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.
Mutual fund performance and manager style by james l. davis(11)bfmresearch
This document provides a 3-sentence summary of the given document:
The document analyzes whether certain investment styles reliably produce abnormal returns for mutual funds and whether fund performance is persistent based on style. It finds that none of the styles studied generated positive abnormal returns compared to benchmarks, with value funds showing negative abnormal returns. There is some evidence that top performing growth managers and worst performing small-cap managers show persistence for a year, but abnormal performance tends to disappear quickly. The results cast doubt on the economic value of active fund management.
This document provides a 3-sentence summary of a research paper that develops a multifactor model to forecast the 1-year returns of actively managed equity mutual funds. The model uses forecasts of a fund's manager skill, style (based on factors like market, size, value, and momentum), and expected factor returns. When tested on German equity funds, the multifactor model substantially improved forecasts compared to a naive model, reducing the mean squared error by up to 30% and yielding returns over 200 basis points higher for top-decile funds.
Short term persistence in mutual fund performance(12)bfmresearch
This study examines the short-term persistence of mutual fund performance using daily returns data over quarterly periods. The researchers estimate stock selection and market timing models for mutual funds and rank funds into deciles based on their estimated abnormal returns each quarter. They then measure the average abnormal return of each decile in the following quarter. They find that the top-performing decile in a given quarter generates a statistically significant average abnormal return of 25-39 basis points in the subsequent quarter, providing evidence of short-term persistence in performance. However, this persistence disappears when funds are evaluated over longer periods using a concatenated time series approach.
Dimensional investors are able to capture the value premium where others fail through an integrated investment process. Their process begins with clear investment principles of efficient markets and targeting dimensions of expected return like value and size. They design strategies for continuous exposure to these premium-generating factors. Their portfolio engineering, management, and trading are dynamically integrated to minimize costs from factors like momentum and provide liquidity. This allows Dimensional to reliably deliver excess returns to investors from targeting premiums.
The study was undertaken to investigate the possibility of momentum and contrarian strategies to outperform and generate a superior return to the investor i.e. returns over and above the benchmark index. Analysis of the data collected over four years (2016-2019) for quarterly, half-yearly and yearly holding periods resulted in rejecting the possibility of the momentum and contrarian strategies to outperform index consistently, even though they provide huge returns sometimes, in the Indian stock market for the period under study
Fundamental factors that affect stock value include earnings per share and valuation multiples like the price-to-earnings ratio. Technical factors beyond company fundamentals can also influence stock prices, such as inflation, economic strength of the market and industry peers, availability of substitutes, incidental transactions, demographics of investors, trends, liquidity, and market sentiment. Together, fundamental and technical factors determine stock prices in both efficient and inefficient markets.
1. The document analyzes alternative benchmarks for evaluating the performance of mutual funds that invest in real estate investment trusts (REITs). It compares using a simple REIT index to using multiple-factor models that account for characteristics like firm size, book-to-market ratio, and property type.
2. The study finds that including additional factors like these improves the explanatory power of performance models and lowers estimates of abnormal returns for many REIT mutual funds. Including returns of non-REIT real estate firms like homebuilders and real estate operating companies also enhances the models.
3. While benchmark choice has a modest effect on measured performance of the overall REIT mutual fund market, individual fund performance can be more
Managers does longevityimplyexpertise-costabfmresearch
This study analyzes the performance of 1,042 mutual funds from 1986 to 1995 to determine if fund managers with longer tenure (at least 10 years) generate higher risk-adjusted returns than less experienced managers. The results show:
1) Funds overall had positive risk-adjusted returns of 0.16% per month on average, but funds with long-tenured managers did not outperform those with less experienced managers.
2) Positive returns were largely due to a few crucial years common to all funds, not manager tenure.
3) A manager's ability to generate positive returns over 3 years did not indicate the ability to do so consistently in subsequent periods, regardless of tenure.
Mutual fund performance and manager style by james l. davis(11)bfmresearch
This document provides a 3-sentence summary of the given document:
The document analyzes whether certain investment styles reliably produce abnormal returns for mutual funds and whether fund performance is persistent based on style. It finds that none of the styles studied generated positive abnormal returns compared to benchmarks, with value funds showing negative abnormal returns. There is some evidence that top performing growth managers and worst performing small-cap managers show persistence for a year, but abnormal performance tends to disappear quickly. The results cast doubt on the economic value of active fund management.
This document provides a 3-sentence summary of a research paper that develops a multifactor model to forecast the 1-year returns of actively managed equity mutual funds. The model uses forecasts of a fund's manager skill, style (based on factors like market, size, value, and momentum), and expected factor returns. When tested on German equity funds, the multifactor model substantially improved forecasts compared to a naive model, reducing the mean squared error by up to 30% and yielding returns over 200 basis points higher for top-decile funds.
Short term persistence in mutual fund performance(12)bfmresearch
This study examines the short-term persistence of mutual fund performance using daily returns data over quarterly periods. The researchers estimate stock selection and market timing models for mutual funds and rank funds into deciles based on their estimated abnormal returns each quarter. They then measure the average abnormal return of each decile in the following quarter. They find that the top-performing decile in a given quarter generates a statistically significant average abnormal return of 25-39 basis points in the subsequent quarter, providing evidence of short-term persistence in performance. However, this persistence disappears when funds are evaluated over longer periods using a concatenated time series approach.
Dimensional investors are able to capture the value premium where others fail through an integrated investment process. Their process begins with clear investment principles of efficient markets and targeting dimensions of expected return like value and size. They design strategies for continuous exposure to these premium-generating factors. Their portfolio engineering, management, and trading are dynamically integrated to minimize costs from factors like momentum and provide liquidity. This allows Dimensional to reliably deliver excess returns to investors from targeting premiums.
The study was undertaken to investigate the possibility of momentum and contrarian strategies to outperform and generate a superior return to the investor i.e. returns over and above the benchmark index. Analysis of the data collected over four years (2016-2019) for quarterly, half-yearly and yearly holding periods resulted in rejecting the possibility of the momentum and contrarian strategies to outperform index consistently, even though they provide huge returns sometimes, in the Indian stock market for the period under study
Fundamental factors that affect stock value include earnings per share and valuation multiples like the price-to-earnings ratio. Technical factors beyond company fundamentals can also influence stock prices, such as inflation, economic strength of the market and industry peers, availability of substitutes, incidental transactions, demographics of investors, trends, liquidity, and market sentiment. Together, fundamental and technical factors determine stock prices in both efficient and inefficient markets.
1. The document analyzes alternative benchmarks for evaluating the performance of mutual funds that invest in real estate investment trusts (REITs). It compares using a simple REIT index to using multiple-factor models that account for characteristics like firm size, book-to-market ratio, and property type.
2. The study finds that including additional factors like these improves the explanatory power of performance models and lowers estimates of abnormal returns for many REIT mutual funds. Including returns of non-REIT real estate firms like homebuilders and real estate operating companies also enhances the models.
3. While benchmark choice has a modest effect on measured performance of the overall REIT mutual fund market, individual fund performance can be more
Managers does longevityimplyexpertise-costabfmresearch
This study analyzes the performance of 1,042 mutual funds from 1986 to 1995 to determine if fund managers with longer tenure (at least 10 years) generate higher risk-adjusted returns than less experienced managers. The results show:
1) Funds overall had positive risk-adjusted returns of 0.16% per month on average, but funds with long-tenured managers did not outperform those with less experienced managers.
2) Positive returns were largely due to a few crucial years common to all funds, not manager tenure.
3) A manager's ability to generate positive returns over 3 years did not indicate the ability to do so consistently in subsequent periods, regardless of tenure.
Volatility trading strategies seek to profit from changes in a asset's volatility. Volatility measures how much the price of an asset fluctuates over time. There are several types of volatility strategies including volatility dispersion trading which buys options on index components and sells options on the overall index, volatility spreads which use option combinations to profit from different implied volatilities, and gamma trading which aims to benefit from unexpected events causing large price moves. Volatility is important for options as their pricing depends on assumptions about future volatility.
2017 maqsood & kousar stock market terminologiesTehmina Kousar
The document defines various stock market terminology over 27 pages. It defines terms like index, short sale, cross, odd lot, board lot, ask, beta, blue chip company, diversification, liquidity, limit order, call option, market capitalization, quote, trading volume, going long, bid ask spread, bid, defensive stock, bull, buy-in, falling knife, street price, capital structure, stock split, bull market, convertible securities, bear market, fill or kill, lame duck of the market, over the counter market, buy-and-hold, fund of funds, arbitrage, dumping, rigging, demurrage, strading, clogging, bagel land, and bear
- The document reviews several studies from the 1960s and 1970s that analyzed the performance of mutual funds relative to benchmarks like the Dow Jones Industrial Average.
- The earliest studies by Friend, Sharpe, and Jensen found that on average mutual funds did not outperform the market and in some cases had lower risk-adjusted returns.
- Later studies critiqued the risk measures used and developed new measures, finding more mixed results and that some funds did outperform depending on factors like objectives, risks taken, and time periods analyzed.
- Overall the literature showed an ongoing effort to develop better ways to measure and compare mutual fund performance over time while accounting for risks.
Stock Return Synchronicity and Technical Trading Rules (Global Development Fi...Koon Boon KEE
This document discusses a study examining the relationship between stock return synchronicity and the returns of technical trading rules. It begins by noting debates around the profitability and intellectual foundations of technical analysis. The study aims to explore if varying degrees of firm-level synchronicity can explain the profits or losses of technical trading rules. It reviews literature showing a decline in technical trading profitability over time in the US and lower stock price synchronicity. The study examines this relationship in Chinese stock markets, which had high synchronicity in 1995 but a now larger market. It measures synchronicity using stock return correlations and discusses debates around interpreting this measure.
The document discusses volatility arbitrage hedge funds and their performance relative to other hedge funds during the market downturn of 2008-2009. It notes that volatility arbitrage funds returned 7.3% through August 2008 while most other hedge fund strategies struggled. A number of reasons for the strong performance are provided, including high volatility, opportunities from illiquid market conditions, and avoidance of exposure to sub-prime credit. The document proposes a new volatility arbitrage fund that will utilize flexible strategies tailored to changing market conditions in order to generate positive returns across different market environments. Brief biographies of the proposed fund's trading team are also included.
Parametric provides strategies for exploiting increased market volatility, including rebalancing portfolios and using options strategies. Rebalancing reduces concentration risks and volatility over time by selling assets that have increased in value and buying those that have decreased, capturing returns from volatility. Options strategies can also provide downside protection for portfolios while retaining upside potential. Parametric implemented an options overlay for a client in 2008 that protected against a 5-20% market decline while retaining upside to 30%, balancing protection and participation in gains.
This document discusses using quantitative strategies to systematically manage client-driven trading flows. It describes how a dealer can proactively price, hedge, and manage risk when facilitating client trades, rather than reactively. Specific topics covered include the large volume and order flow in the US options market, an overview of flow trading and hedging client trades, and examples of relative value arbitrage strategies used to systematically hedge less liquid client trades.
Stock Return Forecast - Theory and Empirical EvidenceTai Tran
The document discusses several models for stock return forecasting including CAPM, the Fama-French three-factor model, a four-factor model with momentum, and a five-factor model including asset growth. Empirical evidence is presented analyzing daily returns of Coca-Cola stock in 2005, finding that momentum is highly significant in predicting returns, while beta is less so. Multi-factor models, particularly the four and five-factor models, provide improved forecasting over CAPM alone, though with increasing complexity. Limitations include selection bias and issues with beta estimation.
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.
Addressing Retail Flowback in Unlisted REITs (real estate investment trusts)Spencer Johnson
The document discusses potential strategies for addressing the "flowback issue" faced by unlisted real estate investment trusts (REITs) pursuing a listing transaction. Specifically, it examines doing nothing, implementing lock-ups to control shareholder selling, and having the REIT enter the market to absorb some of the shareholder selling. Case studies of previous REITs that did nothing showed significant downward pressure on stock prices from retail shareholder selling. Implementing lock-ups through a recapitalization is an effective but costly strategy requiring shareholder approval. Having the REIT purchase some shares could help but is restricted by regulations around the listing process.
The document examines how the shift from active to passive investing affects financial stability. It finds that the shift both increases and decreases certain risks:
1) The growth of ETFs, which are largely passive and do not redeem in cash, has likely reduced risks from liquidity transformation and destabilizing redemptions compared to mutual funds.
2) However, some passive strategies like leveraged ETFs amplify market volatility.
3) The shift has also increased asset management industry concentration, potentially exacerbating risks from operational problems at large firms.
4) Evidence is mixed on whether passive investing increases comovement of asset returns and liquidity through "index inclusion effects."
1) Market timing and tactical asset allocation (TAA) share the goal of "buy low, sell high" but differ in important ways. While market timing takes a binary "all in or all out" approach, TAA varies the degree of exposure to different asset classes.
2) TAA practitioners also consider the economic environment to help time when valuations may revert to fair value, unlike simple market timing strategies based only on metrics like price-to-earnings.
3) By taking a global approach and diversifying across many timing decisions, TAA aims to add value over shorter time horizons that are more tolerable to investors, unlike the longer timeframes often needed for market timing to be
This document provides 11 tips for staying calm during volatile markets:
1. Have a predetermined investment plan and guidelines to prevent emotional decisions.
2. Understand your portfolio's holdings and why you own each asset.
3. Remember that diversification and comparing performance to benchmarks can provide perspective on fluctuations.
4. Volatility is temporary and markets have historically recovered, so don't make hasty decisions.
5. Learn from past mistakes and use experience to prepare for future ups and downs. Expert advice can also help navigate volatility.
6. Consider defensive sectors that may be less volatile during market swings. Dividends can also provide stability.
7. Continuing regular savings
Fund performance persistence and competition keswanibfmresearch
This document summarizes a study that examines how competition within a sector affects mutual fund performance persistence at the sector level in the UK market. The study uses data on UK unit trusts from 1991-2001 that includes annual returns, assets, and sector classifications. Several variables are constructed to measure competition within each sector, such as the number of funds, proportion of mature funds, and concentration of assets. The main finding is that performance persistence is higher in sectors where concentration of assets under management is higher, indicating less competition is associated with greater persistence. This suggests the degree of persistence exhibited by a sector's funds depends on how competitive that sector is.
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 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.
1) The document analyzes perverse incentives created by common hedge fund fee structures, noting fees as high as 2-3% annually plus 20% of gains can incentivize inappropriate investments.
2) Research cited finds hedge funds from 1995-2003 returned an average of 8.82% annually, but estimates actual returns for investors were 500-1000 bps lower due to biases.
3) While hedge funds provided some diversification, correlations with stocks were around 0.3 on average, meaning investors paid fees for passive stock exposure rather than skill.
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 introduces a new measure called Active Share to quantify active portfolio management. Active Share describes the percentage of portfolio holdings that differ from the portfolio's benchmark index. It argues that Active Share, combined with tracking error, provides a comprehensive picture of a fund's active management approach. The authors apply this two-dimensional framework to analyze mutual funds, finding that the most active stock pickers outperform, while closet indexers and funds focusing on factor bets underperform after fees.
1) A study found that when people learn that their choice agrees with expert opinions, the reward center of their brain is activated similarly to when experiencing pleasures like food or money.
2) This suggests that conformity feels intrinsically rewarding on a basic biological level, explaining why investor sentiment can suddenly change as people want to agree with the group.
3) When others value something differently than you initially did, it makes you question your own valuation and want to conform to be part of the group, even if just subconsciously.
Volatility trading strategies seek to profit from changes in a asset's volatility. Volatility measures how much the price of an asset fluctuates over time. There are several types of volatility strategies including volatility dispersion trading which buys options on index components and sells options on the overall index, volatility spreads which use option combinations to profit from different implied volatilities, and gamma trading which aims to benefit from unexpected events causing large price moves. Volatility is important for options as their pricing depends on assumptions about future volatility.
2017 maqsood & kousar stock market terminologiesTehmina Kousar
The document defines various stock market terminology over 27 pages. It defines terms like index, short sale, cross, odd lot, board lot, ask, beta, blue chip company, diversification, liquidity, limit order, call option, market capitalization, quote, trading volume, going long, bid ask spread, bid, defensive stock, bull, buy-in, falling knife, street price, capital structure, stock split, bull market, convertible securities, bear market, fill or kill, lame duck of the market, over the counter market, buy-and-hold, fund of funds, arbitrage, dumping, rigging, demurrage, strading, clogging, bagel land, and bear
- The document reviews several studies from the 1960s and 1970s that analyzed the performance of mutual funds relative to benchmarks like the Dow Jones Industrial Average.
- The earliest studies by Friend, Sharpe, and Jensen found that on average mutual funds did not outperform the market and in some cases had lower risk-adjusted returns.
- Later studies critiqued the risk measures used and developed new measures, finding more mixed results and that some funds did outperform depending on factors like objectives, risks taken, and time periods analyzed.
- Overall the literature showed an ongoing effort to develop better ways to measure and compare mutual fund performance over time while accounting for risks.
Stock Return Synchronicity and Technical Trading Rules (Global Development Fi...Koon Boon KEE
This document discusses a study examining the relationship between stock return synchronicity and the returns of technical trading rules. It begins by noting debates around the profitability and intellectual foundations of technical analysis. The study aims to explore if varying degrees of firm-level synchronicity can explain the profits or losses of technical trading rules. It reviews literature showing a decline in technical trading profitability over time in the US and lower stock price synchronicity. The study examines this relationship in Chinese stock markets, which had high synchronicity in 1995 but a now larger market. It measures synchronicity using stock return correlations and discusses debates around interpreting this measure.
The document discusses volatility arbitrage hedge funds and their performance relative to other hedge funds during the market downturn of 2008-2009. It notes that volatility arbitrage funds returned 7.3% through August 2008 while most other hedge fund strategies struggled. A number of reasons for the strong performance are provided, including high volatility, opportunities from illiquid market conditions, and avoidance of exposure to sub-prime credit. The document proposes a new volatility arbitrage fund that will utilize flexible strategies tailored to changing market conditions in order to generate positive returns across different market environments. Brief biographies of the proposed fund's trading team are also included.
Parametric provides strategies for exploiting increased market volatility, including rebalancing portfolios and using options strategies. Rebalancing reduces concentration risks and volatility over time by selling assets that have increased in value and buying those that have decreased, capturing returns from volatility. Options strategies can also provide downside protection for portfolios while retaining upside potential. Parametric implemented an options overlay for a client in 2008 that protected against a 5-20% market decline while retaining upside to 30%, balancing protection and participation in gains.
This document discusses using quantitative strategies to systematically manage client-driven trading flows. It describes how a dealer can proactively price, hedge, and manage risk when facilitating client trades, rather than reactively. Specific topics covered include the large volume and order flow in the US options market, an overview of flow trading and hedging client trades, and examples of relative value arbitrage strategies used to systematically hedge less liquid client trades.
Stock Return Forecast - Theory and Empirical EvidenceTai Tran
The document discusses several models for stock return forecasting including CAPM, the Fama-French three-factor model, a four-factor model with momentum, and a five-factor model including asset growth. Empirical evidence is presented analyzing daily returns of Coca-Cola stock in 2005, finding that momentum is highly significant in predicting returns, while beta is less so. Multi-factor models, particularly the four and five-factor models, provide improved forecasting over CAPM alone, though with increasing complexity. Limitations include selection bias and issues with beta estimation.
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.
Addressing Retail Flowback in Unlisted REITs (real estate investment trusts)Spencer Johnson
The document discusses potential strategies for addressing the "flowback issue" faced by unlisted real estate investment trusts (REITs) pursuing a listing transaction. Specifically, it examines doing nothing, implementing lock-ups to control shareholder selling, and having the REIT enter the market to absorb some of the shareholder selling. Case studies of previous REITs that did nothing showed significant downward pressure on stock prices from retail shareholder selling. Implementing lock-ups through a recapitalization is an effective but costly strategy requiring shareholder approval. Having the REIT purchase some shares could help but is restricted by regulations around the listing process.
The document examines how the shift from active to passive investing affects financial stability. It finds that the shift both increases and decreases certain risks:
1) The growth of ETFs, which are largely passive and do not redeem in cash, has likely reduced risks from liquidity transformation and destabilizing redemptions compared to mutual funds.
2) However, some passive strategies like leveraged ETFs amplify market volatility.
3) The shift has also increased asset management industry concentration, potentially exacerbating risks from operational problems at large firms.
4) Evidence is mixed on whether passive investing increases comovement of asset returns and liquidity through "index inclusion effects."
1) Market timing and tactical asset allocation (TAA) share the goal of "buy low, sell high" but differ in important ways. While market timing takes a binary "all in or all out" approach, TAA varies the degree of exposure to different asset classes.
2) TAA practitioners also consider the economic environment to help time when valuations may revert to fair value, unlike simple market timing strategies based only on metrics like price-to-earnings.
3) By taking a global approach and diversifying across many timing decisions, TAA aims to add value over shorter time horizons that are more tolerable to investors, unlike the longer timeframes often needed for market timing to be
This document provides 11 tips for staying calm during volatile markets:
1. Have a predetermined investment plan and guidelines to prevent emotional decisions.
2. Understand your portfolio's holdings and why you own each asset.
3. Remember that diversification and comparing performance to benchmarks can provide perspective on fluctuations.
4. Volatility is temporary and markets have historically recovered, so don't make hasty decisions.
5. Learn from past mistakes and use experience to prepare for future ups and downs. Expert advice can also help navigate volatility.
6. Consider defensive sectors that may be less volatile during market swings. Dividends can also provide stability.
7. Continuing regular savings
Fund performance persistence and competition keswanibfmresearch
This document summarizes a study that examines how competition within a sector affects mutual fund performance persistence at the sector level in the UK market. The study uses data on UK unit trusts from 1991-2001 that includes annual returns, assets, and sector classifications. Several variables are constructed to measure competition within each sector, such as the number of funds, proportion of mature funds, and concentration of assets. The main finding is that performance persistence is higher in sectors where concentration of assets under management is higher, indicating less competition is associated with greater persistence. This suggests the degree of persistence exhibited by a sector's funds depends on how competitive that sector is.
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 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.
1) The document analyzes perverse incentives created by common hedge fund fee structures, noting fees as high as 2-3% annually plus 20% of gains can incentivize inappropriate investments.
2) Research cited finds hedge funds from 1995-2003 returned an average of 8.82% annually, but estimates actual returns for investors were 500-1000 bps lower due to biases.
3) While hedge funds provided some diversification, correlations with stocks were around 0.3 on average, meaning investors paid fees for passive stock exposure rather than skill.
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 introduces a new measure called Active Share to quantify active portfolio management. Active Share describes the percentage of portfolio holdings that differ from the portfolio's benchmark index. It argues that Active Share, combined with tracking error, provides a comprehensive picture of a fund's active management approach. The authors apply this two-dimensional framework to analyze mutual funds, finding that the most active stock pickers outperform, while closet indexers and funds focusing on factor bets underperform after fees.
1) A study found that when people learn that their choice agrees with expert opinions, the reward center of their brain is activated similarly to when experiencing pleasures like food or money.
2) This suggests that conformity feels intrinsically rewarding on a basic biological level, explaining why investor sentiment can suddenly change as people want to agree with the group.
3) When others value something differently than you initially did, it makes you question your own valuation and want to conform to be part of the group, even if just subconsciously.
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.
Superior performance by combining Rsik Parity with Momentum?Wilhelm Fritsche
This document examines different strategies for global asset allocation between equities, bonds, commodities and real estate. It finds that applying trend following rules substantially improves risk-adjusted performance compared to traditional buy-and-hold portfolios. It also finds trend following to be superior to risk parity approaches. Combining momentum strategies with trend following further improves returns while reducing volatility and drawdowns. A flexible approach that allocates capital based on volatility-weighted momentum rankings of 95 markets produces attractive, consistent risk-adjusted returns.
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.
This document summarizes research on the momentum factor in equities. It finds that stocks with strong recent performance tend to continue outperforming, known as the momentum effect. The biggest challenge for capturing momentum is its high inherent turnover. Using optimization in portfolio construction can successfully capture momentum while controlling turnover. Adding momentum to portfolios with other factors like value provides diversification benefits due to its negative correlation with value.
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.
Plan sponsors hire and fire investment management firms to manage retirement plan assets totaling $6.3 trillion. The study examines the hiring and firing decisions of 3,400 plan sponsors between 1994-2003. It finds that plan sponsors hire managers after periods of strong excess returns, but these returns do not continue afterward. Managers are terminated for various reasons, including but not limited to underperformance, and excess returns after firings are indistinguishable from zero. When comparing returns from fired vs. newly hired managers, staying with fired managers would have yielded similar returns. Hiring and firing patterns vary based on plan sponsor characteristics.
This document presents a model of market momentum. The model shows that:
[1] Momentum is more pronounced in a confident market where investors incorporate new information into prices more slowly.
[2] Only idiosyncratic shocks, not systematic shocks, can produce momentum, as systematic shocks do not affect cross-sectional stock returns.
[3] Empirical evidence supports the predictions, finding momentum is greater when volatility (and uncertainty) is lower, and when stocks experience larger idiosyncratic shocks.
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
This study examines the stock picking and market timing abilities of 10 UK investment trusts between 1995 and 2016. Results show little evidence of outperformance against the FTSE All Share index. Only 1 fund showed evidence of superior stock picking, while no funds showed evidence of superior market timing. Consistent with other studies, funds with more concentrated portfolios tended to perform better. The study aims to evaluate the investment skills of UK fund managers and determine if fund concentration impacts performance.
This document summarizes a study examining IPO underpricing explanations using data from the Stock Exchange of Singapore. The key points are:
1) The study analyzes both application and allocation data from IPOs in Singapore, which allows reconstruction of underlying investor demand schedules.
2) They find large investors preferentially request shares in IPOs with higher initial returns, consistent with them having better information.
3) Inferences differ substantially between looking at just applications versus allocations, since allocations may not reflect true underlying demand due to rationing practices.
This document summarizes a study on the relationship between firm investment and financial status. The study uses a sample of 1,317 firms from 1987 to 1994 to examine how investment decisions differ across financially constrained and unconstrained firms. It finds that investment is most sensitive to internal funds for firms that are least financially constrained, consistent with the findings of Kaplan and Zingales (1997). Statistical tests show this difference is statistically significant. Additionally, firms that reduced dividends exhibited traditional signs of greater financial constraints such as lower current ratios and profitability compared to firms that increased dividends. The study uses multiple discriminant analysis and regression analysis to classify firms and compare investment-cash flow sensitivities between financially constrained and unconstrained groups.
The document discusses different forms of market efficiency according to the Efficient Market Hypothesis (EMH). It defines weak, semi-strong, and strong forms of efficiency based on what information is reflected in market prices. Weak-form tests whether past prices predict future prices, semi-strong tests whether public information is reflected, and strong tests whether insider information provides advantages. The document also discusses methods for testing each form of efficiency through analyses like event studies and anomalies. Overall, evidence supports weak and semi-strong forms but not strong form efficiency.
The document discusses different forms of market efficiency according to the Efficient Market Hypothesis (EMH). It defines weak, semi-strong, and strong forms of efficiency based on what information is reflected in market prices. Weak-form tests whether past prices predict the future, semi-strong tests if public information is reflected, and strong tests if insider information provides advantages. The document also summarizes various empirical studies that have tested different forms of market efficiency through approaches like event studies and analyzing returns.
The document discusses different forms of market efficiency according to the Efficient Market Hypothesis (EMH). It defines weak, semi-strong, and strong forms of efficiency based on what information is reflected in market prices. Weak-form efficiency means prices reflect all historical price information, semi-strong means they reflect all public information, and strong form means they reflect all public and private information. The document summarizes various studies and evidence related to testing each form of efficiency through analyses of market anomalies, event studies, and performance of professional investors.
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 discusses a study analyzing the efficient market hypothesis (EMH) as it relates to four small Asian stock markets: Singapore, Malaysia, Korea, and Indonesia. The study examines daily stock return data to determine if the markets exhibit predictable properties that would contradict the weak form of the EMH. Previous research has found evidence against weak-form efficiency in larger markets, but little study had been done on these smaller Pacific basin markets. The analysis found that the daily returns in the four markets did exhibit predictable properties, indicating the weak-form EMH does not hold for these markets. This suggests daily patterns in stock prices and returns can be predicted to some degree.
The document summarizes research on the performance of trend-following investing across global markets from 1903 to 2012. Key findings include:
1) Trend-following strategies have delivered consistently strong positive returns each decade for over a century, with low correlation to traditional assets.
2) Trend-following strategies performed best during large equity market declines, helping diversify traditional portfolios.
3) Backtesting shows that allocating 20% of a 60% stock/40% bond portfolio to trend-following from 1903 to 2012 would have increased returns, lowered volatility, and reduced maximum drawdown.
The document discusses several studies on mutual fund performance from 1965 to 2002. Some key findings include:
- Early studies from 1965-1972 found mutual funds generally did not outperform markets and there was little evidence managers could predict price movements.
- Later studies from the 1970s-1980s found mixed results, with some funds showing above average performance but no consistency over time. Expense ratios were negatively correlated with performance.
- Studies in the 1990s provided evidence mutual fund performance was not related to expenses and turnover as predicted by market efficiency arguments. Larger funds had lower expenses due to economies of scale.
- International studies found mutual fund managers generally unable to offer higher risk-adjusted returns through stock selection or market timing
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.
This document summarizes a study that examines whether mutual fund managers can pick stocks by analyzing the performance of stocks that funds buy and sell around subsequent quarterly earnings announcements. The study finds:
1) On average, stocks that mutual funds buy outperform stocks they sell by about 10 basis points in the 3 days around the next earnings announcement.
2) This performance persists after benchmarking against stocks with similar characteristics, and funds that perform best tend to have a growth style.
3) Mutual fund trades forecast future earnings surprises, indicating managers can predict fundamentals.
4) Abnormal returns around earnings announcements account for 18-51% of total abnormal returns to stocks funds trade.
Similar to Momentum investingperformanceandherding grinblatt (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.
Performance emergingfixedincomemanagers joi_is age just a numberbfmresearch
1) Younger fixed-income managers tend to outperform older, more established managers in terms of gross returns. Returns are significantly higher for emerging managers in their first year and first five years compared to later years.
2) The study examines 54 fixed-income managers formed since 1985 that had majority employee ownership. Most were formed before 2000, when barriers to entry increased.
3) Business risk is low for emerging managers, as only 6.8% of the 88 examined managers are no longer in business. Higher first-year and early-period returns for emerging managers indicate they provide alpha during their hungry startup phase.
This document analyzes different categories of active mutual fund management based on measures of Active Share and tracking error. It finds that the most active stock pickers have outperformed their benchmarks after fees, while closet indexers and funds focusing on factor bets have underperformed after fees. Performance patterns were similar during the 2008-2009 financial crisis. Closet indexing has become more popular recently. Fund performance can be predicted by cross-sectional stock return dispersion, favoring active stock pickers when dispersion is higher.
The document summarizes findings from the Standard & Poor's Indices Versus Active Funds (SPIVA) Scorecard, which compares the performance of actively managed mutual funds to relevant benchmarks. Some key points:
- Over the past 3 years, the majority (over 50%) of actively managed large-cap, mid-cap, small-cap, global, international, and emerging market funds underperformed their benchmarks.
- Over the past 5 years, indices outperformed a majority of active managers in nearly all major domestic and international equity categories based on equal-weighted returns. Asset-weighted averages also showed underperformance in 11 out of 18 domestic categories.
- For fixed income funds, over 50% under
This document summarizes research on the relationship between portfolio turnover and investment performance. Recent studies have found no evidence that higher portfolio turnover leads to lower returns, as was previously thought. Trading costs have declined over time, and portfolio turnover is not a good proxy for actual trading costs, which depend more on trade size and type of security traded. A 2007 study directly estimated trading costs and found no clear correlation between costs and returns. The author's own analysis of mutual funds from 2007-2008 also found little relationship between turnover and performance. Therefore, advisors should not assume higher turnover means lower returns.
This document discusses using active share and tracking error as measures of portfolio manager skill. It defines active share as the percentage of a fund's portfolio that differs from its benchmark index. Tracking error measures systematic factor risk by capturing how much a fund's returns vary from its benchmark. Research shows funds with high active share and moderate tracking error tend to outperform on average. The document examines how active share and tracking error can help identify skillful managers by focusing on their portfolio construction process rather than just past returns.
This document is a guide to the markets published by JPMorgan that provides data and analysis across various asset classes including equities, fixed income, international markets, and the economy. It includes sections on returns by investment style and sector for equities, economic indicators and drivers, interest rates and other data for fixed income, international market returns and valuations, and asset class performance and correlations. The guide contains over 60 charts and analyses global and domestic financial trends and investment opportunities.
The document discusses whether the concept of "Alpha" is a useful performance metric for investors. It makes two main arguments:
1) Alpha alone does not determine if a portfolio has superior risk-adjusted returns, as portfolio volatility and correlation to benchmarks also influence risk-adjusted returns.
2) Alpha is dependent on leverage - a higher reported Alpha could simply be due to using leverage rather than superior investment skill.
The document concludes that Alpha is a misleading performance measure and not suitable as the sole metric, especially for investors concerned with total risk and returns rather than just a single return component.
Fis group study on emerging managers performance drivers 2007bfmresearch
This study examined the performance of emerging investment managers over three years ending in 2006. It found that:
1) For large cap managers, increased firm assets were negatively correlated with risk-adjusted returns for core and growth strategies, but not for value. This may be because increased assets led to less concentrated core portfolios, lowering returns.
2) For small cap managers, risk-adjusted returns were highest for firms with less than $500 million in assets, possibly due to added resources like analysts. Returns leveled off between $500 million and $1 billion, and declined above $1 billion.
3) Having more research analysts was consistently positively correlated with higher risk-adjusted returns across strategies, while the impact
The document discusses Barclays' process for evaluating and selecting investment managers. It states that identifying the right asset allocation and implementing it properly are both important for achieving investment goals. The process involves both science, through a formal and structured methodology, and art, by applying judgment and philosophy. Barclays aims to identify managers most likely to perform well through rigorous due diligence and ongoing monitoring. The paper will explain Barclays' comprehensive approach to manager analysis, selection, and review.
Active managementmostlyefficientmarkets fajbfmresearch
This survey of literature on active vs passive management shows:
1) On average, actively managed funds do not outperform the market after accounting for fees and expenses, though a minority do add value.
2) Studies suggest some investors may be able to identify superior active managers in advance using public information.
3) Investors who identify superior active managers could improve their risk-adjusted returns by including some exposure to active strategies.
This document summarizes recent academic research on active equity managers who deliver persistent outperformance. It discusses studies finding that:
1) While the average equity manager underperforms after fees, a minority of managers have demonstrated persistent outperformance that cannot be attributed to chance alone.
2) Managers with higher "active share" (the degree to which their portfolio composition differs from the benchmark) tend to generate greater risk-adjusted returns.
3) Managers with lower portfolio turnover and a focus on strong stock selection, rather than market timing, are more likely to outperform over time.
The document evaluates how Brown Advisory's investment approach aligns with the characteristics identified in these studies as being associated with persistent
The document discusses China's transition to a consumer-driven economy. It provides analysis from CLSA China Macro Strategist Andy Rothman on trends in China's economy including the declining importance of exports, strong growth in domestic consumption, increasing incomes driving spending, and continued growth in infrastructure investment. The analysis suggests China's economy remains healthy and growing despite slowing external demand.
This report provides an analysis of defined contribution retirement plans based on 2010 Vanguard recordkeeping data. Some key findings include:
- Median and average account balances reached their highest levels since tracking began in 1999, recovering from market declines.
- Use of target-date funds as investment options and default investments continues to grow significantly, with 42% of participants using them and 20% wholly invested in a single target-date fund.
- Professionally managed investment options like target-date funds are being used by an increasing number of participants, with 29% solely invested in an automatic investment program in 2010 compared to just 9% in 2005.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
This paper examines the relationship between mutual fund manager ownership stakes in the funds they manage and the performance of those funds. The author hypothesizes that greater manager ownership will be positively associated with fund returns and negatively associated with fund turnover, as higher ownership would better align manager and shareholder interests by reducing agency costs. Using a dataset of manager ownership disclosures from 2004-2005, the author finds that funds with higher manager ownership had higher returns and lower turnover, supporting the hypotheses. However, manager ownership was not related to a fund's tax burden.
Information ratio mgrevaluation_bossertbfmresearch
This document discusses using the Information Ratio (IR) to evaluate mutual fund managers. The IR measures excess return over a benchmark relative to excess return volatility. While commonly used, the IR has limitations that depend on benchmark choice, data frequency, and fund return distributions. The document aims to empirically analyze IR characteristics across different asset classes and countries to determine if it is a reliable performance measure or if guidelines are needed for its use.
This document summarizes a study comparing the performance of mutual funds managed by individual managers versus teams of managers. The study finds that funds managed by teams have similar risk-adjusted performance to individually-managed funds, despite team-managed funds growing at a faster rate. Additionally, team-managed funds have significantly lower risk, lower cross-sectional performance differences, lower expenses, and lower portfolio factor loadings than individually-managed funds. The study uses a large sample of domestic and international mutual funds to test these findings.
This document discusses returns-based style analysis (RBSA), a technique developed by William Sharpe to determine the style of a portfolio or mutual fund using only returns data. The document provides an overview of RBSA and compares it to holdings-based style analysis. It then describes how to implement RBSA using Excel by constructing a portfolio of indices to minimize the tracking error between the returns of the portfolio being analyzed and the index portfolio returns. The document concludes by providing an example RBSA using the Dodge & Cox Balanced Fund to illustrate the technique.
How to Identify the Best Crypto to Buy Now in 2024.pdfKezex (KZX)
To identify the best crypto to buy in 2024, analyze market trends, assess the project's fundamentals, review the development team and community, monitor adoption rates, and evaluate risk tolerance. Stay updated with news, regulatory changes, and expert opinions to make informed decisions.
How to Invest in Cryptocurrency for Beginners: A Complete GuideDaniel
Cryptocurrency is digital money that operates independently of a central authority, utilizing cryptography for security. Unlike traditional currencies issued by governments (fiat currencies), cryptocurrencies are decentralized and typically operate on a technology called blockchain. Each cryptocurrency transaction is recorded on a public ledger, ensuring transparency and security.
Cryptocurrencies can be used for various purposes, including online purchases, investment opportunities, and as a means of transferring value globally without the need for intermediaries like banks.
13 Jun 24 ILC Retirement Income Summit - slides.pptxILC- UK
ILC's Retirement Income Summit was hosted by M&G and supported by Canada Life. The event brought together key policymakers, influencers and experts to help identify policy priorities for the next Government and ensure more of us have access to a decent income in retirement.
Contributors included:
Jo Blanden, Professor in Economics, University of Surrey
Clive Bolton, CEO, Life Insurance M&G Plc
Jim Boyd, CEO, Equity Release Council
Molly Broome, Economist, Resolution Foundation
Nida Broughton, Co-Director of Economic Policy, Behavioural Insights Team
Jonathan Cribb, Associate Director and Head of Retirement, Savings, and Ageing, Institute for Fiscal Studies
Joanna Elson CBE, Chief Executive Officer, Independent Age
Tom Evans, Managing Director of Retirement, Canada Life
Steve Groves, Chair, Key Retirement Group
Tish Hanifan, Founder and Joint Chair of the Society of Later life Advisers
Sue Lewis, ILC Trustee
Siobhan Lough, Senior Consultant, Hymans Robertson
Mick McAteer, Co-Director, The Financial Inclusion Centre
Stuart McDonald MBE, Head of Longevity and Democratic Insights, LCP
Anusha Mittal, Managing Director, Individual Life and Pensions, M&G Life
Shelley Morris, Senior Project Manager, Living Pension, Living Wage Foundation
Sarah O'Grady, Journalist
Will Sherlock, Head of External Relations, M&G Plc
Daniela Silcock, Head of Policy Research, Pensions Policy Institute
David Sinclair, Chief Executive, ILC
Jordi Skilbeck, Senior Policy Advisor, Pensions and Lifetime Savings Association
Rt Hon Sir Stephen Timms, former Chair, Work & Pensions Committee
Nigel Waterson, ILC Trustee
Jackie Wells, Strategy and Policy Consultant, ILC Strategic Advisory Board
South Dakota State University degree offer diploma Transcriptynfqplhm
办理美国SDSU毕业证书制作南达科他州立大学假文凭定制Q微168899991做SDSU留信网教留服认证海牙认证改SDSU成绩单GPA做SDSU假学位证假文凭高仿毕业证GRE代考如何申请南达科他州立大学South Dakota State University degree offer diploma Transcript
Confirmation of Payee (CoP) is a vital security measure adopted by financial institutions and payment service providers. Its core purpose is to confirm that the recipient’s name matches the information provided by the sender during a banking transaction, ensuring that funds are transferred to the correct payment account.
Confirmation of Payee was built to tackle the increasing numbers of APP Fraud and in the landscape of UK banking, the spectre of APP fraud looms large. In 2022, over £1.2 billion was stolen by fraudsters through authorised and unauthorised fraud, equivalent to more than £2,300 every minute. This statistic emphasises the urgent need for robust security measures like CoP. While over £1.2 billion was stolen through fraud in 2022, there was an eight per cent reduction compared to 2021 which highlights the positive outcomes obtained from the implementation of Confirmation of Payee. The number of fraud cases across the UK also decreased by four per cent to nearly three million cases during the same period; latest statistics from UK Finance.
In essence, Confirmation of Payee plays a pivotal role in digital banking, guaranteeing the flawless execution of banking transactions. It stands as a guardian against fraud and misallocation, demonstrating the commitment of financial institutions to safeguard their clients’ assets. The next time you engage in a banking transaction, remember the invaluable role of CoP in ensuring the security of your financial interests.
For more details, you can visit https://technoxander.com.
What Lessons Can New Investors Learn from Newman Leech’s Success?Newman Leech
Newman Leech's success in the real estate industry is based on key lessons and principles, offering practical advice for new investors and serving as a blueprint for building a successful career.
Every business, big or small, deals with outgoing payments. Whether it’s to suppliers for inventory, to employees for salaries, or to vendors for services rendered, keeping track of these expenses is crucial. This is where payment vouchers come in – the unsung heroes of the accounting world.
Monthly Market Risk Update: June 2024 [SlideShare]Commonwealth
Markets rallied in May, with all three major U.S. equity indices up for the month, said Sam Millette, director of fixed income, in his latest Market Risk Update.
For more market updates, subscribe to The Independent Market Observer at https://blog.commonwealth.com/independent-market-observer.
Madhya Pradesh, the "Heart of India," boasts a rich tapestry of culture and heritage, from ancient dynasties to modern developments. Explore its land records, historical landmarks, and vibrant traditions. From agricultural expanses to urban growth, Madhya Pradesh offers a unique blend of the ancient and modern.
Budgeting as a Control Tool in Government Accounting in Nigeria
Being a Paper Presented at the Nigerian Maritime Administration and Safety Agency (NIMASA) Budget Office Staff at Sojourner Hotel, GRA, Ikeja Lagos on Saturday 8th June, 2024.
Explore the world of investments with an in-depth comparison of the stock market and real estate. Understand their fundamentals, risks, returns, and diversification strategies to make informed financial decisions that align with your goals.
In World Expo 2010 Shanghai – the most visited Expo in the World History
https://www.britannica.com/event/Expo-Shanghai-2010
China’s official organizer of the Expo, CCPIT (China Council for the Promotion of International Trade https://en.ccpit.org/) has chosen Dr. Alyce Su as the Cover Person with Cover Story, in the Expo’s official magazine distributed throughout the Expo, showcasing China’s New Generation of Leaders to the World.
The Rise and Fall of Ponzi Schemes in America.pptxDiana Rose
Ponzi schemes, a notorious form of financial fraud, have plagued America’s investment landscape for decades. Named after Charles Ponzi, who orchestrated one of the most infamous schemes in the early 20th century, these fraudulent operations promise high returns with little or no risk, only to collapse and leave investors with significant losses. This article explores the nature of Ponzi schemes, notable cases in American history, their impact on victims, and measures to prevent falling prey to such scams.
Understanding Ponzi Schemes
A Ponzi scheme is an investment scam where returns are paid to earlier investors using the capital from newer investors, rather than from legitimate profit earned. The scheme relies on a constant influx of new investments to continue paying the promised returns. Eventually, when the flow of new money slows down or stops, the scheme collapses, leaving the majority of investors with substantial financial losses.
Historical Context: Charles Ponzi and His Legacy
Charles Ponzi is the namesake of this deceptive practice. In the 1920s, Ponzi promised investors in Boston a 50% return within 45 days or 100% return in 90 days through arbitrage of international reply coupons. Initially, he paid returns as promised, not from profits, but from the investments of new participants. When his scheme unraveled, it resulted in losses exceeding $20 million (equivalent to about $270 million today).
Notable American Ponzi Schemes
1. Bernie Madoff: Perhaps the most notorious Ponzi scheme in recent history, Bernie Madoff’s fraud involved $65 billion. Madoff, a well-respected figure in the financial industry, promised steady, high returns through a secretive investment strategy. His scheme lasted for decades before collapsing in 2008, devastating thousands of investors, including individuals, charities, and institutional clients.
2. Allen Stanford: Through his company, Stanford Financial Group, Allen Stanford orchestrated a $7 billion Ponzi scheme, luring investors with fraudulent certificates of deposit issued by his offshore bank. Stanford promised high returns and lavish lifestyle benefits to his investors, which ultimately led to a 110-year prison sentence for the financier in 2012.
3. Tom Petters: In a scheme that lasted more than a decade, Tom Petters ran a $3.65 billion Ponzi scheme, using his company, Petters Group Worldwide. He claimed to buy and sell consumer electronics, but in reality, he used new investments to pay off old debts and fund his extravagant lifestyle. Petters was convicted in 2009 and sentenced to 50 years in prison.
4. Eric Dalius and Saivian: Eric Dalius, a prominent figure behind Saivian, a cashback program promising high returns, is under scrutiny for allegedly orchestrating a Ponzi scheme. Saivian enticed investors with promises of up to 20% cash back on everyday purchases. However, investigations suggest that the returns were paid using new investments rather than legitimate profits. The collapse of Saivian l
An accounting information system (AIS) refers to tools and systems designed for the collection and display of accounting information so accountants and executives can make informed decisions.
“Amidst Tempered Optimism” Main economic trends in May 2024 based on the results of the New Monthly Enterprises Survey, #NRES
On 12 June 2024 the Institute for Economic Research and Policy Consulting (IER) held an online event “Economic Trends from a Business Perspective (May 2024)”.
During the event, the results of the 25-th monthly survey of business executives “Ukrainian Business during the war”, which was conducted in May 2024, were presented.
The field stage of the 25-th wave lasted from May 20 to May 31, 2024. In May, 532 companies were surveyed.
The enterprise managers compared the work results in May 2024 with April, assessed the indicators at the time of the survey (May 2024), and gave forecasts for the next two, three, or six months, depending on the question. In certain issues (where indicated), the work results were compared with the pre-war period (before February 24, 2022).
✅ More survey results in the presentation.
✅ Video presentation: https://youtu.be/4ZvsSKd1MzE
1. Momentum Investment Strategies,
Portfolio Performance, and Herding:
A Study of Mutual Fund Behavior
By M A R K GRINBLATT, SHERIDAN TITMAN, AND R U S S W E R M E R S *
This study analyzes the extent to which mutual funds purchase stocks based on
their past returns as well as their tendency to exhibit "herding" behavior (i.e.,
buying and selling the same stocks at the same time). We find that 77 percent of
the mutual funds were "momentum investors," buying stocks that were past
winners; however, most did not systematically sell past losers. On average, funds
that invested on momentum realized significantly better performance than other
funds. We also find relatively weak evidence that funds tended to buy and sell
the same stocks at the same time. (JEL G14, G23)
The amount of wealth managed by institu- the herd, in combination with the alleged ten-
tional investors has grown considerably over dency of institutions to follow momentum-
the past 20 years. Due perhaps to decreased based fads by buying past winners and
trading costs, brought about by the termination selling past losers is of concern, since this
of fixed commissions in May 1975, these in- behavior could potentially exacerbate stock-
stitutional investors have become much more price volatility.
active traders and, as a result, have become Momentum trading strategies and herding
increasingly important in terms of setting mar- behavior are also used by academics to mo-
ket prices.' The growing influence of institu- tivate models of seemingly irrational mar-
tional investors has led to increased scrutiny kets. Fischer Black (1986) and Brett
both by policymakers and by journalists, who Trueman (1988) provide reasons why insti-
tend to believe that these investors trade ex- tutional investors may trade excessively, and
cessively and move in and out of stocks in a a number of recent theory papers provide ra-
herd-hke manner. This tendency to invest with tionales to explain why institutional inves-
tors would analyze the same groups of stocks
and trade in the same direction.^ In addition,
J. Bradford De Long et al. (1990) describe
* Grinblatt: John E. Anderson Graduate School of what they call "positive-feedback traders,"
Management, tJniversity of California, Los Angeles, Box who have a tendency to buy stocks after they
951481, Los Angeles, CA 90095-1481; Titman: Depart- perform well.
ment of Finance, Carroll School of Management, Boston
College, Chestnut Hill, MA 02167; Wermers: Division of
Our study provides empirical evidence on
Finance and Economics, Graduate School of Business and the trading patterns of fund managers by ex-
Administration, tJniversity of Colorado, Boulder, CO amining the quarterly holdings of 155 mutual
80309. The authors are grateful to Narasimhan Jegadeesh, funds over the 1975-1984 period. We char-
as well as to seminar participants at National Taiwan Uni-
versity, National Chinese tJniversity, Osaka University,
University of California at Berkeley, University of Chi-
cago, Stanford University, UCLA, University of Texas,
and Yale University for comments on earlier drafts. ^ These papers include Robert J. Shiller and John
' Institutional holdings are now about 50 percent of to- Pound (1989), Michael Brennan (1990), David S. Scharf-
tal equity holdings in the United States, while institutional stein and Jeremy C. Stein (1990), Josef Lakonishok et al.
trading, when added to member trading, accounted for (1991), Abhijit Banerjee (1992), Sushil Bikhchandani et
about 70 percent of total NYSE volume in 1989 (Robert al. (1992), Kenneth A. Froot et al. (1992), and David
Schwartz and James Shapiro, 1992). Hirshleiferetal. (1994).
1088
2. VOL. 85 NO. 5 GRINBLATT ET AL: MOMENTUM INVESTMENT AND HERDING 1089
acterize the portfolio choices of these funds the observed performance was generated by
to determine the extent to which they pur- the simple momentum strategy of buying past
chase stocks based on their past returns and winners and selling past losers, as described in
the extent to which they "herd," that is, the Narasimhan Jegadeesh and Titman (1993).
extent to which the group either predomi- This simple strategy would generate abnormal
nantly buys or predominantly sells the same performance with either of the Grinblatt and
stock at the same time. We tJien examine the Titman (1989a, 1993) performance measures,
extent to which herding and momentum in- as well as with any of the more traditional
vesting affect the performance of the funds.^ measures.
If either irrationality or agency problems gen- The paper is organized as follows. Section
erate these trading styles (as discussed, for I describes the data, while Section II de-
example, by Scharfstein and Stein [1990]), scribes the methodology used to compute the
then mutual funds that exhibit these behaviors degree of momentum (or contrarian) invest-
will tend to push the prices of stocks that they ing behavior exhibited by a fund. Section III
purchase above intrinsic values, thereby re- presents empirical results on momentum in-
alizing lower future performance. However, vestment styles and performance. Section IV
if this type of behavior arises because in- investigates the tendency of the funds to en-
formed portfolio managers tend to pick the gage in herding behavior and also considers
same underpriced stocks, then funds that ex- the relation of herding behavior to momen-
hibit these styles should realize high future tum investing and performance. Finally,
performance. Section V summarizes and concludes the
Our analysis of momentum investing and paper.
performance is also motivated by two previous
studies (Gdnblatt and Titman, 1989a, 1993), I. Data
which indicate that, at least before transaction
costs, a number of mutual funds earned sig- Quarterly portfolio holdings for 274 mutual
nificant risk-adjusted abnormal returns. This funds that existed on December 31,1974, were
observed performance is not related to known purchased from CDA Investment Technolo-
anomalies that involve cross-sectional differ- gies, Inc. of Silver Springs, Maryland. These
ences in expected returns, like the small-firm mutual fund data, used previously by Grinblatt
effect. However, before we conclude that these and Titman (1989a, 1993) to examine fund
abnormal returns are generated by either su- performance, include 155 funds that existed
perior information or analysis, we would also during the entire 10-year time period of De-
like to rule out the possibility that the observed cember 31,1974, to December 31,1984.'* Cen-
abnormal performance was generated by ex- ter for Research in Security Prices (CRSP)
ploiting time-series anomalies. Specifically, monthly returns for each NYSE- and AMEX-
we would like to determine the extent to which listed stock held by the funds were computed
by compounding returns in the CRSP daily re-
turns file. Over-the-counter (OTC) stocks and
fixed-income holdings were treated as missing
' Irwin Friend et al. (1970) were perhaps the first to
examine the trading patterns of mutual funds. They found, values in a manner that we will describe
among other things, that there was a tendency of some shortly.
mutual funds to follow the prior investment choices of
their more successful counterparts. Alan Kraus and Hans
R. Stoll (1972) examined the tendency of mutual funds
and bank trusts to buy and sell the same stocks at the same
time but did not find evidence of herding beyond that due " The analysis in Grinblatt and Titman (1989a) and Ste-
to chance. Lakonishok et al. (1992) examined the amount phen J. Brown and William N. Goetzmann (1995) indi-
of herding exhibited by pension fund managers. They cates that this fund-survival requirement has only a small
found only weak evidence of the funds either buying or effect on inference tests of performance abilities. In our
selling in herds (above chance occurrences) and a weak later analysis of the herding of funds into individual
relation between herding in stocks and the past returns of stocks, we expand our sample to include all 274 funds,
the stocks. which includes nonsurvivors.
3. 1090 THE AMERICAN ECONOMIC REVIEW DECEMBER 1995
II. Methodology 1 and /t = 2 are the two measures that we will
focus on, although we will present some re-
A. The Momentum Measures sults for k> 2. We will refer to equation (2)
as "lag-0 momentum" (LOM) when ^ = 1,
A momentum investor buys past' 'winners'' and as "lag-1 momentum" (LIM) when
and sells past "losers." A contrarian investor k = 2.
does the opposite. Our measure of momentum
investing is B. Statistical Inference
. T N As described by equation (2), the differ-
enced portfolio weights were updated every
•' / = I J = I
calendar quarter, while the differenced port-
where vv,,, is the portfolio weight on security 7 folio return was generated each month.* This
at date t, and Rj,-k+ is the return of security process resulted in a time series of 120
j {j = 1, . . . , N) from date t - kXo date t - monthly return differences for each mutual
k + 1, the historical benchmark period. fund. If the return differences associated with
This statistic is designed to measure the de- these measures are serially uncorrelated under
gree to which a fund manager tilts his portfolio the null hypothesis of no momentum invest-
in the direction of stocks that have experienced ing, then inference-testing for the significance
high returns in some historical benchmark pe- of the measures is simple. Testing whether the
riod, and away from stocks that have experi- momentum measure has a mean value of zero
enced low returns. Since this measure equals is identical to a test of whether two given port-
the difference between two portfolio returns folios (with dynamic weight vectors) have the
during the benchmark period, a positive mea- same mean return.'
sure means that, on average, the fund's current We employ many cross-sectional regres-
portfolio had higher returns than the portfolio sions in our analysis, mainly of fund perfor-
that the fund would have held had no portfolio mance on fund characteristics. Statistical
revisions been made. significance cannot be infe^rred from the
Since the mutual fund holdings are only cross-sectional t and F statistics typically re-
available quarterly, while stock returns are ported in such regressions, since the regression
available monthly, further modifications of the residuals are correlated across mutual funds.
measure given by equation (1) are needed to Thus, we use alternative t and F tests that are
arrive at the measures of momentum that are derived from a time-series procedure (see
implemented. Given that we have 41 quarters Grinblatt and Titman, 1994).
of holdings, with three monthly returns per
quarter, equation (1) is modified as follows:'
""The differenced weights are identical for any three
I 40 3 A/ months in the same calendar quarter, except when return
data are not available for one or more securities in some
(but not all) of the three months. For example, if returns
for the Boeing Corporation are available in January and
Since the most recent returns are probably of February, but not in March, then the differenced portfolio
the greatest interest to portfolio managers, k = weight of Boeing is set to zero only for March, and it is
identical for January and February.
' Since most portfolios of interest, such as value-
weighted portfolios, have changing weights, the ordinary
^ Using monthly returns rather than quarterly returns t tests that are usually applied in these tests are technically
reduces the problem of missing returns. For example, if inappropriate. However, if securities returns are serially
returns for the Boeing Corporation common stock are uncorrelated, the central-limit theorem can be applied and
available from the CRSP for January and February, but asymptotic z tests and chi-square tests are valid for non-
not March, then Boeing drops out of our momentum in- normal portfolio returns. Given the length of our time se-
vesting measure in only one observation out of 120 (using ries, these asymptotic test statistics are virtually identical
monthly returns), instead of one out of 40 (using quarterly to the f and F statistics used here and have negligibly dif-
returns). See also footnote 6. ferent significance levels.
4. VOL. 85 NO. 5 GRINBLATT ET AL.: MOMENTUM INVESTMENT AND HERDING 1091
C. Modifying the Measures to Eliminate Here, we subtract means from returns in or-
' 'Passive Momentum Investing'' der to have measures that asymptotically ap-
proach zero under the null hypothesis of no
The portfolio weights of winning (losing) momentum investing. The monthly return
stocks increase (decrease) even if the number from 12 months ^head for security 7 is used
of shares held stays constant. In this case, the as a proxy for Rj.'° We also use a similar
lag-0 momentum measure would indicate decomposition of LIM into "Buy L I M "
momentum investing for buy-and-hold in- and "Sell L I M . "
vestment strategies. To correct this, we cal- While the LOM and LIM statistics are ap-
culate the weights using the average of the propriate measures of the extent to which
beginning- and end-of-quarter share prices.* past returns affect the total holdings of a
For consistency, we make the same modifi- fund, we also use a turnover-adjusted LOM
cation for all momentum measures, even (TALOM) which measures the extent to
though passive momentum investing affects which past returns affect portfolio trades, in-
only the lag-0 momentum measure.' dependent of the number of trades made by
a fund during a time period. This measure is
D. Extensions of the Measure given by
We will also use decomposed versions of the (4) TALOM
LOM measure, called "Buy LOM" and "Sell
LOM." A high Buy (SeU) LOM measure for a
fiind means that it bought winners (sold losers)
strongly, on average. These two measures are the T20 ,
decomposition of equation (2) into partial sums:
The turnover-adjusted measure (TALOM)
(3a) Buy LOM is the LOM measure, normalized so that the
changes in weights of the stocks purchased
(and the changes in weights of the stocks
r-l ,-.| »5,,, sold) sum to 1 during each quarter. The re-
sults give a more accurate picture of the av-
(3b) Sell LOM erage difference in past returns between
stocks purchased and stocks sold across all
quarters by a mutual fund, since a constant
/ = l *,.,,<*;_„-, $1 is invested and shorted each quarter. A
mutual fund that trades very little, but buys
past extreme winners and sells past extreme
losers, will have a very high TALOM mea-
* If the beginning-of-quarter price was not available for sure, even though the unmodified LOM mea-
a given security in a given quarter, the end-of-quarter price sure will be relatively small. Analogous to
was used, and vice versa. Buy LOM and Sell LOM, "Buy TALOM"
' Because each fund's stock holdings are observed only
quarterly in our data set, it is tempting to think that I^M
and "Sell TALOM" decompose TALOM
may spuriously be nonzero because of fund performance. It into terms having vvy,,, > Wy,3,_3 and vvy,,, <
is true that when the fund manager can achieve superior M>j,3, _ 3, respectively.
returns, the actual portfolio weights are correlated with fu-
ture returns. However, since LOM uses differenced portfolio
weights, a bias only arises when the portfolio revisions are
predominantly at the beginning of the quarter (spuriously
indicating momentum investing) or at the end of the quarter
(spuriously indicating contrarian investment behavior).
Since there is no a priori reason to believe that portfolio '" Admittedly, this is a noisy proxy for the expected
revisions that occur for the purpose of achieving superior return, but since there are large numbers of stocks and time
performance should occur closer to the beginning of a quar- periods averaged in the measures we report, the noisiness
ter than to its end, we do not believe that a bias exists. of the proxy has a negligible effect on our results.
5. 1092 THE AMERICAN ECONOMIC REVIEW DECEMBER 1995
TABLE 1—MOMENTUM-INVESTING SUMMARY STATISTICS FOR SAMPLE OF 155 MUTUAL FUNDS (QUARTERLY FUND
PORTFOLIO HOLDINGS ARE FOR THE PERIOD DECEMBER 31,1974, THROUGH DECEMBER 31,1984)
Venture-
capital/
Aggressive- Growth- Special- special-
Total sample growth Balanced Growth income Income purpose situations
Statistic (N = 155) (^N = 45) (N = 10) (N = 44) (N = 37) (TV = 13) (N = 3) (N = 3)
LOM (percent/quarter) 0.74 1.25 0.29 0.89 0.32 0.17 -0.05 0.95
t statistic 10.96** 9.80** 3.83** 10.71** 6.33** 1.63 -0.33 3.17**
Percentage positive 76.8 88.9 60.0 81.8 67.6 61.5 66.7 66.7
Wilcoxon probability 0.0001 0.0001 0.44 0.0001 0.01 0.30 0.64 0.64
Fl-statistic (LOM in every category = 0): E = 51.57**
F2 statistic (LOM is equal across categories: F = 18.24**
Buy LOM (percent/quarter) 1.03 1.53 0.50 1.07 0.64 0.69 0.28 1.70
t statistic 2.63** 2.90** 1.76+ 2.65** 2.14* 2.08* 1.56 2.77**
Percentage positive 55.8 58.3 52.5 55.8 54.2 48.3 45.0 57.5
Wilcoxon probability 0.10 0.02 0.47 0.10 0.23 0.63 0.15 0.03
F l statistic (Buy LOM in every category = 0): F = 5.85**
F 2 statistic (Buy LOM is equal across categories): E = 0.04
Sell LOM (percent/quarter) -0.29 -0.40 -0.12 -0.13 -0.31 -0.51 -0.06 -0.60
/ statistic -0.86 -0.88 -0.55 -0.37 -1.18 — L65+ - 0 . 6 0 -1.16
Percentage positive 50.8 50.0 50.0 50.8 50.8 48.3 57.5 46.7
Wilcoxon probability 0.81 1.00 1.00 0.81 0.81 0.63 0.01 0.34"
F l statistic (Sell LOM in every category = 0): F = 0.62
F2 statistic (Sell LOM is equal across categories): F = 0.02
LIM (percent/quarter) 0.30 0.53 -0.02 0.43 -0.04 0.03 0.40 1.08
(statistic 5.46** 4.18** -0.33 6.16** - 1 . 0 1 0.36 2.18* 4.01**
Percentage positive 58.7 68.9 40.0 75.0 35.1 46.2 66.7 66.7
Wilcoxon probability 0.005 0.001 0.44 0.0001 0.02 0.74 0.64 0.64
F l statistic (LIM in every category = 0): F = 12.16**
F 2 statistic (LIM is equal across categories): F = 8.41**
Buy L I M (percent/quarter) 0.85 1.34 0.32 0.83 0.45 0.56 0.38 1.99
t statistic 2.23* 2.72** 1.13 2.07* 1.50 2.01* 0.80 3.34**
Percentage positive 57.5 58.3 49.2 55.8 50.8 50.0 45.8 60.0
Wilcoxon probability 0.03 0.02 0.81 0.10 0.81 1.00 0.23 0.004
F l statistic (Buy L I M in every category = 0): F = 3.36**
F 2 statistic (Buy L I M is equal across categories): F = 0.06
Sell LIM (percent/quarter) -0.44 -0.72 -0.23 -0.28 -0.37 -0.41 0.05 -0.73
/ statistic -1.56 -1.78+ -1.27 -0.97 -1.75+ -1.77+ 0.37 -1.60
Percentage positive 47.5 44.2 46.7 50.0 49.2 48.3 50.0 47.5
Wilcoxon probability 0.47 0.10 0.34 0.99 0.81 0.63 0.47 0.47
F l statistic (Sell LIM in every category = 0): F = 1.91+
F 2 statistic (Sell LIM is equal across categories): F = 0.06
TALOM (percent/quarter) 2.07 3.39 0.60 2.98 0.54 0.14 0.45 2.50
t statistic 9.50** 9.75** 1.16 9.27** 1.71 + 0.36 0.37 2.42*
Percent positive 72.3 82.2 50.0 77.3 56.8 76.9 100.0 66.7
Wilcoxon probability 0.0001 0.0001 0.97 0.0001 0.29 0.01 0.06 0.64
F l statistic (TALOM in every category = 0): F = 30.39**
F 2 statistic (TALOM is equal across categories): F = 9.30**
6. VOL. 85 NO. 5 GRINBLATT ET AL: MOMENTUM INVESTMENT AND HERDING 1093
TABLE 1—Continued.
Venture-
capital/
Aggressive- Growth- Special- special-
Total sample growth Balanced Growth income Income purpose situations
Statistic (N = 155) (N = 45) (N = 10) (N = 44) (N = 37) (N = 13) (N = 3 ) (N = 3)
Buy TALOM (percent/quarter) 2.31 3.64 1.29 2.78 0.98 0.93 0.02 3.35
{ statistic 1.93* 2.56* 1.32 2.18* 0.89 0.96 0.02 1.95+
Percentage positive 51.7 56.7 49.2 52.5 52.5 46.7 44.2 58.3
Wilcoxon probability 0.63 0.06 0.81 0.47 0.47 0.34 0.10 0.02
E statistic (Buy TALOM in every category = 0): F = 2.48*
Fl statistic (Buy TALOM is equal across categories): F = 0.06
Notes: The LOM statistic is the measure of momentum investing based on stock returns in the same quarter as the portfolio
revisions. The LIM statistic is the measure of momentum investing based on stock returns in the quarter before the
portfolio revisions. "TALOM" is the LOM statistic, with portfolio revisions normalized so that $1 of stocks are bought
each quarter and $1 are sold. For each category above, an equally weighted portfolio of all funds in that category is
formed. Then, the appropriate momentum-investing statistic is calculated for that mean portfolio for each month. Finally,
the time-series mean and t statistic are calculated for that portfolio across all 120 months. Wilcoxon probability is the
probability that the absolute value of the Wilcoxon-Mann-Whitney rank z statistic is greater than the absolute value of
the observed z statistic, under the null hypothesis.
* Statistically significant at the 10-percent level.
* Statistically significant at the 5-percent level.
** Statistically significant at the I-percent level.
in. Results sell "losers," as defined by the LOM measure.
The average LOM measure for all 155 funds
A. Summary Data on the Degree of over the 10-year period is 0.74 percent per
Momentum Investing quarter, indicating that, on average, the stocks
held by a fund at the end of a given quarter
Table 1 presents the average LOM measure had returns 0.74-percent higher, during that
for the entire sample, as well as for various quarter, than the stocks held at the end of the
investment-objective categories." According previous quarter, which was highly statis-
to this table, about 77 percent of the mutual tically significant.'^ F tests strongly reject
funds, 119 out of 155, buy "winners" and/or both that the average LOM is equal across
investment-objective categories and that it is
zero across categories. In unreported results,
" The mutual funds in this study were subdivided into we also find that funds with the greatest ten-
seven investment-objective categories, according to their dency to buy winners in the first five years of
stated objectives. Aggressive-growth and growth funds in-
vest in the common stock of growth companies, with the the sample period are more prone to buy win-
primary aim of achieving capital gains instead of dividend ners in tiie second five years of the sample pe-
income. Growth-income funds seek to provide both capital riod, indicating that some managers follow
gains and a steady stream of income by buying the shares consistent "styles."
of high-yielding conservative stocks. Balanced funds in-
vest in both stocks and bonds, intending to provide capital Table 1 also provides the average Buy
gains and income while preserving principal. Income LOM and Sell LOM measures for each cate-
funds seek to provide high current income by buying gov- gory. Note that the results for the Buy LOM
ernment and corporate bonds as well as high-yielding measure are largely similar to the LOM
common and preferred stock. Finally, special-purpose and
venture-capital/special-situations funds, as their names
suggest, have very specialized strategies that vary from
fund to fund. These two categories represent a very small " Nonparametric test results, designated by Wilcoxon
portion of our sample. probabilities, generally agree with the standard t statistics.
7. 1094 THE AMERICAN ECONOMIC REVIEW DECEMBER 1995
results. For example, the aggressive-growth, significant. The results for the turnover-
growth, and growth-income categories have adjusted measures, TALOM and Buy TALOM
the highest average LOM measures among (also shown in Table 1), provide a more dra-
the five investment-objective categories with matic confirmation of the momentum invest-
nonnegligible numbers of funds, while the ing behavior of the funds. For both measures,
aggressive-growth, growth, and income cat- the average difference between buy and sell
egories have the highest Buy LOM measures. portfolios across all 155 funds was about 2
Note, however, that the Sell LOM measures percent per quarter, confirming that buying
are insignificant (at the 5-percent level) for winners is the chief method of momentum in-
every category, and a joint test of signifi- vestment. In unreported results, we found that
cance cannot reject that the seven average the top 10 funds, ranked by TALOM, bought
Sell LOM measures are all equal to zero. portfolios of stocks with returns that were
Therefore, momentum investing appears to more than 8 percent (quarterly) greater than
be almost entirely driven by funds buying the portfolios of stocks they sold, on average;
winners, and not by selling losers. the top 25 had a difference of about 6 percent.
The results for LIM, Buy LIM, and Sell The TALOM results in Table 1 also confirm
LIM are much the same. The LIM measure is that the aggressive-growth and growth funds
significantly positive, on average, indicating were much more likely to have traded on mo-
that fund managers had a tendency to select mentum than funds in other large categories:
stocks based on superior returns over the prior their larger LOM measures were not primarily
quarter. The average one-quarter-lagged mo- due to higher turnover than other categories
mentum measure is about 0.30 percent per (since TALOM is adjusted for turnover).
quarter, suggesting that, on average, the most Again, results from nonparametric tests gen-
recent quarter's returns were more important erally agree with the standard f-statistic results.
determinants of portfolio choice (as shown by In results not reported in Table 1, we also
LOM) than the returns realized in the more dis- computed Buy LOM measures for partitions of
tant past (as shown by LIM). In unreported stocks in the portfolio based on the market
regressions, we found a strongly positive capitalization of the stocks, in order to mea-
cross-sectional correlation between the LOM sure the relative contribution of buying win-
and LIM measures of the funds. The regres- ners in different size deciles to the overall
sion of LIM on LOM gave a coefficient of LOM measure. For all objective categories,
0.48, with a time-series t statistic of 5.5; the and for the total sample of ftinds, buying large-
reverse regression of LOM on LIM gave a co- capitalization past winners provided almost all
efficient of 0.88, with a time-series t statistic of the contribution to the observed momentum-
of 10.0).'^ As with the LOM measure, the investing behavior. We also found no signifi-
aggressive-growth and growth categories had, cant evidence of selling past losers in any size
on average, the highest levels of LIM- decile.
measured momentum investing, which is
due, in part, to a larger percentage of these B. The Relation between Momentum
funds trading on momentum, relative to other Investing and Superior Portfolio
funds (about 89 percent and 82 percent of Performance
aggressive-growth and growth funds, respec-
tively, followed momentum strategies, accord- In this subsection, we examine the extent to
ing to their LOM measures). which a fund's tendency to hold past winners
Despite its statistical significance, the 0.74- relates to its performance. As mentioned ear-
percent (0.30-percent) quarterly return for the lier, past research (Jegadeesh and Titman,
LOM (LIM) measure seems economically in- 1993) suggests that stocks that perform rela-
tively well over a 3-6-month period tend to
realize relatively good performance during the
" These two regressions imply a correlation of 0.65 next year. Hence, mutual funds that hold
between LOM and LIM. stocks that performed well in the recent past
8. VOL. 85 NO. 5 GRINBLATT ET AL: MOMENTUM INVESTMENT AND HERDING 1095
should realize better performance than those funds, the 119 funds using momentum invest-
funds that hold stocks that did not perform ment strategies clearly outperformed the 36
well. funds using contrarian strategies over the
In order to measure mutual fund perfor- ten-year period. The performance of the mo-
mance, we employ the method developed by mentum investors averaged about 2.6
Grinblatt and Titman (1993), which does not percent per year, while the contrarians had
require that we select a benchmark portfolio. an insignificant average performance of
The performance measure ( a ) developed in about 0.1 percent per year. Similar results
that paper uses a four-quarter change in (un- held for most of the individual investment-
modified) portfolio weights and multiplies objective categories.
the differenced weights by a future return; Table 2B repeats this analysis with LIM as
that is. the momentum investing measure. The 91
LIM momentum investors outperformed the
64 LIM contrarians by about 1.8 percent per
777 year, on average. The LIM momentum inves-
tors actually had slightly higher performance
With this measure, the benchmark used to th£tn the LOM momentum investors (although
adjust the return of a portfolio for its risk in a there is a large degree of overlap between the
given month is the current month's return two). The LIM contrarians also achieved bet-
earned by the portfolio holdings four quarters ter performance than the LOM contrarians, and
prior to the current quarter's holdings. There- their performance was statistically significant
fore, a represents the mean return of a zero- (but relatively small in magnitude).'*
investment portfolio.'" If the systematic risks Table 2 also shows that the investment-
of the current and benchmark portfolios are the objective categories having the best perfor-
same from the point of view of an investor mance are those that most strongly used a
with no selectivity or timing abilities (as de- momentum strategy in selecting stocks (see
fined by Grinblatt and Titman [1989b]), the the "total" columns). Of the three categories
performance represented by a should be insig- with significant (at the 99-percent confi-
nificant for that investor.'^ dence level) performance (aggressive growth,
We first split our sample of 155 funds into growth, and income funds), two have signifi-
momentum and contrarian investors, based on cantly positive LOM and LIM measures. In
the sign of LOM and LIM, and examined the fact, among the five major categories, the
performance of these two subgroups. Table 2A aggressive-growth category ranks first in
compiles mean LOM measures f^or the total performance, LOM, and LIM, while the
sample and for the five largest investment- growth category ranks second in each of
objective categories. For the sample of all 155 these three measures." Interestingly, these
'* The weights of this zero-investment portfolio repre- "•At first glance, it seems surprising that the LIM
sent the difference between the vector of fund portfolio trend-following and contrarian portfolios both outperform
weights in the current period and the vector of fund port- their LOM counterparts. However, this follows from the
folio weights four quarters earlier. We found that an al- fact that LIM classifies fewer funds as trend-followers.
ternative performance measure which uses a one-quarter We expect that the sample of LIM trend-followers will
lag rather than a four-quarter lag revealed relatively little contain stronger trend-followers than the LOM trend-
performance, on average, which indicates that the stocks followers. For this reason, the average returns of the LIM
picked by these funds performed well in the following four trend-followers are higher. In addition, since the LIM con-
quarters, and not simply in the first quarter the stocks were trarians include some of the funds that were classified as
held. This finding rules out the possibility that funds may trend-followers by the LOM criteria, we also expect the
be affecting their measured performance by heavily buy- average returns of the LIM contrarians to be higher than
ing (or selling) the same stock during consecutive quar- the expected returns of the LOM contrarians.
ters. " Tlie special-purpose (SP) and the venture-capital/
"Grinblatt and Titman (1993) provide evidence that special-situations (VS) categories each had only three
the two portfolios have the same market betas. funds.
9. 1096 THE AMERICAN ECONOMIC REVIEW DECEMBER 1995
TABLE; 2—MEAN PORTFOLIO STATISTICS
1
Mean portfolios
All 155 funds Aggressive growth Balanced
Total Momentum Contrarians Total Momentum Contrarians Total Momentum Contrarians
A, Based on LOM statistic: (N = 155) (W= 119) (W = 36) (N = 45) (N = 40) (N = 5) (N = 10) (yv = 6) (/V = 4)
LOM (percent/quarter) 0,74 1,06 -0,30 1,25 1,45 -0,31 0,29 0,62 -0,20
(10,96**) (10,78**) (-5,20**) (9,80**) (10,07**) (-2,92**) (3,83**) (5,27**) (-2,18*)
Performance (percent/year) 2,04 2,61 0,15 3,40 3,75 0,63 0,01 0,03 -0,01
(3,16**) (3,25**) (0,51) (3,55**) (3,56**) (0,71) (0,03) (0,05) (-0,03)
Performance of differenced
portfolio (percent/
year) 2,46 3,12 0,04
(3,32**) (2,49*) (0,06)
B, Based on LIM statistic: {N = 155) (/V = 91) (W = 64) (/V = 45) (A' = 31) (A'= 14) (N = 10) (A/= 4) (^ = 6)
LIM (percent/quarter) 0,30 0,74 -0,33 0,53 0,94 -0,38 -0,02 0,35 -0,27
(5,46**) (8,81**) (-7,93**) (4,18**) (6,65**) (-2,48*) (-0,33) (2,75**) (-4,13**)
Performance (percent/year) 2,04 2,79 0,97 3,40 3,92 2,26 0,01 0,16 -0,09
(3,16**) (3,02**) (2,82**) (3,55**) (3,14**) (3,10**) (0,03) (0,19) (-0,23)
Performance of differenced
portfolio (percent/
year) 1,81 1,66 0,25
(2,44**) (1,36) (0,27)
Notes: For each category, the funds were separated into two sets: those with a positive momentum investing measure ("Momentum"), and
those with a negative measure ("Contrarians"), Equally weighted portfolios were then formed, and time-series mean and t statistics are
shown in the table. The "differenced portfolio" is long the momentum-investing portfolio and short the contrarian portfolio. Numbers in
parentheses are / statistics,
' Statistically significant at the 10-percent level,
* Statistically significant at the 5-percent level,
** Statistically significant at the 1-percent level.
categories also tend to have the highest the LOM measure, increases performance by
amount of portfolio turnover and tend to be about L27 percent.'*
the smallest in terms of the size of total as- Multiple regressions of performance on
sets managed. Only the income-fund cate- LOM and LIM and of performance on LOM,
gory shows significant performance and an LIM, L2M, L3M, and L4M show that LOM
insignificant level of momentum investing. provides the main explanatory power. This is
Other categories of funds show some degree not surprising, given the high correlation be-
of momentum investing, but their levels are tween LOM and the momentum measures
relatively small. based on longer lags. The next two regressions
Regression results, all of which control for
investment objective,, are found in Table 3.
The first two regressions show a strong cor-
relation between performance and momentum '" Note that the turnover-adjusted LOM (TALOM) was
investing, whether measured with LOM or tiot included as one of these momentutn investing mea-
LIM. For the sample of all 155 funds, the es- sures because it is not a metric of the tendency of a fund
to choose a portfolio based on past returns of stocks,
timated regression coefficient of L27 for the
TALOM was only used to compare the tendency of funds
first regression indicates that an increase of 1 to invest on momentum, without regard to differences in
percent in momentum investing, according to the intensity of trading across funds.
10. VOL 85 NO. 5 GRINBLATT ET AL: MOMENTUM INVESTMENT AND HERDING 1097
TABLE 2—Extended.
Mean portfolios
Growth Growth—income Income
Total Momentum Contrarians Total Momentum Contrarians Total Momentum Contrarians
(N = 44) (N = 36) (N = 8) (JV = 37) (N = 25)
1 (N = 12) (A/= 13) (/V = 8) (^ = 5)
0.89 1.17 -0.39 0.32 0.58 -0.23 0.17 0.48 -0.33
(10.71**) (10.85**) (-4.67**) (6.33** ) (7.44**) (-2.83**) (1.63) (3.77**) (-2.04*)
2.41 2.82 0.56 0.83 1.19 0.08 1.33 1.85 0.51
(2.94**) (2.94**) (1.00) (1.75') (1.99*) (0.22) (2.64**) (2.38*) (0.81)
2.26 l.ll 1.35
(2.37*) (2.16*) (1.26)
(/V = 44) (N = 33) (/V=ll) (/V = 37) (yv= 13) (N = 24) (W = 13) {N = (,) (N = 7)
0.43 0.73 -0.45 -0.04 0.39 -0.28 0.03 0.37 -0.26
(6.16**) (8.08**) (-6.50**) (-1.01) (4.99**) (-4.96**) (0.36) (2.69**) (-2.34*)
2.41 2.69 1.54 0.83 1.34 0.55 1.33 1.88 0.86
(2.94**) (2.69**) (3.05**) (1.96*) (1.27) (2.64**) (2.19*) (1.52)
1.15 0.79 1.02
(1.31) (1-52) (1.00)
show that LOM and Sell LOM do not explain was 17.9 percent per year, while that of the
performance, after controlling for Buy LOM 36 contrarian investors was 17.2 percent per
(similar results are shown for Buy LIM and year." The mean return of the CRSP index
Sell LIM). Thisfindingis consistent with our during this period was about 14.7 percent per
prior finding that momentum investing was year. From Table 2A, the average difference
concentrated in buying large-capitalization between the risk-adjusted performance of
winners. The regression in the last row con- momentum and contrarian investors was 2.5
firms that Buy LOM explains performance bet- percent (per year) during this period, which
ter than Buy LIM. was higher than the average difference be-
In unreported results, we compared the tween their gross returns (0.7 percent per
hypothetical gross (not risk-adjusted) port- year). Contrarians held more priced risk in
folio returns of momentum investors and their portfolios by holding smaller stocks
contrarians with a market benchmark, the than momentum investors.
value-weighted CRSP index (with daily div-
idend reinvestment). In calculating gross rv.. The Herding Behavior of the Mutual Funds
returns, we assumed that the portfolio indi-
cated by the beginning-of-quarter holdings The preceding analysis indicates that mutual
was held constant until the end of the quar- funds show a tendency to buy stocks based on
ter, when the weights were updated. We
found that, in general, momentum investors
realized higher gross returns than contrari-
ans, and both realized higher returns than the " The top 20 percent of LOM momentum investors had
CRSP index. For example, the mean gross an average hypothetical gross return of 19.0 percent per
year, while the bottom 20 percent (the most contrarian
return of the 119 LOM momentum investors investors) had an average return of 17.5 percent per year.
11. 1098 THE AMERICAN ECONOMIC REVIEW DECEMBER 1995
TABLE 3—CROSS-SECTIONAL REGRESSIONS ACROSS ALL 155 FUNDS,
DEPENDENT VARIABLE = PERFORMANCE (PERCENT PER YEAR)
Regression
Independent
variable (i) (ii) (iii) (iv) (V) (vi) (vii) (viii)
LOM 1.27 1.15 1.13 0.66
(2.67**) (2.53*) (2.33*) (1.00)
LIM 1.20 0.27 0.32
(2.02*) (0.50) (0.58)
L2M -0.51
(-0.81)
L3M 0.31
(0.46)
L4M 0.44
(0.70)
Buy LOM 0.94 1.66 1.33
(1.65t) (3.26**) (2.48*)
Sell LOM 0.63
(0.84)
Buy LIM 1.42) 0.44
(2.22*) (0.71)
Sell LIM 0.14
(0.22)
R^: 0.03 0.29 0.39 0.39 0.40 0.40 0.34 0.39
F: 3.68 2.49 6.03 6.19 3.66 5.48
(0.03) (0.03) (0.003) (0.002) (0.03) (0.01)
Notes: In each regression, the time-series average fund performance (in percent/year) is regressed, cross-sectionally, on
the time-series average momentum investing measures (in percent/quarter). For example, in regression (i), the time-series
mean performance is regressed, across funds, on the time-series mean LOM measure. The method of computing / and F
statistics is based on a time-series procedure (see Grinblatt and Titman [1994] for details). Separate dummy intercepts
were used for funds in different investment objective categories to control for differences in non-momentum-investing-
related performance across categories. Therefore, the common intercept was fixed at zero. The t statistics are given in
parentheses beneath coefficient estimates; numbers in parentheses beneath F statistics are p values.
* Statistically significant at the 10-percent level.
* Statistically significant at the 5-percent level.
** Statistically significant at the 1-percent level.
their past returns. This, by itself, suggests that W. Vishny (1992) (henceforth, LSV) on our
mutual funds should show some (possibly sample of mutual funds. LSV calculated a sta-
weak) tendency to herd (i.e., buy and sell the tistic, described by equation (6), that mea-
same stocks in the same quarter). For exam- sures the average tendency, of pension funds
ple, we would expect to observe more mutual either to buy or to sell particular stocks at the
funds buying than selling those stocks that same time:
have recently increased in price. In this sec-
tion, we examine this tendency to herd more (6) UHM,,, = pi,-p, - Ep,,, - p,I
generally.
As a starting point, we replicate the analysis where /?,,, equals the proportion of funds, trad-
of Lakonishok, Andrei Shleifer, and Robert ing in stock i during quarter t, that are buyers;
12. VOL. 85 NO. 5 GRINBLATT ET AL: MOMENTUM INVESTMENT AND HERDING 1099
Pi, which is the expected value of p,,,, is cal- level of herding does not seem economically
culated as the mean of p,., over all stocks dur- significant, and it is similar to the mean level
ing quarter t. Therefore, p, is the proportion of that LSV found for pension funds, 2.7
fund trades in quarter t that are buys, for the percent.
average stock. We refer to the statistic given Not surprisingly. Table 4A shows that the
by equation (6) as the "unsigned herding set of all funds exhibits more herding in buy-
measure" (UHM) to distinguish it from what ing past winners than in buying past losers.
we later describe as the "signed herding mea- However, herding that occurs on the sell side,
sure" (SHM), which separates buy and sell although positive, appears to be less related to
herding. past returns. These findings are consistent with
Table 4A presents the mean unsigned herd- the average fund being a momentum investor
ing statistics, averaged over all stock-quarters that buys past winners but does not systemat-
(see the "total" column) and averaged over ically sell past losers, which results in several
two subgroups of stock-quarters (see the funds herding into (but not out of) the same
"buy" and "sell" columns). Membership of groups of stocks based on their past-quarter
a stock-quarter in one of the subgroups was returns.
determined by whether the set of all funds was The average herding measure for the set of
buying or selling the stock during the quarter all funds appears to be small. Two explana-
to a greater degree than would be expected tions for this are examined in Table 4. The first
with random buying and selling (i.e., stock i has to do with the possibility that we are mea-
during quarter t was considered to be a ' 'buy suring herding over a sample of investors that
herding" stock-quarter if p,,, > p,; similarly, is too broad. For example, by definition, all
stock "sell herding" categorization occurred investors cannot be buying and selling as a
when Pij < PI). This partition allows us to herd, since, in the aggregate, the buys must
determine whether herding was stronger on equal the sells. As a result, if our sample of
the buy side than on the sell side of institu- mutual funds is representative of a large frac-
tional trades. Analogous to LSV, the statis- tion of trading, then we would not expect to
tics given in Table 4 are from the perspective find much evidence of herding. However,
of individual stocks (instead of from a fund herding may exist among various subsets of
perspective) and are based on the entire sam- the mutual funds. For this reason, we also ap-
ple of 274 mutual funds (including nonsur- ply the LSV herding measure [equation (6)]
vivors) that existed on December 31, 1974. to measure imbalances between buys and sells
In addition, we segregated the stock-quarters of the smaller subgroups represented by the
by whether they had a return among the top investment-objective categories. The results in
50 percent of NYSE and AMEX returns dur- Table 4A indicate that we find even less evi-
ing the quarter, or among the bottom 50 dence of herding within investment-objective
percent. category subgroups.
The herding statistic of 2.5 percent (in Table A second reason why we may not have
4A under the "total" column for all 274 found strong evidence of herding is that the
funds) is the unsigned herding measure, herding measure was aggregated across all
averaged over all NYSE and AMEX stock- stock-quarters, including those with very little
quarters (where trades by at least one fund oc- trading by the mutual funds. Intuitively, it
curred in that stock) during the period from makes sense to condition the herding measure
December 31, 1974 to December 31, 1984. on the number of funds trading in the stock
This overall herding measure can be thought during the particular quarter. It is certainly
of as meaning that, for the average stock- much more meaningful to analyze the ten-
quarter, if 100 funds traded in that stock- dency of funds to be either simultaneously
quarter, 2.5 more funds traded on the same buying or selling a particular stock that several
side of the market than would be expected funds are trading in a particular quarter than a
under the null hypothesis that the stocks stock which only a few funds are trading. Be-
were picked independently. This overall cause of this, we present the average herding
13. 1100 THE AMERICAN ECONOMIC REVIEW DECEMBER 1995
TABLE 4—MEAN HERDING STATISTICS, SEGREGATED BY PAST-QUARTER-RETURN DECILES AND
BY " B U Y " OR " S E L L " HERDING
Aggressive-growth funds
Total sample of funds (N = 274) (N = 73) Balanced funds (N = 19)
Statistic Buy Sell Total Buy Sell Total Buy Sell Total
A. Mean Herding Statistic (Percentage) for Volume of Trade ^ 1:
Past losers 1.11 3.48 2.29 0.02 3.57 1.98 1.63 -2.22 -0.45
(Number of stock-quarters) (9,631) (9,527) (19,158) (5,700) (6,987) (12,687) (1,705) (2,003) (3,708)
Past winners 2.51 2.85 2.68 1.03 2.66 1.82 0.95 -1.91 -0.51
(Number of stock-quarters) (11,462) (11,285) (22,747) (7,426) (6,930) (14,356) (1,942) (2,028) (3,970)
All stock-quarters 1.87 3.14 2.50 0.59 3.12 1.89 1.26 -2.07 -0.48
(Number of stock-quarters) (21,093) (20,812) (41,905) (13,126) (13,917) (27,043) (3,647) (4,031) (7,678)
B. Mean Herding Statistic (Percentage) for Volume of Trade ^ 5;
Past losers 3.11 4.89 4.08 5.18 7.53 6.46 4.92 1.38 3.10
(Number of stock-quarters) (3,341) (3.987) (7,328) (586) (700) (1,286) (56) (59) (115)
Past winners 4.58 4.47 4.53 6.50 5.04 5.80 3.82 -1.26 1.68
(Number of stock-quarters (4,079) (4,267) (8,346) (823) (760) (1,583) (62) (45) (107)
All stock-quarters 3.92 4.68 4.32 5.95 6.23 6.10 4.34 0.23 2.42
(Number of stock-quarters) (7,420) (8,254) (15,674) (1,409) (1,460) (2,869) (118) (104) (222)
C. Mean Herding Statistic (Percentage) for Volume of Trade ^ 10:
Past losers 5.26 • 5.42 5.35 7.86 9.14 8.59 14.55 0.18 8.80
(Number of stock-quarters) (1.350) (1,653) (3,003) (106) (138) (244) (3) (2) (5)
Past winners 5.94 5.35 5.63 9.06 6.77 7.89 6.55 -0.10 3.42
(Number of stock-quarters) (1,687) (1,816) (3,503) (127) (132) (259) (9) (8) (17)
All stock-quarters 5.64 5.38 5.50 8.52 7.98 8.23 8.55 -0.04 4.64
(Number of stock-quarters) (3,037) (3,469) (6,506) (233) (270) (503) (12) (10) (22)
Notes: Past-quarter returns are defined as those returns during the same quarter as the portfolio revisions. Individual herding statistics are
calculated as p — E(p) — Ep — E(p), where p = the proportion of funds buying the given stock during the given quarter among all
funds that traded that stock during that quarter. E(p) and Ep — E(p) are calculated under the null hypothesis of no intentional herding.
The "mean herding statistic" is the average of the individual herding statistics across time and across stocks, for a given category. The
column labeled "total" is the mean herding statistic calculated over all stock-quarters having at least the volume of trade by the funds
indicated in the panel. "Buy" is calculated as the average over only those stock-quarters where p > E(p), that is, the proportion of buys
was greater than the expected proportion of buys. "Sell" is calculated as the average only over those stock-quarters for which p < E{p).
"Past losers" are those stocks having past returns in the lower 50 percent among all NYSE and AMEX stocks during the given quarter,
while "past winners" are those having past returns in the upper 50 percent.
measures over all stock-quarters with at least jective categories with the highest average
five active funds in Table 4B, and over all performance (aggressive growth, growth,
stock-quarters with at least ten active funds in and income [see Table 4A]) showed the
Table 4C. greatest tendency to herd in the average
Panels B and C of Table 4 show that, when stock-quarter.
we limit our analysis to stock-quarters with The next step in our analysis is to
at least five or ten trades, respectively, evi- characterize individual funds by the extent to
dence of herding increases significantly. For which they "go with the crowd." In order to
example, for the entire sample of funds, the measure a particular fund's tendency to herd,
average herding measure is about 5.5 percent wefirstdevelop what we call a "signed'' stock
when we include only stock-quarters with at herding measure (SHM), defined below,
least ten funds active. Note that, when at which provides an indication of whether a
least ten funds were active, funds in the ob- fund is "following the crowd" or "going
14. VOL 85 NO. 5 GRINBLATT ET AL: MOMENTUM INVESTMENT AND HERDING 1101
TABLE 4—Extended.
Growth-iticotne funds
Growth funds(N = 81) (N = 57) Income funds {N = 31)
Buy Sell Total Buy Sell Total Buy Sell Total
0,60 1,98 1,34 0,76 1,54 1,16 -0,95 3,22 1,15
(6.115) (7.147) (13.262) (5,318) (5,525) (10.843) (2.709) (2,750) (5,459)
1,49 2,01 1,75 1,22 2,08 1,66 -1,25 • 2,64 0,64
(7.258) (7.171) (14,429) (5,637) (5.771) (11,408) (3,182) (3,014) (6,196)
1,08 1,99 1,55 1.00 1,81 1,41 -1,11 2,92 0,88
(13.373) (14,318) (27,691) (10.955) (11,296) (22,251) (5,891) (5,764) (11,635)
3,73 3,97 3,87 4,38 3,26 3,81 4,27 3,18 3,75
(916) (1,165) (2,081) (676) (700) (1.376) (73) (67) (140)
4,50 3,45 3,98 4,29 4,02 4,15 3,92 8,13 6,07
(1,206) (1.205) (2,411) (718) (785) (1.503) (110) (115) (225)
4,17 3,71 3,93 4,34 3,66 3,99 4,06 6,31 5,18
(2,122) (2,370) (4,492) (1,394) (1.485) (2,879) (183) (182) (365)
5,81 4,89 5,30 6,92 4,74 5,78 7,24 5,78 6,57
(245) (314) (559) (162) (178) (340) (7) (6) (13)
7,14 4,62 5,98 4,28 2,93 3,62 5,58 5,25 5,39
(296) (250) (546) (161) (156) (317) (11) (14) (25)
6,54 4,77 5,64 5,60 3,89 4,73 6,22 5,41 5,79
(541) (564) (1,105) (323) (334) (657) (18) (20) (38)
against the crowd'' in a particular stock during Note that SHM,, = 0 if a stock-quarter shows
a particular quarter: negative herding or if only a small number of
funds have traded it, since there is no mean-
(7) SHM,, ingful way in which the fund can herd (or in-
vest against the herd) in these cases. Also,
= /,,, X UHM,, - X UHM,,,] Iij = 1 if the fund trades "with the herd" in
stock i during quarter t, and /,, = — 1 if the
where SHM,,, = 0 if fewer than 10 funds fund trades ' 'against the herd'' in that stock-
traded stock / during quarter t. Otherwise, quarter. The second term in SHM,, is calcu-
lated under the null hypothesis of no herding
'o if l p , , , - p , | < £ | p , , , - p , | ; hy the funds in the stock-quarter (above that
due to chance).^"
I if/?,./ — P, > £Pij — Pi and the mutual fund is a
buyer of stock i during quarter /. or if — (p,,, — p,) >
EPfj - P, and the fund is a seller (i,e,, the fund
/,,,= ' "follows the crowd");
-1 if p,,, - p,> Epi,, - p, and the mutual fund is a ™ Under the null hypothesis of independent trading de-
cisions among funds, the number of trading funds that are
seller of stock i during quarter r, or if - (p,., - p,) >
buyers is binomially distributed. We can calculate the
Epi., - p, and the fund is a buyer (i,e,. the fund value of £ ( / X UHM) for stock i in quarter t starting with
"goes against the crowd"), the following known binomial parameters:
15. 1102 THE AMERICAN ECONOMIC REVIEW DECEMBER 1995
TABLE 5—MEAN PORTFOLIO STATISTICS
Venture-
capital/
Total Aggressive Growth- Special- special-
sample growth Balanced Growth income Income purpose situations
Measure (N = 155) {N = 45) {N = 10) (A/= 44) (N = 37) (N= 13) {N = 3) (yv = 3)
FHM 0.84 1.05 0.66 0.89 0.72 0.60 0.12 0.83
(percent) (6.73**) (6.49**) (5.99**) (6.95**) (6.84**) (4.46**) (1.920 (6.73**)
LOM 0.74 1.25 0.29 0.89 0.32 0.17 -0.05 0.95
(percent/quarter) (10.96**) (9.80**) (3.83**) (10.71**) (6.33**) (1.63) (-0.33) (3.17**)
Performance 2.04 3.40 0.01 2.41 0.83 1.33 0.21 2.66
(percent/year) (3.16**) (3.55**) (0.03) (2.94**) (1.75+) (2.64**) (0.19) (1.43)
Notes: For each category above an equally weighted portfolio of all funds in that category is formed. Then, for the "fund
herding measure" (FHM), we calculate the portfolio-weighted "signed herding measure" (SHM) of the stocks held by
that equally weighted portfolio at the end of a quarter, less the portfolio-weighted SHM of the stocks held at the beginning
of that quarter, based on the herding measure of the stocks during that quarter. Finally, the time-series mean and t statistic
are calculated across all 40 quarters. For the LOM measure, the same procedure is followed, but the portfolio-weighted
stock returns are used instead of the portfolio-weighted herding measure (giving a time series of 120 months of data).
For the performance measure, we calculate for each quarter the portfolio-weighted stock returns (of the next quarter)
based on the end-of-quarter portfolio, less the portfolio-weighted returns (of the next quarter) based on the portfolio held
four quarters previously. This procedure gives a time series of 111 months of data. Time-series t statistics are given in
parentheses.
* Statistically significant at the 10-percent level.
** Statistically significant at the 1-percent level.
The fund herding measure for an individual As the above equation illustrates, a positive
fund (FHM) is then calculated by substituting (negative) portfolio revision is multiplied by
the signed herding measure in place of the stock a positive (negative) SHM if the set of all
return in equation (2) (for k = 1); that is, funds bought (sold) heavily in a given stock
during a given quarter, giving a positive con-
tribution to that fund's FHM. Conversely, a
(8) FHM=-^i i i(H>,.!, - vv,,,,,-,)SHM;.,,.,,,
positive (negative) portfolio revision is mul-
tiplied by a negative (positive) SHM if the set
of all funds sold (bought) heavily in a given
n = the number of funds trading stock i in quarter t, stock during a given quarter, giving a negative
p = the proportion of trading funds in the population that contribution to that fund's FHM. Hence, funds
are buyers, estimated as described for equation (6). that tend to buy (sell) when other funds are
Note that in the above expectation UHM = UHM(p), also buying (selling) will be characterized as
where p = the proportion of funds trading in stock-quarter herders by this measure.
(i, 0 that are buyers. Then, for stock i in quarter t. Table 5 presents the fund herding results.
£ [ / X UHM] = (2p - l)UHM(p)Pr(p) All categories of funds showed highly signif-
icant levels of FHM, and unreported F tests
strongly rejected that the average FHM fund
(2p - l)UHM(p)Pr(p) herding measure is equal across categories, or
that it is zero for all categories. We can inter-
where, for the n discrete values that p can assume, pret the reported 0.84 value for FHM as mean-
ing that, if the average fund traded 10 percent
of its portfolio each quarter, it bought stocks
Pr(p) = ( " )p"''(l -P)"""- that, on a portfolio-weighted average, had
npl
16. VOL. 85 NO. 5 GRINBLATT ET AL: MOMENTUM INVESTMENT AND HERDING 1103
TABLE 6—CROSS-SECTIONAL REGRESSIONS OF FUND PERFORMANCE
Venture-
capital/
Total Aggressive Growth- Special- special-
Independent sample growth Balanced Growth income Income purpose situations
variable (N = 155) (N = 45) (N = 10) iN = 44) (N = 37) (N= 13) (Af=3) (AT = 3 )
A. Cross-Sectional Regressions of Fund Performance on Fund Herding Measure (FHM):
Constant 2.99 0.21 -0.25 -1.17 2.35 1.60 -2.79
(3.01**) (0.33) (-0.45) (-2.16*) (2.44*) (0.60) (1.26)
FHM 1.61 0.40 -0.30 2.97 2.78 -1.69 -11.67 9.93
(2.82**) (0.57) (-0.29) (3.37**) (2.80**) (-1.29) (-.85) (2.38*)
Adjusted R^) 0.25 -0.0001 0.02 0.32 0.25 0.04 0.73 0.56
B. Cross-Sectionat Regressions of Eund Performance on LOM and FHM:
a
Constant 2.65 0.20 0.49 -0.35 2.39 —"
(3.02**) (0.30) (0.67) (-0.53) (2.30*)
LOM 1.24 0.87 -0.02 1.08 1.22 2.86
(2.23*) (1.42) (-0.02) (1.54) (1.91+) (2.07*)
FHM 0.12 -0.32 -0.28 1.06 1.10 -0.21
(0.20) (-0.39) (-0.24) (1.12) (1.01) (-1.79)
F: 4.39 1.10 0.04 6.00 4.47 2.66
(0.01) (0.34) (0.96) (0.003) (0.01) (0.07)
Adjusted R 0.39 0.12 0.02 0.42 0.35 0.39
Notes: In panel A, for each category, the benchmark-free fund performance measure (percent/year) is regressed, across
funds, on the fund herding measure (FHM, as a percentage). In panel B, for each category the benchmark-free fund
performance measure (percent/year) is regressed, across funds, on the fund momentum-investing measure (LOM, percent/
quarter) and on FHM. The method of computing t and F statistics is given in Grinblatt and Titman (1994). For the
regressions across all 155 funds, separate dummy intercepts were used for funds in different investment-objective cate-
gories to control for differences in non-regressor-related performance across categories. Therefore, the common intercept
was fixed at zero for those regressions. Student / statistics are in parentheses below the coefficient estimates; the numbers
in parentheses beneath the F statistics £ire the associated p values.
" Insufficient data.
* Statistically significant at the 10-percent level.
* Statistically significant at the 5-percent level.
** Statistically significant at the 1-percent level.
about 8.4-percent excessive buying by all fund categories). Funds that invest on mo-
funds, or sold stocks that, on a portfolio- mentum are more likely to invest in herds and
weighted average, had 8.4-percent excessive are more likely to perform.
selling by all funds (or some combination of Table 6A presents results for cross-sectional
these two extreme outcomes); while an aver- regressions of performance on FHM. The
age aggressive-growth fund trading 10 percent results show that fund performance is signifi-
each quarter bought (sold) stocks with about cantly correlated with the tendency of a fund
10.5-percent excessive buying (selling) by the to herd (FHM). This finding by itself would
set of all 155 funds. support the idea in some theoretical herding
Note that aggressive-growth funds had the papers that informed investors have a tendency
highest average levels of FHM, LOM, and per- to herd (Brennan, 1990; Froot et al., 1992;
formance, while the growth funds were second Hirshleifer et al., 1994). However, this is due
in all three categories (among the five major to the high correlation between the tendency
17. 1104 THE AMERICAN ECONOMIC REVIEW DECEMBER 1995
to herd and the tendency to buy past winners, and Titman (1993) disappear in the future,
which was confirmed in an unreported regres- then the performance of these funds is likely
sion of FHM on LOM. Table 6B shows that, to diminish.
at the margin, FHM does not significantly ex-
plain fund performance, given the explanatory REFERENCES
power already provided by LOM. Therefore,
on average, performing funds tend to buy past Banerjee, Abhijit. " A Simple Model of Herd
winners, with herding in past winners appar- Behavior." Quarterly Journal of Eco-
ently occurring as a result. nomics, August 1992, 707(3), pp. 7 9 7 -
817.
V. Conclusion Bikhchandani, Sushil; Hirshleifer, David and
Welch, Ivo. "A Theory of Fads, Fashion,
This paper characterizes some of the Custom, and Cultural Change as Informa-
investment strategies of mutual funds and an- tional Cascades." Journal of Political
alyzes how these strategies relate to realized Economy, October 1992, 700(5), pp. 9 9 2 -
performance. The evidence indicates that mu- 1026.
tual funds have a tendency to buy stocks based Black, Fischer. "Presidential Address: Noise."
on their past returns, and that they tend to buy Journal of Finance, July 1986, 47(3), pp.
and sell the same stocks at the same time (i.e., 529-44.
herd) in excess of what one would expect from Brennan, Michael J. "Presidential Address:
pure chance. The average level of herding and Latent Assets." Journal of Finance, July
momentum investing was statistically signifi- 1990, 45(3), pp. 709-30.
cant, but not particularly large. However, there Brown, Stephen J. and Goetzmann, William N.
was a significant degree of cross-sectional dis- "Performance Persistence." Journal of Fi-
persion across funds in their tendency to buy nance, June 1995, 50(2), pp. 679-98.
past winners and to trade with the herd. De Long, J. Bradford; Shieifer, Andrei; Summers,
The tendency of individual funds to buy past Lawrence H. and Waldmann, Robert J. ' 'Pos-
winners as well as to herd was shown to be itive Feedback Investment Strategies and
highly correlated with fund performance over Destabilizing Rational Speculation." Jour-
our period of study. The relation between the nal of Finance, June 1990, 45(2), pp. 3 7 9 -
tendency to buy past winners and performance 96.
was especially strong. On average, those funds Friend, Irwin; Blume, Marshall and Crockett,
following momentum strategies realized sig- Jean. Mutual funds and other institu-
nificant excess performance, while contrarian tional investors. New York: McGraw-
funds realized virtually no performance. The Hill, 1970.
relation between a fund's tendency to go with Froot, Kenneth A.; Scharfstein, David S.
the herd and its performance was less con- and Stein, Jeremy C. "Herd on the Street: In-
vincing, and it largely disappeared after con- formational Inefficiencies in a Market with
trolling for the fund's tendency to buy past Short-Term Speculation." Journal of Fi-
winners. nance, September 1992, 47(4), pp. 1461-
This research provides some insights 84.
about the extent to which mutual funds are Grinblatt, Mark and Titman, Sheridan D.
able to profit from their security-analysis ef- "Mutual Fund Performance: An Analysis
forts. The positive relation between momen- of Quarterly Portfolio Holdings." Journal
tum trading and performance suggests that of Business, July 1989a, 6 2 ( 3 ) , pp. 3 9 3 -
the positive performance of mutual funds 416.
observed in Grinblatt and Titman (1989a, "Portfolio Performance Evaluation:
1993) may have been at least partially gen- Old Issues and New Insights." Review of
erated by a simple trading rule rather than by Financial Studies, 1989b, 2 ( 3 ) , pp. 3 9 3 -
superior information. This suggests that if 421.
the momentum profits observed in Jegadeesh "Performance Measurement without
18. VOL. 85 NO. 5 GRINBLATT ET AL.: MOMENTUM INVESTMENT AND HERDING 1105
Benchmarks: An Examination of Mutual Dressing by Pension Fund Managers."
Fund Returns." Journal of Business, Janu- American Economic Review, May 1991
ary 1993, 66(1), pp. 47-68. iPapers and Proceedings), S7(2), pp.
.. "A Study of Monthly Mutual Fund 227-31.
Returns and Performance Evaluation Tech- Lakonisbok, Josef; Sbleifer, Andrei and Visbny,
niques." Journal of Financial and Quanti- Robert W. "The Impact of Institutional
tative Analysis, September 1994, 29(3), pp. Trading on Stock Prices." Journal of Fi-
419-44. nancial Economics, August 1992, 52(1),
Hirshleifer, David; Subrahmanyam, Avanidhar pp. 23-43.
and Titman, Sheridan, D. "Security Analysis Scbarfstein, David S. and Stein, Jeremy C. "Herd
and Trading Patterns When Some Investors Behavior and Investment." American Eco-
Receive Information Before Others." Jour- nomic Review, June 1990, SO(3), pp. 4 6 5 -
nal of Finance, December 1994, 49(5), pp. 79.
1665-98. Scbwartz, Robert and Sbapiro, James. "The
Jegadeesh, Narasimhan and Titman, Sheridan D. Challenge of Institutionalization for the Eq-
"Returns to Buying Winners and Selling uity Market." Working paper. New York
Losers: Implications for Stock Market Ef- University, 1992.
ficiency." Journal of Finance, March 1993, Sbiller, Robert J. and Pound, Jobn.' 'Survey Ev-
48(1), pp. 65-91. idence on Diffusion of Interest and Infor-
Kraus, Alan and Stoll, Hans R.' 'Parallel Trading mation Among Investors." Journal of
by Institutional Investors." Journal of Fi- Economic Behavior and Organization, Au-
nancial and Quantitative Analysis, Decem- gust 1989, 72(1), pp. 47-66.
ber 1972, 7(5), pp. 2107-38. Trueman, Brett. "A Theory of Noise Trading
Lakonishok, Josef; Sbleifer, Andrei; Thaler, Ri- in Securities Markets." Journal of Finance,
chard H. and Vishny, Robert W. "Window March 1988, 45(1), pp. 83-95.