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.
Liquidity, investment style, and the relation between fund size and fund perf...bfmresearch
This document summarizes a study that examines the effect of liquidity and investment style on the relationship between fund size and fund performance. The study finds:
1) Fund performance declines as fund size increases, consistent with prior research.
2) This inverse relationship is stronger for funds holding less liquid portfolios, providing evidence that liquidity issues contribute to performance declining with size.
3) The negative effect of size on performance is also more pronounced for growth funds and high-turnover funds, which tend to have higher trading costs.
4) Controlling for other fund characteristics, performance is still negatively related to size, and this effect is stronger for less liquid funds.
This document discusses a research paper that investigates why mutual fund performance does not persist over the long run. It finds that fund flows and manager changes act as mechanisms that prevent persistent outperformance or underperformance. For winner funds, high inflows reduce future performance, and losing a top manager also lowers returns. However, winner funds not experiencing high inflows or a manager change outperform those facing both by 3.6% annually. For loser funds, internal governance through manager replacement is more important than external governance from outflows. Firing an underperforming manager and experiencing outflows together improves future performance more than the individual effects alone.
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.
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.
The document discusses liquidity assumptions in finance and their implications for security pricing and valuation. It notes that while finance literature recognizes several dimensions of liquidity, the core issue is the immediacy assumption that market orders have immediate execution, which is not always realistic. Research shows liquid securities have significantly higher returns than illiquid ones, demonstrating the effect of liquidity on pricing. The document also discusses liquidity risk, modeling liquidity risk as a function of trade size and price impact, and the challenges in measuring liquidity and volatility for valuation purposes.
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 document summarizes a research paper that develops a dynamic stochastic general equilibrium (DSGE) model to explain how monetary policy affects risk in financial markets and the macroeconomy. The key feature of the model is that asset and goods markets are segmented because it is costly for households to transfer funds between the markets. The model generates endogenous movements in risk as the fraction of households that rebalance their portfolios varies over time in response to real and monetary shocks. Simulation results indicate the model can account for evidence that monetary policy easing reduces equity premiums and helps explain the response of stock prices to monetary shocks.
Liquidity, investment style, and the relation between fund size and fund perf...bfmresearch
This document summarizes a study that examines the effect of liquidity and investment style on the relationship between fund size and fund performance. The study finds:
1) Fund performance declines as fund size increases, consistent with prior research.
2) This inverse relationship is stronger for funds holding less liquid portfolios, providing evidence that liquidity issues contribute to performance declining with size.
3) The negative effect of size on performance is also more pronounced for growth funds and high-turnover funds, which tend to have higher trading costs.
4) Controlling for other fund characteristics, performance is still negatively related to size, and this effect is stronger for less liquid funds.
This document discusses a research paper that investigates why mutual fund performance does not persist over the long run. It finds that fund flows and manager changes act as mechanisms that prevent persistent outperformance or underperformance. For winner funds, high inflows reduce future performance, and losing a top manager also lowers returns. However, winner funds not experiencing high inflows or a manager change outperform those facing both by 3.6% annually. For loser funds, internal governance through manager replacement is more important than external governance from outflows. Firing an underperforming manager and experiencing outflows together improves future performance more than the individual effects alone.
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.
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.
The document discusses liquidity assumptions in finance and their implications for security pricing and valuation. It notes that while finance literature recognizes several dimensions of liquidity, the core issue is the immediacy assumption that market orders have immediate execution, which is not always realistic. Research shows liquid securities have significantly higher returns than illiquid ones, demonstrating the effect of liquidity on pricing. The document also discusses liquidity risk, modeling liquidity risk as a function of trade size and price impact, and the challenges in measuring liquidity and volatility for valuation purposes.
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 document summarizes a research paper that develops a dynamic stochastic general equilibrium (DSGE) model to explain how monetary policy affects risk in financial markets and the macroeconomy. The key feature of the model is that asset and goods markets are segmented because it is costly for households to transfer funds between the markets. The model generates endogenous movements in risk as the fraction of households that rebalance their portfolios varies over time in response to real and monetary shocks. Simulation results indicate the model can account for evidence that monetary policy easing reduces equity premiums and helps explain the response of stock prices to monetary shocks.
1. The document analyzes how differences in the strength of risk management functions at bank holding companies (BHCs) may explain differences in risk-taking and performance.
2. It constructs a Risk Management Index (RMI) to measure the importance and independence of risk management at BHCs. Higher RMI is associated with lower tail risk and better performance during the financial crisis.
3. While risk management is endogenous, the evidence suggests BHCs with a strong risk culture tend to have both lower risk-taking and stronger risk management, as opposed to strategically choosing high risk with strong controls.
This document discusses systemic risk in interconnected financial systems. It presents a model for determining a "clearing payment vector" that clears the obligations of members in a financial system given their liabilities, limited liabilities, and operating cash flows. The paper shows there always exists a unique clearing vector and develops an algorithm to efficiently compute it. Comparative statics imply increased volatility lowers the total value of firms in the system even without direct costs of insolvency.
STRESS TESTING IN BANKING SECTOR FRAMEWORKDinabandhu Bag
This document summarizes a study analyzing default correlation in retail banking portfolios in India. It discusses:
1) Literature on default correlation and factor modeling approaches to estimate correlation. Previous studies found correlation varies over time and across industries/ratings.
2) Analysis of a test portfolio with 4 retail segments showing migration of exposures between segments over 14 months. Segments showed varying default rate trends over time.
3) The study builds a multi-factor linear model to test if external economic factors significantly impact default correlations between segments over time.
This document summarizes a presentation on core deposit modeling best practices. It discusses topics like rate sensitivities in a rising rate environment, valuation of core deposits, sensitivity analysis, liquidity concerns, core deposit studies and behavioral inputs. Key points covered include using historical data to model rate sensitivities, the GAAP definition of valuing core deposits as the present value of average balances discounted by alternative funding costs, and the importance of sensitivity analysis and considering different scenarios in modeling.
Prior performance and risk taking ammannbfmresearch
This document discusses using a dynamic Bayesian network approach to analyze the behavior of mutual fund managers, specifically how prior performance impacts risk-taking. The key findings are:
1) In contrast to some theories and studies, the analysis found that prior performance has a positive impact on the choice of risk level - successful fund managers take on more risk in the following year by increasing measures like volatility, beta, and tracking error.
2) Poor-performing fund managers were found to switch to more passive strategies.
3) Bayesian networks allow capturing nonlinear patterns and assigning probabilities to different outcomes, providing a more robust approach than previous studies on this topic.
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.
This presentation will survey and discuss various quantitative considerations in liquidity risk for a financial institution. This includes the concept of liquidity-at-risk (LaR) as a determinant of buffers, as well as how one defines and quantifies such buffers. We will also examine issues such as limit-related input for liquidity policy and transfer pricing as an alternative concept. Two stylized models of liquidity risk are presented and analyzed.
This document reviews research on the relationship between investor sentiment and stock market fluctuations. It discusses how investor sentiment can influence irrational investor behavior, though efficient market theory says stock prices fully reflect all public information. Several studies find connections between sentiment indices and stock returns, though sentiment is not a purely "financial" factor and can be driven by human psychology. The conclusion suggests incorporating sentiment into stock valuation to help analyze portfolios, while noting sentiment cannot predict events like the 2008 recession and emphasizing the need to understand how psychological factors influence human behavior and the market.
The document discusses 10 important banking metrics for evaluating bank performance and value. It defines each metric and provides details on what numbers are considered good. Return on equity measures profitability and banks generally want to see over 10%. Return on assets also measures profitability but doesn't reflect leverage like return on equity. Net interest margin shows how much a bank earns from its invested assets. The efficiency ratio measures operating expenses to see if a bank is a low-cost operator.
This document provides an introduction and literature review for a study comparing the efficiency of Islamic and conventional banks in the United Arab Emirates. The introduction gives background on banking in the UAE and the differences between Islamic and conventional banking models. The literature review summarizes several previous studies that have examined bank efficiency in various countries and time periods using different methodologies. The methodology section outlines the research questions, data, and analytical approach that will be used to compare bank efficiency in the UAE, including the use of regression analysis and testing of hypotheses.
This document discusses concepts related to risk and return in investments. It defines key terms like return, components of return, risk, types of risk, and risk aversion.
Return represents the reward for undertaking an investment and has two components - current return (periodic income) and capital return (price appreciation/depreciation). Total risk is divided into unsystematic (unique) risk and market risk. Unsystematic risk can be reduced through diversification, while market risk cannot. The document also discusses different sources of risk like business risk, financial risk, interest rate risk, market risk, and purchasing power risk. It explains how risk aversion and speculation are not inconsistent concepts, using examples of risk premiums and heterogeneous
The aim of this paper is to analyze the liquidity levels of various banks in the UAE for the period 2005-2009. To understand the behavior of liquidity indicators especially during the financial crisis, the researcher will analyze the four liquidity indicators over the years 2005 to 2009. The findings highlight how the banks in question have been impacted by the 2007-2008 crisis. This can most obviously be seen in the notable decline of each of the banks liquidity level in 2009. The effect of loans to total assets, loans to customers’ deposit, and investment to total assets ratios for the five banks was most notable in 2009. Two liquidity ratios were analyzed in order to determine the banks’ ability to honor its debt obligations, these being loans to total assets and loans to customers respectively. The third ratio was the total equity to total assets to assess the liquidity level in the capital structure, while the fourth ratio was the investment to total assets to measure the managing of liquidity. While Bank liquidity was affected by the crisis, bank performance remained relatively stable, as measured by coefficient of variation, since these banks were able to yield more control over cash flows in comparison to revenues and costs.
A research study on investors behaviour regarding choice of asset allocation ...SubmissionResearchpa
This document discusses asset allocation and factors that influence investors' choices. It begins by defining asset allocation as balancing risk and reward by distributing investments across asset classes like equities, fixed income, and cash. Key factors in selecting an asset allocation include time horizon, risk tolerance, and goals. Diversification across asset classes reduces risk through offsetting performance. The document then examines various investor profiles defined by characteristics like age, wealth source, personality. Loss aversion and framing effects can influence decisions by making investors reluctant to realize losses or consider choices separately.
The document discusses market liquidity in fixed income markets post-financial crisis. Several factors have contributed to reduced liquidity, including decreased broker-dealer trading inventories due to regulations. This has increased execution risk for investors. The document recommends asset managers adapt by evolving trading strategies, portfolio construction, and risk management. It proposes a three-pronged approach: modernizing market structure; enhancing fund tools and regulation; and supporting new products to address liquidity challenges.
Mercer Capital's Community Bank Stress Testing: What You Need to KnowMercer Capital
While there is no legal requirement for community banks to perform stress tests, recent regulatory commentary suggests that community banks should be developing and implementing some form of stress testing on at least an annual basis.
Whether you are considering performing the test in-house or with outside assistance, this webinar will be of interest to you. This webinar: covers the basics of community bank stress testing; reviews the economic scenarios published by the Federal Reserve; provides detail on the key steps to developing a sound community bank stress test; and discusses how to analyze and act upon the outputs of your stress tests.
This document summarizes a webinar presented by Mike Lubansky on stress testing loan portfolios. The webinar covered regulatory requirements for stress testing, the objective and importance of stress testing, different types of stress testing approaches for community banks, challenges with data collection, scenario selection, and maximizing the value of stress test reports. Sample stress test outputs were presented and common mistakes were discussed. The webinar provided an overview of effective stress testing practices for community banks.
This document summarizes a research paper about mutual fund flows and performance. It contains the following key points:
1) The paper presents a rational model of active portfolio management that can reproduce many observed patterns in mutual fund performance and flows, without relying on investor irrationality.
2) In the model, fund flows rationally respond to past performance even though performance is not persistent on average, due to competitive capital allocation to managers.
3) The model shows that lack of performance persistence does not imply managers lack skill or that evaluating performance is wasteful, as differential ability exists but is not consistently rewarded due to competitive capital allocation.
Over a 32-year period, the study found that only 0.6% of funds delivered positive alpha (returns in excess of their benchmarks) through skill rather than luck. The proportion of skilled managers decreased over time, with only 0.6% found to be skilled in 2006 compared to 14.4% in 1990. No funds in the Growth & Income category exhibited skill, while the Aggressive Growth funds showed the most skill. Expenses eliminated the good performance of many managers who appeared skilled. The authors believe the movement of skilled managers to the higher-paying hedge fund industry best explains the decline in mutual fund manager skill over this period.
The document discusses recent academic research on active equity managers who deliver persistent outperformance. Some key findings include:
1) Strategies with a high "active share" (the degree to which the portfolio differs from its benchmark) are more likely to outperform their benchmarks and peers.
2) Top-decile performers over periods like three years have generated positive average annual alphas the following year, while bottom-decile performers saw negative alphas.
3) Brown Advisory's equity strategies tend to have high active shares, holding a concentrated number of stocks based on in-house research rather than mirroring index weights. This approach is aligned with characteristics the research associates with persistent outperformance.
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.
1. The document analyzes how differences in the strength of risk management functions at bank holding companies (BHCs) may explain differences in risk-taking and performance.
2. It constructs a Risk Management Index (RMI) to measure the importance and independence of risk management at BHCs. Higher RMI is associated with lower tail risk and better performance during the financial crisis.
3. While risk management is endogenous, the evidence suggests BHCs with a strong risk culture tend to have both lower risk-taking and stronger risk management, as opposed to strategically choosing high risk with strong controls.
This document discusses systemic risk in interconnected financial systems. It presents a model for determining a "clearing payment vector" that clears the obligations of members in a financial system given their liabilities, limited liabilities, and operating cash flows. The paper shows there always exists a unique clearing vector and develops an algorithm to efficiently compute it. Comparative statics imply increased volatility lowers the total value of firms in the system even without direct costs of insolvency.
STRESS TESTING IN BANKING SECTOR FRAMEWORKDinabandhu Bag
This document summarizes a study analyzing default correlation in retail banking portfolios in India. It discusses:
1) Literature on default correlation and factor modeling approaches to estimate correlation. Previous studies found correlation varies over time and across industries/ratings.
2) Analysis of a test portfolio with 4 retail segments showing migration of exposures between segments over 14 months. Segments showed varying default rate trends over time.
3) The study builds a multi-factor linear model to test if external economic factors significantly impact default correlations between segments over time.
This document summarizes a presentation on core deposit modeling best practices. It discusses topics like rate sensitivities in a rising rate environment, valuation of core deposits, sensitivity analysis, liquidity concerns, core deposit studies and behavioral inputs. Key points covered include using historical data to model rate sensitivities, the GAAP definition of valuing core deposits as the present value of average balances discounted by alternative funding costs, and the importance of sensitivity analysis and considering different scenarios in modeling.
Prior performance and risk taking ammannbfmresearch
This document discusses using a dynamic Bayesian network approach to analyze the behavior of mutual fund managers, specifically how prior performance impacts risk-taking. The key findings are:
1) In contrast to some theories and studies, the analysis found that prior performance has a positive impact on the choice of risk level - successful fund managers take on more risk in the following year by increasing measures like volatility, beta, and tracking error.
2) Poor-performing fund managers were found to switch to more passive strategies.
3) Bayesian networks allow capturing nonlinear patterns and assigning probabilities to different outcomes, providing a more robust approach than previous studies on this topic.
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.
This presentation will survey and discuss various quantitative considerations in liquidity risk for a financial institution. This includes the concept of liquidity-at-risk (LaR) as a determinant of buffers, as well as how one defines and quantifies such buffers. We will also examine issues such as limit-related input for liquidity policy and transfer pricing as an alternative concept. Two stylized models of liquidity risk are presented and analyzed.
This document reviews research on the relationship between investor sentiment and stock market fluctuations. It discusses how investor sentiment can influence irrational investor behavior, though efficient market theory says stock prices fully reflect all public information. Several studies find connections between sentiment indices and stock returns, though sentiment is not a purely "financial" factor and can be driven by human psychology. The conclusion suggests incorporating sentiment into stock valuation to help analyze portfolios, while noting sentiment cannot predict events like the 2008 recession and emphasizing the need to understand how psychological factors influence human behavior and the market.
The document discusses 10 important banking metrics for evaluating bank performance and value. It defines each metric and provides details on what numbers are considered good. Return on equity measures profitability and banks generally want to see over 10%. Return on assets also measures profitability but doesn't reflect leverage like return on equity. Net interest margin shows how much a bank earns from its invested assets. The efficiency ratio measures operating expenses to see if a bank is a low-cost operator.
This document provides an introduction and literature review for a study comparing the efficiency of Islamic and conventional banks in the United Arab Emirates. The introduction gives background on banking in the UAE and the differences between Islamic and conventional banking models. The literature review summarizes several previous studies that have examined bank efficiency in various countries and time periods using different methodologies. The methodology section outlines the research questions, data, and analytical approach that will be used to compare bank efficiency in the UAE, including the use of regression analysis and testing of hypotheses.
This document discusses concepts related to risk and return in investments. It defines key terms like return, components of return, risk, types of risk, and risk aversion.
Return represents the reward for undertaking an investment and has two components - current return (periodic income) and capital return (price appreciation/depreciation). Total risk is divided into unsystematic (unique) risk and market risk. Unsystematic risk can be reduced through diversification, while market risk cannot. The document also discusses different sources of risk like business risk, financial risk, interest rate risk, market risk, and purchasing power risk. It explains how risk aversion and speculation are not inconsistent concepts, using examples of risk premiums and heterogeneous
The aim of this paper is to analyze the liquidity levels of various banks in the UAE for the period 2005-2009. To understand the behavior of liquidity indicators especially during the financial crisis, the researcher will analyze the four liquidity indicators over the years 2005 to 2009. The findings highlight how the banks in question have been impacted by the 2007-2008 crisis. This can most obviously be seen in the notable decline of each of the banks liquidity level in 2009. The effect of loans to total assets, loans to customers’ deposit, and investment to total assets ratios for the five banks was most notable in 2009. Two liquidity ratios were analyzed in order to determine the banks’ ability to honor its debt obligations, these being loans to total assets and loans to customers respectively. The third ratio was the total equity to total assets to assess the liquidity level in the capital structure, while the fourth ratio was the investment to total assets to measure the managing of liquidity. While Bank liquidity was affected by the crisis, bank performance remained relatively stable, as measured by coefficient of variation, since these banks were able to yield more control over cash flows in comparison to revenues and costs.
A research study on investors behaviour regarding choice of asset allocation ...SubmissionResearchpa
This document discusses asset allocation and factors that influence investors' choices. It begins by defining asset allocation as balancing risk and reward by distributing investments across asset classes like equities, fixed income, and cash. Key factors in selecting an asset allocation include time horizon, risk tolerance, and goals. Diversification across asset classes reduces risk through offsetting performance. The document then examines various investor profiles defined by characteristics like age, wealth source, personality. Loss aversion and framing effects can influence decisions by making investors reluctant to realize losses or consider choices separately.
The document discusses market liquidity in fixed income markets post-financial crisis. Several factors have contributed to reduced liquidity, including decreased broker-dealer trading inventories due to regulations. This has increased execution risk for investors. The document recommends asset managers adapt by evolving trading strategies, portfolio construction, and risk management. It proposes a three-pronged approach: modernizing market structure; enhancing fund tools and regulation; and supporting new products to address liquidity challenges.
Mercer Capital's Community Bank Stress Testing: What You Need to KnowMercer Capital
While there is no legal requirement for community banks to perform stress tests, recent regulatory commentary suggests that community banks should be developing and implementing some form of stress testing on at least an annual basis.
Whether you are considering performing the test in-house or with outside assistance, this webinar will be of interest to you. This webinar: covers the basics of community bank stress testing; reviews the economic scenarios published by the Federal Reserve; provides detail on the key steps to developing a sound community bank stress test; and discusses how to analyze and act upon the outputs of your stress tests.
This document summarizes a webinar presented by Mike Lubansky on stress testing loan portfolios. The webinar covered regulatory requirements for stress testing, the objective and importance of stress testing, different types of stress testing approaches for community banks, challenges with data collection, scenario selection, and maximizing the value of stress test reports. Sample stress test outputs were presented and common mistakes were discussed. The webinar provided an overview of effective stress testing practices for community banks.
This document summarizes a research paper about mutual fund flows and performance. It contains the following key points:
1) The paper presents a rational model of active portfolio management that can reproduce many observed patterns in mutual fund performance and flows, without relying on investor irrationality.
2) In the model, fund flows rationally respond to past performance even though performance is not persistent on average, due to competitive capital allocation to managers.
3) The model shows that lack of performance persistence does not imply managers lack skill or that evaluating performance is wasteful, as differential ability exists but is not consistently rewarded due to competitive capital allocation.
Over a 32-year period, the study found that only 0.6% of funds delivered positive alpha (returns in excess of their benchmarks) through skill rather than luck. The proportion of skilled managers decreased over time, with only 0.6% found to be skilled in 2006 compared to 14.4% in 1990. No funds in the Growth & Income category exhibited skill, while the Aggressive Growth funds showed the most skill. Expenses eliminated the good performance of many managers who appeared skilled. The authors believe the movement of skilled managers to the higher-paying hedge fund industry best explains the decline in mutual fund manager skill over this period.
The document discusses recent academic research on active equity managers who deliver persistent outperformance. Some key findings include:
1) Strategies with a high "active share" (the degree to which the portfolio differs from its benchmark) are more likely to outperform their benchmarks and peers.
2) Top-decile performers over periods like three years have generated positive average annual alphas the following year, while bottom-decile performers saw negative alphas.
3) Brown Advisory's equity strategies tend to have high active shares, holding a concentrated number of stocks based on in-house research rather than mirroring index weights. This approach is aligned with characteristics the research associates with persistent outperformance.
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 document summarizes research on how the metric "active share" can predict potential investment returns (alpha) and risks. The researchers find:
1) Managers with high active share (meaning their portfolio holdings differ significantly from the benchmark) have generally outperformed those with low active share, especially in US, global, and international equity categories.
2) Active share predicts future returns (alpha) well in most categories except large-cap growth and small-caps.
3) Active share also forecasts relative risk levels comparably to other risk measurement tools.
4) However, high active share strategies experience larger drawdowns so may not be practical for all institutional investors. Diversifying across several high active
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.
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.
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.
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.
This document summarizes research on index effects that occur around index rebalancing dates. It discusses the growth of passive investing, hypotheses for why abnormal returns may occur on additions and deletions from indexes, and evaluates whether an index effect exists in the S&P/TSX Composite index in Canada. The methodology examines stock returns around rebalancing dates to identify any cumulative abnormal returns.
The document presents a model showing that mutual fund managers have an incentive to distort new investments toward stocks their fund already holds large positions in near the end of quarters. This increases the fund's reported returns and attracts more inflows, allowing for greater distortion next quarter. However, the price impact is temporary, so each quarter starts with a larger return deficit until it cannot be overcome, explaining the empirical evidence of short-term persistence and long-term reversal in fund performance. The model also provides insights into why some funds underperform and explains behaviors of smaller and younger funds.
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.
Does fund size erode mutual fund performance the role of liquidity and organ...bfmresearch
1) The study investigates how fund size affects mutual fund performance. Using data from 1962-1999, they find that fund returns decline as fund size increases, even after accounting for benchmarks and fund characteristics.
2) They find this negative effect of size on performance is most pronounced for funds that invest in small, illiquid stocks. This suggests liquidity issues related to size are important.
3) Controlling for its own size, a fund's performance is not negatively impacted by the total size of the fund family it belongs to. This indicates scale is not inherently bad and depends on organizational structure.
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.
Fund performance, mgmt behavior, and investor costs dowenbfmresearch
This document summarizes a study that examined mutual fund performance, management behavior, and costs for investors over a 5-year period. The study looked at both equity and fixed income funds. For equity funds, the study found that increased trading activity by managers was negatively related to fund returns, while expense ratios were not significantly related to returns. Potential capital gains exposure and tax costs were positively related to returns. For fixed income funds, increased trading activity was positively related to returns, while higher expense ratios and tax costs were negatively related to returns. The study also found evidence that mutual funds experience economies of scale, with lower costs for larger funds that are part of larger fund families.
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.
Equity fund investors have historically earned lower returns than stock market indexes due to costs. Mutual funds charge management fees around 1.19% of assets annually on average as well as transaction costs of 1.44% from frequent trading. These costs total around 2.63% annually and help explain why fund investor returns have lagged the market by around 8 percentage points annually over 30 years. While some fund company practices like late trading and directed brokerage have been banned, high costs remain a major reason for the "missing return" of equity fund investors compared to market indexes.
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.
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 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 due to variations based on security type and trade size. A 2007 study directly estimated trading costs and found no statistical relationship between costs and returns. The author's own analysis of mutual funds from 2007-2008 also found little correlation between turnover and performance. Therefore, advisors should not assume higher turnover indicates lower potential returns.
Phone Charges Per Roommate for FebruaryBasic Monthly Service Rat.docxrandymartin91030
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The Role of PRivaTe equiTy in u.S. CaPiTal MaRkeTS
The Role of Private equity in u.S. Capital Markets
Robert J. Shapiro and nam D. Pham
october 2008
S O N E C O N
1The Role of PRivaTe equiTy in u.S. CaPiTal MaRkeTS
The Role of Private equity in u.S. Capital Markets
Robert J. Shapiro and nam D. Pham1
inTRoDuCTion
Private equity transactions and operations in the United States have grown dramatically over the last genera-
tion. The number and value of U.S. private buyout-related deals rose from 12 transactions in 1970, involv-
ing less than $13 million in direct capital raised and invested, to 2,474 deals in 2007 for which buyout firms
directly raised and invested approximately $70 billion (net of leverage or borrowing).2 Including leverage, the
value of U.S. buyout deals averaged about $100 billion per-year from 2000 to 2005.3 However, these sharp
increases in private-equity buyouts, virtually all of them leveraged and some very highly so, have raised
concerns about the economy’s vulnerability to a systemic financial market event if a large firm, or a series of
firms purchased in a highly-leveraged buyout or a major private equity firm should suddenly fail. These con-
cerns have been heightened by the systemic crisis and enormous costs triggered by the large-scale failures
of mortgage-backed securities and their derivatives, including the collapse of Bear Stearns, Lehman Broth-
ers, AIG, Fannie Mae, Freddie Mac and Countrywide Financial Corp., and the severe financial stresses and
extraordinary government interventions on behalf of other major financial institutions. This report examines
the basis for these concerns. Based on the data and analysis, we conclude that the organization of private
equity buyout funds and the nature and dimensions of their investments are fundamentally different from the
conditions which have produced our current systemic instability, and that the private equity sector does not
seem to present a systemic risk for U.S. capital markets or the economy.
Private equity funds play a distinctive role in U.S. financial markets and the economy by organizing and chan-
neling capital and skilled managers to acquire and operate firms based on their analysis of the returns those
firms could generate if new management brings about significant changes in those firms’ operations. Private
equity investors may buy struggling firms; they may purchase solid but unwanted divisions of conglomerates,
and they may acquire strong companies with significant growth potential. Regardless, their role contrasts
clearly wit.
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.
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.
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.
This document discusses an analysis of investors' risk perceptions towards mutual fund services. It begins with an abstract that outlines the goal of understanding investors' perceptions and expectations to support financial decision making for mutual funds.
The introduction provides background on mutual funds and how they have diversified their product offerings over time. However, these changes still need to align with investors' expectations. The literature review covers previous research on investors' rationality regarding risk-return tradeoffs, investment expectations, and financial innovations in mutual funds.
The document then discusses potential service quality gaps for mutual funds, including ambiguity in investors' expectations and gaps in designing fund services. It introduces the concept of a "tolerance zone" to depict investors' acceptable levels of
Liquidity Risk and Expected Stock Returns Lubos Pastor and Robert F- S.docxLucasmHKChapmant
Liquidity Risk and Expected Stock Returns Lubos Pastor and Robert F. Stambaugh NBER Working Paper No. 8462 September 2001 JEL No. G12 ABSTRACT This study investigates whether market-wide liquidity is a state variable important for asset pricing. We find that expected stock returns are related cross-sectionally to the sensitivities of returns to fluctuations in aggregate liquidity. Our monthly liquidity measure, an average of individual-stock measures estimated with daily data, relies on the principle that order flow induces greater return reversals when liquidity is lower. Over a 34-year period, the average retum on stocks with high sensitivities to liquidity exceeds that for stocks with low sensitivities by 7.5% annually, adjusted for exposures to the market return as well as size, value, and momentum factors. 1. Introduction In standard asset pricing theory, expected stock returns are related cross-sectionally to returns' senxitivities to state variables with pervasive effects on consumption and invertment opportunities. The basic intuition is that a security whose lowest returns tend to accompany unfavorable shifts in quantities afferting an imvestor's overall welfare must offer additional compensation to the investor for holding that security. Liquidity appears to be a good candidate for a priced state variable. It is often viewed as important for investment decisions, and recent studies find that fluctuations in various measures of liquidity are correlated acroos stocks." This empirical study investigates whether market-wide liquidity is indeed priced. That is, we ask whether cross-sectional differences in expected stock returns are rehated to the sensitivities of returns to fluctuations in aggregate liquidity. 2 Liquidity is a broad and elusive concept that generally denotes the ability to trade large quantities quickly, at low cost, and without moving the price. We focus on an aspect of liquidity associated with temporary price fluctuations induced by order flow. Our monthly aggregate liquidity measure is a cross-sectional average of individual-stock liquidity measures. Each stock's liquidity in a given month, etimated using that stock's within-month daily returns and volume, represents the average effect that a given volume on day d has on the return for day d + 1 , when the volume is given the same sign as the return on day d . The basic idea is that, if signed volume is viewed ronghly as "order flow," then lower liquidity is reflected in a greater tendency for order flow in a given direction on day d to be followed by a price change in the opposite direction on day d + 1 . Esentially, lower liquidity corresponds to stronger volume-related return reversals, and in this respect our liquidity measure follows the same line of reasoning as the model and empirical evidence presented by Campbell, Groseman, and Wang (1993). They find that sturns accompanied by high volume tend to be reversed more strongly, and they explain how this result i.
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.
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.
Similar to Fund flow volatility and performance rakowski (20)
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.
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.
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 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
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.
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.
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.
Morningstar ratings and fund performance blake moreybfmresearch
This study examines the ability of Morningstar ratings to predict the future performance of mutual funds compared to alternative predictors. The authors analyze two samples of US equity funds: seasoned funds from 1992-1997 and complete funds from 1993. They assess predictive ability using out-of-sample performance over 1, 3, and 5 year horizons, adjusting for loads and styles. The results indicate that low Morningstar ratings generally predict relatively poor future performance, but there is little evidence that top-rated funds outperform similar funds. Morningstar ratings do only slightly better than alternative predictors in forecasting future fund performance.
Fund returnsandperformanceevaluationtechniques grinblattbfmresearch
This paper empirically compares three techniques for evaluating mutual fund performance: the Jensen Measure, the Positive Period Weighting Measure, and the Treynor-Mazuy Measure of Total Performance. It does so using a sample of 279 mutual funds and 109 passive portfolios constructed from firm characteristics and industries. The study finds that 1) the performance measures can yield different inferences depending on the benchmark used, 2) measures may detect timing ability differently, and 3) cross-sectional regressions of performance on fund characteristics may provide insights even when individual performance measures lack statistical power.
- Investors spend an estimated 0.67% of the total value of the US stock market each year on active investing strategies seeking returns above the market.
- This amounts to at least a 10% capitalized cost of the current market value to facilitate price discovery through active investing.
- Under reasonable assumptions, a typical investor could increase average annual returns by 67 basis points over 1980-2006 by switching to a passive market portfolio instead of active strategies.
This study analyzes the trading behaviors of 155 mutual funds between 1975 and 1984 to determine if they exhibited momentum investing and herding behaviors. The researchers find that 77% of funds were "momentum investors," buying stocks that had outperformed in the past, though most did not systematically sell past underperformers. Funds exhibiting momentum behaviors on average realized significantly better risk-adjusted returns than other funds. The study also finds weak evidence that funds tended to buy and sell the same stocks at the same time, known as herding behavior.
Performance of trades and stocks of fund managers pinnuckbfmresearch
This document summarizes a study that examines the performance of stock holdings and trades of Australian fund managers. The study finds:
1) The stocks held by fund managers realize abnormal returns on average, consistent with some stock selection ability.
2) Stocks purchased by fund managers realize abnormal returns on average, while stocks they sell do not, supporting the idea that fund managers have superior information.
3) Large stocks are more likely to benefit from fund managers' superior information than small stocks.
However, the superior returns from stock holdings are not delivered to unit holders, possibly due to fees and poor market timing. The results provide out-of-sample support for recent U.S. studies finding fund managers
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.
Fabular Frames and the Four Ratio ProblemMajid Iqbal
Digital, interactive art showing the struggle of a society in providing for its present population while also saving planetary resources for future generations. Spread across several frames, the art is actually the rendering of real and speculative data. The stereographic projections change shape in response to prompts and provocations. Visitors interact with the model through speculative statements about how to increase savings across communities, regions, ecosystems and environments. Their fabulations combined with random noise, i.e. factors beyond control, have a dramatic effect on the societal transition. Things get better. Things get worse. The aim is to give visitors a new grasp and feel of the ongoing struggles in democracies around the world.
Stunning art in the small multiples format brings out the spatiotemporal nature of societal transitions, against backdrop issues such as energy, housing, waste, farmland and forest. In each frame we see hopeful and frightful interplays between spending and saving. Problems emerge when one of the two parts of the existential anaglyph rapidly shrinks like Arctic ice, as factors cross thresholds. Ecological wealth and intergenerational equity areFour at stake. Not enough spending could mean economic stress, social unrest and political conflict. Not enough saving and there will be climate breakdown and ‘bankruptcy’. So where does speculative design start and the gambling and betting end? Behind each fabular frame is a four ratio problem. Each ratio reflects the level of sacrifice and self-restraint a society is willing to accept, against promises of prosperity and freedom. Some values seem to stabilise a frame while others cause collapse. Get the ratios right and we can have it all. Get them wrong and things get more desperate.
New Visa Rules for Tourists and Students in Thailand | Amit Kakkar Easy VisaAmit Kakkar
Discover essential details about Thailand's recent visa policy changes, tailored for tourists and students. Amit Kakkar Easy Visa provides a comprehensive overview of new requirements, application processes, and tips to ensure a smooth transition for all travelers.
Solution Manual For Financial Accounting, 8th Canadian Edition 2024, by Libby...Donc Test
Solution Manual For Financial Accounting, 8th Canadian Edition 2024, by Libby, Hodge, Verified Chapters 1 - 13, Complete Newest Version Solution Manual For Financial Accounting, 8th Canadian Edition by Libby, Hodge, Verified Chapters 1 - 13, Complete Newest Version Solution Manual For Financial Accounting 8th Canadian Edition Pdf Chapters Download Stuvia Solution Manual For Financial Accounting 8th Canadian Edition Ebook Download Stuvia Solution Manual For Financial Accounting 8th Canadian Edition Pdf Solution Manual For Financial Accounting 8th Canadian Edition Pdf Download Stuvia Financial Accounting 8th Canadian Edition Pdf Chapters Download Stuvia Financial Accounting 8th Canadian Edition Ebook Download Stuvia Financial Accounting 8th Canadian Edition Pdf Financial Accounting 8th Canadian Edition Pdf Download Stuvia
A toxic combination of 15 years of low growth, and four decades of high inequality, has left Britain poorer and falling behind its peers. Productivity growth is weak and public investment is low, while wages today are no higher than they were before the financial crisis. Britain needs a new economic strategy to lift itself out of stagnation.
Scotland is in many ways a microcosm of this challenge. It has become a hub for creative industries, is home to several world-class universities and a thriving community of businesses – strengths that need to be harness and leveraged. But it also has high levels of deprivation, with homelessness reaching a record high and nearly half a million people living in very deep poverty last year. Scotland won’t be truly thriving unless it finds ways to ensure that all its inhabitants benefit from growth and investment. This is the central challenge facing policy makers both in Holyrood and Westminster.
What should a new national economic strategy for Scotland include? What would the pursuit of stronger economic growth mean for local, national and UK-wide policy makers? How will economic change affect the jobs we do, the places we live and the businesses we work for? And what are the prospects for cities like Glasgow, and nations like Scotland, in rising to these challenges?
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."
"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.
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.
What's a worker’s market? Job quality and labour market tightness
Fund flow volatility and performance rakowski
1. JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Vol. 45, No. 1, Feb. 2010, pp. 223–237
COPYRIGHT 2010, MICHAEL G. FOSTER SCHOOL OF BUSINESS, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195
doi:10.1017/S0022109009990500
Fund Flow Volatility and Performance
David Rakowski∗
Abstract
This paper provides a detailed analysis of the impact of daily mutual fund flow volatility
on fund performance. I document a significant negative relationship between the volatil-
ity of daily fund flows and cross-sectional differences in risk-adjusted performance. This
relationship is driven by domestic equity funds, as well as small funds, well-performing
funds, and funds that experience inflows over the sample period. My results are consis-
tent with performance differences arising from the transaction costs of nondiscretionary
trading driven by daily fund flows, but not with performance differences arising from the
suboptimal cash holdings that arise from fund flows.
I. Introduction
Open-end mutual funds in the United States possess two characteristics that
distinguish them from most other types of investments. First, the daily net asset
value (NAV) pricing mechanism of mutual funds provides investors with a large
amount of liquidity that is not available when holding securities directly. In most
no-load funds, investors may buy or sell shares at a fixed price each day without
paying commissions or bid-ask spreads and with few limits on the depth or the
number of shares they may trade. Investors do not pay for this liquidity directly.
Instead, the costs are paid by all shareholders in the fund and are reflected in lower
fund returns. This brings me to the second interesting characteristic of open-end
mutual funds—that they tend to underperform their benchmarks (Sharpe (1966),
Jensen (1968), Ippolito (1989), Malkiel (1995), and Gruber (1996)). High trans-
action costs have been suggested as a reason for this underperformance (Grinblatt
∗ Rakowski, rakowski@cba.siu.edu, College of Business, Southern Illinois University Carbondale,
1025 Lincoln Dr., Carbondale, IL 62901. This paper derives from the first essay of my dissertation at
Georgia State University. I am sincerely grateful for the valuable comments of my committee mem-
bers: Jason Greene (chair), Jayant Kale, Omesh Kini, and Conrad Ciccotello. I thank Stephen Brown
(the editor), Owen Beelders, Andrew Clark, Pete Dadalt, Naveen Daniel, Alex Fayman, Charles
Hodges, Ping Hu, Patrick Kelly, Thomas M. Smith (the referee), Laura Starks, and the seminar partic-
ipants at the WFA 2002 Annual Meeting, the FMA 2003 Annual Meeting, the Atlanta Finance Work-
shop, the Securities and Exchange Commission, St. Joseph’s University, the College of New Jersey,
and Southern Illinois University, as well as Lipper, CRSP, Trimtabs, and Morningstar for providing
data. All errors are the sole property of the author.
223
2. 224 Journal of Financial and Quantitative Analysis
and Titman (1989), Chalmers, Edelen, and Kadlec (2001a), Edelen (1999), and
Wermers (2000)), but the exact factors driving these costs have not been fully ex-
amined. Johnson (2004) shows that short-term fund shareholders impose higher
liquidity costs on a fund than long-term shareholders, consistent with transaction
costs arising from short-term fund flows being an important factor driving cross-
sectional differences in performance. This study analyzes high-frequency trading
by a fund’s investors and argues that the flow volatility caused by trading is related
to a fund’s performance.
There are several reasons to expect that fund performance could be affected
by erratic fund flows (“flows” here are defined as net daily purchases or redemp-
tions by a fund’s shareholders). Flows can cause a fund manager to trade more
frequently, incurring transaction costs, such as commissions and having to pay
bid-ask spreads. Another possibility is that flows will constrain a manager from
following her optimal investment strategy. For example, if market prices decline
and a manager wishes to purchase securities, she may instead be forced to sell
in order to pay redeeming shareholders. If the fund manager chooses not to hold
enough cash to meet unexpected redemptions, then she faces the risk of acting as
a liquidity trader in response to fund flows and therefore can be expected to suffer
losses to more informed traders (Kyle (1985)).
A fund manager’s main option to avoid liquidity trading is to hold excess
levels of cash to meet unexpected redemptions. However, holding cash also de-
presses performance during periods of positive returns due to the low returns on
cash holdings (Ferson and Warther (1996)). This situation is referred to here as
“cash drag.” Even if a fund manager responds to unexpected flows in other man-
ners, such as through lines of credit, the costs are still nonzero and should be
proportional to the amount of unexpected flows. Furthermore, the calculation of
mutual fund prices by NAV leads to short-term predictabilities in prices that can
be exploited by investors, with costs paid by the fund’s nontrading shareholders
through lower overall performance (Greene and Hodges (2002), Chalmers et al.
(2001b), Goetzmann, Ivkovic, and Rouwenhorst (2001), Zitzewitz (2003), and
Bhargava and Dubofsky (2001)). This will also result in unexpected or volatile
flows having a negative impact on fund performance.
Of course, not all investors trade frequently enough to make short-term flows
so volatile that they impact performance. However, all investors need not trade fre-
quently for such effects to manifest themselves. It is only necessary for different
investors to trade often enough so that flows reach levels where they influence a
fund manager’s trading and allocation strategies. Not all of these flows will end
up as fund trading, but if only a small amount does, then this can represent sub-
stantial transaction costs, or trading constraints, that must be incurred by fund
managers. However, it is an open question as to whether these daily flows are a
significant factor influencing returns, which is the primary question that this study
addresses. The possibility that fund flows are impacting returns gives this study
its research hypothesis:
Flow Volatility Hypothesis. Daily fund flow volatility is negatively related to
cross-sectional differences in performance.
3. Rakowski 225
The alternative to this is that flow volatility does not add any power to explain
differences in performance once cash holdings, turnover, and standard control
variables have been corrected for. Cross-sectional regressions testing the above
hypothesis make up the bulk of the analysis presented here. In addition to flow
volatility, I also examine if unexpected daily flows are negatively related to fund
performance.
My results indicate that both flow volatility and unexpected flows are neg-
atively related to fund performance and confirm my hypothesis. However, while
this finding applies to funds in general, there are important subsets of my sam-
ple that drive this relationship. In particular, domestic equity funds, small funds,
well-performing funds, and funds that experience net cash inflows provide the
strongest evidence of a negative relationship between daily flow volatility and
performance.
These results provide several important extensions to the findings of
Edelen (1999), who demonstrates that unexpected monthly fund flows are cor-
related with underperformance for domestic equity mutual funds. First, I show
how the relationship between flow volatility differs across alternate investment
objectives and with several fund characteristics. Second, I provide 2 new high-
frequency measures to proxy for the potential trading costs of fund flows: daily
flow volatility and unexpected daily flows. These measures have not been used
at this frequency in evaluating fund performance and are a more direct mea-
sure of a fund’s potential liquidity-driven trading costs than the monthly flows
examined by Edelen (1999). Most importantly, I demonstrate the importance of
these measures, and thus a fund’s probable trading transaction costs, as a factor
in mutual fund underperformance, as opposed to the portfolio reallocation deci-
sions that would likely be driven by the longer-term flows employed by Edelen
(1999).
These findings suggest that the pricing structure of mutual funds and the liq-
uidity provided have important effects on fund performance. Mutual funds cannot
be viewed simply as collections of individual securities, as their prices and trans-
action costs do not represent the sum of these costs for each security in the port-
folio. Investors should consider both the costs and benefits of this liquidity option
that they are purchasing when entering a mutual fund. Fund managers must rec-
ognize that their performance is tied to the behavior of their investors, and not
simply to their ability to choose securities. Managers have often been accused of
trading excessively, due to possible agency problems (Lakonishok, Shleifer, and
Vishny (1992), Shapira and Venezia (2001), and Brown (1996)). However, the
interaction of turnover, fund flows, and performance documented here is consis-
tent with fund managers trading excessively, not because they are attempting to
“churn” the portfolio, but because they must trade in order to manage investors’
liquidity demands.
This study proceeds as follows. Section II describes the data set employed,
and Section III presents the cross-sectional analysis of daily fund flow volatility
and performance. Section IV describes robustness tests for the primary cross-
sectional analysis, while Section V discusses the impact of alternative calcula-
tions of daily flow. Section VI extends the analysis to fund groups, Section VII to
investment objectives, and conclusions are presented in Section VIII.
4. 226 Journal of Financial and Quantitative Analysis
II. Data
Data from several sources are used to characterize fund flow volatility and its
impact on fund performance. Lipper provides daily data from March 2000 until
October 2006 on mutual fund total net assets (TNA) and returns (adjusted for
distributions), which are used to calculate daily flows. Although Lipper reports
daily ending TNA for each fund, this TNA figure does not include the day’s net
fund flows. Therefore, I calculate daily flows as
at+1
(1) ct = − at ,
1 + rt+1
where at is total net assets on day t, rt is the fund’s return on day t, and ct is fund
flow on day t. To get percentage flows, I then divide equation (1) by at /(1 + rt ).
Fund flow volatility (SD FLOW) is measured by the standard deviation of daily
percentage fund flows over the sample period. Further procedures for verifying
the accuracy of TNA observations and the calculation of daily flows are discussed
later in this section.
Cross-sectional variables are taken from the Center for Research in Security
Prices (CRSP) mutual fund database for each year of my sample period. Expense
ratios are decomposed into 12b-1 (12B 1) and non-12b-1 (NON 12B 1) compo-
nents. Load fees are classified as front (FRONT) or deferred (DEFER). The mea-
sure of fund size (SIZE) used in my analysis is the natural logarithm of a fund’s
average daily TNA. Fund turnover ratios (TURNOVER) are used as a measure
of a fund’s potential transaction costs, and cash holdings (CASH) represents the
percentage of the fund’s assets held in cash and cash equivalents, as a measure of
liquidity. My primary measure of performance is the intercept (αi ) from a 3-factor
model of daily returns.1
Different share classes of the same fund are treated as separate funds due to
the different flows, loads, and fees of each share class. I put the data through rigor-
ous screens for errors, eliminating extreme observations (absolute flows of greater
than 50% per day), and manually checking the remaining extreme observations
for validity. I delete all funds with average daily TNA of less than $10 million due
to the extremely erratic nature of percentage flows for these funds. The sample is
restricted to domestic equity, domestic bond, and international equity investment
objectives. Funds with less than 800 daily observations are eliminated from the
analysis.
Table 1 describes my sample. Average daily percentage flows are approxi-
mately 16.5 basis points (bp) of TNA, while the average standard deviation (my
measure of flow volatility) is about 4% of TNA each day. While average daily
flows are positive, there is considerable variation in the behavior of flows across
funds, with only 60% of funds in the sample displaying positive average daily
1 The 3 factors are calculated from daily returns using the standard model of Fama and French
(1992), with data from Ken French’s Web site (http://mba.tuck.dartmouth.edu/pages/faculty/ken
.french/data library.html). I use these U.S.-based equity factors due to the fact that most U.S. mu-
tual funds’ shares are owned by U.S. households (ICI (2006)). I prefer to use a common benchmark so
that I may compare the impact of flow volatility across investment objectives while holding constant
the method of performance measurement.
5. Rakowski 227
flows. Average (median) fund size is $297 million ($85 million). The average
(median) raw return is 2.19 bp (2.32 bp) per day, with annual average (median)
returns of 5.07% (5.27%). The average (median) 3-factor-adjusted performance
measure is 1.11 bp (1.25 bp) per day. Keep in mind that I include all investment
objectives in the sample, and so comparisons with previous performance studies
of only domestic equity funds are not appropriate. For the typical fund, the dis-
tribution of daily flows (not reported) are nonnormal, with positive skewness and
more weight in the tails.
TABLE 1
Descriptive Statistics of Cross-Sectional Characteristics
Table 1 presents descriptive statistics for the sample of 4,772 open-end mutual funds over the sample period from March
2000 to October 2006. Daily fund flows, returns, and total net assets (TNA) are from Lipper, while annual data are from
CRSP. a percentage greater than 1%; b percentage greater than $100 million; c percentage greater than 100%.
Averages Medians % of Funds > 0
Panel A. Flow
Average daily flow (%) 0.1646 0.0277 60.0
Standard deviation of daily flow (%) 3.97 1.18 60.9 a
Average daily flow ($thousands) 11.59 4.78 55.1
Average annual flow (%) 25.56 11.13 55.1
Average annual flow ($millions) 18.83 5.71 55.1
Panel B. Returns
Average daily return (%) 0.0219 0.0232 83.4
Average annual return (%) 5.07 5.27 80.0
Average 3-factor alpha, daily (%) 0.0111 0.0125 69.9
Panel C. Fund Characteristics
Size ($millions) 297.32 84.57 45.5b
Cash holdings (%) 4.36 3.03 95.5
Turnover (%) 97.18 68.80 32.9c
12b-1 fees (%) 0.41 0.25 68.0
Non-12b-1 fees (%) 0.99 0.97 100.0
Front load (%) 1.39 0.00 32.7
Deferred load (%) 1.16 0.43 53.9
III. Cross-Sectional Regression Analysis
My regression analysis is performed with the control variables commonly
used in published studies of fund performance (Ippolito (1989), Malkiel (1995),
Gruber (1996), Carhart (1997), Chevalier and Ellison (1999a), (1999b), and
Chalmers et al. (2001a)). My primary model seeks to explain cross-sectional dif-
ferences in fund performance and takes the form
(2) αi = β0 + β1 SD FLOWi + β2 MEAN FLOWi + β3 SIZEi
+ β4 FRONTi + β5 DEFERi + β6 12B 1i + β7 NON 12B 1i
+ β8 TURNOVERi + β9 CASHi + ei .
Results from the ordinary least squares (OLS) estimation2 of this model are
presented in column 1 of Table 2, where one can see that flow volatility takes
2 For scaling purposes, flows and returns are entered as percentages while decimals are used for
fees and turnover.
6. 228 Journal of Financial and Quantitative Analysis
a significant negative coefficient. Consistent with previous studies, average daily
flows take a significant positive coefficient. Fees, size, and turnover take signif-
icant negative coefficients, while front-end loads take a positive coefficient. The
R2 measure indicates that about 6.6% of the variation in performance is explained.
Overall, these results support the flow volatility hypotheses.
TABLE 2
Cross-Sectional Regression Analysis
Table 2 presents the results of OLS and 2SLS regressions explaining cross-sectional differences in performance for the
full sample of 4,772 open-end mutual funds. Here, αi is the intercept from a daily 3-factor model of fund i ’s returns;
SD FLOW is the standard deviation of daily percentage flows; expense ratios are decomposed into 12B 1 and NON 12B 1
fees; SIZE is the natural log of average daily total net assets. In models (2) and (4), SD FLOW is replaced with UNEX-
PECTED DAILY FLOW, the root mean squared error from a model of expected daily flows. Heteroskedasticity and auto-
correlation consistent (HAC) (White (1980)) t -statistics are given in parentheses. The instruments used in the 2SLS models
include lagged values of all model variables plus indicators for investment objectives. Lagged values are obtained from
the panel of funds each year over the 6-year sample period. Pooled 2SLS is used in models (3) and (4) with the en-
dogenous variables being flow volatility (SD FLOW), unexpected flows (UNEXPECTED DAILY FLOW), average daily flow
(MEAN FLOW), turnover (TURNOVER), and cash holdings (CASH). * and ** indicate significance at the 5% and 1% levels,
respectively. The general model is
(2) αi = β0 + β1 SD FLOWi + β2 MEAN FLOWi + β3 SIZEi + β4 FRONTi + β5 DEFERi
+ β6 12B 1i + β7 NON 12B 1i + β8 TURNOVERi + β9 CASHi + ei .
OLS 2SLS
Independent Variables (1) (2) (3) (4)
Intercept 0.0256** 0.0256** 0.0220** 0.0212**
(18.71) (18.69) (6.58) (8.60)
SD FLOW –0.0055* — –0.0067* —
(–2.41) (2.18)
UNEXPECTED DAILY FLOW — –0.0046* — –0.0077**
(–2.01) (–3.76)
MEAN FLOW 0.0626** 0.0539** 0.1408 0.1049
(3.01) (2.72) (0.44) (0.54)
SIZE –0.0006** –0.0006** –0.0008 –0.0002
(–3.25) (–3.26) (–0.18) (–0.75)
TURNOVER –0.0010** –0.0009** 0.00239 0.0066**
(–3.54) (–3.52) (0.85) (2.94)
DEFER –0.0021 –0.0023 0.0232 0.0488
(–0.09) (–0.10) (0.44) (1.21)
FRONT 0.0271* 0.0271* 0.0144 0.0031
(2.03) (2.03) (0.49) (0.14)
12B 1 –0.3192** –0.3166** –0.2389 –0.5136**
(–3.22) (–3.19) (–1.01) (–2.85)
NON 12B 1 –1.0832** –1.0829** –0.4771** –0.3811**
(–10.12) (–10.10) (–2.97) (–3.16)
CASH 0.0051 0.0047 0.0109 –0.0181
(1.77) (1.62) (0.40) (–0.91)
2
R 6.6% 6.5% 0.4% 1.8%
IV. Robustness of Cross-Sectional Regression Analysis
My hypothesis that volatile flows act to depress performance is based on the
argument that fund managers incur costs from flows that they cannot predict and
prepare for. I proxy for these unexpected flows with flow volatility. Another option
is to model expected flows and then to use these to compute a more direct measure
7. Rakowski 229
of unexpected daily fund flows. Edelen and Warner (2001) and Warther (1995)
both have shown that lagged flows have some predictive power in explaining
current short-term aggregate flows. I therefore model expected daily flows for
each fund i with a simple autoregressive model of daily flows:
5
(3) DAILY FLOWt = αi + βa,i DAILY FLOWt−a,i + ei .
a=1
I then take the root mean squared error from this model as a measure of unex-
pected fund flows for each fund over the sample period. I experiment with sev-
eral variations of the model based on different lag lengths, incorporating various
assumptions regarding the structure of the error terms and including lagged re-
turns and fund size. These alternative models all yield essentially identical results
in my analysis. Therefore I report results only for a simple model incorporat-
ing 5 lags of flows and not including lagged returns or lagged TNA. Results for
these OLS regressions are reported in model (2) of Table 2 and document that
unexpected daily flow also takes a significant negative coefficient in explaining
performance.
In addition to using unexpected flows as an alternative to flow volatility, I
also examine several alternative measures of performance. I find that both flow
volatility and unexpected flows also take significant negative coefficients (not
reported) in explaining raw returns, load-adjusted returns, and both 1-factor
(market-model) alphas and 4-factor alphas (including a momentum factor). I con-
duct several further robustness tests, such as using average absolute flows instead
of flow volatility and eliminating the control variables. The use of absolute flows
is an important robustness check because the possible nonnormality of flows could
lead to biases when using the simple standard deviation of flows. Average abso-
lute flows take the same negative signs and at similar significance levels as flow
volatility. The elimination of my control variables, either concurrently or one at a
time, generally does not change my results for flow volatility or unexpected flows
and is therefore not reported. In particular, the elimination of turnover and/or cash
holdings does not change the signs or significance of the coefficients for flow
volatility or unexpected daily flows.
One motivation for the additional checks concerning the variables for
turnover and cash holdings is that they may be endogenous with respect to flows.
Therefore I also correct for this possibility by repeating the regressions using two-
stage least squares (2SLS) and using lagged values of the indicators for investment
objectives as instruments. Turnover, cash holdings, flow volatility/unexpected
flow, and average daily flow are the endogenous variables. The inclusion of flow
volatility and unexpected flows as endogenous variables is motivated by the pos-
sibility that it could be the fund’s performance that is driving the behavior of
flows. To obtain lagged values, I compute annual values of all variables for each
year during the 6-year sample period and estimate a pooled 2SLS to estimate the
model. The equation for the endogenous variables is
8. 230 Journal of Financial and Quantitative Analysis
(4) ENDOGENOUS VARIABLEi,t = β0
+ β1 MEAN DAILY PERCENTAGE FLOWi,t−1
+ β2 SD FLOWi,t−1 + β3 SIZEi,t−1 + β4 FRONTi,t−1
+ β5 DEFERi,t−1 + β6 12B 1i,t−1 + β7 NON 12B 1i,t−1
+ β8 TURNOVERi,t−1 + β9 CASHi,t−1
+ β10 INTERNATIONAL INDICATORi,t−1
+ β11 BOND INDICATORi,t−1 + ei,t .
In computing unexpected flows, I used lagged unexpected flows as an instru-
ment rather than lagged flow volatility. Results are presented in models (3) and (4)
of Table 2. The results remain qualitatively similar to the OLS regressions, with
significant negative coefficients for flow volatility and unexpected flows. The re-
sults are robust to various adjustments for the time-series properties of this model,
such as including fixed effects for each year. I therefore limit the reported results
to the simple pooled 2SLS estimates. The results of the 2SLS regressions are
consistent with flow volatility and unexpected flows being negatively related to
performance after adjusting for the possible endogeneity of flows.
In order to further examine the causal relationship between flow volatility
and performance, I now take the top and bottom quartiles of the sample based
on performance, flow volatility, and unexpected flows. Table 3 summarizes the
average values for performance, flow volatility, and unexpected flow for these
groups. One can observe that the measure of unexpected flow takes values very
close to the calculations of flow volatility. The t-tests for significant differences
between group means reveal that both high-volatility funds and funds with large
levels of unexpected flows have significantly lower average performance. Funds
with high risk-adjusted performance do not show significantly different levels of
flow volatility or unexpected flows when compared to low-performing funds. An
additional observation is that while funds with more volatile flows have higher
raw returns, this relationship does not persist once other variables are included in
a multivariate regression, as noted earlier. Overall, these findings further support
the conclusion that it is flow volatility that is driving differences in performance
rather than performance driving flow volatility.
One further explanation for my findings that must be addressed is that an
asymmetric flow-performance relationship could lead to a spurious correlation
between the standard deviation of flows and performance.3 This is based on the
possibility that the asymmetric flow-performance relationship that has been doc-
umented for long-term flows (Chevalier and Ellison (1997), Sirri and Tufano
(1999), and Huang, Wei, and Yan (2007)) also applies to my sample of daily data.
I therefore test for an asymmetric flow-performance relationship with a piecewise
3 This analysis is motivated by the following example of spurious correlation generously provided
by the editor: There is by now a large literature that has documented an asymmetric relationship be-
tween performance and fund flow. This relationship implies a correlation between fund flow volatility
and performance. To see this, assume for simplicity that fund excess returns, R, are normally dis-
tributed with mean 0 and that this asymmetry is captured by the empirical relation: flow equals aR
when R > 0, and flow equals 0 when R ≤ 0. Here, a > 0. Then, from the properties of the truncated
normal distribution one may infer that the cross-sectional sample correlation between the sample stan-
dard deviation of flow and the sample mean of excess return is 0.441 (Johnson and Kotz (1970)).
9. Rakowski 231
TABLE 3
Top and Bottom Quartiles of Performance, Flow Volatility, and Unexpected Flows
Table 3 presents average statistics for the top and bottom quartiles of the sample based on performance, flow volatility,
and unexpected flows. Performance is measured by the intercept (α) from a 3-factor model of daily returns. Flow volatility
(SD FLOW) is the standard deviation of percentage daily fund flows. Unexpected flows (UNEXPECTED DAILY FLOW) are
measured by the average root mean squared error from a model of expected daily flows. t -tests are for differences in means
between the top and bottom quartiles. * and ** represent a significant difference at the 5% and 1% levels, respectively.
There are approximately 1,193 funds in each quartile. All figures are reported as percentages.
Top Quartile Based on Bottom Quartile Based on
Performance Performance
Performance (α) 0.0322** –0.0124**
Annual return 9.84** 0.10**
SD FLOW 2.78 3.19
UNEXPECTED DAILY FLOW 2.75 3.08
Top Quartile Based on Bottom Quartile Based on
Flow Volatility (SD FLOW) Flow Volatility (SD FLOW)
Performance (α) 0.0094** 0.0183**
Annual return 5.98** 4.73**
SD FLOW 10.93** 0.48**
UNEXPECTED DAILY FLOW 10.74** 0.46**
Top Quartile Based on Bottom Quartile Based on
Unexpected Flows Unexpected Flows
(UNEXPECTED DAILY FLOW) (UNEXPECTED DAILY FLOW)
Performance (α) 0.0094** 0.0181**
Annual return 6.04** 4.73**
SD FLOW 10.88** 0.51**
UNEXPECTED DAILY FLOW 10.75** 0.46**
linear regression of flow on a fund’s performance ranking. Both flows and perfor-
mance are measured as in the rest of my study (average daily percentage flows
and performance ranks based on a 3-factor model). All coefficient estimates for
performance ranks are insignificant in these tests, with no patterns in the sign
or magnitude of coefficient estimates as one looks across ranks (results available
from the author). These tests suggest that there is no asymmetric pattern in flow
and performance for my sample of daily data. Therefore, I can be confident that
my findings are not driven by any asymmetric pattern between fund flows and
performance. However, the lack of an asymmetric relationship between flows and
performance does further demonstrate that the behavior of daily fund flows dif-
fers substantially from the long-term flow patterns documented by other studies
of mutual fund performance.
V. Alternative Calculation of Daily Flows
The Lipper database reports daily TNA not including the current day’s flows.
Therefore, funds do not suffer from the time constraint that exists when they must
report end-of-day TNA including the current day’s flows. From these data, I can
compute an accurate end-of-day measure of TNA on day t including day t’s flows
by taking the end-of-day TNA on day t + 1 and discounting by the return on day
t + 1.
The reliability of funds consistently reporting the current day’s net flows is
an issue of concern in other databases of daily fund flows such as the Trimtabs
database used by Edelen and Warner (2001), Greene and Hodges (2002), Zitzewitz
(2003), and Chalmers et al. (2001b). Trimtabs reports daily fund TNAs for
10. 232 Journal of Financial and Quantitative Analysis
many funds that include the current day’s net flows. Therefore, flows are cal-
culated as
(5) ct = at − [at−1 (1 + rt )],
where at is total net assets on day t, rt is the fund’s return on day t, and ct is
fund flow on day t. To get percentage flows, I then divide equation (5) by at−1 .
However, this figure for TNA suffers from the problem that funds themselves do
not have an accurate measure of their TNA at the end of each trading day. There-
fore, some funds actually report TNAs including the current day’s flows, while
other funds report TNAs not including the current day’s flows. Some funds even
report TNA partially including the day’s flows, with the remaining flows being
included in the next day’s TNA. This obviously leads to difficulties in accurately
calculating daily flows from such data.
There are several reasons why I do not believe that this potential problem
should invalidate the results. First, Lipper’s procedure of reporting fund TNA not
including the day’s flows leaves much less potential for misreporting by funds.
Second, any discrepancy in the inclusion of flows in the current day’s TNA should
only result in a one-day bias in daily flows that is correlated with returns from the
previous day. Therefore, any bias in the calculation of flow volatility over the
sample period should be largely eliminated, as I consider the standard deviation
of flows over extended periods of time. Furthermore, because all common mutual
fund databases, including CRSP, Lipper, Morningstar, and Trimtabs, round their
reported TNA (usually to $100,000s), any error in TNA will tend to be within the
rounding difference for most observations. I am therefore confident that the issues
concerning TNA reporting that have been raised about past daily flow data are not
relevant to this study or to the Lipper database.
In order to fully examine any possible influence of mismeasured TNA, I
repeat all analysis with flows calculated assuming that TNAt includes the cur-
rent day’s flows, as given in equation (5). This actually strengthens the results,
with flow volatility and unexpected flows still taking negative coefficients (not re-
ported) for both OLS and 2SLS regressions, but with slightly higher significance
levels.
VI. Cross-Sectional Regression Analysis of Fund Groups
While the alternative measures of performance and flow volatility do not
lead to major changes in the findings, when I examine certain subgroups from the
overall sample I do find variation on the relationship between flow volatility and
performance. Because the liquidity of a security is often influenced by the size
of the security (Demsetz (1968)), I first split the sample based on fund size. This
allows me to examine if the link between flow volatility and performance is driven
by the economies of scale faced by the fund manager.
The impact of flows on the fund manager could also differ based on whether
there is an inflow or an outflow. Unfortunately I observe only net flows each day,
and many of these flows may be reversed before the fund manager is forced to
trade. Therefore I split the sample based on the net long-term flows to each fund
over the entire sample period. Each fund is classified as a net inflow fund or an
11. Rakowski 233
outflow fund based on its total cumulative flows. This allows me to examine if
there is a long-term difference between cumulative inflows and outflows.
Third, because I know that the link between flow and performance is non-
linear (Sirri and Tufano (1998)), it is also reasonable that the link between flow
volatility and performance is nonlinear. As a simple examination of this possi-
bility, I split the sample based on those funds whose risk-adjusted performance
is positive and those for which is it negative. I then repeat the basic regression
analysis. Results are reported in Table 4.
TABLE 4
Cross-Sectional Regression Analysis
Table 4 presents the results of OLS regressions explaining cross-sectional differences in performance for the sample of
4,772 open-end mutual funds partitioned by total net assets (TNA), relative performance, and average level of flows. Here,
αi is the intercept from a daily 3-factor model of fund i ’s returns; SD FLOW is the standard deviation of daily percentage
flows; expense ratios are decomposed into 12B 1 and NON 12B 1 fees; SIZE is the natural log of average daily TNA; “Big”
funds are those with average TNA greater than the median of $84.57 million; “Inflow” funds are those with positive average
monthly flows; “Positive α” funds are those with a positive value for α. * and ** indicate significance at the 5% and 1%
levels, respectively. HAC (White (1980)) t -statistics are given in parentheses. The model is
(2) αi = β0 + β1 SD FLOWi + β2 MEAN FLOWi + β3 SIZEi + β4 FRONTi + β5 DEFERi
+ β6 12B 1i + β7 NON 12B 1i + β8 TURNOVERi + β9 CASHi + ei .
Independent Big Small Positive Negative Inflow Outflow
Variables Funds Funds α α Funds Funds
Intercept 0.0304** 0.0193** 0.0215** –0.0048 0.0187** 0.0333**
(12.99) (6.54) (19.70) (–1.83) (10.63) (16.11)
SD FLOW –0.0052 –0.0090* –0.0038* –0.0107 –0.0075* –0.0027
(–1.30) (–2.21) (–2.28) (–1.35) (–2.50) (–0.87)
MEAN FLOW 0.1069 0.0767** 0.0421** 0.1529 0.0708* 0.0364
(1.64) (2.69) (2.96) (0.89) (2.34) (1.51)
SIZE –0.0017** 0.0016* –0.0007** 0.0007* –0.0004 –0.0008**
(–5.09) (2.31) (–5.28) (2.27) (–0.17) (–2.79)
DEFER –0.0500 0.0224** 0.0226 –0.0445 0.0453* 0.0689*
(–1.57) (–2.84) (1.26) (–1.43) (–2.38) (2.18)
FRONT 0.0405* –0.0028 0.0126 0.0421* 0.0244 0.0266
(2.51) (0.72) (1.15) (2.22) (1.37) (1.40)
12B 1 –0.3242* –0.2938 –0.4096** 0.5736** –0.2257 –0.7038**
(–2.30) (–0.12) (–5.22) (3.96) (1.38) (–4.70)
NON 12B 1 –0.8398** –1.2646* 0.1631 –1.0802** –0.4458 –2.0653**
(–5.90) (–2.14) (1.79) (–6.35) (–1.79) (–12.89)
TURNOVER –0.0010* –0.0009** 0.0001 –0.0014 –0.0010** –0.0006
(–2.04) (–8.28) (0.35) (–1.47) (–3.41) (–1.87)
CASH 0.0038 0.0065 –0.0086** 0.0042 0.0017 0.0001
(0.63) (1.90) (–4.21) (0.35) (0.53) (0.02)
R2 6.2% 8.3% 2.2% 10.8% 1.2% 18.2%
N 2,386 2,386 3,594 1,178 2,628 2,144
The results indicate that there is considerable variation across these groups
in the relationship between flow volatility and performance. A significant nega-
tive coefficient is found for small funds but not large funds, suggesting that the
economies of scale present for larger funds are successful in alleviating the prob-
lem of volatile fund flows.
Funds with positive risk-adjusted performance display a significant negative
coefficient, but not funds with negative risk-adjusted performance. This is con-
sistent with a nonlinear relationship between flow volatility and performance.
The finding therefore extends the asymmetric nature of the flow-performance
12. 234 Journal of Financial and Quantitative Analysis
relationship to the second moment of the flow distribution. This conclusion is fur-
ther supported by the last partition of the sample, between funds that experience
net inflows or outflows over the sample period. Here I find that there is a signif-
icant negative coefficient for inflow funds but not for funds that experience net
outflows. These results are independent of the previously documented link be-
tween flow and performance (Chevalier and Ellison (1997), Sirri and Tufano
(1999), and Huang et al. (2007)) for several reasons. First, the asymmetry present
here is between flow volatility and performance, not simply the level of flow and
performance. Second, if the traditional flow-performance relationship were driv-
ing the results, then I would expect to find a positive coefficient for flow volatility
and performance, while I instead obtain a negative coefficient estimate. Third,
as mentioned above, the data do not display a significant asymmetric relation-
ship between daily flow and performance, as is the case for monthly or quarterly
flows. Therefore, the asymmetries displayed here are unique to daily data and to
the second moment of the flow distribution.
VII. Cross-Sectional Regression Analysis Based on
Investment Objectives
For the final cross-sectional tests of daily flow volatility, I partition the sam-
ple based on investment objectives as reported in the CRSP mutual funds database,
with descriptive statistics reported in Table 5. The sample contains 2,593 domestic
equity funds, 1,583 domestic bond funds, and 597 international equity funds.
TABLE 5
Descriptive Statistics of Cross-Sectional Characteristics by Investment Objective
Table 5 presents descriptive statistics for the sample of open-end mutual funds over the sample period from March 2000
to October 2006, by investment objective. Daily fund flows, returns, and TNA are from Lipper, while annual data are from
CRSP. * and ** represent a significant difference at the 5% and 1% levels, respectively, in t -tests for differences in means
when international equity and bond funds are compared to domestic equity funds.
Domestic Domestic International
Equity Bond Equity
Funds Funds Funds
Panel A. Flow
Average daily flow (%) 0.1794 0.1245 0.2063
SD of daily flow (%) 4.22 3.21 4.94
Average daily flow ($thousands) 5.71 –1.72** 72.37*
Average annual flow (%) 25.25 13.90 57.73**
Average annual flow ($millions) 10.07 –1.96** 111.79**
Panel B. Returns
Average daily return (%) 0.0204 0.0221* 0.0289**
Average annual return (%) 4.35 5.36** 7.87**
Average 3-factor alpha, daily (%) 0.0071 0.0215** 0.0010**
Panel C. Fund Characteristics
Size ($millions) 333.87 217.84** 349.50
Cash holdings (%) 4.21 4.77* 3.79
Turnover (%) 92.81 108.47** 86.22
12b-1 fees (%) 0.43 0.37** 0.41
Non-12b-1 fees (%) 1.08 0.70** 1.37**
Front load (%) 1.40 1.34 1.54
Deferred load (%) 1.22 1.05** 1.20
N 2,593 1,583 597
13. Rakowski 235
The regression tests proceed as before, with results presented in Table 6.
Domestic equity funds take a significant negative coefficient for flow volatility.
The coefficient estimates for bond and international funds are also negative,4 but
insignificant. The lack of significance for international funds is surprising, con-
sidering the findings of Greene and Hodges (2002) and Zitzewitz (2003), whose
analysis of market-timing trading implies a negative relationship between flow
volatility and performance. However, it is consistent with the smaller number of
international equity funds than domestic equity or bond funds in the sample, and
the fact that international equity funds exhibit more noisy observations of the vari-
ables included in the tests. Overall, this suggests that the effect of flow volatility
is not due simply to the marketing-timing trades of international funds.
TABLE 6
Cross-Sectional Regression Analysis by Fund Type
Table 6 presents the results of OLS regressions explaining cross-sectional differences in performance for the sample of
4,772 open-end mutual funds partitioned by investment objective. Here, α is the intercept from a daily 3-factor model of
fund i ’s returns; SD FLOW is the standard deviation of daily percentage flows; expense ratios are decomposed into 12B 1
and NON 12B 1 fees; SIZE is the natural log of average daily total net assets. * and ** indicate significance at the 5% and
1% levels, respectively. HAC (White (1980)) t -statistics are given in parentheses. The model is
(2) αi = β0 + β1 SD FLOWi + β2 MEAN FLOWi + β3 SIZEi + β4 FRONTi + β5 DEFERi
+ β6 12B 1i + β7 NON 12B 1i + β8 TURNOVERi + β9 CASHi + ei .
Domestic Domestic International
Independent Equity Bond Equity
Variables Funds Funds Funds
Intercept 0.0022 0.0022 0.0338** 0.0338** –0.0155 –0.0156
(1.28) (1.27) (30.15) (30.25) (–1.88) (–1.88)
SD FLOW –0.0188** –0.0014 –0.0365
(–2.61) (–0.49) (–0.94)
UNEXPECTED –0.0173* –0.0010 –0.0320
DAILY FLOWS (–2.57) (–0.40) (–0.89)
MEAN FLOW 0.4562* 0.4301* 0.0460 0.0372 0.9148 0.8324
(2.38) (2.44) (0.82) (0.79) (0.79) (0.76)
SIZE 0.0006** 0.0006** –0.0008** –0.0004** 0.0017 0.0017
(2.83) (2.80) (–6.01) (–6.00) (1.82) (1.82)
DEFER –0.0342 –0.0347 0.0729** 0.0728** –0.1769 –0.1774
(–1.24) (–1.25) (4.37) (4.36) (–1.78) (–1.79)
FRONT –0.0330* –0.0330* 0.0611** 0.0611** –0.0120 –0.0134
(–2.10) (–2.10) (6.17) (6.12) (–0.23) (–0.26)
12B 1 –0.3579** –0.3547** –0.6242** –0.6233** 0.6285 0.6284
(–2.88) (–2.85) (–8.11) (–8.06) (1.62) (1.62)
NON 12B 1 0.5291** 0.5279** –0.8560** –0.8562** 0.7955 0.7932
(3.80) (3.79) (–7.10) (–7.10) (1.79) (1.78)
TURNOVER –0.0035** –0.0035** –0.0004** –0.0004** –0.0043 –0.0043
(–4.52) (–4.53) (–3.17) (–3.11) (–1.53) (–1.53)
CASH –0.0002 0.0001 –0.0209** 0.0212** 0.0662** 0.0672**
(–0.04) (0.04) (–3.97) (–3.88) (2.90) (2.94)
R2 6.6% 6.5% 20.5% 20.5% 4.8% 4.6%
From the descriptive statistics presented in Table 5, the means of most
variables for domestic equity funds fall in between those of domestic bond funds
4 The use of alternative performance measures, such as using international equity and bond indices
in the computation of α, do yield significant negative coefficient estimates (not reported) for flow
volatility and unexpected flows.
14. 236 Journal of Financial and Quantitative Analysis
and international equity funds. However, domestic equity funds do exhibit lower
raw returns and higher 12b-1 fees and deferred loads than either domestic bond
funds or international equity funds. This is consistent with higher marketing
expenditures leading to changes in flows that could then have a detrimental im-
pact on performance. Such a conjecture follows from the work of Jain and Wu
(2000), who document that marketing effort does impact flows, while not being
positively related to future returns.
VIII. Conclusions
This paper documents a significant negative relationship between daily
mutual fund flow volatility and performance. A nearly identical relationship is
documented between unexpected daily flows and performance. The negative re-
lationship between fund flow volatility and performance is strongest for domestic
equity funds.
The fact that flow volatility remains significant after correcting for funds’
turnover suggests that it is not simply the increased trading by fund managers
that drives the link between flow volatility and performance. The evidence here
is consistent with the short-term discretionary trading of fund mangers, proxied
for by turnover, being positively related to performance for equity funds, after
correcting for its correlation with other variables. Short-term liquidity-motivated
trading, proxied for by daily flow volatility and unexpected flows, is negatively
related to performance.
The results of this study indicate that trading by fund investors plays an im-
portant role in determining cross-sectional differences in fund performance. The
findings do not suggest that high portfolio turnover is the result of excessive trad-
ing, or “churning” by fund managers, but that it is the response to erratic daily
flows from fund investors. It seems that there are more complex factors driv-
ing differences in performance across funds than previous studies have indicated,
and that although flow-induced transaction costs are important, more research is
needed to better understand the precise interaction between fund flows and fund
managers’ trading, as well as how far this interaction goes in explaining the unre-
solved issues regarding mutual fund performance.
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