This document analyzes trading costs for a sample of 132 equity mutual funds between 1984 and 1991. It finds that trading costs, including spread costs and brokerage commissions, average 0.78% of fund assets per year and vary substantially across funds. Higher trading costs are negatively associated with fund returns, even after controlling for expense ratios. Turnover explains some but not all of the variation in trading costs. Fund investment objectives are related to average trading costs but variation within objectives is greater than across objectives.
This document summarizes a journal article that examines how stale prices impact the performance evaluation of mutual funds. The article introduces a model to estimate "true alpha" based on the true returns of underlying fund assets, independent of biases from stale pricing. Empirical tests show true alpha is about 40 basis points higher than observed alpha and remains positive on average. The difference between the two alphas consists of three components - a small statistical bias, dilution from long-term fund flows, and a large and significant dilution effect primarily from short-term arbitrage flows exploiting stale prices.
Price and performance in funds gil bazobfmresearch
This study examines the relationship between mutual fund fees and performance. It finds a puzzling negative relationship: funds with worse risk-adjusted performance before fees charge higher fees. The study explores several potential explanations for this relationship, including strategic fee setting by funds and the role of fund governance. Some evidence suggests funds with stronger governance structures have fees more aligned with performance.
This research investigates the determinants of the capital structure of firms listed service sector on BIST(Borsa Istanbul) and the adjustment process towards this target. The econometric analysis employs the Generalized Method of Moments estimators (GMM-Sys, GMM difference) techniques that controls for unobserved firm-specific effects and the endogeneity problem. The findings of the paper suggest that firms have target leverage ratios and they adjust to them relatively fast. Consistent with the predictions of capital structure theories and the findings of the empirical literature, the results of this paper suggest that size, assets tangibility, profitability, growth opportunity except earnings volatility have significant effects on the capital structure choice of hotels and restaurants.The capital structure or leverage is measured by total debt ratio. Analysis results indicates that firms with high profits, sizable, high fixed assets ratio and high total sales and more growth opportunities tend to have relatively less debt in their capital structures.
This document summarizes an experimental study on the effects of strategic and natural risk on entrepreneurship decisions. The study finds:
1) When only strategic risk is present, entry and investment levels equalize with the outside option over time as predicted by theory.
2) When both strategic and natural risks are present, entrepreneurs consistently earn less than the outside option due to excessive entry and investment.
3) Adding natural risk to the outside option "democratizes" entrepreneurship, leading to more switching between entrepreneurship and the outside option over time.
This document summarizes a study on the relationship between firm investment and financial status. The study uses a sample of 1,317 firms from 1987 to 1994 to examine how investment decisions differ across financially constrained and unconstrained firms. It finds that investment is most sensitive to internal funds for firms that are least financially constrained, consistent with the findings of Kaplan and Zingales (1997). Statistical tests show this difference is statistically significant. Additionally, firms that reduced dividends exhibited traditional signs of greater financial constraints such as lower current ratios and profitability compared to firms that increased dividends. The study uses multiple discriminant analysis and regression analysis to classify firms and compare investment-cash flow sensitivities between financially constrained and unconstrained groups.
The document summarizes David Durand's commentary on Modigliani and Miller's 1958 propositions about capital structure and cost of capital. Durand exposed difficulties justifying Proposition I in the real world where arbitrage is usually impossible. He also commented that MM underestimated how market imperfections like restrictions on margin buying affect their arguments. In a reply, Modigliani and Miller defended their original positions and argued that Durand misinterpreted some of their assumptions. They acknowledged not providing an explicit model for how growth opportunities affect share prices.
This study analyzes the motives and geographical structure of Finnish mergers and acquisitions (M&As) between 1989-2000 using a multilogit model. The study finds that characteristics indicating a firm's ability to monitor and internalize synergies, such as a highly educated workforce, increase the probability of acquiring a distant target, especially internationally. Having research and development (R&D) capital from investments also increases the likelihood of international acquisitions. For targets, having R&D capital increases the probability of being acquired across all categories but especially internationally. The study suggests factors like education and R&D abilities help firms overcome barriers to internalizing synergies from more distant M&As.
The Relationship Between Firm Investment and Financial StatusSudarshan Kadariya
This document summarizes a study that examined the relationship between firm investment and financial status using a sample of 1,317 public firms between 1987-1994. The study found that:
1) Firms classified as facing fewer financial constraints (NFC) had stronger financial ratios and investment sensitivity to cash flow compared to financially constrained (FC) firms.
2) Investment levels were more sensitive to internal cash flow for NFC firms compared to partially financially constrained and FC firms.
3) The study validated prior research finding that investment decisions of more creditworthy firms are more sensitive to internal funds availability.
This document summarizes a journal article that examines how stale prices impact the performance evaluation of mutual funds. The article introduces a model to estimate "true alpha" based on the true returns of underlying fund assets, independent of biases from stale pricing. Empirical tests show true alpha is about 40 basis points higher than observed alpha and remains positive on average. The difference between the two alphas consists of three components - a small statistical bias, dilution from long-term fund flows, and a large and significant dilution effect primarily from short-term arbitrage flows exploiting stale prices.
Price and performance in funds gil bazobfmresearch
This study examines the relationship between mutual fund fees and performance. It finds a puzzling negative relationship: funds with worse risk-adjusted performance before fees charge higher fees. The study explores several potential explanations for this relationship, including strategic fee setting by funds and the role of fund governance. Some evidence suggests funds with stronger governance structures have fees more aligned with performance.
This research investigates the determinants of the capital structure of firms listed service sector on BIST(Borsa Istanbul) and the adjustment process towards this target. The econometric analysis employs the Generalized Method of Moments estimators (GMM-Sys, GMM difference) techniques that controls for unobserved firm-specific effects and the endogeneity problem. The findings of the paper suggest that firms have target leverage ratios and they adjust to them relatively fast. Consistent with the predictions of capital structure theories and the findings of the empirical literature, the results of this paper suggest that size, assets tangibility, profitability, growth opportunity except earnings volatility have significant effects on the capital structure choice of hotels and restaurants.The capital structure or leverage is measured by total debt ratio. Analysis results indicates that firms with high profits, sizable, high fixed assets ratio and high total sales and more growth opportunities tend to have relatively less debt in their capital structures.
This document summarizes an experimental study on the effects of strategic and natural risk on entrepreneurship decisions. The study finds:
1) When only strategic risk is present, entry and investment levels equalize with the outside option over time as predicted by theory.
2) When both strategic and natural risks are present, entrepreneurs consistently earn less than the outside option due to excessive entry and investment.
3) Adding natural risk to the outside option "democratizes" entrepreneurship, leading to more switching between entrepreneurship and the outside option over time.
This document summarizes a study on the relationship between firm investment and financial status. The study uses a sample of 1,317 firms from 1987 to 1994 to examine how investment decisions differ across financially constrained and unconstrained firms. It finds that investment is most sensitive to internal funds for firms that are least financially constrained, consistent with the findings of Kaplan and Zingales (1997). Statistical tests show this difference is statistically significant. Additionally, firms that reduced dividends exhibited traditional signs of greater financial constraints such as lower current ratios and profitability compared to firms that increased dividends. The study uses multiple discriminant analysis and regression analysis to classify firms and compare investment-cash flow sensitivities between financially constrained and unconstrained groups.
The document summarizes David Durand's commentary on Modigliani and Miller's 1958 propositions about capital structure and cost of capital. Durand exposed difficulties justifying Proposition I in the real world where arbitrage is usually impossible. He also commented that MM underestimated how market imperfections like restrictions on margin buying affect their arguments. In a reply, Modigliani and Miller defended their original positions and argued that Durand misinterpreted some of their assumptions. They acknowledged not providing an explicit model for how growth opportunities affect share prices.
This study analyzes the motives and geographical structure of Finnish mergers and acquisitions (M&As) between 1989-2000 using a multilogit model. The study finds that characteristics indicating a firm's ability to monitor and internalize synergies, such as a highly educated workforce, increase the probability of acquiring a distant target, especially internationally. Having research and development (R&D) capital from investments also increases the likelihood of international acquisitions. For targets, having R&D capital increases the probability of being acquired across all categories but especially internationally. The study suggests factors like education and R&D abilities help firms overcome barriers to internalizing synergies from more distant M&As.
The Relationship Between Firm Investment and Financial StatusSudarshan Kadariya
This document summarizes a study that examined the relationship between firm investment and financial status using a sample of 1,317 public firms between 1987-1994. The study found that:
1) Firms classified as facing fewer financial constraints (NFC) had stronger financial ratios and investment sensitivity to cash flow compared to financially constrained (FC) firms.
2) Investment levels were more sensitive to internal cash flow for NFC firms compared to partially financially constrained and FC firms.
3) The study validated prior research finding that investment decisions of more creditworthy firms are more sensitive to internal funds availability.
Fund flow volatility and performance rakowskibfmresearch
This paper analyzes the impact of daily mutual fund flow volatility on fund performance. The author finds that higher daily flow volatility is negatively associated with risk-adjusted fund performance. This relationship is strongest for domestic equity funds, smaller funds, better performing funds, and those that experienced net inflows. The results suggest daily fund flows impose liquidity costs through unnecessary trading that reduces returns.
This document discusses the risks associated with derivative transactions and the impact of regulation in limiting these risks. It analyzes price risk, default risk, and systemic risk in derivatives markets. The document argues that default risk has been exaggerated and misunderstood. It claims that systemic risk simply aggregates individual default risks, which are lower than assumed due to the nature of derivatives. The document also discusses "agency risk" arising from compensation structures that can encourage excessive risk taking.
This document summarizes a study that examines the relationship between corruption and firm investment in Vietnam using survey data from Vietnamese small and medium enterprises. The study tests two hypotheses: that corruption hinders firm investment by increasing costs and promoting rent-seeking behaviors, or that corruption boosts investment by helping firms overcome bureaucratic obstacles. The study employs both a simple logistic regression model and a bivariate probit model with a corruption instrument variable to address potential endogeneity between corruption and investment. The results provide evidence that corruption hinders firm investment in Vietnam, which may partially explain the negative effect of corruption on firm performance found in previous research.
This document is a bachelor thesis that investigates the impact of capital structure choice on investment decisions of firms. Specifically, it examines the effect of leverage on investment decisions for all Dutch AEX-listed firms as well as separately for high-growth and low-growth firms. The thesis begins with an introduction and literature review on the relationship between leverage and investment. It then describes the regression model that will be used for analysis and defines the variables. Finally, it presents the results of the regression analysis and draws conclusions regarding the effect of leverage on investment decisions.
Distress risk and stock returns in an emerging marketAlexander Decker
This research study examines the relationship between financial distress and stock returns of listed companies in Pakistan. The study uses Altman's Z-score model to measure distress risk and subsequent realized stock returns as a proxy for market performance. The sample includes 17 distressed and 17 non-distressed firms listed on the Karachi Stock Exchange between 2006-2011. The results found a positive but insignificant relationship between distress risk and stock returns for distressed firms, suggesting distress risk may be a systematic risk for the Pakistani stock market to some extent. For non-distressed firms, distress risk was negatively correlated with stock returns and this relationship was statistically significant. The study is inconclusive on whether distress risk explains stock returns in Pakistan.
International Journal of Business and Management Invention (IJBMI) inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
This document discusses a study that uses a simple screening method based on publicly available financial statements to select stocks from the Russell 2000 index that consistently outperform the index from 2001-2005. The screen generates an annualized excess return (alpha) of 7.58% over this period. The document estimates that widespread adoption of XBRL could reduce the cost of capital for small public companies by 1.75-3.03% by enabling easier analysis of financial statements, particularly for companies that currently lack analyst coverage. The returns to the simple screening method provide evidence that information from financial statements not fully incorporated into stock prices, and that XBRL could help reduce this inefficiency.
Agency Problems and Reputation in Expert Services - Evidence From Auto Repair...ANTISMOG
This document summarizes a study that investigates agency problems and the ability of reputation to limit them in the auto repair market. The study analyzes data from 91 undercover garage visits. It finds clear patterns of agency problems, including unnecessary repairs in 27% of visits representing 61% of charges, and serious undertreatment in 77% of visits. Reputation does not meaningfully improve service quality or limit inefficiencies. Mechanics charged lower diagnosis fees when appearing as potential repeat customers, but service quality was still often poor. The study estimates agency problems in auto repair generate $8.2 billion in welfare loss annually in the US, representing 22% of industry revenue. Reputation fails to solve information problems in auto repair due to
1) The document discusses emerging fixed-income managers and whether their age or assets under management is a better indicator of performance.
2) It analyzes monthly return data for 317 fixed-income firms from 1985-present to determine if younger or smaller firms outperform their larger counterparts.
3) The research aims to address gaps in prior studies that focused only on equities and hedge funds, and used assets under management rather than age to define emerging managers.
Morningstar fund investor_fees_predictorbfmresearch
The document summarizes research on how expense ratios and Morningstar star ratings can predict the future success of mutual funds. Some key findings:
- Funds in the lowest expense ratio quintile significantly outperformed those in the highest quintile in terms of total returns and survival rates across all asset classes except international stocks.
- Funds with the highest Morningstar ratings (5 stars) generally had higher total returns and survival rates than 1-star funds, though expense ratios were a slightly better predictor of success.
- The star rating beat expense ratios as a predictor in less than half of asset class comparisons between 2005-2010, taking into account funds that failed or were liquidated.
Examination of hedged mutual funds agarwalbfmresearch
Hedge funds have traditionally only been available to accredited investors while providing lighter regulation and stronger performance incentives compared to mutual funds. Recently, some mutual funds have adopted hedge fund-like strategies but remain subject to tighter regulation. This study examines the performance of these "hedged mutual funds" relative to both hedge funds and traditional mutual funds. It finds that despite using similar strategies as hedge funds, hedged mutual funds underperform due to their tighter regulation and weaker incentives. However, hedged mutual funds outperform traditional mutual funds, with the superior performance driven by those with managers having hedge fund experience.
1) The study investigates how fund size affects performance in the active money management industry. Specifically, it analyzes whether fund returns decline as fund size increases.
2) The results show that both gross and net fund returns decline as lagged fund size increases, even after accounting for various performance benchmarks and fund characteristics. This suggests that larger fund size erodes performance.
3) However, controlling for its own size, a fund's performance does not deteriorate as the size of the family it belongs to increases. This indicates that scale itself does not necessarily harm performance, depending on how the fund is organized.
Should investors avoid active managed funds baksbfmresearch
This document summarizes a study that analyzes mutual fund performance from an investor's perspective. The study develops a Bayesian method to evaluate mutual fund manager performance using flexible prior beliefs about managerial skill. The method is applied to a sample of over 1,400 equity mutual funds. The study finds that even with extremely skeptical prior beliefs about manager skill, some allocation to actively managed funds is justified. The economic importance is quantified by estimating the portfolio share and certainty equivalent loss from excluding all active managers.
The document discusses a new approach to asset allocation that focuses on diversifying risk factors rather than asset classes. It argues that traditional approaches underestimate market dynamics and fail to hedge "fat tail" risks. The new approach takes a forward-looking, macroeconomic view and aims to diversify risks across equity, bond, currency and commodity risk factors. It also notes that one extremely bad year can erase gains from multiple good years, emphasizing the importance of hedging rare but severe loss events.
The S&P Persistence Scorecard seeks to analyze whether past mutual fund performance is indicative of future performance. It tracks the consistency of top performers over consecutive periods and measures performance persistence through transition matrices. The key findings are that very few funds consistently repeat top-half or top-quartile performance over consecutive periods. Additionally, screening for only top-quartile funds may be inappropriate as a healthy number of future top performers come from the second and third quartiles in prior periods. The bottom quartile funds have a high probability of being merged or liquidated and screening these out may be reasonable.
This study examines how hedge fund manager characteristics impact fund performance. The authors analyze data on over 1,000 hedge fund managers, including SAT scores, education levels, work experience, and age. They find managers from higher-SAT undergraduate institutions tend to have higher raw and risk-adjusted returns, more inflows, and take less risk. Unlike mutual funds, the study also finds hedge fund flows do not negatively impact future performance.
These documents summarize several academic studies on hedge fund performance and investor returns:
1) One study finds that annualized returns for hedge fund investors are 3-7% lower than buy-and-hold returns for the same funds, due to poor timing of capital flows. Risk-adjusted returns are close to zero.
2) Another examines how fund life cycles are affected by flows, size, competition and performance. It finds increasing competition in a category decreases fund survival probabilities.
3) A third study finds macroeconomic risk explains a significant portion of hedge fund return dispersion, but not for mutual funds. Higher macroeconomic risk is positively related to future hedge fund returns.
This paper examines how hedge funds use derivatives and how that relates to risk taking. The paper finds that 71% of hedge funds in the sample use derivatives. After controlling for other factors, derivatives users on average exhibit lower risks such as market risk, downside risk, and event risk. This risk reduction is especially pronounced for directional-style funds. Derivatives users also engage in less risk shifting and are less likely to liquidate during poor market conditions. However, investors do not appear to differentiate between derivatives users and non-users when making investment decisions.
Prior performance and risk chen and pennacchibfmresearch
This document summarizes a research paper that models how a mutual fund manager's choice of portfolio risk is affected by the fund's prior performance and the manager's compensation structure. The model shows that when compensation cannot fall to zero, managers take on more tracking error risk (deviation from the benchmark portfolio) as performance declines. However, increased total return volatility is not necessarily predicted. Empirical tests on over 6,000 funds from 1962-2006 find evidence managers increase tracking error, but not return, volatility during underperformance, especially for longer-tenured managers. This supports implications of the theoretical model.
Prior performance and risk chen and pennacchibfmresearch
This document summarizes a research paper that models how a mutual fund manager's choice of portfolio risk is affected by the fund's prior performance and the manager's compensation structure. The model shows that when compensation cannot fall to zero, managers take on more tracking error risk (deviation from the benchmark portfolio) as performance declines. However, increased total return volatility is not necessarily predicted. Empirical tests on over 6,000 funds from 1962-2006 find evidence managers increase tracking error, but not return, volatility during poor performance, especially longer-tenured managers. This adds new insights to research on risk-shifting incentives in mutual fund tournaments.
This document summarizes a research paper that analyzes the mutual fund industry worldwide. It finds:
1) Explicit index funds are less prevalent outside the US, comprising 7% of assets globally compared to 20% in the US. Many actively managed funds closely track their benchmarks, amounting to "closet indexing".
2) Countries with more explicit indexing have lower fees for active funds and a weaker link between fees and active management. Active funds have higher "active share" under more indexing pressure.
3) Countries with more closet indexing have higher fees for active funds, indicating less competitive pressure.
4) Globally, actively managed funds with higher active share charge higher fees but outperform after fees
Fund flow volatility and performance rakowskibfmresearch
This paper analyzes the impact of daily mutual fund flow volatility on fund performance. The author finds that higher daily flow volatility is negatively associated with risk-adjusted fund performance. This relationship is strongest for domestic equity funds, smaller funds, better performing funds, and those that experienced net inflows. The results suggest daily fund flows impose liquidity costs through unnecessary trading that reduces returns.
This document discusses the risks associated with derivative transactions and the impact of regulation in limiting these risks. It analyzes price risk, default risk, and systemic risk in derivatives markets. The document argues that default risk has been exaggerated and misunderstood. It claims that systemic risk simply aggregates individual default risks, which are lower than assumed due to the nature of derivatives. The document also discusses "agency risk" arising from compensation structures that can encourage excessive risk taking.
This document summarizes a study that examines the relationship between corruption and firm investment in Vietnam using survey data from Vietnamese small and medium enterprises. The study tests two hypotheses: that corruption hinders firm investment by increasing costs and promoting rent-seeking behaviors, or that corruption boosts investment by helping firms overcome bureaucratic obstacles. The study employs both a simple logistic regression model and a bivariate probit model with a corruption instrument variable to address potential endogeneity between corruption and investment. The results provide evidence that corruption hinders firm investment in Vietnam, which may partially explain the negative effect of corruption on firm performance found in previous research.
This document is a bachelor thesis that investigates the impact of capital structure choice on investment decisions of firms. Specifically, it examines the effect of leverage on investment decisions for all Dutch AEX-listed firms as well as separately for high-growth and low-growth firms. The thesis begins with an introduction and literature review on the relationship between leverage and investment. It then describes the regression model that will be used for analysis and defines the variables. Finally, it presents the results of the regression analysis and draws conclusions regarding the effect of leverage on investment decisions.
Distress risk and stock returns in an emerging marketAlexander Decker
This research study examines the relationship between financial distress and stock returns of listed companies in Pakistan. The study uses Altman's Z-score model to measure distress risk and subsequent realized stock returns as a proxy for market performance. The sample includes 17 distressed and 17 non-distressed firms listed on the Karachi Stock Exchange between 2006-2011. The results found a positive but insignificant relationship between distress risk and stock returns for distressed firms, suggesting distress risk may be a systematic risk for the Pakistani stock market to some extent. For non-distressed firms, distress risk was negatively correlated with stock returns and this relationship was statistically significant. The study is inconclusive on whether distress risk explains stock returns in Pakistan.
International Journal of Business and Management Invention (IJBMI) inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
This document discusses a study that uses a simple screening method based on publicly available financial statements to select stocks from the Russell 2000 index that consistently outperform the index from 2001-2005. The screen generates an annualized excess return (alpha) of 7.58% over this period. The document estimates that widespread adoption of XBRL could reduce the cost of capital for small public companies by 1.75-3.03% by enabling easier analysis of financial statements, particularly for companies that currently lack analyst coverage. The returns to the simple screening method provide evidence that information from financial statements not fully incorporated into stock prices, and that XBRL could help reduce this inefficiency.
Agency Problems and Reputation in Expert Services - Evidence From Auto Repair...ANTISMOG
This document summarizes a study that investigates agency problems and the ability of reputation to limit them in the auto repair market. The study analyzes data from 91 undercover garage visits. It finds clear patterns of agency problems, including unnecessary repairs in 27% of visits representing 61% of charges, and serious undertreatment in 77% of visits. Reputation does not meaningfully improve service quality or limit inefficiencies. Mechanics charged lower diagnosis fees when appearing as potential repeat customers, but service quality was still often poor. The study estimates agency problems in auto repair generate $8.2 billion in welfare loss annually in the US, representing 22% of industry revenue. Reputation fails to solve information problems in auto repair due to
1) The document discusses emerging fixed-income managers and whether their age or assets under management is a better indicator of performance.
2) It analyzes monthly return data for 317 fixed-income firms from 1985-present to determine if younger or smaller firms outperform their larger counterparts.
3) The research aims to address gaps in prior studies that focused only on equities and hedge funds, and used assets under management rather than age to define emerging managers.
Morningstar fund investor_fees_predictorbfmresearch
The document summarizes research on how expense ratios and Morningstar star ratings can predict the future success of mutual funds. Some key findings:
- Funds in the lowest expense ratio quintile significantly outperformed those in the highest quintile in terms of total returns and survival rates across all asset classes except international stocks.
- Funds with the highest Morningstar ratings (5 stars) generally had higher total returns and survival rates than 1-star funds, though expense ratios were a slightly better predictor of success.
- The star rating beat expense ratios as a predictor in less than half of asset class comparisons between 2005-2010, taking into account funds that failed or were liquidated.
Examination of hedged mutual funds agarwalbfmresearch
Hedge funds have traditionally only been available to accredited investors while providing lighter regulation and stronger performance incentives compared to mutual funds. Recently, some mutual funds have adopted hedge fund-like strategies but remain subject to tighter regulation. This study examines the performance of these "hedged mutual funds" relative to both hedge funds and traditional mutual funds. It finds that despite using similar strategies as hedge funds, hedged mutual funds underperform due to their tighter regulation and weaker incentives. However, hedged mutual funds outperform traditional mutual funds, with the superior performance driven by those with managers having hedge fund experience.
1) The study investigates how fund size affects performance in the active money management industry. Specifically, it analyzes whether fund returns decline as fund size increases.
2) The results show that both gross and net fund returns decline as lagged fund size increases, even after accounting for various performance benchmarks and fund characteristics. This suggests that larger fund size erodes performance.
3) However, controlling for its own size, a fund's performance does not deteriorate as the size of the family it belongs to increases. This indicates that scale itself does not necessarily harm performance, depending on how the fund is organized.
Should investors avoid active managed funds baksbfmresearch
This document summarizes a study that analyzes mutual fund performance from an investor's perspective. The study develops a Bayesian method to evaluate mutual fund manager performance using flexible prior beliefs about managerial skill. The method is applied to a sample of over 1,400 equity mutual funds. The study finds that even with extremely skeptical prior beliefs about manager skill, some allocation to actively managed funds is justified. The economic importance is quantified by estimating the portfolio share and certainty equivalent loss from excluding all active managers.
The document discusses a new approach to asset allocation that focuses on diversifying risk factors rather than asset classes. It argues that traditional approaches underestimate market dynamics and fail to hedge "fat tail" risks. The new approach takes a forward-looking, macroeconomic view and aims to diversify risks across equity, bond, currency and commodity risk factors. It also notes that one extremely bad year can erase gains from multiple good years, emphasizing the importance of hedging rare but severe loss events.
The S&P Persistence Scorecard seeks to analyze whether past mutual fund performance is indicative of future performance. It tracks the consistency of top performers over consecutive periods and measures performance persistence through transition matrices. The key findings are that very few funds consistently repeat top-half or top-quartile performance over consecutive periods. Additionally, screening for only top-quartile funds may be inappropriate as a healthy number of future top performers come from the second and third quartiles in prior periods. The bottom quartile funds have a high probability of being merged or liquidated and screening these out may be reasonable.
This study examines how hedge fund manager characteristics impact fund performance. The authors analyze data on over 1,000 hedge fund managers, including SAT scores, education levels, work experience, and age. They find managers from higher-SAT undergraduate institutions tend to have higher raw and risk-adjusted returns, more inflows, and take less risk. Unlike mutual funds, the study also finds hedge fund flows do not negatively impact future performance.
These documents summarize several academic studies on hedge fund performance and investor returns:
1) One study finds that annualized returns for hedge fund investors are 3-7% lower than buy-and-hold returns for the same funds, due to poor timing of capital flows. Risk-adjusted returns are close to zero.
2) Another examines how fund life cycles are affected by flows, size, competition and performance. It finds increasing competition in a category decreases fund survival probabilities.
3) A third study finds macroeconomic risk explains a significant portion of hedge fund return dispersion, but not for mutual funds. Higher macroeconomic risk is positively related to future hedge fund returns.
This paper examines how hedge funds use derivatives and how that relates to risk taking. The paper finds that 71% of hedge funds in the sample use derivatives. After controlling for other factors, derivatives users on average exhibit lower risks such as market risk, downside risk, and event risk. This risk reduction is especially pronounced for directional-style funds. Derivatives users also engage in less risk shifting and are less likely to liquidate during poor market conditions. However, investors do not appear to differentiate between derivatives users and non-users when making investment decisions.
Prior performance and risk chen and pennacchibfmresearch
This document summarizes a research paper that models how a mutual fund manager's choice of portfolio risk is affected by the fund's prior performance and the manager's compensation structure. The model shows that when compensation cannot fall to zero, managers take on more tracking error risk (deviation from the benchmark portfolio) as performance declines. However, increased total return volatility is not necessarily predicted. Empirical tests on over 6,000 funds from 1962-2006 find evidence managers increase tracking error, but not return, volatility during underperformance, especially for longer-tenured managers. This supports implications of the theoretical model.
Prior performance and risk chen and pennacchibfmresearch
This document summarizes a research paper that models how a mutual fund manager's choice of portfolio risk is affected by the fund's prior performance and the manager's compensation structure. The model shows that when compensation cannot fall to zero, managers take on more tracking error risk (deviation from the benchmark portfolio) as performance declines. However, increased total return volatility is not necessarily predicted. Empirical tests on over 6,000 funds from 1962-2006 find evidence managers increase tracking error, but not return, volatility during poor performance, especially longer-tenured managers. This adds new insights to research on risk-shifting incentives in mutual fund tournaments.
This document summarizes a research paper that analyzes the mutual fund industry worldwide. It finds:
1) Explicit index funds are less prevalent outside the US, comprising 7% of assets globally compared to 20% in the US. Many actively managed funds closely track their benchmarks, amounting to "closet indexing".
2) Countries with more explicit indexing have lower fees for active funds and a weaker link between fees and active management. Active funds have higher "active share" under more indexing pressure.
3) Countries with more closet indexing have higher fees for active funds, indicating less competitive pressure.
4) Globally, actively managed funds with higher active share charge higher fees but outperform after fees
This document summarizes a study that examines whether mutual fund managers can pick stocks by analyzing the performance of stocks that funds buy and sell around subsequent quarterly earnings announcements. The study finds:
1) On average, stocks that mutual funds buy outperform stocks they sell by about 10 basis points in the 3 days around the next earnings announcement.
2) This performance persists after benchmarking against stocks with similar characteristics, and funds that perform best tend to have a growth style.
3) Mutual fund trades forecast future earnings surprises, indicating managers can predict fundamentals.
4) Abnormal returns around earnings announcements account for 18-51% of total abnormal returns to stocks funds trade.
Electronic copy available at httpssrn.comabstract=1629786.docxSALU18
Electronic copy available at: http://ssrn.com/abstract=1629786
1
Behavioral Portfolio Analysis of Individual Investors
1
Arvid O. I. Hoffmann
*
Maastricht University and Netspar
Hersh Shefrin
Santa Clara University
Joost M. E. Pennings
Maastricht University, Wageningen University, and University of Illinois at Urbana-Champaign
Abstract: Existing studies on individual investors’ decision-making often rely on observable socio-demographic
variables to proxy for underlying psychological processes that drive investment choices. Doing so implicitly ignores
the latent heterogeneity amongst investors in terms of their preferences and beliefs that form the underlying drivers
of their behavior. To gain a better understanding of the relations among individual investors’ decision-making, the
processes leading to these decisions, and investment performance, this paper analyzes how systematic differences in
investors’ investment objectives and strategies impact the portfolios they select and the returns they earn. Based on
recent findings from behavioral finance we develop hypotheses which are tested using a combination of transaction
and survey data involving a large sample of online brokerage clients. In line with our expectations, we find that
investors driven by objectives related to speculation have higher aspirations and turnover, take more risk, judge
themselves to be more advanced, and underperform relative to investors driven by the need to build a financial
buffer or save for retirement. Somewhat to our surprise, we find that investors who rely on fundamental analysis
have higher aspirations and turnover, take more risks, are more overconfident, and outperform investors who rely on
technical analysis. Our findings provide support for the behavioral approach to portfolio theory and shed new light
on the traditional approach to portfolio theory.
JEL Classification: G11, G24
Keywords: Behavioral Portfolio Theory, Investment Decisions, Investor Performance, Behavioral Finance
*
Corresponding author: Arvid O. I. Hoffmann, Maastricht University, School of Business and Economics,
Department of Finance, P.O. Box 616, 6200 MD, The Netherlands. Tel.: +31 43 38 84 602. E-mail:
[email protected]
1
The authors thank Jeroen Derwall and Meir Statman for thoughtful comments and suggestions on previous
versions of this paper. Any remaining errors are our own.
Electronic copy available at: http://ssrn.com/abstract=1629786
2
I. Introduction
The combination of increased self-responsibility for retirement and an aging population has led a
growing number of people to become accountable for their own financial futures. Considering
the significant impact of current investment choices on future lifestyles (Browning and Crossley,
2001), it is important to understand how individual investors differ when it comes to the
triangular relationshi ...
Accounting Research Center, Booth School of Business, Universi.docxnettletondevon
Accounting Research Center, Booth School of Business, University of Chicago
Comparing the Accuracy and Explainability of Dividend, Free Cash Flow, and Abnormal
Earnings Equity Value Estimates
Author(s): Jennifer Francis, Per Olsson and Dennis R. Oswald
Source: Journal of Accounting Research, Vol. 38, No. 1 (Spring, 2000), pp. 45-70
Published by: Wiley on behalf of Accounting Research Center, Booth School of Business,
University of Chicago
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Journal of Accounting Research
Vol. 38 No. 1 Spring 2000
Printed in US.A.
Comparing the Accuracy and
Explainability of Dividend, Free
Cash Flow, and Abnormal Earnings
Equity Value Estimates
JENNIFER FRANCIS,* PER OLSSON,t
AND DENNIS R. OSWALD:
1. Introduction
This study provides empirical evidence on the reliability of intrinsic
value estimates derived from three theoretically equivalent valuation
models: the discounted dividend (DIV) model, the discounted free cash
flow (FCO) model, and the discounted abnormal earnings (AE) model.
We use Value Line (VL) annual forecasts of the elements in these models
to calculate value estimates for a sample of publicly traded firms fol-
lowed by Value Line during 1989-93.1 We contrast the reliability of value
*Duke University; tUniversity of Wisconsin; London Business School. This research
was supported by the Institute of Professional Accounting and the Graduate School of
Business at the University of Chicago, by the Bank Research Institute, Sweden, and Jan
Wallanders och Tom Hedelius Stiftelse for Samhallsvetenskaplig Forskning, Stockholm,
Sweden. We appreciate the comments and suggestions of workshop participants at the
1998 EAA meetings, Berkeley, Harvard, London Business School, London School of Eco-
nomics, NYU, Ohio State, Portland State, Rochester, Stockholm School of Economics,
Tilburg, and Wisconsin, and from Peter Easton, Frank Gigler, Paul Healy, Thomas Hem-
mer, Joakim Levin, Mark Mitchell, Krishna Palepu, Stephen Penman, Richard Ruback,
Linda Vincent, Terry Warfield, and Jerry Zimmerman.
I We collect third-quarter annual forecast data over a five-year .
Standard & poor's 16768282 fund-factors-2009 jan1bfmresearch
This document summarizes a study by Standard & Poor's on factors that predict investment fund performance. The study analyzed both qualitative factors like fund size, expenses, and age as well as quantitative metrics like Jensen's alpha and information ratio. The key findings were:
- For developed markets, larger funds with lower expenses tended to outperform. But for emerging markets, smaller funds did better due to differences in liquidity.
- Jensen's alpha and information ratio best predicted future performance of developed market equity funds over shorter time periods.
- Past performance was informative over 2 years but less so over 1 year due to noise. Fund selection should focus on factors predicting shorter term outperformance.
Fiduciary or paper money is issued by the Central Bank on the basis of
computation of estimated demand for cash. Monetary policy guides the Central
Bank’s supply of money in order to achieve the objectives of price stability (or low
inflation rate), full employment, and growth in aggregate income.
This document summarizes a study examining 125 equity mutual funds that closed to new investment between 1993 and 2004. The study tests three hypotheses about why funds close: 1) The "good steward" hypothesis argues funds close to restrict inflows and maintain performance, and will perform well after reopening. 2) The "cheap talk" hypothesis posits closing has no real cost if fees increase and existing investors contribute, compensating managers. 3) The "family spillover" hypothesis claims closing diverts attention to other funds in the same family. The study finds little support for good steward performance, but evidence managers raise fees consistent with cheap talk, and little family benefit except briefly around closure.
An Empirical Study On The Determinants Of An Investor S Decision In Unit Trus...Sara Alvarez
This document summarizes a research study on the factors that influence an investor's decision to invest in unit trusts in Malaysia. The study hypothesized that financial status, risk tolerance, expected investment returns, and access to investment information would significantly impact investment decisions. A survey of 202 investors found that financial status, risk tolerance, and sources of information did significantly influence investment behavior, but expected returns did not have a clear relationship. The findings help financial institutions understand investor preferences to better target customers and promote unit trust investments.
This paper presents a model to value cash holdings for all-equity financed firms with growth opportunities. The model considers the tradeoff between agency costs of free cash flow and costs of external financing. It derives the optimal dynamic cash retention policy and shows that firms optimally retain only a fraction of cash flows. The model implies that high cash flow volatility decreases the value of cash and that optimal cash retention can delay investment timing. Empirical tests on US firm data from 1980-2010 confirm these implications, finding a negative relationship between cash value and volatility in the context of growth options.
JP Morgan Chase The Balance Between Serving Customers and Maxim.docxpauline234567
JP Morgan Chase: The Balance Between Serving Customers and Maximizing Shareholder Wealth
Penelope Bender
William Woods University
BUS 585: Integrated Studies in Business Administration
Dr. Leathers
Abstract
This paper investigates why JP Morgan Chase and other financial institutions struggle to balance client interests over maximizing wealth.
It is an exploratory study done through literature review.
Often financial institutions, like JP Morgan, put profits ahead of the interests of those they serve.
The paper contributes to better understanding of corporate culture.
This paper investigates why JP Morgan Chase and other financial institutions struggle to balance client interests over maximizing shareholder wealth. This exploratory study is done through a literature review to answer why financial institutions, specifically JP Morgan, often put profits ahead of those they serve. The study will provide evidence of the complex nature of balancing client interests over maximizing shareholder and individual wealth and the need for tighter internal and external oversight. This paper contributes to a better understanding of why corporate culture encourages profit over stakeholders’ interests.
2
Research Question
Why does JP Morgan Chase and other financial institutions struggle to balance client interests over maximizing shareholder wealth?
Employees of JP Morgan Chase and other large banks work in their best interests to increase wealth and succeed by meeting management goals. However, because of the complex nature of large banks, an individual(s), unethical behavior can go unchecked.
3
Problem Statement
JP Morgan Chase competes globally and faces competition from other large banks in the US and abroad.
JP Morgan Chase is part of a complex system of regulation, self-interests, and wealth creation.
The interests of shareholders and investors is sometimes overshadowed by agents working in their own best interests.
Financial markets are a complex web of interests, and because of opportunities for individual profits, regulating individual’s actions without stricter regulations and internal oversight is impossible.
The study is not meant to be a moral or ethical analysis but merely why the complex relationship exists and will continue to exist in capitalist society. This paper contributes to a better understanding of why capitalism or financialism’s (Clarke, 2014) fundamentals encourage wealth creation. Financial markets are a complex web of interests, and because of opportunities for individual profits, regulating individual’s actions without stricter regulations and internal oversight is impossible.
4
Literature Review
The literature review showed a connection between self-interests, regulators, competition, and risk, which all lead to a complex system of conflicting agendas.
5
How Self-Interests Influence Behavior
Ross (1973) explains that all employment relationships are agency relationships and moral hazards are generally .
This document summarizes a study on the determinants of capital structure in Thailand. The study analyzed data on 144 Thai listed companies from 2000 to 2011 to examine how firm-specific factors like size, profitability, asset tangibility, growth opportunities, and volatility influence a company's leverage ratios. The results showed that leverage ratios increased significantly with firm size but decreased significantly with profitability, in line with trade-off and pecking order theories. However, tangibility, growth, and volatility did not have significant relationships with leverage ratios. Therefore, the study concluded that firm size and profitability are the main determinants of capital structure for companies in Thailand.
Transaction costs AND INFORMATION EFFICIENCY IN CREDIT INTERMEDIATIONDinabandhu Bag
This document summarizes a research paper that examines how transaction costs impact lending efficiency between borrowers and lenders. It discusses how transaction costs, such as information costs, screening costs, and monitoring costs, can influence lending decisions and portfolio quality. The document then presents a model to test if incorporating borrower transaction information, like home ownership and profession, in addition to traditional loan characteristics, can help reduce transaction costs and delinquency rates compared to only using traditional variables. An analysis of loan data from an Indian bank finds that a model including both traditional and transaction variables has higher explanatory power for delinquency than a model with just traditional variables.
2013 Callan Cost of Doing Business Survey: U.S. Funds and TrustsCallan
The survey found that on average funds spent 54 basis points of total assets to operate in 2012. External investment management fees represented 90% of total expenses, the largest portion. These fees have risen 55% since 1998. Non-investment management external advisor fees, the second largest expense, increased 115% over the same period. Overall, average total fund expenses have increased more than 50% since 1998.
1) The study uses a new database to analyze mutual fund performance by decomposing returns into stock-picking ability, style, transactions costs, and expenses.
2) It finds that funds hold stocks that outperform the market by 1.3% annually but their net returns underperform by 1% due to the costs of active management.
3) High expenses and transactions costs account for most of the 2.3% difference between stock picking returns and net returns, while style differences account for some of the rest.
The NMS Exchange For Endwments and Foundations 2013 Keith Dixson
The document discusses several topics:
1) Implementing a risk factor framework for institutional investors to allocate risk budgets across factors rather than asset classes.
2) Governance being the centerpiece of sustainable value creation, with institutional investors needing to focus on long-term goals and align interests between managers and investments.
3) An article questioning if alpha is the best measure of hedge fund performance and whether opportunity cost may be a better gauge.
Similar to Transaction cost expenditures and the relative performance of mutual funds(13) (20)
Performance emergingfixedincomemanagers joi_is age just a numberbfmresearch
1) Younger fixed-income managers tend to outperform older, more established managers in terms of gross returns. Returns are significantly higher for emerging managers in their first year and first five years compared to later years.
2) The study examines 54 fixed-income managers formed since 1985 that had majority employee ownership. Most were formed before 2000, when barriers to entry increased.
3) Business risk is low for emerging managers, as only 6.8% of the 88 examined managers are no longer in business. Higher first-year and early-period returns for emerging managers indicate they provide alpha during their hungry startup phase.
This document analyzes different categories of active mutual fund management based on measures of Active Share and tracking error. It finds that the most active stock pickers have outperformed their benchmarks after fees, while closet indexers and funds focusing on factor bets have underperformed after fees. Performance patterns were similar during the 2008-2009 financial crisis. Closet indexing has become more popular recently. Fund performance can be predicted by cross-sectional stock return dispersion, favoring active stock pickers when dispersion is higher.
The document summarizes findings from the Standard & Poor's Indices Versus Active Funds (SPIVA) Scorecard, which compares the performance of actively managed mutual funds to relevant benchmarks. Some key points:
- Over the past 3 years, the majority (over 50%) of actively managed large-cap, mid-cap, small-cap, global, international, and emerging market funds underperformed their benchmarks.
- Over the past 5 years, indices outperformed a majority of active managers in nearly all major domestic and international equity categories based on equal-weighted returns. Asset-weighted averages also showed underperformance in 11 out of 18 domestic categories.
- For fixed income funds, over 50% under
This document summarizes research on the relationship between portfolio turnover and investment performance. Recent studies have found no evidence that higher portfolio turnover leads to lower returns, as was previously thought. Trading costs have declined over time, and portfolio turnover is not a good proxy for actual trading costs, which depend more on trade size and type of security traded. A 2007 study directly estimated trading costs and found no clear correlation between costs and returns. The author's own analysis of mutual funds from 2007-2008 also found little relationship between turnover and performance. Therefore, advisors should not assume higher turnover means lower returns.
This document discusses using active share and tracking error as measures of portfolio manager skill. It defines active share as the percentage of a fund's portfolio that differs from its benchmark index. Tracking error measures systematic factor risk by capturing how much a fund's returns vary from its benchmark. Research shows funds with high active share and moderate tracking error tend to outperform on average. The document examines how active share and tracking error can help identify skillful managers by focusing on their portfolio construction process rather than just past returns.
This document is a guide to the markets published by JPMorgan that provides data and analysis across various asset classes including equities, fixed income, international markets, and the economy. It includes sections on returns by investment style and sector for equities, economic indicators and drivers, interest rates and other data for fixed income, international market returns and valuations, and asset class performance and correlations. The guide contains over 60 charts and analyses global and domestic financial trends and investment opportunities.
The document discusses whether the concept of "Alpha" is a useful performance metric for investors. It makes two main arguments:
1) Alpha alone does not determine if a portfolio has superior risk-adjusted returns, as portfolio volatility and correlation to benchmarks also influence risk-adjusted returns.
2) Alpha is dependent on leverage - a higher reported Alpha could simply be due to using leverage rather than superior investment skill.
The document concludes that Alpha is a misleading performance measure and not suitable as the sole metric, especially for investors concerned with total risk and returns rather than just a single return component.
Fis group study on emerging managers performance drivers 2007bfmresearch
This study examined the performance of emerging investment managers over three years ending in 2006. It found that:
1) For large cap managers, increased firm assets were negatively correlated with risk-adjusted returns for core and growth strategies, but not for value. This may be because increased assets led to less concentrated core portfolios, lowering returns.
2) For small cap managers, risk-adjusted returns were highest for firms with less than $500 million in assets, possibly due to added resources like analysts. Returns leveled off between $500 million and $1 billion, and declined above $1 billion.
3) Having more research analysts was consistently positively correlated with higher risk-adjusted returns across strategies, while the impact
The document discusses Barclays' process for evaluating and selecting investment managers. It states that identifying the right asset allocation and implementing it properly are both important for achieving investment goals. The process involves both science, through a formal and structured methodology, and art, by applying judgment and philosophy. Barclays aims to identify managers most likely to perform well through rigorous due diligence and ongoing monitoring. The paper will explain Barclays' comprehensive approach to manager analysis, selection, and review.
Active managementmostlyefficientmarkets fajbfmresearch
This survey of literature on active vs passive management shows:
1) On average, actively managed funds do not outperform the market after accounting for fees and expenses, though a minority do add value.
2) Studies suggest some investors may be able to identify superior active managers in advance using public information.
3) Investors who identify superior active managers could improve their risk-adjusted returns by including some exposure to active strategies.
This document summarizes recent academic research on active equity managers who deliver persistent outperformance. It discusses studies finding that:
1) While the average equity manager underperforms after fees, a minority of managers have demonstrated persistent outperformance that cannot be attributed to chance alone.
2) Managers with higher "active share" (the degree to which their portfolio composition differs from the benchmark) tend to generate greater risk-adjusted returns.
3) Managers with lower portfolio turnover and a focus on strong stock selection, rather than market timing, are more likely to outperform over time.
The document evaluates how Brown Advisory's investment approach aligns with the characteristics identified in these studies as being associated with persistent
The document discusses China's transition to a consumer-driven economy. It provides analysis from CLSA China Macro Strategist Andy Rothman on trends in China's economy including the declining importance of exports, strong growth in domestic consumption, increasing incomes driving spending, and continued growth in infrastructure investment. The analysis suggests China's economy remains healthy and growing despite slowing external demand.
This report provides an analysis of defined contribution retirement plans based on 2010 Vanguard recordkeeping data. Some key findings include:
- Median and average account balances reached their highest levels since tracking began in 1999, recovering from market declines.
- Use of target-date funds as investment options and default investments continues to grow significantly, with 42% of participants using them and 20% wholly invested in a single target-date fund.
- Professionally managed investment options like target-date funds are being used by an increasing number of participants, with 29% solely invested in an automatic investment program in 2010 compared to just 9% in 2005.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
This study explores performance persistence in mutual funds. The authors find:
1) Funds that perform relatively poorly compared to peers and benchmarks are more likely to disappear, indicating survivorship bias can be relevant in mutual fund studies.
2) Mutual fund performance persists from year to year on a risk-adjusted basis, though much of the persistence is due to repeated underperformance relative to benchmarks.
3) Persistence patterns vary dramatically between time periods, suggesting performance is correlated across managers due to common strategies not captured by risk adjustments. Poorly performing funds also persist instead of being fully eliminated by the market.
This study examines persistence in mutual fund performance over 1962-1993 using a survivorship-bias-free database. The author finds:
1) Common factors in stock returns and differences in mutual fund expenses explain almost all persistence in mutual fund returns, with the exception of strong underperformance by the worst-performing funds.
2) The "hot hands effect" documented in prior literature is driven by the one-year momentum effect in stock returns, but individual funds do not earn higher returns from actively following momentum strategies after accounting for costs.
3) Expenses have a negative impact on performance of at least one-for-one, and higher turnover also negatively impacts performance, reducing returns by around 0.95
This paper examines the relationship between mutual fund manager ownership stakes in the funds they manage and the performance of those funds. The author hypothesizes that greater manager ownership will be positively associated with fund returns and negatively associated with fund turnover, as higher ownership would better align manager and shareholder interests by reducing agency costs. Using a dataset of manager ownership disclosures from 2004-2005, the author finds that funds with higher manager ownership had higher returns and lower turnover, supporting the hypotheses. However, manager ownership was not related to a fund's tax burden.
Information ratio mgrevaluation_bossertbfmresearch
This document discusses using the Information Ratio (IR) to evaluate mutual fund managers. The IR measures excess return over a benchmark relative to excess return volatility. While commonly used, the IR has limitations that depend on benchmark choice, data frequency, and fund return distributions. The document aims to empirically analyze IR characteristics across different asset classes and countries to determine if it is a reliable performance measure or if guidelines are needed for its use.
This document summarizes a study comparing the performance of mutual funds managed by individual managers versus teams of managers. The study finds that funds managed by teams have similar risk-adjusted performance to individually-managed funds, despite team-managed funds growing at a faster rate. Additionally, team-managed funds have significantly lower risk, lower cross-sectional performance differences, lower expenses, and lower portfolio factor loadings than individually-managed funds. The study uses a large sample of domestic and international mutual funds to test these findings.
This document discusses returns-based style analysis (RBSA), a technique developed by William Sharpe to determine the style of a portfolio or mutual fund using only returns data. The document provides an overview of RBSA and compares it to holdings-based style analysis. It then describes how to implement RBSA using Excel by constructing a portfolio of indices to minimize the tracking error between the returns of the portfolio being analyzed and the index portfolio returns. The document concludes by providing an example RBSA using the Dodge & Cox Balanced Fund to illustrate the technique.
OJP data from firms like Vicinity Jobs have emerged as a complement to traditional sources of labour demand data, such as the Job Vacancy and Wages Survey (JVWS). Ibrahim Abuallail, PhD Candidate, University of Ottawa, presented research relating to bias in OJPs and a proposed approach to effectively adjust OJP data to complement existing official data (such as from the JVWS) and improve the measurement of labour demand.
"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
My study abroad in Bali, Indonesia, inspired this research topic as I noticed how globalization is changing the culture of its people. I learned their language and way of life which helped me understand the beauty and importance of cultural preservation. I believe we could all benefit from learning new perspectives as they could help us ideate solutions to contemporary issues and empathize with others.
Vicinity Jobs’ data includes more than three million 2023 OJPs and thousands of skills. Most skills appear in less than 0.02% of job postings, so most postings rely on a small subset of commonly used terms, like teamwork.
Laura Adkins-Hackett, Economist, LMIC, and Sukriti Trehan, Data Scientist, LMIC, presented their research exploring trends in the skills listed in OJPs to develop a deeper understanding of in-demand skills. This research project uses pointwise mutual information and other methods to extract more information about common skills from the relationships between skills, occupations and regions.
In a tight labour market, job-seekers gain bargaining power and leverage it into greater job quality—at least, that’s the conventional wisdom.
Michael, LMIC Economist, presented findings that reveal a weakened relationship between labour market tightness and job quality indicators following the pandemic. Labour market tightness coincided with growth in real wages for only a portion of workers: those in low-wage jobs requiring little education. Several factors—including labour market composition, worker and employer behaviour, and labour market practices—have contributed to the absence of worker benefits. These will be investigated further in future work.
[4:55 p.m.] Bryan Oates
OJPs are becoming a critical resource for policy-makers and researchers who study the labour market. LMIC continues to work with Vicinity Jobs’ data on OJPs, which can be explored in our Canadian Job Trends Dashboard. Valuable insights have been gained through our analysis of OJP data, including LMIC research lead
Suzanne Spiteri’s recent report on improving the quality and accessibility of job postings to reduce employment barriers for neurodivergent people.
Decoding job postings: Improving accessibility for neurodivergent job seekers
Improving the quality and accessibility of job postings is one way to reduce employment barriers for neurodivergent people.
2. Elemental Economics - Mineral demand.pdfNeal Brewster
After this second you should be able to: Explain the main determinants of demand for any mineral product, and their relative importance; recognise and explain how demand for any product is likely to change with economic activity; recognise and explain the roles of technology and relative prices in influencing demand; be able to explain the differences between the rates of growth of demand for different products.
Seminar: Gender Board Diversity through Ownership NetworksGRAPE
Seminar on gender diversity spillovers through ownership networks at FAME|GRAPE. Presenting novel research. Studies in economics and management using econometrics methods.
5 Tips for Creating Standard Financial ReportsEasyReports
Well-crafted financial reports serve as vital tools for decision-making and transparency within an organization. By following the undermentioned tips, you can create standardized financial reports that effectively communicate your company's financial health and performance to stakeholders.
Independent Study - College of Wooster Research (2023-2024) FDI, Culture, Glo...AntoniaOwensDetwiler
"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
My study abroad in Bali, Indonesia, inspired this research topic as I noticed how globalization is changing the culture of its people. I learned their language and way of life which helped me understand the beauty and importance of cultural preservation. I believe we could all benefit from learning new perspectives as they could help us ideate solutions to contemporary issues and empathize with others.
STREETONOMICS: Exploring the Uncharted Territories of Informal Markets throug...sameer shah
Delve into the world of STREETONOMICS, where a team of 7 enthusiasts embarks on a journey to understand unorganized markets. By engaging with a coffee street vendor and crafting questionnaires, this project uncovers valuable insights into consumer behavior and market dynamics in informal settings."
How Does CRISIL Evaluate Lenders in India for Credit RatingsShaheen Kumar
CRISIL evaluates lenders in India by analyzing financial performance, loan portfolio quality, risk management practices, capital adequacy, market position, and adherence to regulatory requirements. This comprehensive assessment ensures a thorough evaluation of creditworthiness and financial strength. Each criterion is meticulously examined to provide credible and reliable ratings.
Abhay Bhutada, the Managing Director of Poonawalla Fincorp Limited, is an accomplished leader with over 15 years of experience in commercial and retail lending. A Qualified Chartered Accountant, he has been pivotal in leveraging technology to enhance financial services. Starting his career at Bank of India, he later founded TAB Capital Limited and co-founded Poonawalla Finance Private Limited, emphasizing digital lending. Under his leadership, Poonawalla Fincorp achieved a 'AAA' credit rating, integrating acquisitions and emphasizing corporate governance. Actively involved in industry forums and CSR initiatives, Abhay has been recognized with awards like "Young Entrepreneur of India 2017" and "40 under 40 Most Influential Leader for 2020-21." Personally, he values mindfulness, enjoys gardening, yoga, and sees every day as an opportunity for growth and improvement.
Transaction cost expenditures and the relative performance of mutual funds(13)
1. Financial Transaction-cost Expenditures and
Institutions the Relative Performance of
Center Mutual Funds
by
John M.R. Chalmers
Roger M. Edelen
Gregory B. Kadlec
00-02
2. The Wharton Financial Institutions Center
The Wharton Financial Institutions Center provides a multi-disciplinary research approach to
the problems and opportunities facing the financial services industry in its search for competitive
excellence. The Center's research focuses on the issues related to managing risk at the firm level
as well as ways to improve productivity and performance.
The Center fosters the development of a community of faculty, visiting scholars and Ph.D.
candidates whose research interests complement and support the mission of the Center. The
Center works closely with industry executives and practitioners to ensure that its research is
informed by the operating realities and competitive demands facing industry participants as they
pursue competitive excellence.
Copies of the working papers summarized here are available from the Center. If you would like
to learn more about the Center or become a member of our research community, please let us
know of your interest.
Anthony M. Santomero
Director
The Working Paper Series is made possible by a generous
grant from the Alfred P. Sloan Foundation
3. Transaction-cost Expenditures and the Relative Performance of
Mutual Funds *
John M.R. Chalmers
Lundquist College of Business
1208 University of Oregon
Eugene, OR 97403-1208
jchalmer@oregon.uoregon.edu
Roger M. Edelen
The Wharton School
University of Pennsylvania
Philadelphia, PA 19104-6367
edelen@wharton.upenn.edu
Gregory B. Kadlec
Pamplin College of Business
Virginia Tech
Blacksburg, VA 24060-0221
kadlec@vt.edu
This Version: November 23, 1999
First Version: March 15, 1998
*
We thank Grant Cullen, Diane Del Guercio, Jarrad Harford, Craig MacKinlay, Abon Mozumdar, Wayne Mikkelson, Megan
Partch, Russ Wermers, and Lu Zheng for helpful suggestions. This work has also benefited from the comments of seminar
participants at the Massachusetts Institute of Technology, the University of Illinois-Champaign-Urbana, the University of
Maryland, the University of Oregon, Virginia Tech, the 14th Annual Pacific Northwest Finance Conference, the 1999 Western
Finance Association meetings, and the Micro workshop at Wharton. We thank Julia Acton, John Blease, Chris Henshaw, Sergey
Sanzhar, and Paul Vu for excellent research assistance. We thank Mark Carhart and Andrew Metrick for providing data used in
this study. This paper was previously circulated under the title: “Evaluating mutual fund managers by the operational efficiency
of their trades.”
4. An analysis of mutual fund trading costs
Abstract
We directly estimate annual trading costs for a sample of equity mutual funds and find that these
costs are large and exhibit substantial cross sectional variation. Trading costs average 0.78% of
fund assets per year and have an inter-quartile range of 0.59%. Trading costs, like expense
ratios, are negatively related to fund returns and we find no evidence that on average trading
costs are recovered in higher gross fund returns. We find that our direct estimates of trading
costs have more explanatory power for fund returns than turnover. Finally, trading costs are
associated with investment objectives. However, variation in trading costs within investment
objectives is greater than the variation across objectives.
5. 1. Introduction
Mutual fund returns are negatively related to fund expense ratios as documented by
Jensen (1968), Elton, et al, (1993), Malkiel (1995), and Carhart (1997) among others. While
less visible than expense ratios, trading costs are another potentially important cost to mutual
funds. There are ample references to trading costs and their likely effect on fund returns in the
literature, dating back at least to Jensen (1968). However, a direct analysis of fund trading
costs and their relation to fund returns has not been conducted. 1 Rather, most research has
used fund turnover as a proxy for fund trading costs. We estimate mutual funds’ equity trading
costs and the association between those costs and fund returns. The trading costs that we focus
on are spread costs and brokerage commissions. Spread costs are tallied using a fund-by-fund,
quarter-by-quarter examination of stocks traded, accompanied by a transaction-based estimate
of the cost of each trade. Brokerage commissions are disclosed in the Securities and Exchange
Commission’s (SEC) N-SAR filing. We combine our analysis of fund trading costs with an
analysis of fund expense ratios, which do not include trading costs, to provide a comprehensive
evaluation of fund costs and their association with fund returns.
Mutual fund costs are critical to analysis of the value of active portfolio management.
Grossman and Stiglitz (1980) suggest that informed investors trade only to the extent that the
expected value of their private information is greater than the costs incurred to gather the
information and implement the trades. Fund expense ratios can be interpreted as information
gathering costs while fund trading costs can be interpreted as the cost of implementing an
investment strategy. Our results confirm the negative relation found between expense ratios
and fund returns and extend the conclusions drawn in indirect analyses of the relation between
1
Keim and Madhavan (1993, 1995), Chan and Lakonishok (1995), and Jones and Lipson (1999) examine trade execution costs
for specific institutional trades. By contrast, we aggregate fund’s trading costs and show the accumulated effect on fund returns.
1
6. fund trading costs and fund returns (see e.g. Grinblatt and Titman (1989), Elton et. al. (1993),
Carhart (1997) and Edelen (1999)).
Our analysis directly quantifies trading costs. We find that, trading costs incurred by
mutual funds are large. As a fraction of assets under management, spread costs average .47%
and brokerage commissions average .30% annually. More importantly, there is substantial
variation in these costs across funds. For example, the difference in trading costs between
funds in the 25th and 75th percentile is 59 basis points. This is greater than the 48 basis point
difference in expense ratios across the same range.
We decompose trading costs into three components: turnover, average spread of fund
holdings, and fund managers’ sensitivity to trading costs. Turnover, measures trading
frequency and is a common proxy for trading costs. Turnover captures roughly 55% of the
variation in trading costs. The average spread of fund holdings is a measure of a fund’s
average cost per trade and captures 30% of the variation in trading costs. Finally, fund
managers’ sensitivity to trading costs measures the degree to which the fund manager executes
trades that are more or less expensive than the average stock in the portfolio. Trading
sensitivity captures 5% of the variation in trading costs.
Our analysis sheds light on the value of active fund management. We examine the
relation among expense ratios, trading costs and fund returns. We find that fund returns
(measured as raw returns, CAPM-adjusted returns, or Carhart four factor-adjusted returns) are
significantly negatively related to both expense ratios and trading costs. Consistent with
indirect analyses of fund returns and trading costs (Elton et al (1993) Carhart (1997)) we find a
negative relation between turnover and fund returns. However, the relation between turnover
and fund returns is weaker than that between our direct estimates of trading costs and fund
2
7. returns. In fact, regressions using our direct estimates of trading costs imply that trading costs
have more power than expense ratios in explaining fund returns. We find no evidence that
trading costs are recovered in higher gross fund returns.
Finally, given the widespread use of fund investment objectives to classify fund types, we
analyze the relation between investment objective and trading costs. We find that on average
investment objectives are related to fund costs in the manner one would expect, that is aggressive
growth funds have higher average costs than growth and income funds. However, we also find
that variation within investment objectives is much larger than variation across investment
objectives. Thus, the impact of trading costs goes beyond the standard classification of funds’
investment objectives.
The remainder of this paper is organized as follows. We first describe our sample and the
methods we use to estimate trading costs. We then provide simple descriptions of trading costs
distributed by fund size and explain how the trading costs are related to returns, in panel data and
in Fama MacBeth (1973) style regressions. We decompose trading costs and assess each
component’s association with returns. Finally we analyze the relation between investment
objective and trading costs to determine to what extent a fund’s investment objective informs
investors about the level of trading costs.
2. Data
2.1. Sample selection and data sources
Following Edelen (1999), 165 funds are randomly sampled from the 1987 summer volume
of Morningstar’s Sourcebook, Twenty-nine funds are dropped because portfolio holdings data
are unavailable. Four funds are dropped because the funds held less than 50% of their assets in
3
8. equity for the entire sample period (1984-1991). We require a minimum of 50% of assets in
equity because our trading cost data are limited to equity securities. The sample of 132 funds
represents a variety of investment objectives. Using CRSP mutual fund investment objective
classifications, our sample is 25% aggressive growth funds, 39% growth funds, 28% growth and
income funds, and 8%, income funds.
Table 1 compares our sample funds to the 341 mutual funds in the CRSP database during
1987 that hold at least 50% of their assets in equity. The 50% equity requirement restricts our
sample to 92 funds during 1987. Our sample is representative of the funds in the CRSP mutual
fund database in terms of style classification, age, total assets, expense ratio, turnover, average
return, and survival.
We use holdings data to infer funds’ trading decisions. The equity holdings for each fund
are hand-collected from volumes of Spectrum II, a publication of CDA Investment Technologies,
Inc. Spectrum II provides quarterly snapshots of funds’ equity holdings and are used extensively
by Grinblatt and Titman (1989) and Wermers (1998). 2 We collect the holdings data from
January 1984 through December 1991. We have an average of 18 time-series observations of
holdings data per fund. Quarterly holdings data are available for 90% of the sample while 10%
of the holdings are observed semi-annually. Figure 1 shows the distribution of holdings data for
our sample funds across time and investment objective. Using these holdings data, we infer
funds’ trading activity from changes in the position of each stock held by each fund after
adjusting for stock splits and CDA reporting adjustments. 3
2
Wermers (1999) provides an excellent description of the data-collection process used by CDA. Because we have collected the
data from hard-copy volumes, our data may not reflect updates that CDA has made to correct errors, and we may have introduced
errors by way of data entry.
3
CDA reports are issued at quarter-end in March, June, September, and December. For funds that report holdings for other
quarter-end months, say January, April, July, October, CDA reports January’s holdings in March. However, CDA updates the
January holdings for any splits or other stock distributions that occur in February or March.
4
9. Because data for estimating trading costs of foreign stocks are not available from our data
sources, purchases and sales of foreign stocks are dropped. These omissions are likely to be
minor since foreign stocks account for less than .4% of the sample funds’ holdings. In addition,
the snapshot nature of the portfolio-holdings data limits our proxy for funds’ trading activity.
For example, if a stock was bought and sold between disclosure dates we would not capture the
trade. Finally, we cannot capture trading in bonds and other fixed-income securities with the
CDA data. For 1,700 of our 2,315 fund quarters of holdings data, we have data from the SEC’s
N-SAR filings reporting fund total purchases and sales activity on a semi-annual basis. We use
these data to gauge how well the CDA portfolio-changes capture trading activity. On average
our proxy captures 87% of the trading reported in the N-SAR report.
We obtain data on fund returns, turnover, and expense ratios from the Center for Research
in Security Prices (CRSP) mutual fund database. Data on fund brokerage commissions, client
flows, and total purchases and sales are taken from the SEC’s N-SAR report. We obtain data on
stock returns, prices, and shares outstanding from the CRSP daily returns files and data on book
value of equity from Compustat’s industrial research and tertiary file. Finally, data on bid-ask
quotes, transaction prices, and transaction volumes are obtained from the Institute for the Study
of Securities Markets (ISSM) transaction files.
2.2. Estimating trading costs
In our analysis of mutual fund trading costs we consider brokerage commissions and spread
costs, which we label direct costs of trading. We also consider tax costs due to the realization of
capital gains, which we label indirect costs since these costs influence investors’ returns but not
the mutual funds’ returns. We discuss these costs in turn.
2.2.1. Brokerage commissions
5
10. Brokerage commissions are available for 99 of the 132 funds from N-SAR reports filed
with the SEC. Specifically, we have brokerage commission data for 42% of all fund-quarter
observations. To estimate brokerage commissions for the missing fund-quarter observations, we
assign funds to quintiles on the basis of turnover and expense ratios. 4 Our brokerage fee estimate
for each missing observation is the median brokerage fee for its corresponding turnover-expense
ratio quintile. As a gauge of the in-sample reliability of these estimates, the R-square for the
regression of brokerage commissions on turnover rank and expense ratio rank is .42.
2.2.2. Spread costs
We estimate a fund’s spread cost when trading stock i in quarter t using the volume-
weighted average effective spread for all trades recorded in the ISSM database for stock i in
quarter t:
K
P − M ik −
effective spread it = ∑ ik ,
Shrsik
⋅ K (1)
M ik −
k =1
∑ Shrsik
k =1
where k ranges over the set of all transactions in the ISSM database for stock i in quarter t; Pik is
the transaction price; Mik- is the midpoint of the bid and ask quotes immediately preceding
transaction k; and Shrsik is the number of shares traded. Given that mutual fund trades are
relatively large, the volume weighted effective spread of equation (1) is more relevant for
estimating spread costs for mutual fund trades than an equally weighted effective spread because
it places greater weight on the cost of larger trades. We estimate annual spread costs for each
fund as the product of the dollar value of each trade multiplied by the effective spread estimate in
4
In estimating brokerage commissions we considered several potential factors in addition to turnover and expense ratios
including: fund size, number of trades, and average trade size.
6
11. equation (1) summed over all trades for the fund each quarter and divided by the value of the
fund’s assets/equity. 5
The ISSM transaction data cover stocks that are listed on either the AMEX or the NYSE.
By value 20% of our sample fund holdings are listed on the NASDAQ. We do not have
transaction data to calculate the effective spreads of NASDAQ stocks directly. To estimate
effective spreads for these stocks, each quarter we assign all stocks listed on CRSP (NYSE,
AMEX and NASDAQ) to deciles based on share price. Within each share-price decile we assign
stocks to deciles according to market value of equity. We then estimate the effective spreads of
NASDAQ stocks using the median effective spread for the corresponding price-size cell in the
ten-by-ten grid of NYSE/AMEX effective spreads. As a gauge of the in-sample reliability of
these estimates, the average R-square for the cross-sectional regressions of effective spreads on
the corresponding price-rank and size-rank for NYSE/AMEX stocks is .71.
A potential limitation of our spread cost estimates is that, for a given stock in a given
quarter, we calculate spread costs using the same effective spread for all fund trades regardless of
trade size. Trading costs are likely to depend on the size of the total trade package (i.e., the
quarterly position change) as well as how the trade package is broken up into individual trades.
Using data on actual trade packages of institutional investors, Chan and Lakonishok (1995) find
that 78% of institution’s trade packages are executed over two or more days and that the
estimated price impact cost is 1% for buys and 0.35% for sells. Since we do not know how fund
position changes are executed during the quarter we apply the same effective spread to all fund
trades in a given stock during a given quarter. While this may introduce noise into our estimates
of fund trading costs, our evidence suggests that it does not introduce significant biases.
5
We scale by equity when necessary to reduce heterogeneity in spread cost estimates due to our inability to estimate spread costs
for funds’ non equity holdings.
7
12. 2.2.3. Estimating capital gains
To estimate capital gains associated with fund managers’ trading decisions we first
estimate the tax-basis for each stock held by each fund. We assume that: the tax-basis for the
fund's initial holdings in our database is the price of each stock one-year prior to the first
observation of holdings data; 6 the tax-basis for all subsequent holdings is equal to the stock’s
price at the midpoint of the quarter in which it was purchased; and funds use average costing (the
average cost basis) to determine the tax-basis of shares sold as opposed to specific identification
which provides a unique cost basis to each share purchase. Specific identification gives fund
managers greater flexibility in managing the tax-timing options in the fund’s portfolio.
Nonetheless, Huddart and Narayanan (1997) report that most funds use average costing.
As in Huddart and Narayanan (1997), the basic variable in the analysis of capital gains is
the estimated capital gain or loss of each position as a fraction of its current value:
Pit − basis jit
gain jit = , (2)
Pit
where Pit is the price of stock i as of the midpoint of quarter t, and basisjit is the basis of stock i as
of the midpoint of quarter t for fund j. gainjit is bounded above by one but can be an arbitrarily
large negative number. There are very few extreme negative observations and truncating these
observations has no effect on the analysis. An unrealized gain is the capital gain that would arise
if a position were sold during the quarter while a realized gain is the gain that arises from an
actual sale.
2.3. Characteristics of the sample funds and their holdings
6
This assumption is motivated by the fact that the average holding period of each stock of our sample funds is approximately one
year. If stock price data are not available one year prior we roll forward in 3 month increments until stock price data are
available.
8
13. Table 2, Panel A, presents characteristics of the sample of 132 mutual funds. The average
fund has $374 million in total assets, with 81% invested in equity. The median fund is much
smaller with $153 million in assets, while the median proportion invested in equity is
comparable to the mean at 85%. The average fund earned an annualized return of 13.2% and the
median return is 13.9% during the sample period. Average (median) turnover is 76% (70%) of
assets managed, where turnover is defined as fund sales scaled by total fund assets. Funds at the
25th percentile have less than half the turnover of funds at the 75th percentile, indicating that
turnover varies substantially across funds. Finally, the average fund has a ratio of annual client
inflow to assets of 39% and annual client outflows to assets of 40%.
Table 2, Panel B presents characteristics of the stocks held by the sample funds. The
average fund holds 82 stocks. To compare the stocks held by these funds to the universe of
stocks traded on the NYSE, AMEX, or NASDAQ, each quarter we form deciles based on
various stock characteristics for the universe of stocks. We report equally weighted means of the
ranks and characteristics in Panel B.
Relative to the median stock (in decile 5.5), the stocks held by the funds in our sample have
much larger capitalization (decile 9.2), high dividend yields (decile 7.0), high share prices (decile
8.7), low effective spreads (decile 2.1), low return volatility (decile 3.6), average beta risk (decile
5.4), slightly below average book to market ratios (decile 5.2), and high prior-year returns (decile
6.8). These results are consistent with those of Del Guercio (1996), Falkenstein (1996), Gompers
and Metrick (1998), and Daniel, Grinblatt, Titman and Wermers (1997) who report that
institutional investors have preferences for large, liquid stocks with high past returns, and median
to slightly below median book-to-market ratios.
9
14. 3. Fund Costs
In this section we report our estimates of annual fund costs and examine the relation
between fund costs and fund size.
3.1. Cost estimates
Table 3 provides summary statistics of fund expense ratios, brokerage commissions, spread
costs, and capital gains. Expense ratios include the fund management fee, administrative costs,
and other operating expenses such as audit fees, directors’ fees and taxes and 12b-1 distribution
fees. From Table 3, the average fund expense ratio is 1.07% of assets and the median expense
ratio is 1.03%. Our sample average expense ratios are comparable to the average expense ratio
of 1.08% in Carhart (1997) and 1.13% reported in Gruber (1996). There is considerable
variation in expense ratios across funds as seen by the 48 basis point difference between the
expense ratios of the 25th and 75th percentiles.
Expense ratios do not reflect trading costs, which include brokerage commissions and
spread costs, nor do they include the costs associated with the realization of capital gains. From
Table 3, average annual brokerage commissions are 0.30% of fund assets. As with expense
ratios, there is considerable variation across funds in brokerage-commission costs as seen by the
30 basis point difference between the 25th and 75th percentiles. The average fund spends 0.47%
of fund assets each year on spread costs. 7 This estimate likely understates the actual cost for two
reasons. First, our estimate is based on average effective spreads and may not fully reflect the
price impact of large trades. Second, we fail to capture 13% of trading activity using quarterly
snapshots of holdings, implying that, ceteris paribus, an unbiased estimate would be scaled up
7
This number is higher than reported in prior versions of this paper (.34%) because our prior results used equally weighted
averages to estimate the effective spread. Using volume weighted effective spreads increases the spread estimates because larger
trades tend to have larger effective spreads.
10
15. by 0.87-1 or 1.15. Again, we find considerable variation across funds in spread costs, as seen by
the 37 basis point difference between the 25th and 75th percentiles. Summing brokerage
commissions and spread costs, on average funds spend .78% of their assets on trading each year.
More importantly, the inter-quartile range for these trading costs is 59 basis points which is
greater than the 48 basis point inter-quartile range for expense ratios.
For comparison purposes we estimate funds’ trading costs using the methodology proposed
by Grinblatt and Titman (1989). Grinblatt and Titman estimate funds’ trading costs by
comparing actual fund returns to hypothetical fund returns. Hypothetical fund returns are
calculated using CDA portfolio holdings data and CRSP stock returns data. They reflect the
returns to the portfolio of stocks held by funds, but, unlike actual fund returns, hypothetical fund
returns do not include expense ratios, brokerage commissions, or spread costs. Thus, the
difference between the two returns provides an estimate of total fund costs. An estimate of
trading costs is obtained by subtracting the expense ratio from the difference in returns.
This indirect approach to estimating trading costs is noisy relative to our direct estimates.
The noise is due in large part to the assumptions that must be made concerning the price at which
stocks are purchased and sold during the quarter in the hypothetical portfolio. 8 This noise is
evident in the indirect estimates we compute for our sample funds. Following Grinblatt and
Titman, the implicit trading cost for the 25th percentile of all fund-quarter observations is –1.09%
of fund assets. In fact, the implicit trading cost is negative for more than 35% of the fund-quarter
observations. Despite the evident noise, the Grinblatt and Titman measure should provide an
unbiased estimate of fund trading costs. We find that the average estimate using their procedure
8
Our direct estimate of trading costs also relies on estimated prices. Recall that, we use mid-quarter prices when calculating
trade value. However, the trade value is then multiplied by spread cost, which is on the order of 1%. Thus, the impact of errors in
price estimates on our transaction cost estimate is small. By contrast, in the indirect approach errors in price estimates carry
through directly to the transaction cost estimate.
11
16. comes remarkably close to our direct estimates. If we limit our analysis to funds with at least
90% equity in their portfolio, a sample of 88 funds, 9 the average direct estimate of annual trading
cost is 0.92% of fund assets while the average implicit estimate is 1.03% of fund assets. These
two estimates are statistically indistinguishable. This suggests that our estimates of fund spread
costs using volume-weighted average effective spreads of eq. (1) are not materially biased.
Finally, we examine an indirect trading cost, capital gains realizations, which influence
investors’ after-tax returns. From Table 3, the average fund realizes net capital gains of 5.32%
of total assets annually. The net capital gain is measured in excess of tax-loss carry forwards and
therefore estimates the taxable capital gains passed through to investors.
This gain realization accelerates the due date for capital-gains taxes, but it does not
represent the incremental tax cost of the fund managers’ trading activity. When investors redeem
fund shares, capital gains are recognized irrespective of the fund managers’ trading activity.
Given an annual redemption rate of 40% of fund assets from Table 2, the typical capital-gains
deferral is 2.5 years. Therefore, turnover of equity holdings in the fund portfolio accelerates
capital-gains realization by about 2.5 years. For investors facing a 28% capital-gains tax rate, this
acceleration of gain recognition by the fund manager increases the present value of tax payments
( )
by roughly 0.28 1 − e − 0. 11* 2. 5 ≅ 6.7% per dollar of capital gain realized, assuming an 11%
discount rate. Thus, the estimated tax cost imposed on investors from the 5.32% annual gain
realization is 0.36% per year. This assumes all accounts are fully taxable. Of course, for tax-
deferred accounts, the incremental cost of gain recognition by the fund manager’s turnover is
zero. During the sample period (1984-1991) less than 20% of mutual fund assets were held in
tax-deferred accounts (Investment Company Institute 1998 Fact Book).
9
For funds with large fixed income/cash holdings returns calculated from equity holdings will tend to overstate the return on the
12
17. Summing brokerage commissions, spread costs, and tax costs, funds’ trading activities cost
an average of 1.14% of fund assets annually. The magnitude of these invisible costs, as
Bogle(1994) refers to them, is comparable to the magnitude of the more easily observed expense
ratio. More importantly, the cross-sectional variation in these invisible costs is twice that of
expense ratios. In particular, the inter-quartile range of invisible costs is 96 basis points while
the inter-quartile range of expense ratios is 48 basis points. Thus, if one seeks to discriminate
among funds on the basis of fund costs, trading costs and tax costs add economically relevant
information relative to expense ratios alone.
3.2 Fund costs and fund size
In this section we examine the relation between fund costs and fund size. Studies by
Collins and Mack (1997) and Tufano and Sevick (1997) find that fund expense ratios generally
decline with fund assets, indicating a large fixed-cost component to expense ratios. We find a
weaker relation between trading costs and fund size.
Figure 2 depicts the level of various fund costs across fund-size quartiles. We assign funds
to size quartiles using each fund’s average assets under management during each year of the
sample period. In panel A of Fig. 2 the average brokerage commission, spread cost, and expense
ratio is plotted in the bar graphs for each size quartile. The bar graphs illustrate that smaller funds
have higher expense ratios than larger funds. In contrast to expense ratios, spread costs and
brokerage commissions are relatively constant proportions of assets managed across size
quartiles 1-3. However, the largest funds do appear to have lower spread costs and brokerage
commissions.
fund’s portfolio, and thus, overstate the estimate of total fund expenditures.
13
18. To further assess these relations, Panel B of Fig. 2 reports the correlation between the
various fund costs and fund size. Specifically, each year we calculate the cross-sectional
correlation between total fund assets and each of cost variables for that year. We average the
correlation coefficients over time and present them in bottom row. The correlation between
expense ratios and fund size is -0.56, where the correlation between spread costs and fund size is
-0.32 and the correlation between brokerage commissions and fund size is -.30. We interpret the
evidence in Fig. 2 to imply that expense ratios have characteristics more like fixed costs, while
trading costs do not benefit to the same extent from scale economies.
4. Fund costs and fund returns
Grossman and Stiglitz (1980) theorize that in a competitive market, traders with superior
information earn abnormal returns that just offset their opportunity and implementation costs.
In a delegated portfolio-management context, this implies that the portfolio return should on
average offset the fees and trading costs imposed by the investment manager. In this section,
we examine the association between fund returns, expense ratios, and trading costs. That
expense ratios are negatively associated with fund returns is well documented (see e.g. Jensen
(1968), Elton et. al. (1993), Malkiel (1995), Carhart (1997)). However, a direct analysis of the
relation between fund trading costs and fund returns has not been undertaken. Rather there has
been a necessarily loose connection made between fund turnover and trading costs. (See for
example Bogle (1994) p. 202-205, Carhart (1997), Metrick and Gompers (1998).) While fund
turnover is likely to be related to trading costs, it is also likely that fund holdings and trade
discretion play important roles in determining trading costs. Thus, it is of interest to examine
14
19. the relation between fund returns, fund expense ratios, and our direct measures of fund trading
costs.
4.1 Panel analysis
Table 4 presents a simple yet powerful demonstration of the association between fund
costs and fund returns. There are four panels in Table 4. Each panel presents a rank ordering
of funds on the basis of a measure, or proxy, for fund costs. In each panel, we report the
average total fund cost and its separate components: expense ratios, brokerage costs, and
spread costs. We also report the average fund return using three measures of returns: raw
returns, CAPM-adjusted returns, and Carhart-adjusted returns. Raw returns are fund returns
net of expenses, fees (excluding load fees), and trading costs. CAPM-adjusted returns are raw
returns minus expected returns as specified by the CAPM. Carhart-adjusted returns are raw
returns minus expected returns as specified by the Carhart four-factor model. We estimate the
expected returns of these models using the same procedure as Carhart (1997) for both the
CAPM-adjusted returns and for the Carhart-adjusted returns. 10 Specifically, the Carhart-
adjusted return for fund j in month t is,
) ) ) )
Carhart adjusted return jt ≡ R jt − RFt − b jt −1 RMRF t − s jt −1 SMB t − h jt −1 HMLt − p jt −1 PR1YRt , (3)
where Rjt is the return on fund j in month t, RFt is the three-month t-bill in month t, RMRF t,
SMBt, HMLt, are Fama and French’s (1993) excess return on the market and factor mimicking
portfolios for size and book-to-market, and PR1YRt is the factor-mimicking portfolio Carhart
(1997) creates to capture one-year return momentum. We estimate the coefficients on the
factor mimicking portfolios using up to three years of monthly data up to month t-1. The
10
See page 66-67 of Carhart (1997) for this description.
15
20. CAPM-adjusted returns are calculated using the same procedure above but exclude the terms
involving SMB, HML and PR1YR.
Panel A of Table 4 reports average fund costs and average fund returns for funds
assigned to quintiles according to total fund costs. From Panel A, the range in total fund costs
is substantial -- 222 basis points. 11 However, the striking result is the remarkably close
association between total fund costs and adjusted fund returns. For example, in quintile 1
average total costs are 0.90% and the annual Carhart-adjusted return is -0.77%. In quintile 5
average total costs are 3.12% and the annual Carhart-adjusted return is –4.38%. It appears that
total fund costs bear a strong negative association with fund return performance. At the bottom
of Table 4, a test of the association between fund returns and total fund costs shows that for the
entire sample we cannot reject the hypothesis that the Carhart-adjusted returns plus the total
fund costs are zero. Thus, we cannot reject the hypothesis that the activities generating fund
costs have no beneficial effect on fund returns. In fact, a plausible inference from these results
is that every dollar spent on trading costs results in a dollar less in returns. This is consistent
with Elton et. al. (1993) who find that expense ratios are negatively related to fund’s return
performance and that turnover is weakly associated with lower returns. We will return to this
issue in the next section with regression results.
The remaining panels in Table 4 indicate the degree to which the association between
fund returns and total fund costs is captured using alternative measures, or proxies, for fund
costs. From Panel B, sorting on expense ratios provides information on funds’ total costs and
returns. However, the range of returns when sorted by expense ratios is in all cases tighter than
the dispersion provided by sorting on total fund costs. It is interesting that much of the
16
21. variation in returns when sorting on the expense ratio is associated with higher trading costs.
Trading costs in the high expense-ratio quintile, 1.20%, are substantially larger than in the low
expense-ratio quintile, 0.51%. In fact, trading costs increase monotonically across expense-
ratio quintiles. Thus, the evidence on the relation between expenses and returns found
elsewhere in the literature is likely to be related to a more complicated relation involving
trading costs.
Sorting on trading costs, like the expense ratio, provides information on total fund costs
and fund returns. From panel C, the return measures are nearly monotonic across trading cost
quintiles and the typical range of return discrimination is very similar to the range when sorting
on total fund costs. Of course, the correlation between trading costs and expense ratios seen in
panel B is apparent here as well. This raises a natural question concerning the extent to which
these different panels are in fact identifying separate associations with returns. There is clearly
some overlap between expenses and trading costs. We address this issue in the next section
with regression analysis.
Finally, sorting on fund turnover also provides information on total fund costs and fund
returns, confirming results found elsewhere (e.g. Elton et. al. (1993), and Carhart (1997)).
However, the variation captured by turnover is considerably less than that captured by the other
measures of fund costs. From panel D, the paired comparison between total fund costs of
quintiles 1 and 5 is significant, however, the paired comparison between returns of quintiles 1
and 5 is insignificant.
11
The variation in fund costs in Panel A is greater than we observe in Table 3 because Table 3 reports the distribution of the
time-series average of each fund’s cost calculated over the entire sample period, while in Table 4 the average fund cost funds are
calculated from funds that are assigned to quintiles each quarter.
17
22. 4.2 Regression analysis
Our regression analysis uses the cross-sectional time-series procedure developed by
Fama-Macbeth (1973). Table 5 reports time-series averages of coefficient estimates from
cross-sectional regressions of monthly fund return measures on expense ratios and spread
costs. We do not include brokerage costs in the regressions. 12 Not surprisingly the results
confirm the negative association between fund returns and expense ratios, and spread costs
seen in section 4.1. The most important result in this table is the fact that each of the two costs
retains statistical significance controlling for the other. This indicates that both costs exert an
independent influence on returns, despite their positive correlation.
The levels of the coefficient estimates are provide evidence to assess the value of active
fund management. For example, total fund costs provide for investments in security research
and the implementation of trading strategies. Therefore, higher costs should lead to higher
gross returns in a Grossman and Stiglitz (1980) world. A coefficient of –1 on the expense ratio
or spread cost variable would indicate a one-to-one inverse relation between trading costs and
returns, suggesting that every dollar spent is not recovered in higher adjusted returns. The
point estimates of the coefficients on spread costs are for the most part more than two standard
errors below –1 suggesting that funds experience worse than 1 for 1 losses in spread costs.
However, this conclusion must be interpreted cautiously for two reasons. First, the spread
costs variable is scaled by equity value in these regressions, not asset value, and as a result the
coefficient estimate will be understated. Second the high correlation between spread costs and
12
Recall that the sparseness of the actual brokerage commission data forced us to estimate brokerage commissions for much of
the sample and these estimates are derived from turnover and expense ratios and therefore potentially introduce collinearity that
is mechanically related to variables of interest. Including the brokerage commission data in the regressions reduces the statistical
significance of the expense ratio, but the other coefficients remain similar is size and significance.
18
23. brokerage fees seen in Table 3 helps to explain the larger coefficient on spread costs since
brokerage fees are not included in the regression spread costs are likely to pick up those costs.
In the case of the expense ratio we cannot reject the hypothesis that the coefficient on
the expense ratio is equal to –1 in any of the specifications. These results are similar to
Malkiel (1995) where he finds a coefficient on expense ratios of -1.92 which is not statistically
different from -1.
5. The determinants of fund trading costs
Table 4 shows that turnover captures only a portion of the variation in fund trading costs
and that it does a relatively poor job of explaining fund returns. Turnover is surely an important
determinant of trading costs, but it is just one component. This section analyzes the determinants
of trading costs more completely.
5.1 A decomposition of trading costs
Trading costs for fund j in period t may be expressed as
trading costs jt = ∑ (Brok jit + Spd jit )TradeSize jit
I
(3)
i =1
where i ranges over all stocks in fund j’s investment opportunity set (I in total); Brok jit and Spdjit
are the percentage brokerage commission and effective spread, respectively; and TradeSizejit is
the trade size as a percentage of total assets. Because data on brokerage commissions at the
individual-trade level are unavailable and are likely to be roughly constant across trades we
consider only the spread component of trading costs in what follows.
It is convenient to rewrite Eq. (3), excluding brokerage commissions, as
( )
I I
jt ∑
spread costs jt = Spd TradeSize jit + ∑ Spd jit − Spd jt TradeSize jit (4)
i =1 i =1
19
24. where Spd jt denotes the value-weighted average spread of the stocks in fund j in period t.
Denote the turnover (total trading volume scaled by total assets) of fund j in period t as
I
T jt ≡ ∑ TradeSize jit and the weight of stock i in fund j in period t as wjit. The first term in Eq.
i =1
(4) can be written:
TradeSize jit = ∑ Spd jit (w jit T jt ).
I I
∑ Spd jt
(5)
i =1 i =1
Therefore, the fund’s spread costs can be written:
spread costs jt = Spd jt T jt + ∑ (TradeSize jit − w jitT jt )Spd jit .
I
(6)
i =1
The first term in Eq. (6) is the average spread of the stocks in the fund’s portfolio times
fund turnover. The second term is an adjustment to account for what we call the spread
sensitivity of the fund’s trading. If a fund’s trading were proportional to its holdings but
(otherwise) indifferent to the spread, the expected volume of trade in stock i would be wjitTjt.
The difference between a fund’s actual trading and this naïve benchmark, (Tradesizejit -wjitTjt),
indicates the degree to which a fund’s trading is tilted toward stocks with lower or higher spreads
relative to its portfolio average. Thus, Eq. (6) decomposes a fund’s total spread costs into three
components: turnover, the average spread of the portfolio, and the spread sensitivity of trading.
This analysis points out two conditions that must be met for turnover to fully capture
variation in funds’ trading costs. First, the average spread of funds’ portfolio holdings must be
constant across funds and over time. Second, after normalizing against holdings, a fund’s volume
of trading in a stock must be unrelated to the stock’s spread. Sections 5.2 and 5.3 examine these
20
25. two conditions, respectively. Both sections conclude that the respective assumption is not valid. 13
This explains the Table 4 findings that variation in turnover does a relatively poor job of
capturing variation in trading costs.
5.2. Turnover, average spreads, and trading costs
Figure 3 plots the distribution across funds in turnover, Tjt, and the average spread of fund
holdings (Spd ) .
jt The figure is constructed by independently ranking funds into turnover
quintiles and spread quintiles, and then forming a five-by-five partition of the sample of funds
according to these quintile rankings. Panel A presents the number of funds in each cell of the
five-by-five partition. The area of each dot graphically represents the relative number of funds in
the corresponding cell. Panel B presents the average annual spread costs of the funds in each cell,
with the area of the dot graphically representing the relative magnitude of that cost. The average
turnover of the funds in each turnover quintile is listed in the row headings. Similarly, the
average spread of the funds’ holdings in each effective-spread quintile is listed in the column
heading.
There are several observations from Fig. 3. First, there is substantial variation in the
average spread of holdings across funds. Section 5.1 shows that turnover will be an accurate
proxy for trading costs only if trading costs are proportional to turnover (spread sensitivity close
to zero) and the proportionality coefficient (the fund’s average spread) is constant across funds.
Fig. 3 shows that one cannot apply a single proportionality coefficient in relating trading costs to
turnover without losing significant explanatory power. Both turnover and the average spread of
holdings are important determinants of funds’ spread costs. For example, the five funds in
turnover quintile 1 and spread quintile 5 have average annual spread costs of .46%, a magnitude
13
Chalmers and Kadlec (1998) use similar logic to argue that investors’ amortized spread costs are not fully captured by the
21
26. similar to that for the three funds in the opposite corner, turnover quintile 5 and spread quintile 1,
with annual spread costs averaging .57%. There are a number of similar examples in Panel B
which demonstrate that a fund’s annual spread costs depends on both the frequency of trade and
the average spread of the funds’ holdings.
Second, in Panel A, the number of funds along the diagonal, where turnover and average
spread of holdings are directly related, is surprising. For example, among funds that hold the
highest-spread stocks (average spread = 1.28%) the greatest concentration of funds occurs in the
highest turnover quintile (average turnover = 1.35). Recall that the quintile rankings are
independent, so funds in the high-turnover quintile have high turnover relative to all funds, not
just relative to other high-spread funds. One might expect that funds holding higher-spread
stocks would have relatively lower turnover rates, given that their turnover is particularly costly.
For example, Amihud and Mendelson (1986) discuss a clientele effect whereby investors with
short holding periods hold stocks with relatively low spreads and investors with long holding
periods hold stocks with relatively high spreads. A similar argument applies to the funds in the
lowest spread quintile. Most funds in this quintile are relatively inactive traders, despite the fact
that they hold relatively low spread stocks. These patterns are statistically significant: the p-value
for the null that there is no association between the row and column variable in Panel A, using a
chi-square test, is 0.015.
5.3 Spread sensitivity of trading
The spread sensitivity component of trading costs is negative if trading volume within the
fund is inversely related to the spread. We use a summary measure of spread sensitivity (the
second term in Eq. (6)) in decomposing trading costs. A more comprehensive measure of spread
spread and must consider turnover as well.
22
27. sensitivity is provided by regressing trading volume on spreads across portfolio holdings. This
section focuses on this measure. We have considered many specifications for this regression. All
have some disadvantage, econometric or otherwise. However, the conclusions regarding trading
behavior are robust across procedures and they collectively provide evidence that fund managers
are somewhat sensitive to costs imposed by the spread, after controlling for the fund’s holdings.
For brevity, we present only one of the many methods used to evaluate fund manager’s trading
sensitivity to spread costs.
For each fund, and each quarter, we estimate a regression where the dependent variable is
the volume of trade in various spread categories or bins, and the independent variables are the
total portfolio weight of stocks in the bin and the average spread of the stocks in the bin. Using
this approach, all stocks held or traded during the quarter are allocated into one of forty bins
according to their effective spread. The bins range from a spread of less than 0.20% to 4.0%,
with 0.10% increments. The total portfolio weight in each bin is determined, each quarter, as is
the total volume of transacting by the fund in each bin, each quarter. Thus, for each fund, and
each quarter we estimate a regression with 40 observations. Intuitively, the regressions test
whether the trading activity associated with a stock depends on the spread, after controlling for
the fund’s tendency to hold such stocks.
Table 6 reports average coefficient estimates from the above regressions. Perhaps not
surprisingly, there is a strong association between funds’ trading activity and their holdings. That
is, if a fund holds more of particular stock it tends to trade it more. The interesting result is the
incremental negative relation between trading activity and spread after controlling for holdings.
The t-statistic for this relation is –12, and in the various specifications attempted was never less
than –4. This indicates that on average, funds pay some attention to the spread in making trading
23
28. decisions. Thus, according to equation (6), this factor is likely to provide information about the
fund’s overall trading costs.
5.4 Fund returns and the components of trading costs
In results not tabulated we present a regression decomposition of funds’ annual spread
costs into the three components, turnover, average spread of holdings, and spread sensitivity.
The coefficient estimates are 0.005 (t=42), 0.100 (t=34) and 0.002 (t=4), respectively. This
suggests that all three components are important determinants of fund trading costs. In this
section we examine the relative contribution of the three components in explaining fund returns.
Table 7 reports time-series average coefficient estimates from regressions of fund returns
on expense ratios and the three components of spread costs. From Table 7, fund returns retain a
negative relation to expense ratios, though not as significantly as in the regressions of Table 5.
As in Carhart (1997), we find that fund returns are negatively related to fund turnover, although
the significance is generally marginal. The average coefficient estimate for turnover are –.02 (t-
statistic=-1.46), -.02 (t-statistic=-1.84), and -.03 (t-statistic=-2.60) for the regressions using raw
returns, CAPM-adjusted returns, and Carhart-adjusted returns, respectively.
We also find evidence that the other components of spread costs, average spread of
holdings, and spread sensitivity, help to explain fund returns. For example, the coefficient
estimate for average spread of holdings is negative in all three regressions and significant in the
regressions using raw returns and CAPM-adjusted returns. In particular, the average coefficient
estimate for average spread of holdings are –.96 (t-statistic=-1.76), -1.47 (t-statistic=-2.83), and -
0.12 (t-statistic=-0.55) for the regressions using raw returns, CAPM-adjusted returns, and
Carhart-adjusted returns, respectively. The coefficient estimate for spread sensitivity is also
negative in all three regressions, though it insignificant in each of the three regressions. From
24
29. this analysis we conclude that there is weak explanatory power to the non-turnover components
of trading costs.
6. Investment objectives and fund costs
Fund investment objective classifications are an important descriptor of mutual funds.
However, they are inherently subjective. Brown and Goetzman (1997) attack the subjective
nature of the classification system by examining the returns of mutual funds and classifying
them by their return characteristics, arguing that these are much more objective measures by
which funds can be categorized. Given the strong explanatory power for performance, it strikes
us that the expense ratios and trading costs that funds impose on investors provide useful
measures by which funds can be characterized. With this in mind, we provide evidence on the
question: to what extent is cross sectional variation in total fund costs, expense ratios and
trading costs, explained by existing investment-objective classifications?
Table 8 provides statistics on costs broken down by CRSP’s 1987 investment objectives
for our sample funds. In Panel A fund characteristics, most importantly the expense ratio and
trading costs, are presented. There is some variation in total costs across investment
objectives, particularly for maximum capital gains and growth categories. However, in
comparing this range of explained variation to Table 4 it is apparent that existing objective
classifications have little association with costs and that most of the cross sectional variation in
costs occurs within objectives instead of across objectives. To highlight this point, the 25th –
75th percentile ranges for total fund costs (not reported in Table 8) are 159 bp to 254 bp for
Maximum capital gain, 149 bp to 231 bp for Growth funds, and 105 to 178 for Growth and
Income funds. The large variation within investment objective supports the notion that
25
30. classification based upon trading costs provides valuable information beyond that provided by
the investment objective.
Table 8 Panel B summarizes the characteristics of the stocks held by investment
objective. The investment objectives do appear to correspond with stock characteristics in the
manner that one might expect. For example, average effective spreads are .59% maximum
capital gain and .49% for growth funds while the stocks that Growth and Income funds own
have average effective spreads of .35%. In addition, the other stock characteristic variables
conform to the observation that more risky stock attributes are associated with maximum
capital gain and growth objectives. Even so, the riskiest objective holds very large stocks, size
rank of 8.6, with low effective spreads (rank=2.8), below median standard deviations (rank =
4.3), and above median dividend yields (rank=6.1).
7. Conclusions
We estimate the annual costs of fund managers’ trades and find these costs to have a
substantial negative association with return performance. There are many interesting contexts
in which to interpret the evidence in our paper. Grossman and Stiglitz (1980) suggest that an
informed trader will not trade in stocks where the expected value of the information is less than
the costs of executing the trade. One interpretation of our evidence is that mutual fund
managers do not follow this rule. Alternatively, it could be that much of the trading costs we
observe are related to the provision of liquidity as discussed in Edelen (1999). To the extent
that this trading is unavoidable, the negative association between fund returns and trading costs
suggests that it pays to have fund managers who mitigate the cost of such trades. Finally, a
plausible, although unlikely interpretation for our results is that poor returns cause higher
26
31. trading costs because investors leave funds with poor returns which generates additional
trading costs. We find this unlikely because inflows also create liquidity costs and Sirri and
Tufano (1998) and Del Guercio and Tkac (1999) find that inflow tends to follow good
performance, while fund outflows are relatively insensitive to fund returns.
A practical issue that arises from our analysis is that it is costly to obtain direct estimates
of funds’ trading costs. Given their importance in explaining fund returns, a low cost proxy for
trading costs may be valuable. Our evidence suggests that a truly discriminating proxy needs
to go beyond turnover. Unfortunately, the other two components of trading costs, the
weighted-average spread and cost sensitivity of trading, are not readily observable. Thus, the
search for a low-cost proxy for these components would have significant practical value.
27
32. References
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33. Falkenstein, E. G., 1996, Preferences for stock characteristics as revealed by mutual fund
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decisions. Working paper, Duke University.
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29
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30
35. Table 1
Comparison of sample funds to the CRSP mutual-fund database
For purposes of comparison, we identify all funds in the CRSP database in 1987 with an investment objective that includes
common stock. Specifically, we drop funds with investment policy identifications of money market, government securities, C &
I, Bonds, Tax-free money market, preferred, and bond and preferred. We also drop funds with no policy classification and less
than 50% stock holdings. After these deletions we are left with 341 funds. In this comparison we drop 40 of our 132 sample
funds because those sample funds do not pass the 50% equity screen in 1987. The column labeled CRSP is the 341 funds in the
CRSP universe. Dead funds refer to funds that are merged or liquidated prior to December 1998. Turnover, is the ratio of the
funds annual sales to assets. Expenses are expense ratios reported by CRSP. Returns are arithmetic averages of the annualized
quarterly returns in 1987. Assets, is the average monthly total net asset value reported in CRSP.
Our Sample Our Sample
Fund objective CRSP Year of inception CRSP
Maximum Capital Gains 16% 21% Before 1980 47% 52%
Growth 46% 35% 1980 to 1985 32% 27%
Growth & income 24% 29% 1985 6% 8%
Income 8% 10% 1986 5% 5%
I-G 6% 5% 1987 10% 8%
Other characteristics Dead funds
Ratio of equity to assets 84% 83% No 89% 89%
Turnover 87% 86% Yes 11% 11%
Expenses .99% 0.92%
Returns 7.85% 7.14%
Assets (millions) 490 513
36. Table 2
Characteristics of sample funds and their holdings
Panel A: Sample funds
Fund assets represent total assets under management, as reported by CDA (quarterly observations). Equity / Fund Assets is the
average equity value held by the fund divided by fund value reported by CDA. Fund returns are annualized monthly data from
CRSP. Turnover is annual fund sales divided by fund assets. Inflow and outflow are the annual cash flows into and out of the
mutual funds scaled by fund assets.
N=132 Funds. Mean 10% 25% Median 75% 90%
Fund assets (millions) 374 35 68 153 468 1,085
Equity / Fund Assets .81 .62 .73 .85 .90 .94
Fund returns (%) 13.2 3.9 10.0 13.9 16.8 22.4
Turnover .76 .27 .45 .70 .99 1.45
Inflow (N = 126 Funds) .39 .09 .16 .25 .48 .76
Outflow (N =126 Funds) .40 .14 .19 .27 .42 .69
Panel B: Stocks held by sample funds
Number of stocks is the average number of stocks held in the fund portfolio each quarter. Standard deviation is the annualized
monthly standard deviation of return. Effective spread is the transaction-size-weighted average effective spread of each stock
over the quarters in which the stock is held. Prior year return is the raw return of the stock in the year prior to the observation.
All rank variables are relative to an equal-weighted decile ranking of the universe of stocks available on CRSP
(NYSE/AMEX/NASDAQ) (1 low, 10 high).
N=132 Funds Mean 10% 25% Median 75% 90%
Number of Stocks 82 39 55 73 98 132
Market Value Equity (millions) 4,662 547 2,514 4,804 7,608 8,674
Rank 9.2 7.0 7.9 8.5 8.8 8.9
Price 39 23 33 40 46 51
Rank 8.7 7.6 8.3 8.9 9.3 9.6
Standard Deviation of return .36 .27 .30 .34 .39 .47
Rank 3.6 2.2 2.7 3.3 4.2 5.3
Dividend Yield 3.0% 1.0% 1.9% 3.0% 4.0% 4.6%
Rank 7.0 5.0 6.0 7.3 8.0 8.7
Effective Spread .46% .29% .33% .40% .50% .62%
Rank 2.11 1.3 1.5 1.8 2.4 3.2
Beta 1.03 .80 .90 1.01 1.15 1.28
Rank 5.35 4.2 4.8 5.3 6.0 6.6
Prior year return .20 .10 .14 .18 .26 .35
Rank 6.8 6.1 6.4 6.7 7.1 7.5
Book-to-market .62 .45 .51 .62 .72 .78
Rank 5.1 4.0 4.5 5.2 5.8 6.3
37. Table 3
Fund expenses, trading costs and capital gains
All data expressed as annual percent of fund assets. Expense ratio is annual expenses scaled by fund assets as reported by CRSP
and does not include sales charges (loads) or brokerage commissions. Spread costs are effective spreads times dollar trade size
summed over all trades in a given year, for a given fund, scaled by fund assets. Brokerage commissions are observed semi-annually
and reported on an annualized basis. We estimate data on brokerage commissions for 33 of 132 funds. Total fund costs is the sum
of the expense ratio, spread costs, and brokerage commissions. Realized gains measures the annualized capital gains realized
during the period. Total gain is the hypothetical capital gain if all positions were liquidated. Correlations are calculated by first
averaging over time each fund’s data, then computing the correlation across funds.
Data (% of fund assets) Mean 10% 25% Median 75% 90%
Expense ratio 1.07 .66 .80 1.03 1.28 1.49
Spread costs .47 .15 .25 .39 .62 .88
Brokerage commissions .31 .10 .18 .28 .42 .54
Spread + Brokerage .78 .30 .44 .70 1.03 1.37
Total Fund Expenditures 1.85 1.01 1.35 1.74 2.28 2.76
Realized gains 5.32 .09 2.58 4.95 8.01 12.07
Total gain 9.57 .45 5.49 9.77 14.26 19.38
Correlation
Expense ratio Spread costs
Brokerage commissions .52 .53
Spread costs .37
Spread + commissions .52
38. Table 4
Average fund returns by cost quintiles
All data expressed in annual terms. Funds are assigned to quintiles each quarter on the basis of total cost, trading costs, expense ratios and turnover (1=low). The table presents the
average value of the indicated variable within quintile sub-samples. Total cost is the sum of expense ratios, spread costs, and brokerage commissions. Spread costs are effective spreads
times dollar trade size summed over all trades in a given year, for a given fund, scaled by total equity. Carhart adjusted returns are raw fund returns minus the predicted return from the
four-factor model presented in Carhart (1997). CAPM adjusted returns are raw fund returns minus the expected return from the CAPM. T-statistics are reported which test the
hypothesis that the mean quintile 1 value minus the mean quintile 5 value is zero. The turnover quintiles exclude 4.6% of the observations because of missing CRSP turnover. Below
the panels the mean value across all observations is reported for the sum of the adjusted return measures and total fund costs. The t-statistic for this value tests the null that the sum is
zero. ** p-value < .05, * p-value < .10.
Total Fund Cost Quintile t-statistic Trading Cost Quintile t-statistic
1 2 3 4 5 (Q1-Q5) 1 2 3 4 5 (Q1-Q5)
Expense Ratio 0.65% 0.86% 1.03% 1.15% 1.40% -60.00** 0.76% 0.95% 1.04% 1.11% 1.24% -36.73**
Spread Cost 0.16% 0.33% 0.45% 0.66% 1.18% -63.51** 0.13% 0.29% 0.45% 0.67% 1.23% -72.93**
Brokerage commissions 0.09% 0.17% 0.26% 0.37% 0.55% -64.34** 0.08% 0.16% 0.25% 0.38% 0.56% -68.73**
Total Costs 0.90% 1.36% 1.74% 2.18% 3.12% -112.94** 0.97% 1.40% 1.74% 2.16% 3.03% -94.71**
Carhart Adjusted Returns -0.77% -0.87% -1.04% -1.73% -4.38% 4.34** -0.25% -0.94% -0.87% -3.09% -3.62% 4.06**
CAPM Adjusted Returns -0.41% -1.75% -1.52% -2.32% -6.26% 6.24** 0.06% -1.68% -1.68% -3.68% -5.25% 5.64**
Returns 14.52% 13.42% 13.93% 12.66% 9.76% 1.95* 14.14% 13.89% 14.06% 11.88% 10.31% 1.60
Expense Ratio quintiles t-statistic Turnover Quintiles t-statistic
1 2 3 4 5 (Q1-Q5) 1 2 3 4 5 (Q1-Q5)
Expense Ratio 0.59% 0.81% 0.99% 1.15% 1.55% -91.18** 0.81% 0.91% 1.01% 1.15% 1.18% -27.71**
Spread Cost 0.35% 0.42% 0.52% 0.73% 0.76% -24.02** 0.24% 0.38% 0.53% 0.66% 0.96% -47.34**
Brokerage commissions 0.16% 0.22% 0.28% 0.33% 0.44% -32.36** 0.11% 0.17% 0.28% 0.35% 0.51% -53.59**
Total Costs 1.10% 1.45% 1.79% 2.21% 2.74% -64.75** 1.16% 1.47% 1.82% 2.15% 2.65% -58.59**
Carhart Adjusted Returns -1.31% -1.70% -1.06% -1.11% -3.63% 2.79** -1.10% -1.32% -1.51% -1.88% -2.52% 1.59
CAPM Adjusted Returns -1.01% -1.95% -1.57% -2.75% -4.98% 4.20** -3.28% -2.05% -2.13% -2.30% -2.82% -.45
Returns 14.50% 13.04% 12.67% 13.12% 10.97% 1.44 11.87% 13.23% 13.67% 13.46% 12.18% -.13
Mean Carhart adjusted return + total fund costs = .11%, t-statistic=.43
Mean CAPM adjusted return + total fund costs = -.59%, t-statistic=-2.12*
39. Table 5
Expenses, spread costs and fund returns
Coefficients reported below are time-series averages of coefficients from 64 cross-sectional regressions of monthly fund returns
on expenses and spread costs following Fama-MacBeth (1973). Raw fund returns are unadjusted fund returns. The returns are
measured net of fund expenses, fees, and transaction costs (excluding load fees). CAPM-adjusted returns are raw fund returns
minus the expected return as specified by the CAPM. Carhart-adjusted returns are raw fund returns minus the expected return as
specified by a four factor model used in Carhart (1997). Expenses are the funds expense ratio from CRSP. Spread costs are the
expenditures we estimate the fund has made on bid-ask spread related costs scaled by equity value. * indicates p-value < 10%
and ** indicates the p-value < 5%.
Independent Raw Fund CAPM- Carhart-
variables Returns adjusted adjusted
Returns Return
Intercept .01** .002** .002**
(2.5) (2.93) (2.22)
Expenses -1.72* -2.22** -1.77**
(-1.74) (-2.72) (-2.90)
Spread Expenditures -3.43* -5.21** -3.22**
(-1.98) (-3.20) (-2.95)
N (cross-sections) 64 64 64
40. Table 6
The relation between trade choice, spreads, and portfolio weights
For each fund, in each quarter, we place all stocks held or traded during the quarter into one of forty bins according to the stock’s
effective spread. The bins range from a spread of less than 0.20% to 4.0%, with 0.10% increments. We sum the portfolio weight
of all of a given fund’s stocks in each bin and sum the trading activity for each bin where buys and sells are both positive
numbers. Regressions are estimated separately for each fund, each quarter. There are 40 observations in each regression. The
regressions estimate the relation between summed trading and the summed holdings and the midpoint of the bin’s spread range.
The coefficient estimates for each quarter are first averaged across all quarters of data for each fund. The panel presents the
distribution of the average coefficient estimates across the 132 sample funds.
Mean: estimate t-statistic 10% 25% Median 75% 90%
Intercept .21 8.5 .05 .09 .17 .24 .37
Spread -1.02 -12.5 -2.3 -1.65 -.86 -.34 .11
Portfolio weight .43 405 .24 .32 .41 .53 .72
41. Table 7
Fund returns and the components of spread costs
Coefficients reported below are time-series averages of coefficients from 64 cross-sectional Fama-MacBeth (1973) regressions of
monthly fund returns on expenses, turnover, average effective spread of holdings, and the spread sensitivity. Raw fund returns
are unadjusted fund returns. Fund returns are measured net of fund expenses, fees, and transaction costs, excluding load fees.
CAPM-adjusted returns are raw fund returns minus the expected return as specified by the CAPM. Carhart-adjusted returns are
raw fund returns minus the expected return as specified by a four factor model used in Carhart (1997). Expenses are the funds
expense ratio from CRSP. Fund turnover is estimated from the CDA data and is measured as the average of purchases plus sales
divided by fund assets. Average spread is the value-weighted average effective spread of the fund’s stock holdings. Spread
sensitivity is defined in eq. (6) and measures the tendency of the fund manager to trade stocks that are more or less costly to trade
than the average stock that the fund holds. * indicates p-value < 10% and ** indicates the p-value < 5%.
Independent Raw Fund CAPM- Carhart-
variables Returns adjusted adjusted
Returns Return
Intercept .02** .005** .002**
(2.99) (2.81) (2.01)
Expenses -.81 -1.17 -1.81**
(-.97) (-1.60) (-2.79)
Fund Turnover -1.90 -.02* -.03**
(-1.46) (-1.84) (-2.60)
Average Spread -.96* -1.47** -.12
(-1.76) (-2.83) (-.55)
Spread Sensitivity -.05 -.07 -.03
(-.87) (-1.33) (-.61)
N (cross-sections) 64 64 64
42. Table 8
Characteristics of funds and their holdings by investment objective
Using the CRSP fund objective codes from 1987 we characterize the average values of funds broken down by investment
objective. Ranks are computed annually relative to the universe of available data in each year. The investment objective we call
aggressive growth is classified by CRSP as maximum captital gains and I-G appears to be synonymous with an investment style
of balanced.
Panel A: Fund characteristics by investment objective
Fund characteristics Aggressive Growth Growth & Income I-G
Growth Income
Number of funds 32 48 33 11 8
Fund Assets (millions) 365 281 527 427 265
Fund Return 14.1% 13.4% 13.9% 8.4% 12.6%
Expense ratio 1.15% 1.13% 0.95% 0.98% 0.96%
Spread Cost 0.65% 0.51% 0.35% 0.34% 0.16%
Brokerage Commission 0.35% 0.35% 0.26% 0.29% 0.14%
Total Fund Costs 2.15% 1.99% 1.56% 1.61% 1.39%
Realized Capital Gains 2.73% 6.61% 6.76% 4.56% 2.97%
Total Gains 6.81% 11.75% 11.89% 2.21% 8.04%
Turnover .94 .77 .63 .88 .65
Ratio of Equity to Assets .83 .83 .83 .83 .56
Fund Inflows .46 .48 .25 .35 .25
Fund Outflows .47 .51 .25 .26 .25
Panel B: Stock holdings’ characteristics by investment objective
All rank variables are relative to an equal-weighted decile ranking of the universe of stocks available on CRSP
(NYSE/AMEX/NASDAQ) with 1 low and 10 high. Number of stocks is the average number of stocks held in the fund portfolio
each quarter. Standard deviation is the annualized monthly standard deviation of return. Effective spread is the average effective
spread for the stocks held by each fund. The effective spread for each stock is a transaction-size-weighted average effective
spread over the quarter in which the stock is held by any fund. Prior year return is the raw return of the stock in the year prior to
the observation.
Stock Holdings’ Aggressive Growth Growth& Income I-G
Growth Income
Number of stocks held 94 89 71 81 47
Market Value Equity (mil) 2,781 3,715 6,396 6,181 8,636
Rank 8.6 9.1 9.6 9.6 9.8
Price 36.27 35.84 44.60 41.08 47.03
Rank 8.0 8.5 9.2 9.0 9.4
Standard Deviation of Return .40 .38 .31 .31 .27
Rank 4.3 4.0 2.9 2.8 2.3
Dividend yield 2.06% 2.62% 3.62% 4.44% 4.16%
Rank 6.1 6.5 7.9 8.4 8.5
Effective spread .59% 0.49% 0.35% .37% .30%
Rank 2.8 2.3 1.6 1.7 1.4
Prior year return .22 .23 .19 .15 .15
Rank 6.6 6.8 6.8 6.5 6.9
Book-to-Market .59 .57 .66 .74 .67
Rank 4.9 4.9 5.4 5.9 5.5
Beta 1.1 1.1 .9 .9 .8
Rank 5.9 5.7 4.8 4.7 4.3
43. Information not in table but used in text.
Note: 25th to 75th percentile for total fund expense in MCG is 159 bp to 254
G is 149 bp to 231
G-I is 105 bp to 178
44. Fig. 1. Distribution of fund observations
The number of fund quarters observed in each year distinguished by the investment objective of the fund.
450
400
350
number of fund quarters observed
300
250
200
150
100
50
0
1984 1985 1986 1987 1988 1989 1990 1991
Agg Growth Growth Growth and Income Income
45. Fig. 2. Fund costs and fund size
Data to assess the relation between fund size and fund costs are presented in Panels A and B.
Panel A. The sample of 132 funds is sorted annually by average assets under management and placed into size quartiles. For each
quartile average expenses ratios, brokerage commissions, and spread costs are calculated for the year. The bars represent the average
values of the cost variables by size quartile. For 33 funds, missing brokerage commissions are replace by estimates from a cross-
sectional regression of brokerage commissions on spread costs, turnover, and the expense ratio using the 99 funds with brokerage
commission data.
2.50%
Costs/Assets
2.00%
Spread Costs
1.50%
Brokerage Commissions
Expense Ratio
1.00%
Total Fund Costs
0.50%
0.00%
1 2 3 4
Fund Size Quartile
Panel B. Each year we average each fund’s assets under management, spread costs, brokerage commissions, expense ratio and total
fund costs. We present the cross-sectional correlation between assets under management and each of the cost variables. The average
reflects the average over time of the cross-sectional correlation estimates. The number of funds in each correlation estimate is
provided in the rightmost column.
Correlation between Assets under management and
Year Spread Cost Brokerage Expense Ratio Total Fund Number of
Commissions Costs Funds
1984 -0.22 -0.27 -0.57 -0.41 70
1985 -0.26 -0.17 -0.56 -0.43 86
1986 -0.33 -0.22 -0.55 -0.45 97
1987 -0.35 -0.33 -0.48 -0.49 116
1988 -0.31 -0.32 -0.50 -0.47 120
1989 -0.31 -0.30 -0.46 -0.46 121
1990 -0.32 -0.28 -0.52 -0.45 114
1991 -0.48 -0.53 -0.83 -0.49 24
Average
Correlation -0.32 -0.30 -0.56 -0.45
46. Fig. 3. Spread Expenses, Average Spreads of Holdings and Turnover.
Each fund is assigned an independent turnover and spread quintile. The quintile is established by ranking funds by year according to
turnover and, independently, according to the weighted average bid-ask spread of each fund’s holdings. Then the ranks are averaged
across years for the 132 funds. Within quintile-by-quintile sub-samples, we compute the average spread expense, and the number of
funds falling into each of cell. The area of each circle represents the average value of the variable of interest for funds falling into each
turnover rank - spread rank cell. The arrows begin at quintile 1 and point in the direction of increasing spread and turnover ranks.
Average turnover of funds by turnover quintiles is noted on the horizontal axis and average effective spreads of spread quintiles are
noted on the vertical axis.
Panel A: Number of Funds Panel B: Spread costs
5 3 4 3 11 .46% .85% .77% .77% 1.09% .64%
2 4 8 9 4 .21% .27% .44% .54% .87% .38%
Spread
5 7 2 7 5 .15% .27% .41% .54% .54%
Quintiles .32%
4 9 6 5 3 .16% .28% .37% .49% .74% .30%
10 4 7 2 3 .10% .25% .31% .41% .57% .26%
.28 .51 .72 .96 1.35 .28 .51 .72 .96 1.35
Turnover Quintiles