This study examines the performance persistence of fixed income mutual funds between 1990 and 1999. It uses a methodology that ranks funds based on their annual returns, with the top 50% labeled as "winners" and bottom 50% labeled as "losers". It then analyzes whether winners and losers in one period remain winners or losers in the next period. The study finds some evidence of short-term persistence in fund performance driven by changes in interest rates, with statistical significance and consistency between the direction of persistence and bond returns. However, the nature of persistence is shown to be dependent on shifts in interest rates over time.
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.
This document is a chapter about learning about return and risk from analyzing historical data. It discusses factors that influence interest rates and defines real and nominal rates. It explains how the equilibrium real rate of interest is determined and shows the relationship between nominal interest rates and expected inflation. The chapter also covers topics like taxes and interest rates, comparing rates of return over different holding periods, and defining expected returns and standard deviation. It provides examples of analyzing the historical record of returns on Treasury bills, stocks, and other asset classes.
This document summarizes a study examining whether the components of the book-to-market ratio (B/M) can improve estimates of expected stock returns compared to using B/M alone. Specifically, the study analyzes how past changes in stock price and book equity, as well as net share issues, relate to future stock returns for different categories of stocks over time periods 1927-1963 and 1963-2005. The results vary across different stock categories and time periods, with the breakdown of B/M into its components generally providing better estimates of expected returns for smaller "Tiny" stocks throughout the sample periods, but not consistently improving estimates for larger stocks excluding Tiny stocks. Net share issues are also found to help predict returns during 1963
"Self fulfilling Debt Crises, Revisited: The Art of the Desperate Deal", by M...ADEMU_Project
1) The document discusses how sovereign governments can engage in "desperate deals" when faced with a failed bond auction, such as obtaining alternative financing at high interest rates or occasionally defaulting.
2) It presents a model where self-fulfilling debt crises occur in a "crisis zone" when a government would repay under normal pricing but default if faced with prices of zero for any positive debt issuance.
3) The model allows for "desperate deal" pricing during crises that makes the government indifferent between repaying and defaulting, and can generate crises with high but variable spreads like in the data.
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 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.
Statistics in Finance - M&A and GDP growthJean Lemercier
This document is a group coursework submission form for a statistics in finance module. It lists the names of three group members, their group number, the module code, title, lecturer, and submission date. At the bottom is the group's project on how mergers and acquisitions affect economic growth. It includes an introduction, motivation, data description with summary statistics, and a linear regression model specification to analyze the relationship between M&A activity and UK GDP growth from 2001-2013.
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.
This document is a chapter about learning about return and risk from analyzing historical data. It discusses factors that influence interest rates and defines real and nominal rates. It explains how the equilibrium real rate of interest is determined and shows the relationship between nominal interest rates and expected inflation. The chapter also covers topics like taxes and interest rates, comparing rates of return over different holding periods, and defining expected returns and standard deviation. It provides examples of analyzing the historical record of returns on Treasury bills, stocks, and other asset classes.
This document summarizes a study examining whether the components of the book-to-market ratio (B/M) can improve estimates of expected stock returns compared to using B/M alone. Specifically, the study analyzes how past changes in stock price and book equity, as well as net share issues, relate to future stock returns for different categories of stocks over time periods 1927-1963 and 1963-2005. The results vary across different stock categories and time periods, with the breakdown of B/M into its components generally providing better estimates of expected returns for smaller "Tiny" stocks throughout the sample periods, but not consistently improving estimates for larger stocks excluding Tiny stocks. Net share issues are also found to help predict returns during 1963
"Self fulfilling Debt Crises, Revisited: The Art of the Desperate Deal", by M...ADEMU_Project
1) The document discusses how sovereign governments can engage in "desperate deals" when faced with a failed bond auction, such as obtaining alternative financing at high interest rates or occasionally defaulting.
2) It presents a model where self-fulfilling debt crises occur in a "crisis zone" when a government would repay under normal pricing but default if faced with prices of zero for any positive debt issuance.
3) The model allows for "desperate deal" pricing during crises that makes the government indifferent between repaying and defaulting, and can generate crises with high but variable spreads like in the data.
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 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.
Statistics in Finance - M&A and GDP growthJean Lemercier
This document is a group coursework submission form for a statistics in finance module. It lists the names of three group members, their group number, the module code, title, lecturer, and submission date. At the bottom is the group's project on how mergers and acquisitions affect economic growth. It includes an introduction, motivation, data description with summary statistics, and a linear regression model specification to analyze the relationship between M&A activity and UK GDP growth from 2001-2013.
1) A managed volatility approach seeks to provide competitive returns compared to a benchmark index while maintaining lower volatility over the long term by constructing a portfolio of stocks with low expected volatility.
2) The document summarizes the results of a simulation of a managed volatility strategy for an EMU portfolio between 1999-2010 which showed an improved Sharpe ratio and higher risk-adjusted returns compared to the benchmark index with over 28% lower volatility.
3) Managed volatility strategies that aim to limit downside risk while maintaining potential upside have become increasingly popular with investors seeking to control risk independently from returns.
"Debt Crises: For Whom the Bell Toll", by Harlold L.Cole, Daniel Neuhann and ...ADEMU_Project
This document discusses a model of debt crises and contagion between two countries. The model explores how information acquisition by investors can generate multiple equilibria and affect sovereign bond prices and debt levels. When some investors are informed about countries' fundamentals while others remain uninformed, bond prices and debt levels may depend on the equilibrium selected. Even small domestic shocks can then lead to large changes in countries' debt burdens. The level of information in the market also influences whether crises are more likely to spread between countries or remain isolated events.
The prospect of rising interest rates continues to pose a risk to bond investors, but how a rise
in interest rates impacts investors depends on multiple factors.
The document provides an overview of the Black-Scholes option pricing model (BSOPM). It describes the key assumptions of the BSOPM, including that the underlying stock pays no dividends, markets are efficient, and prices are lognormally distributed. It also outlines how the BSOPM can be used to calculate theoretical option prices from historical data on the stock price, strike price, time to expiration, interest rate, and volatility. The document discusses implied volatility and how it differs from historical volatility, as well as limitations of the BSOPM.
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.
"Endogenous Political Turnover and Fluctuations in Sovereign Default Risk", b...ADEMU_Project
The document discusses a model that augments the Eaton-Gersovitz framework for sovereign default with endogenous political turnover. It finds that politically induced short-termism by leaders concerned with reelection can microfound high discount rates and cause large fluctuations in sovereign default risk. Calibrating the model to growth rates and debt market statistics of Mexico, Peru, and Turkey, it shows periods of high default risk correspond to increases in the probability of a low-growth political regime taking hold.
Mid caps have historically provided better risk-adjusted returns than small or large caps over 10- and 30-year periods. They have outperformed both asset classes while exposing investors to less risk, as measured by standard deviation and Sharpe ratios. However, mid caps remain an underutilized asset class, receiving only about 5% of investors' assets despite representing approximately 30% of the total market. Their lower levels of research coverage and investor interest may lead to pricing inefficiencies that could be exploited.
1) The document introduces the classical linear regression model, which describes the relationship between a dependent variable (y) and one or more independent variables (x). Regression analysis aims to evaluate this relationship.
2) Ordinary least squares (OLS) regression finds the linear combination of variables that best predicts the dependent variable. It minimizes the sum of the squared residuals, or vertical distances between the actual and predicted dependent variable values.
3) The OLS estimator provides formulas for calculating the estimated intercept (α) and slope (β) coefficients based on the sample data. These describe the estimated linear regression line relating y and x.
Risk management for sovereign financing within a debt sustainability frameworkStavros A. Zenios
This is a presentation of my Working Paper with the ESM, available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3250806
@techreport{ESM-DSA:2018,
Address = {Luxembourg},
Author = {Athanasopoulou, M. and Consiglio, A. and Erce, A. and Gavilan, A. and Moshammer, E. and Zenios, S.A.},
Date-Added = {2017-02-17 19:16:07 +0000},
Date-Modified = {2018-08-21 19:09:44 +0300},
Institution = {European Stability Mechanism},
Number = {31},
Title = {Risk management for sovereign financing within a debt sustainability framework},
Type = {Working Paper},
Year = {2018}}
Apoorva Javadekar -Role of Reputation For Mutual Fund FlowsApoorva Javadekar
As per apoorva javadekar From this ppt
we can conclude that 3.Some 2 nd half risk-sfiting for bad repute funds .Fund Flow heterogeniety could be explained through presence of loss-averse investors
Arbitrage-free Volatility Surfaces for Equity FuturesAntonie Kotzé
This document discusses methods for estimating and representing volatility surfaces and skews from options market data. It examines studies on estimating skews both parametrically, by fitting functions like quadratic curves to market data, and nonparametrically without assuming a function form. For liquid markets like the ALSI, skews derived from data are curved rather than linear. Estimating skews is challenging with limited or illiquid data. The document also discusses applying principal component analysis to decompose the main drivers of skew changes over time.
Morningstar ratings and fund performance blake moreybfmresearch
This study examines the ability of Morningstar ratings to predict the future performance of mutual funds compared to alternative predictors. The authors analyze two samples of US equity funds: seasoned funds from 1992-1997 and complete funds from 1993. They assess predictive ability using out-of-sample performance over 1, 3, and 5 year horizons, adjusting for loads and styles. The results indicate that low Morningstar ratings generally predict relatively poor future performance, but there is little evidence that top-rated funds outperform similar funds. Morningstar ratings do only slightly better than alternative predictors in forecasting future fund performance.
How poor stock mkt perf affects fund f lows shriderbfmresearch
1) The document examines how the determinants of mutual fund flows change depending on market conditions, specifically comparing periods of strong market performance to periods of weak performance.
2) Prior research has found that fund flows depend on both absolute and relative performance measures, but different studies have found different performance measures to be most important.
3) The study analyzes mutual fund data from 2001-2002, a period including record inflows and increasing outflows, to test if the determinants of fund flows differ between periods of good versus poor market performance and fund flow changes.
Fund selection based on fund characteristics budionobfmresearch
- The study investigates whether fund characteristics can help predict mutual fund performance beyond just using past performance.
- By analyzing one group of funds from 1962-2006, the study finds that past performance, ability (a fund's risk-adjusted performance over its lifetime), and turnover ratio significantly predict future fund performance.
- When the researchers implement an investment strategy selecting funds based on predicted performance from these three factors, it generates higher risk-adjusted returns than a strategy just using past performance.
Does Liquidity Masquerade As Size FinalAshok_Abbott
he empirical results presented in this paper suggest a strong role for liquidity in explaining higher raw and excess returns realized by investors in less liquid stocks. Size effect has been studied extensively and it has been suggested that it may be a proxy for another unobserved factor. Our results strongly suggest that liquidity may be that unobserved factor explaining a large part but not all of the size premium.
Value slides 2022_430102343b4e7f831172c529b654a6ed.pptxHafizArslan19
This document summarizes research on the value premium - the tendency for stocks with value characteristics like low price-to-book ratios to outperform growth stocks over the long run. It discusses several potential explanations for the value premium proposed in academic literature, including that investors extrapolate past growth too far into the future for growth stocks and are surprised by mean reversion for value stocks. The document also reviews evidence that investors predict near-term earnings accurately but overestimate longer-term growth, and that value stocks experience positive earnings surprises while growth stocks see negative surprises.
This study reports survivorship bias for the momentum effect. Effectively, this study shows that the Portuguese stock market does not exhibit the “momentum effect” when all listed stocks are considered spanning the period from January 1991 to December 2016. However, this phenomenon was detected when only survivor stocks are used. This study also shows that average returns for momentum portfolios were similar before and after the 2007
financial crisis.
This paper examines common risk factors in stock and bond returns using time-series regressions. It finds five factors: three stock factors related to the overall market, firm size, and book-to-market equity, and two bond factors related to maturity and default risk. Stock returns are explained by the three stock factors, and are also linked to bond returns through the two bond factors. The factors explain average returns on stocks and bonds, though the average premiums for the bond factors are close to zero.
The Financial Review 40 (2005) 1--9Reflections on the Effi.docxtodd771
The document summarizes evidence that actively managed investment funds do not consistently outperform the market. Over periods of 10 years or more, over 80% of actively managed funds underperformed their benchmark indexes. This suggests that markets are generally efficient, as arbitrage opportunities are not being exploited by professional investors. While some active managers do beat the market in individual periods, there is no persistence in performance - past winners often underperform in future periods. Expenses and high portfolio turnover help explain why the average actively managed fund underperforms the market by over 200 basis points after fees. Overall, the evidence supports the efficient market hypothesis and suggests investors are best served by low-cost index funds.
1) The document examines whether market timing strategies based on price-to-earnings ratios, dividend yields, and sentiment indexes can outperform a simple buy-and-hold strategy over the long run.
2) It finds that market timing strategies based on P/E ratios and dividend yields alone do not reliably outperform buy-and-hold, as these valuations measures combine both sentiment and fundamental value, which are difficult to disentangle.
3) A sentiment index may have some advantage over valuation ratios as a market timing tool, but the document concludes that successfully timing the market requires insights into future sentiment and value that are not fully captured by widely available indicators.
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.
1) A managed volatility approach seeks to provide competitive returns compared to a benchmark index while maintaining lower volatility over the long term by constructing a portfolio of stocks with low expected volatility.
2) The document summarizes the results of a simulation of a managed volatility strategy for an EMU portfolio between 1999-2010 which showed an improved Sharpe ratio and higher risk-adjusted returns compared to the benchmark index with over 28% lower volatility.
3) Managed volatility strategies that aim to limit downside risk while maintaining potential upside have become increasingly popular with investors seeking to control risk independently from returns.
"Debt Crises: For Whom the Bell Toll", by Harlold L.Cole, Daniel Neuhann and ...ADEMU_Project
This document discusses a model of debt crises and contagion between two countries. The model explores how information acquisition by investors can generate multiple equilibria and affect sovereign bond prices and debt levels. When some investors are informed about countries' fundamentals while others remain uninformed, bond prices and debt levels may depend on the equilibrium selected. Even small domestic shocks can then lead to large changes in countries' debt burdens. The level of information in the market also influences whether crises are more likely to spread between countries or remain isolated events.
The prospect of rising interest rates continues to pose a risk to bond investors, but how a rise
in interest rates impacts investors depends on multiple factors.
The document provides an overview of the Black-Scholes option pricing model (BSOPM). It describes the key assumptions of the BSOPM, including that the underlying stock pays no dividends, markets are efficient, and prices are lognormally distributed. It also outlines how the BSOPM can be used to calculate theoretical option prices from historical data on the stock price, strike price, time to expiration, interest rate, and volatility. The document discusses implied volatility and how it differs from historical volatility, as well as limitations of the BSOPM.
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.
"Endogenous Political Turnover and Fluctuations in Sovereign Default Risk", b...ADEMU_Project
The document discusses a model that augments the Eaton-Gersovitz framework for sovereign default with endogenous political turnover. It finds that politically induced short-termism by leaders concerned with reelection can microfound high discount rates and cause large fluctuations in sovereign default risk. Calibrating the model to growth rates and debt market statistics of Mexico, Peru, and Turkey, it shows periods of high default risk correspond to increases in the probability of a low-growth political regime taking hold.
Mid caps have historically provided better risk-adjusted returns than small or large caps over 10- and 30-year periods. They have outperformed both asset classes while exposing investors to less risk, as measured by standard deviation and Sharpe ratios. However, mid caps remain an underutilized asset class, receiving only about 5% of investors' assets despite representing approximately 30% of the total market. Their lower levels of research coverage and investor interest may lead to pricing inefficiencies that could be exploited.
1) The document introduces the classical linear regression model, which describes the relationship between a dependent variable (y) and one or more independent variables (x). Regression analysis aims to evaluate this relationship.
2) Ordinary least squares (OLS) regression finds the linear combination of variables that best predicts the dependent variable. It minimizes the sum of the squared residuals, or vertical distances between the actual and predicted dependent variable values.
3) The OLS estimator provides formulas for calculating the estimated intercept (α) and slope (β) coefficients based on the sample data. These describe the estimated linear regression line relating y and x.
Risk management for sovereign financing within a debt sustainability frameworkStavros A. Zenios
This is a presentation of my Working Paper with the ESM, available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3250806
@techreport{ESM-DSA:2018,
Address = {Luxembourg},
Author = {Athanasopoulou, M. and Consiglio, A. and Erce, A. and Gavilan, A. and Moshammer, E. and Zenios, S.A.},
Date-Added = {2017-02-17 19:16:07 +0000},
Date-Modified = {2018-08-21 19:09:44 +0300},
Institution = {European Stability Mechanism},
Number = {31},
Title = {Risk management for sovereign financing within a debt sustainability framework},
Type = {Working Paper},
Year = {2018}}
Apoorva Javadekar -Role of Reputation For Mutual Fund FlowsApoorva Javadekar
As per apoorva javadekar From this ppt
we can conclude that 3.Some 2 nd half risk-sfiting for bad repute funds .Fund Flow heterogeniety could be explained through presence of loss-averse investors
Arbitrage-free Volatility Surfaces for Equity FuturesAntonie Kotzé
This document discusses methods for estimating and representing volatility surfaces and skews from options market data. It examines studies on estimating skews both parametrically, by fitting functions like quadratic curves to market data, and nonparametrically without assuming a function form. For liquid markets like the ALSI, skews derived from data are curved rather than linear. Estimating skews is challenging with limited or illiquid data. The document also discusses applying principal component analysis to decompose the main drivers of skew changes over time.
Morningstar ratings and fund performance blake moreybfmresearch
This study examines the ability of Morningstar ratings to predict the future performance of mutual funds compared to alternative predictors. The authors analyze two samples of US equity funds: seasoned funds from 1992-1997 and complete funds from 1993. They assess predictive ability using out-of-sample performance over 1, 3, and 5 year horizons, adjusting for loads and styles. The results indicate that low Morningstar ratings generally predict relatively poor future performance, but there is little evidence that top-rated funds outperform similar funds. Morningstar ratings do only slightly better than alternative predictors in forecasting future fund performance.
How poor stock mkt perf affects fund f lows shriderbfmresearch
1) The document examines how the determinants of mutual fund flows change depending on market conditions, specifically comparing periods of strong market performance to periods of weak performance.
2) Prior research has found that fund flows depend on both absolute and relative performance measures, but different studies have found different performance measures to be most important.
3) The study analyzes mutual fund data from 2001-2002, a period including record inflows and increasing outflows, to test if the determinants of fund flows differ between periods of good versus poor market performance and fund flow changes.
Fund selection based on fund characteristics budionobfmresearch
- The study investigates whether fund characteristics can help predict mutual fund performance beyond just using past performance.
- By analyzing one group of funds from 1962-2006, the study finds that past performance, ability (a fund's risk-adjusted performance over its lifetime), and turnover ratio significantly predict future fund performance.
- When the researchers implement an investment strategy selecting funds based on predicted performance from these three factors, it generates higher risk-adjusted returns than a strategy just using past performance.
Does Liquidity Masquerade As Size FinalAshok_Abbott
he empirical results presented in this paper suggest a strong role for liquidity in explaining higher raw and excess returns realized by investors in less liquid stocks. Size effect has been studied extensively and it has been suggested that it may be a proxy for another unobserved factor. Our results strongly suggest that liquidity may be that unobserved factor explaining a large part but not all of the size premium.
Value slides 2022_430102343b4e7f831172c529b654a6ed.pptxHafizArslan19
This document summarizes research on the value premium - the tendency for stocks with value characteristics like low price-to-book ratios to outperform growth stocks over the long run. It discusses several potential explanations for the value premium proposed in academic literature, including that investors extrapolate past growth too far into the future for growth stocks and are surprised by mean reversion for value stocks. The document also reviews evidence that investors predict near-term earnings accurately but overestimate longer-term growth, and that value stocks experience positive earnings surprises while growth stocks see negative surprises.
This study reports survivorship bias for the momentum effect. Effectively, this study shows that the Portuguese stock market does not exhibit the “momentum effect” when all listed stocks are considered spanning the period from January 1991 to December 2016. However, this phenomenon was detected when only survivor stocks are used. This study also shows that average returns for momentum portfolios were similar before and after the 2007
financial crisis.
This paper examines common risk factors in stock and bond returns using time-series regressions. It finds five factors: three stock factors related to the overall market, firm size, and book-to-market equity, and two bond factors related to maturity and default risk. Stock returns are explained by the three stock factors, and are also linked to bond returns through the two bond factors. The factors explain average returns on stocks and bonds, though the average premiums for the bond factors are close to zero.
The Financial Review 40 (2005) 1--9Reflections on the Effi.docxtodd771
The document summarizes evidence that actively managed investment funds do not consistently outperform the market. Over periods of 10 years or more, over 80% of actively managed funds underperformed their benchmark indexes. This suggests that markets are generally efficient, as arbitrage opportunities are not being exploited by professional investors. While some active managers do beat the market in individual periods, there is no persistence in performance - past winners often underperform in future periods. Expenses and high portfolio turnover help explain why the average actively managed fund underperforms the market by over 200 basis points after fees. Overall, the evidence supports the efficient market hypothesis and suggests investors are best served by low-cost index funds.
1) The document examines whether market timing strategies based on price-to-earnings ratios, dividend yields, and sentiment indexes can outperform a simple buy-and-hold strategy over the long run.
2) It finds that market timing strategies based on P/E ratios and dividend yields alone do not reliably outperform buy-and-hold, as these valuations measures combine both sentiment and fundamental value, which are difficult to disentangle.
3) A sentiment index may have some advantage over valuation ratios as a market timing tool, but the document concludes that successfully timing the market requires insights into future sentiment and value that are not fully captured by widely available indicators.
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.
This document summarizes critiques of the Capital Asset Pricing Model (CAPM) and presents alternative models. It discusses empirical studies from the 1980s and 1990s that found variables other than beta help explain stock returns, contradicting CAPM. Fama and French's 1992 study found firm size and book-to-market ratio better predict returns than beta. Their three-factor model and the Arbitrage Pricing Theory were proposed as alternatives to CAPM. Overall, the document outlines major empirical challenges to CAPM and influential models that improved on its ability to explain stock returns.
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.
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.
Traditional methods to measure volatility case study of selective developed ...Alexander Decker
This document analyzes stock market volatility across developed and emerging markets from 1997-2009 using traditional measures like standard deviation. Key findings include:
- Returns for all markets showed non-normality, with emerging markets exhibiting more non-normality and higher kurtosis, indicating more peaked return distributions.
- Volatility, as measured by standard deviation, was highest for Turkey, Brazil, and China - all emerging markets. However, some developed markets were found to be more volatile than some emerging markets, suggesting volatility is not unique to emerging markets.
- The analysis concludes volatility should be measured using other methods like extreme value analysis due to the heavy-tailed distributions found in emerging market returns. This could provide better guidance for
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.
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Performance persistence of fixed income funds droms
1. JOURNAL OF ECONOMICS AND FINANCE • Volume 30 • Number 3 • Fall 2006 347
PERFORMANCE PERSISTENCE OF FIXED INCOME
MUTUAL FUNDS
By William G. Droms and David A. Walker*
Abstract
The "winner-winner, winner-loser, gone" methodology allows tests for short-term performance
persistence for government and corporate fixed income mutual funds from 1990 to 1999. Persistence
occurs when “winner” (loser) funds remain “winner” (loser) funds. If intermediate-term (long-term)
bond returns are higher than long-term (intermediate-term) bond returns for successive years, the z-
statistic is positive. Persistence is negative in the opposite case, and the pattern holds for longer lag
periods. Statistical significance and consistency between the sign of persistence and bond returns
indicates persistent returns on bond funds, but the nature of persistence is driven by changes in
interest rates. (JEL G11)
Introduction
This study provides an analysis of performance persistence of fixed income mutual funds. Fund
performance is defined to “persist” if, for consecutive time periods, the fund return is above (below)
the median of all funds after being above (below) the median in the previous period. Studies by
Grinblatt and Titman (1992), Hendricks et al. (1993), Goetzmann and Ibbotson (1994), Brown and
Goetzmann (1995), Malkiel (1995), Elton et al. (1996), Carhart (1997), and Droms and Walker (2001)
have tested the persistence of equity mutual fund total returns over time periods ranging from 10 to 31
years. Grinblatt and Titman (1992) find evidence that differences in performance between funds persist
over time and that this persistence is consistent with the ability of fund mangers to earn abnormal
returns. Hendricks et. al. (1993) find that the relative performance of no-load, growth-oriented mutual
funds persists in the near term, with the strongest evidence for a one-year time horizon. Goetzmann
and Ibbotson (1994) find strong evidence that past mutual fund performance predicts future
performance. Their data suggest that both "winners" (funds with returns above the median) and
"losers" (funds with returns below the median) are likely to repeat, even when performance is adjusted
for relative risk. Brown and Goetzmann (1995) find that relative risk-adjusted performance of mutual
funds persists but that persistence is mostly due to funds that lag the S&P 500; the implication of their
results for investors is that the persistence phenomenon is a useful indicator of which funds to avoid.
Malkiel (1995) finds that funds in the aggregate have underperformed benchmark portfolios even
before deduction of expenses and that while considerable performance persistence existed during the
1970s, there was no consistency of performance during the 1980s. Elton et al. (1996) find that risk-
adjusted performance tends to persist; funds that did well in the past tend to do well in the future.
Using Jensen's alpha as a measure of risk-adjusted performance, their paper shows that primarily one-
year alphas provide information about future performance and that portfolios based on past
performance significantly outperform equally weighted portfolios of funds. Carhart (1997) develops a
31-year data sample free of survivor bias and demonstrates that common factors in stock returns and
investment expenses almost completely explain persistence in equity mutual funds’ mean and risk-
adjusted returns; his results do not support the existence of skilled or informed mutual fund managers.
*McDonough School of Business, Georgetown University, Washington, DC 20057; dromsw@msb.edu; walkerd@msb.edu.
The authors would like to acknowledge the research assistance of Michael Serra and Michael Wieczorek. This research was
supported in part by the McDonough School of Business and the Capital Markets Research Center at Georgetown University.
2. 348 JOURNAL OF ECONOMICS AND FINANCE • Volume 30 • Number 3 • Fall 2006
Droms and Walker (2001) find strong short-term performance persistence for international equity
funds, but no performance persistence for holding periods of two, three, or four years.
These persistence studies focus on equity mutual funds. There is very little published on the
persistence of fixed income mutual funds. Most of the recent literature, such as Busse (2001), Choi and
Murthi (2001), and Wermers (2000), focus on performance of stock mutual funds. Chan et al. (2003)
examine the persistence of long-term stock growth rates on the basis of median operating performance.
Several other recent papers, such as ter Horst and Verbeek (2000), examine various properties of
performance measures and estimators. The current study examines performance persistence of fixed
income mutual funds. The null hypothesis is that there is no persistence between time periods. Whether
or not new funds enter the market, winners in period t are examined to test whether they are winners in
period t+j, for j=1,2,3,4.
Methodology
Research Issue
This study applies the successive “winner-winner, winner-loser” methodology applied in
Goetzmann and Ibbotson (1994), Brown and Goetzmann (1995), and Malkiel (1995). Funds are
ranked ordered by one-year total returns with 50 percent of the funds with the highest returns labeled
“winners” and 50 percent of the funds with the lowest returns labeled “losers.” Funds that ceased
operations in the subsequent year are identified as “gone.” Two-by-three tables are constructed to
identify funds that are “winners” and “losers” in one year and then “winners,” “losers,” or “gone” in
successive years. Persistence is measured by whether winners in one period remain winners in the next
test period. Statistical significance tests (z-scores) are employed following the procedure described in
Brown and Goetzmann (1995).
Approach
To examine the persistence of returns for N funds, the returns are rank ordered from the lowest
return R1 to the highest return RN so that the returns form the vector, R = [R1 , . . . , R.5N , R.5N+1, . . .,
RN]. The lower half of the returns, R1 , . . . , R.5N , define the funds that are “losers” and the upper half
of the returns, R.5N+1, . . . RN, designate the funds that are called the “winners.” Funds with returns
equal to the median are also called “winners.” Let L = [R1 , . . . , R.5N] and W = [R.5N+1, . . ., RN].
If only these same funds operate for the next period, the definition of persistence is quite simple. In
that case, each element of L either remains in the lower half of the returns or shifts to the upper half of
the returns. Likewise, each element of W remains a member of W or does not have returns among the
top half of the rank ordering. If a fund is in L for consecutive periods, it is defined as a loser-loser
(LL). If a fund remains in the upper half of the returns, it is a winner-winner (WW). A fund that shifts
from L to W is a loser-winner (LW) and a fund that shifts from W to L is a winner-loser (WL). Funds
that cease operations that were “winners” (“losers”) during the previous period are designated as
winner-gone (WG) or loser-gone (LG). In matrix form, the path can be described as period t+1
winner loser gone
winner WW WL WG
period t
loser LW LL LG
The classification is somewhat complex for two reasons. Between periods t and t+1, there could
be an increasing number of funds or funds could close. Suppose M new funds are operating in period
t+1, then M+N funds are to be ranked. If K funds close, M+N-K funds are to be ranked. After the
M+N-K funds are ranked, the winners from period t are identified as winners again or losers and the
3. JOURNAL OF ECONOMICS AND FINANCE • Volume 30 • Number 3 • Fall 2006 349
losers from period t are identified as repeat losers or winners. The funds that are new in period t+1 are
ranked, but the matrix path will include only the funds that operate in the consecutive periods.
Data
Annual mutual fund data are collected for a total of 797 corporate and government fixed income
funds that were in operation during the 10-year period from 1990 through 1999. There were a total of
314 funds operating at the beginning of 1990; 175 were government funds and 139 were corporate
funds; 271 funds ceased operations over the 10-year period while 483 new funds began operations.
The data set consists of annual total return data on these funds from the annual Wiesenberger
Investment Companies Service. Returns are measured as the percentage annualized total rate of return
for the fund (treating all dividends as reinvested), net of fees and expenses and before load charges,
where applicable.
Fund Characteristics
Table 1 provides the general characteristics of the data set. The average numbers of government
and corporate funds over the period were 219 and 247, respectively. The maximum numbers of funds
were 255 government funds in 1995 and 316 corporate funds in 1998. By the end of 1999, the number
of government funds had declined to 221, 15 percent below its 1995 peak, while the number of
corporate funds remained near its peak.
Table 1: Bond Fund Returns
Mean, Median and Benchmark Returns (%): 1990-1999
Year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Mean
Number of New Government Funds n.a. 9 16 42 48 34 23 10 8 3 21
Number of "Gone" Government Funds n.a. 11 7 14 14 23 26 20 18 14 16
Total Number of Government Funds 175 173 182 210 244 255 252 242 232 221 219
Mean Total Return on Government Funds 7.89% 13.99% 6.14% 8.46% -3.39% 15.57% 2.77% 7.85% 7.64% -1.04% 6.6%
Median Total Return on Government Funds 8.50% 14.00% 6.10% 7.70% -3.20% 15.00% 2.95% 8.00% 7.40% -0.90% 6.6%
Number of New Corporate Funds n.a. 17 19 57 55 60 32 22 21 7 32
Number of "Gone" Corporate Funds n.a. 6 7 9 4 20 21 19 20 18 14
Total Number of Corporate Funds 139 150 162 210 261 301 312 315 316 305 247
Mean Total Return on Corporate Funds 16.09% 15.92% 7.33% 9.95% -3.08% 15.84% 4.03% 8.39% 6.68% -0.27% 8.1%
Median Total Return on Corporate Funds 16.00% 15.80% 7.10% 9.75% -3.30% 16.30% 3.60% 8.40% 7.05% -0.80% 8.0%
Return on Short-Term Treasury Bills 7.81% 5.60% 3.51% 2.90% 3.90% 5.60% 5.21% 5.26% 4.86% 4.68% 4.9%
Return on Intermediate-Term Government Bonds 9.73% 15.46% 7.19% 11.24% -5.14% 16.80% 2.10% 8.38% 10.21% -1.77% 7.4%
Return on Long-Term Government Bonds 6.18% 19.30% 8.05% 18.24% -7.77% 31.67% -0.93% 15.85% 13.06% -8.96% 9.5%
Return on Long-Term Corporate Bonds 6.78% 19.89% 9.39% 13.19% -5.76% 27.20% 1.40% 12.95% 10.76% -7.45% 8.8%
Notes: Benchmark Data from Stocks, Bonds, Bill and Inflation Yearbook 2003, (Chicago, Ibbotson Associates).Table 1 shows the
total number of funds in the sample each year, the number of new funds introduced each year, the mean and median returns on
the funds in the sample, and the comparable return on the benchmark for each year.
Returns
The mean annual return was 6.6 percent on government funds and 8.1 percent on corporate funds;
the median returns were virtually the same. There was high variance in the returns on both types of
fixed income funds. The range of mean returns on corporate funds was from 16.09 percent in 1990 to
–3.08 percent in 1994. For government funds, the range of mean returns was 13.99 percent in 1991 to
–3.39 percent in 1994.
Benchmark returns on Treasury bills, intermediate-term government bonds, long-term government
4. 350 JOURNAL OF ECONOMICS AND FINANCE • Volume 30 • Number 3 • Fall 2006
bonds, and long-term corporate bonds from Ibbotson Associates are shown in Table 1 to provide a
basis of comparison for bond funds relative to the overall bond market. Bond market returns show
similar patterns to the aggregate groups of bond mutual funds, although percentage variations are
slightly more volatile. The average return on Treasury bills was 4.9 percent over the 1990-99 period,
which was less than the average returns on longer-term bonds and bond funds. The 9.5 percent return
on long-term government bonds was the highest average return among these instruments.
Survivor Bias
One of the key issues to be considered for every time series analysis of mutual fund returns is
potential survivor bias. This bias is minimal in this study because each new fund is added to the
database and merging funds continue to be included. The only bias is that, if any funds closed and did
not merge with an existing fund, that fund would not have returns to be included for the year in which
operations ceased. A total of 271 funds ceased operations while 483 new funds were added.
If a fund in the database merged into another fund also in the database, the surviving fund is
carried forward and the acquired fund is dropped entirely. If a fund in the database merged into a fund
not in the database or if the fund closed, the fund is dropped. Complete total return data were then
assembled for all funds that were in the database during the 10-year period of 1990-1999. Including all
operating funds (new and old) in the returns ranking in each period and separating funds that did not
continue operations avoids virtually all potential survivor bias.
Results
Persistence of Returns
Tables 2 through 5 present the results of persistence tests. For each table, persistence tests are
provided for government bonds in Panel A and for corporate bonds in Panel B. Table 2 provides the
tests for persistence between consecutive periods -- t to t+1. Tables 3, 4 and 5 provide the tests
between periods t and t+2 (two year lag), t and t+3 (three year lag), and t and t+4 (four year lag),
respectively. The significance of persistence of returns is tested by calculation of a z-statistic, which is
distributed normally with a zero mean and a standard deviation of 1.0. A large positive z-statistic is
obtained when a high percentage of the “winners” in one period remain “winners” in the next period
tested. When a high percentage of “winners” in one period become “losers” in the next period, a large
negative z-statistic is found. Small z-statistics are determined when there is no clear pattern in the
returns. If exactly the same winners remain winners and the same losers remain losers between two
periods, the z-statistic would be zero. Statistics are judged at the five- percent level of significance, but
in virtually all cases, the z-statistics are statistically significant at the .01 level, and many are
significant at the .001 level.
Table 2 shows that combined results for a one-year lag period are highly significant and
demonstrate both positive and negative performance persistence among both government bond (Panel
A) and corporate bond (Panel B) funds. Looking at the t+1 data in aggregate, approximately 20
percent of bond fund winners are consecutive winners for both government (374 out of 1,965) and
corporate (447 out of 2,166) bond funds. The two types of funds have comparable percentages among
winner-loser (28 percent), loser-winner (28 percent), and loser-loser (18 percent). Given the
differences in returns from year to year in Table 1, it is surprising to find the similar percentages
moving from one classification to another for both types of funds.
Except for the first period (1990-1991) for government bond funds, the z-statistics for both
government and corporate bond funds are statistically significant. The interesting result is that the
signs of the z-statistics fluctuate from significantly positive to significantly negative and the sign of the
z-statistics are the same for both classes of funds for all pairs of years. Even for the first pair of years
where the z-statistic for government bond funds is not statistically significant, it has the same negative
sign as does the significant statistic for the corporate bond fund in 1990-91. For both classes of funds,
5. JOURNAL OF ECONOMICS AND FINANCE • Volume 30 • Number 3 • Fall 2006 351
the signs are negative for the first pair of years, positive for the next two pairs, negative for the next
four pairs, positive for one year, then negative for the last pair of years (1998-99). When all of the one-
period differences are combined, the z-statistics are –9.045 and –7.288, respectively, for government
and corporate bond funds. The negative signs indicate that, at least in aggregate, winners in period t
are highly likely to become losers in the next period.
Table 2a: Government Bond Return Persistence: 1 Year Lag
Year Total Funds New Winner- Winner- Loser- Loser- Winner- Loser- Cross-Product SD Z
Year 1 Year 2 Funds Winner Loser Winner Loser Gone Gone Ratio Statistic
a b c d
1990-91 175 173 9 38 46 44 36 4 7 0.676 0.314 -1.248
1991-92 173 182 16 55 30 31 50 2 5 2.957 0.322 3.366
1992-93 182 210 42 62 24 27 55 5 9 5.262 0.336 4.940
1993-94 210 244 48 14 85 78 19 6 8 0.040 0.386 -8.341
1994-95 244 255 34 24 85 94 18 13 10 0.054 0.346 -8.435
1995-96 255 252 23 32 87 84 26 9 17 0.114 0.305 -7.121
1996-97 252 242 10 34 84 83 31 8 12 0.151 0.293 -6.457
1997-98 242 232 8 88 28 25 83 5 13 10.434 0.315 7.448
1998-99 232 221 3 27 84 84 23 5 9 0.088 0.323 -7.524
Total n 1965 374 553 550 341 57 90 0.419 0.096 -9.045
Total % 100.0% 19.0% 28.1% 28.0% 17.4% 2.9% 4.6%
Table 2b: Corporate Bond Return Persistence: 1 Year Lag
Year Total Funds New Winner- Winner- Loser- Loser- Winner- Loser- Cross-Product SD Z
Year 1 Year 2 Funds Winner Loser Winner Loser Gone Gone Ratio Statistic
a b c d
1990-91 139 150 17 23 44 49 17 2 4 0.181 0.381 -4.477
1991-92 150 162 19 54 19 21 49 2 5 6.632 0.373 5.071
1992-93 162 210 57 60 18 25 50 3 6 6.667 0.364 5.217
1993-94 210 261 55 25 78 75 28 2 2 0.120 0.319 -6.652
1994-95 261 301 60 33 90 91 27 8 12 0.109 0.299 -7.418
1995-96 301 312 32 47 98 92 43 6 15 0.224 0.256 -5.838
1996-97 312 315 22 61 86 86 60 9 10 0.495 0.237 -2.964
1997-98 315 316 21 98 53 49 95 7 13 3.585 0.245 5.212
1998-99 316 305 7 46 106 106 40 6 12 0.164 0.256 -7.064
Total n 2166 447 592 594 409 45 79 0.520 0.090 -7.288
Total % 100.0% 20.6% 27.3% 27.4% 18.9% 2.1% 3.6%
Notes: Table 2 shows performance persistence with a one-year lag. Winners and losers are ranked relative to the median fund in
year one and re-ranked in year two. Winners are funds with returns above the median and losers are the funds with returns below
the median. Funds ceasing operations are identified as gone.
Tables 3 through 5 show the analogous tests with successive performance lagged by two, three
and four years, respectively. Table 3 indicates an interesting persistence difference across two time
periods, in contrast to single period differences. In Table 3, the aggregate z-statistics for both
government and corporate bond funds are positive and statistically significant in contrast to the
significant and negative z-statistics for one period lags in Table 2. The aggregate z-statistics in Table 3
are 4.668 for government bond funds and 2.770 for corporate bond funds. For the two-year lag periods
reported in Table 3, all of the z-statistics are statistically significant and 9 of the 16 individual z–
statistics are positive, whereas 12 of the 18 z-statistics are negative in Table 2.
6. 352 JOURNAL OF ECONOMICS AND FINANCE • Volume 30 • Number 3 • Fall 2006
Table 3a: Government Bond Return Persistence: 2 Year Lag
Year Total Funds New Winner- Winner- Loser- Loser- Winner- Loser- New- Cross-Product SD Z
Year 1 Year 3 Funds Winner Loser Winner Loser Gone Gone Gone Ratio Statistic
a b c d
1990-92 175 182 25 51 32 33 41 5 13 0 1.980 0.325 2.103
1991-93 173 210 58 63 16 18 56 8 12 1 12.250 0.390 6.431
1992-94 182 244 90 24 57 45 30 10 16 2 0.281 0.339 -3.750
1993-95 210 255 82 70 23 29 56 12 20 5 5.877 0.332 5.337
1994-96 244 252 57 71 28 29 74 23 19 7 6.470 0.313 5.971
1995-97 255 242 33 94 16 14 86 18 27 1 36.089 0.395 9.074
1996-98 252 232 18 35 74 75 31 17 20 1 0.195 0.296 -5.512
1997-99 242 221 11 27 81 80 22 13 19 0 0.092 0.328 -7.294
Total n 1733 1838 435 327 323 396 106 146 1733 1.631 0.105 4.668
Total % 100.0% 25.1% 18.9% 18.6% 22.9% 6.1% 8.4% 100.0% 100.0%
Table 3b: Table 3b: Corporate Bond Return Persistence: 2 Year Lag
Corporate Bond Return Persistence: 2 year lag
Year Total Funds New Winner- Winner- Loser- Loser- Winner- Loser- New- Cross-Product SD Z
Year 1 Year 3 Funds Winner Loser Winner Loser Gone Gone Gone Ratio Statistic
a b c d
1990-92 139 162 36 24 39 44 19 7 7 0 0.266 0.378 -3.509
1991-93 150 210 76 57 13 19 46 5 10 1 10.615 0.411 5.749
1992-94 162 261 112 25 53 45 28 3 7 2 0.294 0.342 -3.587
1993-95 210 301 115 82 16 20 74 7 11 6 18.963 0.372 7.915
1994-96 261 312 92 80 35 32 78 16 20 5 5.571 0.292 5.887
1995-97 301 315 54 108 31 22 102 12 26 2 16.152 0.311 8.943
1996-98 312 316 43 37 96 103 39 21 16 2 0.146 0.270 -7.133
1997-99 315 305 28 42 99 100 39 17 18 3 0.165 0.264 -6.822
Total n 1850 2182 455 382 385 425 88 115 1850 1.315 0.099 2.770
Total % 100.0% 24.6% 20.6% 20.8% 23.0% 4.8% 6.2% 100.0%
Notes: Table 3 shows performance persistence with a two-year lag. Winners and losers are ranked relative to the median fund in
year one and re-ranked in year three. Winners are funds with returns above the median and losers are the funds with returns below
the median. Funds ceasing operations are identified as gone.
Tables 4 and 5 reveal that persistence in the aggregate returns for government and corporate bond
funds is not statistically significant across the 1990 to 1999 period. For three-year lag periods (Table
4), three of the statistically significant z-statistics are positive and four are negative for each category
of fund. For four period horizons (Table 5), half of the z-statistics are significantly positive and half
are significantly negative for each fund category. Comparing government and corporate bond funds,
for each subperiod in Tables 4 and 5, the sign of the z-statistics is the same for the two types of bond
funds; when there is a positive z-statistic for a subperiod for one type of fund, the sign of the statistic
for the other type of fund is the same. For the particular subperiods for three year (Table 4) and four-
year (Table 5) persistence periods, most of the z-statistics are large enough to reject the hypothesis of
no persistence. Of the 14 subperiods examined in Table 4, 12 indicate significant persistence. Of the
12 subperiods identified in Table 5, there was significant persistence for 10 subperiods.
7. JOURNAL OF ECONOMICS AND FINANCE • Volume 30 • Number 3 • Fall 2006 353
Table 4a: Government Bond Return Persistence: 3 Year Lag
Year Total Funds New Winner- Winner- Loser- Loser- Winner- Loser- New- Cross-Product SD Z
Year 1 Year 4 Funds Winner Loser Winner Loser Gone Gone Gone Ratio Statistic
a b c d
1990-93 175 210 67 36 41 41 26 11 20 1 0.557 0.339 -1.726
1991-94 173 244 107 14 58 48 21 15 17 4 0.106 0.396 -5.671
1992-95 182 255 125 56 19 26 43 16 22 14 4.874 0.364 4.356
1993-96 210 252 105 31 56 53 24 18 28 17 0.251 0.333 -4.159
1994-97 244 242 67 28 68 75 20 26 27 16 0.110 0.337 -6.550
1995-98 255 232 41 76 29 27 64 23 36 5 6.212 0.317 5.767
1996-99 252 221 22 83 18 17 81 24 29 3 21.971 0.373 8.294
Total n 1491 1656 324 289 287 279 133 179 1491 1.090 0.117 0.737
Total % 100.0% 21.7% 19.4% 19.2% 18.7% 8.9% 12.0% 100.0%
Table 4b: Corporate Bond Return Persistence: 3 Year Lag
Year Total Funds New Winner- Winner- Loser- Loser- Winner- Loser- New- Cross-Product SD Z
Year 1 Year 4 Funds Winner Loser Winner Loser Gone Gone Gone Ratio Statistic
a b c d
1990-93 139 210 93 26 35 43 16 9 10 3 0.276 0.391 -3.290
1991-94 150 261 131 20 50 42 21 5 12 3 0.200 0.376 -4.280
1992-95 162 301 172 57 18 24 44 6 13 14 5.806 0.371 4.743
1993-96 210 312 147 40 53 49 39 12 17 16 0.601 0.300 -1.700
1994-97 261 315 115 32 75 77 25 24 28 9 0.139 0.312 -6.328
1995-98 301 316 75 85 49 39 75 17 36 7 3.336 0.267 4.517
1996-99 312 305 50 96 33 34 101 27 21 9 8.642 0.283 7.623
Total n 1535 2020 356 313 308 321 100 137 1535 1.185 0.111 1.529
Total % 100.0% 23.2% 20.4% 20.1% 20.9% 6.5% 8.9% 100.0%
Notes: Table 4 shows performance persistence with a three-year lag. Winners and losers are ranked relative to the median fund in
year one and re-ranked in year four. Winners are funds with returns above the median and losers are the funds with returns below
the median. Funds ceasing operations are identified as gone.
Table 5a: Government Bond Return Persistence: 4 Year Lag
Year Total Funds New Winner- Winner- Loser- Loser- Winner- Loser- New- Cross-Product SD Z
Year 1 Year 5 Funds Winner Loser Winner Loser Gone Gone Gone Ratio Statistic
a b c d
1990-94 175 244 116 41 32 19 41 15 27 5 2.765 0.364 2.792
1991-95 173 255 141 54 13 23 43 20 20 19 7.766 0.403 5.090
1992-96 182 252 148 31 40 31 29 20 31 27 0.725 0.352 -0.913
1993-97 210 242 115 58 24 23 51 23 31 29 5.359 0.349 4.806
1994-98 244 232 77 32 56 64 26 34 32 23 0.232 0.321 -4.546
1995-99 255 221 44 25 76 68 18 27 41 10 0.087 0.351 -6.948
Total n 1239 1446 241 241 228 208 139 182 1239 0.912 0.132 -0.694
Total % 100.0% 19.5% 19.5% 18.4% 16.8% 11.2% 14.7% 100.0%
Table 5b: Corporate Bond Return Persistence: 4 Year Lag
Year Total Funds New Winner- Winner- Loser- Loser- Winner- Loser- New- Cross-Product SD Z
Year 1 Year 5 Funds Winner Loser Winner Loser Gone Gone Gone Ratio Statistic
a b c d
1990-94 139 251 148 38 22 16 43 10 10 6 4.642 0.397 3.868
1991-95 150 301 191 50 16 21 38 9 16 15 5.655 0.396 4.380
1992-96 162 312 203 33 40 34 27 8 20 25 0.655 0.349 -1.212
1993-97 210 315 159 70 18 24 59 17 22 25 9.560 0.358 6.299
1994-98 261 316 137 42 58 59 39 31 32 19 0.479 0.289 -2.547
1995-99 301 305 82 37 93 83 27 21 40 17 0.129 0.295 -6.938
Total n 1223 1800 270 247 237 233 96 140 1223 1.075 0.128 0.565
Total % 100.0% 22.1% 20.2% 19.4% 19.1% 7.8% 11.4% 100.0%
Notes: Table 5 shows performance persistence with a four-year lag. Winners and losers are ranked relative to the median fund in
year one and re-ranked in year five. Winners are funds with returns above the median and losers are the funds with returns below
the median. Funds ceasing operations are identified as gone.
8. 354 JOURNAL OF ECONOMICS AND FINANCE • Volume 30 • Number 3 • Fall 2006
The variation in the period-to-period signs of the z-statistics appears to be attributable to changes
in returns in the bond market. In particular, comparing benchmark bond market returns in Table 1 to
one-year persistence periods in Table 2, the data show that if intermediate-term bond returns are higher
than long-term bond returns for successive years, then the z-statistic is positive. Similarly, if long-term
bond returns are higher than intermediate-term bond returns in successive years, persistence is
positive. This pattern holds for all pairs of years. By contrast, if higher returns on intermediate bonds
are followed by a year of higher returns on long-term bonds, or if higher returns on long bonds are
followed by a year of higher returns on intermediate bonds, then persistence is negative. This pattern
also holds for all pairs of years.
This pattern holds for all pairs of two-, three- and four-year lag periods for which the z-statistic is
statistically significant. There is only one exception to the pattern: government funds in 1990-92, a
period for which the government bond fund z-statistic was opposite in sign from the corporate bond
funds and was not statistically significant.
Summary and Conclusions
This study presents the results of an analysis of fixed income mutual fund performance persistence
for government and corporate bond funds. The study applies the "winner-winner, winner-loser"
methodology developed by Goetzmann and Ibbotson (1994), Brown and Goetzmann (1995) and
Malkiel (1995) to test for short-term performance persistence in government and corporate bond funds
over the 10-year period from 1990 to 1999.
When new funds begin operating, they are included in the analysis so that a funds’ persistence is
ranked relative to all funds, new and continuing, operating in each time period. This appears to make it
more difficult to find statistical significance of persistence. Survivorship bias is minimal in this study
because each new fund is added to the database, merging funds continue to be included and funds that
cease to exist are separated for the analysis. The only bias is that, if any funds closed and did not
merge with an existing fund, that fund would not have returns to be included.
Government and corporate bond funds exhibit remarkable performance persistence. Performance
persistence is statistically significant for all but one of 18 one-year lag periods and the signs of the z-
statistics are the same for both categories of funds. Variation in the period-to-period signs of the z-
statistics appears to be attributable to changes in returns in the bond market. In particular, the data
show that if intermediate-term (long-term) bond returns are higher than long-term (intermediate-term)
bond returns for successive years, then the z-statistic is positive. By contrast, if higher returns on
intermediate (long) bonds are followed by a year of higher returns on long (intermediate) bonds, then
persistence is negative. This pattern of persistence also holds for all pairs of two-, three- and four-year
lag periods for which the z-statistic is statistically significant. There is only one exception to the
pattern: government funds in 1990-92, a period for which the government bond fund z-statistic was
opposite in sign from the corporate bond funds and was not statistically significant.
The combination of high levels of statistical significance and the remarkable consistency in the
relationship between the sign of persistence and bond market returns supports the conclusion that
returns on bond funds are strongly persistent, but that the nature of persistence (i.e., “normal” vs.
“perverse” persistence) is driven by changes in interest rates. As changes in market interest rates cause
market leadership to change (i.e., higher returns to intermediate- or long-term bonds), the nature of
persistence changes. Stability of market leadership is associated with positive persistence while
negative persistence is associated with changes in market leadership.
9. JOURNAL OF ECONOMICS AND FINANCE • Volume 30 • Number 3 • Fall 2006 355
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