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.
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.
Enhanced Call Overwriting - Groundbreaking Study Published in 2005Ryan Renicker CFA
- Lehman Brothers provides research on companies it also does business with, so its research may not be entirely objective. Investors should consider this and other factors when making investment decisions.
- The document discusses strategies for overwriting index call options, such as the S&P 500, to potentially enhance returns. It finds that enhanced strategies that adjust the level of overwriting based on implied volatility can further improve risk-adjusted returns compared to static overwriting strategies.
- Specifically, an enhanced strategy that overwrites with fewer calls when implied volatility is high, and more calls when implied volatility is low, performed best in backtests, outperforming simple overwriting strategies and the underlying indices on an absolute and risk-adjusted basis.
This document discusses several theories of stock market fluctuations:
1) The efficient market hypothesis which states prices reflect all known information and movements are due to new information.
2) The random walk hypothesis which claims fluctuations are totally random and unpredictable.
3) Behavioral economics perspectives which emphasize the role of human psychology in driving mass movements.
It also examines the work of Fama on market efficiency forms, Malkiel's refutation of fundamental and technical analysis, and Taleb's criticisms of attempts to explain movements with structured models. The document aims to statistically test and potentially refute these theories of market predictability.
Technical analysis, market efficiency, and behavioral financeBabasab Patil
Technical analysis uses patterns in stock prices and trading volume to predict future market movements and identify trading opportunities. The efficient market hypothesis states that stock prices instantly reflect all available information, making technical analysis ineffective. However, behavioral finance suggests psychological factors influence investor decisions and market anomalies exist, challenging the notion of complete market efficiency.
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
Volatility and Microstructure [Autosaved]Amit Mittal
Volatility emerges as a key effect of the price discovery and order execution processes in financial markets. Microstructure aspects, like non-synchronous trading, price effects of volatility, and volume effects of volatility, can influence volatility though they may be ignored at longer horizons. Measures of order flow, like probability of informed trading (PIN), have been developed to help explain volatility and the transmission of private information in markets.
Volatility in Indian Stock Market: A study to assess volatility, persistence ...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The document discusses the random walk theory and efficient market hypothesis. It defines the random walk theory as the idea that stock prices follow unpredictable and random paths, making it impossible to consistently outperform the market. The efficient market hypothesis suggests that stock prices instantly change to reflect all available public information, such that no investors can use information to earn above-average returns once transaction costs are considered. The document outlines different forms of the efficient market hypothesis based on the type of information reflected in stock prices and provides mixed evidence from empirical tests of the hypotheses.
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.
Enhanced Call Overwriting - Groundbreaking Study Published in 2005Ryan Renicker CFA
- Lehman Brothers provides research on companies it also does business with, so its research may not be entirely objective. Investors should consider this and other factors when making investment decisions.
- The document discusses strategies for overwriting index call options, such as the S&P 500, to potentially enhance returns. It finds that enhanced strategies that adjust the level of overwriting based on implied volatility can further improve risk-adjusted returns compared to static overwriting strategies.
- Specifically, an enhanced strategy that overwrites with fewer calls when implied volatility is high, and more calls when implied volatility is low, performed best in backtests, outperforming simple overwriting strategies and the underlying indices on an absolute and risk-adjusted basis.
This document discusses several theories of stock market fluctuations:
1) The efficient market hypothesis which states prices reflect all known information and movements are due to new information.
2) The random walk hypothesis which claims fluctuations are totally random and unpredictable.
3) Behavioral economics perspectives which emphasize the role of human psychology in driving mass movements.
It also examines the work of Fama on market efficiency forms, Malkiel's refutation of fundamental and technical analysis, and Taleb's criticisms of attempts to explain movements with structured models. The document aims to statistically test and potentially refute these theories of market predictability.
Technical analysis, market efficiency, and behavioral financeBabasab Patil
Technical analysis uses patterns in stock prices and trading volume to predict future market movements and identify trading opportunities. The efficient market hypothesis states that stock prices instantly reflect all available information, making technical analysis ineffective. However, behavioral finance suggests psychological factors influence investor decisions and market anomalies exist, challenging the notion of complete market efficiency.
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
Volatility and Microstructure [Autosaved]Amit Mittal
Volatility emerges as a key effect of the price discovery and order execution processes in financial markets. Microstructure aspects, like non-synchronous trading, price effects of volatility, and volume effects of volatility, can influence volatility though they may be ignored at longer horizons. Measures of order flow, like probability of informed trading (PIN), have been developed to help explain volatility and the transmission of private information in markets.
Volatility in Indian Stock Market: A study to assess volatility, persistence ...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The document discusses the random walk theory and efficient market hypothesis. It defines the random walk theory as the idea that stock prices follow unpredictable and random paths, making it impossible to consistently outperform the market. The efficient market hypothesis suggests that stock prices instantly change to reflect all available public information, such that no investors can use information to earn above-average returns once transaction costs are considered. The document outlines different forms of the efficient market hypothesis based on the type of information reflected in stock prices and provides mixed evidence from empirical tests of the hypotheses.
The document provides information on investment analysis, including definitions, methods, and concepts. It discusses two main types of analysis: fundamental analysis and technical analysis. Fundamental analysis examines basic company data like earnings, sales, and financial statements to determine a stock's intrinsic value. Technical analysis uses historical market data like prices and trading volumes to identify patterns that can predict future price movements. The document also covers the efficient market hypothesis, which proposes that stock prices reflect all publicly available information.
This document discusses incorporating news analysis into investment processes. It describes how news flows can be used to improve short-term risk assessments and condition risk estimates. Various data vendors that provide news analytics are also mentioned, as well as strategies for exploiting news signals, such as responding differently to "good" and "bad" news. Challenges with news-based strategies include defining events, assessing informational content, and managing holding periods.
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 presentation I gave in my investment class about paris trading. I implemented a experiment using R language to identify good pairs from S&P 100 universe. The algorithm is to perform ADF test on the spread of two random stocks and find out the pairs with stationary spread (co-integrated pairs). Pairs identification period is from 2010/11 to 2012/10, test period is from 2012/11 to 2013/12. Finally I got 33 pairs out of 4950 candidates, and I conduct a summary on the experiment result.
1) The document analyzes the stock Exxon Mobil (XOM) using Wyckoff analysis and point and figure charting. It identifies distribution and accumulation patterns in XOM and gold ETFs over several months.
2) Key Wyckoff points like buying climax, automatic reaction, and sign of weakness are identified on vertical charts of XOM and gold ETFs and related to counts on point and figure charts to determine price objectives.
3) The analysis finds XOM to be in a distribution pattern weaker than the market while gold ETFs show accumulation, with estimated reward-to-risk ratios above 3 times for potential trades.
There are three main forms of market efficiency:
1) Weak form - Prices reflect all past price information. Technical analysis is not useful.
2) Semi-strong form - Prices reflect all public information. Fundamental analysis is not useful.
3) Strong form - Prices reflect all public and private information. No analysis is useful.
The Arbitrage Pricing Theory (APT) is a multi-factor model that does not rely on a market portfolio like the Capital Asset Pricing Model (CAPM). The APT allows for multiple factors that influence returns while the CAPM only considers systematic risk relative to the market.
Technical indicators like moving averages and oscillators
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.
Columbia Business School - RBP MethodologyMarc Kirst
This paper describes a methodology called Required Business Performance (RBP) which uses current stock prices to imply expectations of future sales growth. Section 1 outlines the paper. Section 2 summarizes common approaches to estimating intrinsic firm value from dividends, free cash flows, book values or earnings. Section 3 explains how stock prices reflect both public and private information, and how expectations of key value drivers like future sales can be implied from current market prices.
Market efficiency survives the challenge from the literature on long-term return anomalies. Consistent with the market efficiency hypothesis that the anomalies are chance results, apparent overreaction to information is about as common as under-reaction, and post-event continuation of preevent abnormal returns is about as frequent as post-event reversal. Most important, consistent with the market efficiency prediction that apparent anomalies can be due to methodology, most long-term return anomalies tend to
disappear with reasonable changes in technique.
Originally published in 2005. Abstract: Over the years many commodity trading advisors, proprietary traders, and global macro hedge funds have successfully applied various trend following methods to profitably trade in global futures markets. Very little research, however, has been published regarding trend following strategies applied to stocks. Is it reasonable to assume that trend following works on futures but not stocks? We decided to put a long only trend following strategy to the test by running it against a comprehensive database of U.S. stocks that have been adjusted for corporate actions. Delisted companies were included to account for survivorship bias. Realistic transaction cost estimates (slippage & commission) were applied. Liquidity filters were used to limit hypothetical trading to only stocks that would have been liquid enough to trade, at the time of the trade. Coverage included 24,000+ securities spanning 22 years. The empirical results strongly suggest that trend following on stocks does offer a positive mathematical expectancy, an essential building block of an effective investing or trading system.
Pairs trading is a hedge fund strategy that involves buying one security and short selling another security that have historically moved together. When the spread between the two securities widens, the trader will take the opposite position, betting that the prices will converge again. Key aspects of pairs trading include avoiding data snooping to test for higher potential profits, using algorithms to select pairs based on similar historical state prices according to the Law of One Price, and ensuring the component prices are cointegrated with common nonstationary factors to justify the strategy. Bankruptcy risk in one security of a pair can also drive profits if it has a temporarily increasing probability versus the other security with a constant or decreasing probability.
Limited Attention, Information Disclosure, and Financial ReportingDavid Hirshleifer
We model firms' choices between alternative means of presenting information, and the effects of different presentations on market prices when investors have limited attention and processing power. In a market equilibrium with partially attentive investors, we examine the effects of alternative: levels of discretion in pro forma earnings disclosure, methods of accounting for employee option compensation, and degrees of aggregation in reporting. We derive empirical implications relating pro forma adjustments, option compensation, the growth, persistence, and informativeness of earnings, short-run managerial incentives, and other firm characteristics to stock price reactions, misvaluation, long-run abnormal returns, and corporate decisions.
The paper is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=334940
This document provides an introduction to trend following strategies for novice traders. It discusses how markets move based on the constant battle between bullish and bearish investors. When one group gains an advantage over the other, it can be difficult for the losing side to reverse the trend. The document advises traders to take an objective, neutral view of the market and look for major trends rather than trying to time every small movement. It emphasizes the importance of identifying clear support and resistance levels on charts in order to get into trades that have the greatest potential to yield large profits.
Mutual fund performance an analysis of monthly returns of an emerging marketAlexander Decker
1) The document analyzes the monthly performance of over 15 growth-oriented mutual funds on the Dhaka Stock Exchange of Bangladesh compared to benchmark returns.
2) Risk-adjusted performance measures like the Jensen, Treynor, and Sharpe ratios were used to evaluate performance. Most funds performed better on the Jensen and Treynor measures but not as well on the Sharpe ratio.
3) The analysis found that very few funds were well-diversified and reduced unique risk. Growth funds did not outperform in terms of total risk and did not provide the benefits of diversification and professional management that investors seek. Therefore, mutual funds cannot always outperform the market through their expertise.
The document provides risk disclosures and information about trading systems called Checkmate, Synergy, Fusion, and Interplay from Strategic Trading Systems, Inc. It discusses the high risks of commodity trading and that past performance results are hypothetical. It also summarizes the concepts and logic behind the Checkmate and Synergy trading systems, provides examples of trades from the systems, and evaluates their historical performance based on backtesting results.
Jennifer Kaplan, Product Marketing Manager, GovDelivery, discuss 9 rules for digital communications as part of GovDelivery's 2013 Digital Communications Tour.
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 discusses demonstrative pronouns, specifically 'this' and 'that'. 'This' refers to things that are near while 'that' refers to things that are farther away. Both 'this' and 'that' can be used for singular nouns to point out specific objects.
The document provides information on investment analysis, including definitions, methods, and concepts. It discusses two main types of analysis: fundamental analysis and technical analysis. Fundamental analysis examines basic company data like earnings, sales, and financial statements to determine a stock's intrinsic value. Technical analysis uses historical market data like prices and trading volumes to identify patterns that can predict future price movements. The document also covers the efficient market hypothesis, which proposes that stock prices reflect all publicly available information.
This document discusses incorporating news analysis into investment processes. It describes how news flows can be used to improve short-term risk assessments and condition risk estimates. Various data vendors that provide news analytics are also mentioned, as well as strategies for exploiting news signals, such as responding differently to "good" and "bad" news. Challenges with news-based strategies include defining events, assessing informational content, and managing holding periods.
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 presentation I gave in my investment class about paris trading. I implemented a experiment using R language to identify good pairs from S&P 100 universe. The algorithm is to perform ADF test on the spread of two random stocks and find out the pairs with stationary spread (co-integrated pairs). Pairs identification period is from 2010/11 to 2012/10, test period is from 2012/11 to 2013/12. Finally I got 33 pairs out of 4950 candidates, and I conduct a summary on the experiment result.
1) The document analyzes the stock Exxon Mobil (XOM) using Wyckoff analysis and point and figure charting. It identifies distribution and accumulation patterns in XOM and gold ETFs over several months.
2) Key Wyckoff points like buying climax, automatic reaction, and sign of weakness are identified on vertical charts of XOM and gold ETFs and related to counts on point and figure charts to determine price objectives.
3) The analysis finds XOM to be in a distribution pattern weaker than the market while gold ETFs show accumulation, with estimated reward-to-risk ratios above 3 times for potential trades.
There are three main forms of market efficiency:
1) Weak form - Prices reflect all past price information. Technical analysis is not useful.
2) Semi-strong form - Prices reflect all public information. Fundamental analysis is not useful.
3) Strong form - Prices reflect all public and private information. No analysis is useful.
The Arbitrage Pricing Theory (APT) is a multi-factor model that does not rely on a market portfolio like the Capital Asset Pricing Model (CAPM). The APT allows for multiple factors that influence returns while the CAPM only considers systematic risk relative to the market.
Technical indicators like moving averages and oscillators
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.
Columbia Business School - RBP MethodologyMarc Kirst
This paper describes a methodology called Required Business Performance (RBP) which uses current stock prices to imply expectations of future sales growth. Section 1 outlines the paper. Section 2 summarizes common approaches to estimating intrinsic firm value from dividends, free cash flows, book values or earnings. Section 3 explains how stock prices reflect both public and private information, and how expectations of key value drivers like future sales can be implied from current market prices.
Market efficiency survives the challenge from the literature on long-term return anomalies. Consistent with the market efficiency hypothesis that the anomalies are chance results, apparent overreaction to information is about as common as under-reaction, and post-event continuation of preevent abnormal returns is about as frequent as post-event reversal. Most important, consistent with the market efficiency prediction that apparent anomalies can be due to methodology, most long-term return anomalies tend to
disappear with reasonable changes in technique.
Originally published in 2005. Abstract: Over the years many commodity trading advisors, proprietary traders, and global macro hedge funds have successfully applied various trend following methods to profitably trade in global futures markets. Very little research, however, has been published regarding trend following strategies applied to stocks. Is it reasonable to assume that trend following works on futures but not stocks? We decided to put a long only trend following strategy to the test by running it against a comprehensive database of U.S. stocks that have been adjusted for corporate actions. Delisted companies were included to account for survivorship bias. Realistic transaction cost estimates (slippage & commission) were applied. Liquidity filters were used to limit hypothetical trading to only stocks that would have been liquid enough to trade, at the time of the trade. Coverage included 24,000+ securities spanning 22 years. The empirical results strongly suggest that trend following on stocks does offer a positive mathematical expectancy, an essential building block of an effective investing or trading system.
Pairs trading is a hedge fund strategy that involves buying one security and short selling another security that have historically moved together. When the spread between the two securities widens, the trader will take the opposite position, betting that the prices will converge again. Key aspects of pairs trading include avoiding data snooping to test for higher potential profits, using algorithms to select pairs based on similar historical state prices according to the Law of One Price, and ensuring the component prices are cointegrated with common nonstationary factors to justify the strategy. Bankruptcy risk in one security of a pair can also drive profits if it has a temporarily increasing probability versus the other security with a constant or decreasing probability.
Limited Attention, Information Disclosure, and Financial ReportingDavid Hirshleifer
We model firms' choices between alternative means of presenting information, and the effects of different presentations on market prices when investors have limited attention and processing power. In a market equilibrium with partially attentive investors, we examine the effects of alternative: levels of discretion in pro forma earnings disclosure, methods of accounting for employee option compensation, and degrees of aggregation in reporting. We derive empirical implications relating pro forma adjustments, option compensation, the growth, persistence, and informativeness of earnings, short-run managerial incentives, and other firm characteristics to stock price reactions, misvaluation, long-run abnormal returns, and corporate decisions.
The paper is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=334940
This document provides an introduction to trend following strategies for novice traders. It discusses how markets move based on the constant battle between bullish and bearish investors. When one group gains an advantage over the other, it can be difficult for the losing side to reverse the trend. The document advises traders to take an objective, neutral view of the market and look for major trends rather than trying to time every small movement. It emphasizes the importance of identifying clear support and resistance levels on charts in order to get into trades that have the greatest potential to yield large profits.
Mutual fund performance an analysis of monthly returns of an emerging marketAlexander Decker
1) The document analyzes the monthly performance of over 15 growth-oriented mutual funds on the Dhaka Stock Exchange of Bangladesh compared to benchmark returns.
2) Risk-adjusted performance measures like the Jensen, Treynor, and Sharpe ratios were used to evaluate performance. Most funds performed better on the Jensen and Treynor measures but not as well on the Sharpe ratio.
3) The analysis found that very few funds were well-diversified and reduced unique risk. Growth funds did not outperform in terms of total risk and did not provide the benefits of diversification and professional management that investors seek. Therefore, mutual funds cannot always outperform the market through their expertise.
The document provides risk disclosures and information about trading systems called Checkmate, Synergy, Fusion, and Interplay from Strategic Trading Systems, Inc. It discusses the high risks of commodity trading and that past performance results are hypothetical. It also summarizes the concepts and logic behind the Checkmate and Synergy trading systems, provides examples of trades from the systems, and evaluates their historical performance based on backtesting results.
Jennifer Kaplan, Product Marketing Manager, GovDelivery, discuss 9 rules for digital communications as part of GovDelivery's 2013 Digital Communications Tour.
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 discusses demonstrative pronouns, specifically 'this' and 'that'. 'This' refers to things that are near while 'that' refers to things that are farther away. Both 'this' and 'that' can be used for singular nouns to point out specific objects.
The document is a calendar of activities for the Career & Transfer Services center at Los Angeles Harbor College for March 2013. It lists workshops, university representatives visiting the campus each day, and other transfer-related events. Key information includes workshops on transfer topics from March 4-29, university visits from schools like CSUDH, CSULA, UCLA, and Marymount College, and career fairs on March 6th and 13th focused on STEM and CTE fields. The calendar also provides contact information for the center and encourages students to attend workshops and take advantage of resources for transferring.
Un gadget es un dispositivo pequeño y práctico con una función específica. Aunque no está incluido en el diccionario de la Real Academia Española, se usa en círculos tecnológicos para referirse a dispositivos electrónicos. Existen cuatro tipos de gadgets: widgets de escritorio, widgets web, widgets para móviles y gadgets físicos.
Acaso pueda haberse pensado en contratos simulados, o cuyas contraprestaciones resulten tan desfavorables para el futuro causante que hagan pensar que se trataba de una donación disimulada. Pero si en eso se estaba pensando, no hacía falta tratar el tema con rodeos y en la forma que se ha hecho, sino haberlo dicho claramente.
علُم هو إطار عمل ونموذج عام يمكن استخدامه ليساعد أي فرد أو مؤسسة على بناء مقرر إلكتروني ناجح، يمكن أن يتم استخدامه منفردا أو جنبا إلى جنب مع أي منهجيات ومعايير أخرى عند الرغبة في تأسيس وتوفير مقرر الكتروني على الإنترنت لأي مستوى تعليمي.
El documento presenta una guía para un tiempo de reflexión durante el Sábado Santo, invitando a la persona a examinar su vida a la luz de la pasión y muerte de Jesús. Se divide en tres partes: 1) dejarse mover por lo contemplado para seguir la voluntad de Dios, 2) identificar situaciones de desesperación e inmovilismo, y 3) iluminar rincones de dolor con esperanza y amor, siguiendo el ejemplo de María.
La definición más común de música es el arte de combinar sonidos en el tiempo, pero esta definición no explica qué es el arte y asume que algunas combinaciones son buenas y otras malas, lo que puede ser discutible.
Analyzing The Outperforming Sector In Volatile MarketPawel Gautam
This document provides background information on stock market volatility. It discusses what volatility refers to, factors that can influence volatility like economic conditions, news events, and investor psychology. It also covers different ways to measure and analyze volatility like standard deviation and average true range. The history and evolution of stock exchanges in India is briefly outlined to provide context for analyzing outperforming sectors in the volatile Indian market.
1) A study from 1993 found that strategies that bought stocks that performed well in the past 3-12 months and sold stocks that performed poorly generated positive returns over 3-12 month holding periods.
2) Further research confirmed the momentum effect held out of sample and in other markets, though not in Japan. Several potential explanations for the momentum effect were explored, including differences in stock drift rates and tax-related selling, but none fully explained it.
3) Behavioral factors like conservatism bias, overconfidence, and representativeness bias were also found to potentially contribute to the momentum effect by causing underreaction or overreaction to information among investors.
Chapter 06_ Are Financial Markets Efficient?Rusman Mukhlis
The document discusses the efficient market hypothesis (EMH) which states that financial markets are efficient and security prices reflect all available information. It provides an overview of the basic reasoning behind the EMH and examines empirical evidence that both supports and challenges the hypothesis. The evidence is mixed but generally supports the idea that markets are efficient.
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.
The document provides an overview of the efficient market hypothesis (EMH) and evidence related to it.
The EMH states that security prices fully reflect all available information. Empirical evidence is mixed but generally supports the idea. Studies show investment analysts and funds cannot consistently beat the market. Stock prices also reflect publicly available information.
However, some evidence contradicts the EMH. Small firms have abnormally high returns, and returns are higher in January. Market prices also sometimes overreact or are excessively volatile. New information is also not always immediately reflected in stock prices.
Overall, the EMH is a reasonable starting point but does not tell the whole story about financial markets. Behavioral factors and market
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.
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.
There is an abundant literature in finance on overconfidence, however there exists a different psychological trait well known to financial practitioners and psychologists. Which is optimism. This trait has received little attention. Our paper analyses the
consequences of optimism and pessimism on financial markets. We develop a general model of optimism/pessimism where M unrealistic informed traders and N realistic informed traders trade a risky asset with competitive market makers.
According to the EMH, stocks always trade at their fair value on stock exchanges, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the overall market through expert stock selection or market timing, and that the only way an investor can possibly obtain higher returns is by purchasing riskier investments.
* Corresponding author. Tel.: 773 702 7282; fax: 773 702 9937; e-mail: [email protected]
edu.
1 The comments of Brad Barber, David Hirshleifer, S.P. Kothari, Owen Lamont, Mark Mitchell,
Hersh Shefrin, Robert Shiller, Rex Sinquefield, Richard Thaler, Theo Vermaelen, Robert Vishny, Ivo
Welch, and a referee have been helpful. Kenneth French and Jay Ritter get special thanks.
Journal of Financial Economics 49 (1998) 283—306
Market efficiency, long-term returns, and behavioral
finance1
Eugene F. Fama*
Graduate School of Business, University of Chicago, Chicago, IL 60637, USA
Received 17 March 1997; received in revised form 3 October 1997
Abstract
Market efficiency survives the challenge from the literature on long-term return
anomalies. Consistent with the market efficiency hypothesis that the anomalies are
chance results, apparent overreaction to information is about as common as underreac-
tion, and post-event continuation of pre-event abnormal returns is about as frequent as
post-event reversal. Most important, consistent with the market efficiency prediction that
apparent anomalies can be due to methodology, most long-term return anomalies tend to
disappear with reasonable changes in technique. ( 1998 Elsevier Science S.A. All rights
reserved.
JEL classification: G14; G12
Keywords: Market efficiency; Behavioral finance
1. Introduction
Event studies, introduced by Fama et al. (1969), produce useful evidence on
how stock prices respond to information. Many studies focus on returns in
a short window (a few days) around a cleanly dated event. An advantage of this
approach is that because daily expected returns are close to zero, the model for
expected returns does not have a big effect on inferences about abnormal returns.
0304-405X/98/$19.00 ( 1998 Elsevier Science S.A. All rights reserved
PII S 0 3 0 4 - 4 0 5 X ( 9 8 ) 0 0 0 2 6 - 9
The assumption in studies that focus on short return windows is that any lag
in the response of prices to an event is short-lived. There is a developing
literature that challenges this assumption, arguing instead that stock prices
adjust slowly to information, so one must examine returns over long horizons to
get a full view of market inefficiency.
If one accepts their stated conclusions, many of the recent studies on long-
term returns suggest market inefficiency, specifically, long-term underreaction
or overreaction to information. It is time, however, to ask whether this litera-
ture, viewed as a whole, suggests that efficiency should be discarded. My answer
is a solid no, for two reasons.
First, an efficient market generates categories of events that individually
suggest that prices over-react to information. But in an efficient market, appar-
ent underreaction will be about as frequent as overreaction. If anomalies split
randomly between underreaction and overreaction, they are consistent with
market efficiency. We shall see that a roughly even split between apparent
overreaction and underreact ...
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1) Studies of long-term stock returns show about as much evidence of overreaction to information as underreaction, which is consistent with the prediction of market efficiency.
2) Most anomalies in long-term returns tend to disappear or become marginal when using different models for expected returns or statistical techniques, suggesting they can reasonably be attributed to chance rather than inefficiency.
This document summarizes a statistical arbitrage strategy that evaluates mean reversion in stock prices over time. It describes the strategy's assumptions that stock prices temporarily diverge from their equilibrium relative to the market before reverting. The experiment uses S&P 500 stock data to calculate daily returns, correlations, betas and residuals over rolling 60-day windows. When residuals exceed +/-2 standard deviations, positions are taken assuming reversion will occur. While backtested returns are appealing, live trading realities like transaction costs and limited share availability would likely reduce profits versus this theoretical analysis.
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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.
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.
This document summarizes a paper that examines behavioral finance versus the efficient market hypothesis and how they can be used to facilitate capital gains. It discusses how behavioral finance incorporates psychological factors that can lead to market inefficiencies and opportunities for gain, unlike the efficient market theory. The paper will analyze works supporting both theories to argue that incorporating behavioral finance can help assess if price movements reflect real changes in company value or irrational investor behavior.
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.
1) The document discusses the work of Eugene Fama, Lars Peter Hansen, and Robert Shiller, who have developed new methods for studying asset prices and revealed important regularities about how asset prices behave.
2) While it is impossible to predict short-term movements in asset prices, their research showed it is possible to foresee broad movements in prices over longer periods like 3-5 years.
3) Their work helped show that asset prices fluctuate more than can be explained by expected dividends, indicating prices may at times reflect irrational investor behavior rather than always being rationally valued based on expected payments.
Similar to Wang market confidence and momentum (20)
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1. Market confidence and momentum
Kevin Q. Wang and Jianguo Xu
Abstract
We develop a model in which equity fundamentals are subject to random
shocks. Investors learn about the shocks through noisy information. The model
shows that momentum is more pronounced in a more confident market. We
conduct tests of the prediction and find supportive evidence. Specifically, we find
that market volatility negatively predicts momentum profits. This evidence
supports the prediction since a more volatile market is likely to be less confident.
The model also predicts that idiosyncratic shocks, not systematic shocks, produce
momentum. This is consistent with empirical findings from a number of studies.
1
2. Momentum refers to the phenomenon that an arbitrage portfolio comprised of a long
position on stocks that perform better and a short position on stocks that perform worse during
the past 3-12 months (the ranking period) earns a positive profit during the next 3-12 moths (the
holding period). This phenomenon, first documented by Jagadeesh and Titman (1993), has been
confirmed in many later studies. It has been found that momentum extends to later time periods
(Jagadeesh and Titman (2001)), exists in industry portfolios (Moskowitz and Grinblatt (1999)),
value and size portfolios (Lewellen (2002)), and extends to markets other than the United States
(Rouwenhorst (1998)). It has also been found that equity indices also exhibit momentum
(Richards (1995, 1997), Chan, Hameed, and Tong (2000)). The momentum profit is “abnormal”
because it cannot be explained by known risk factors. Fama and French (1996) find that
momentum is the only anomaly that cannot be explained by their three-factor model. Momentum
is particularly annoying because the finding that historical returns help predict future returns
implies that financial markets is not efficient even in the weak form.
Time-varying return patterns of winners and losers in a momentum strategy are
impressive. Figure 1 shows the performance of a 6-6 momentum strategy for the 24 months after
ranking.1 Figure 2 depicts the momentum strategy in a slightly longer time horizon, including
pre-ranking, ranking, and post-holding periods. Together figures 1 and 2 suggest that the winner
(loser) stocks experience positive (negative) shocks. The price changes in the ranking period are
most impressive, suggesting that most of the shocks are incorporated into prices during the
ranking period. For the 6-6 momentum strategy and for the full sample of 1926-2007, the
ranking period winner-loser return spread is about 84% while the holding period return spread is
about 4%. Thus, about 95% of the price adjustments (run-up or run-down) have occurred during
1
A J-K momentum strategy refers to a strategy that ranks past J month returns and hold the portfolio for K months.
Throughout this paper we focus on a 6-6 strategy unless otherwise stated.
2
3. the ranking period, with about 5% left for the holding period which gives rise to the appearance
of momentum profits. It is clear that in terms of the longer time window, momentum profits over
the holding period are of minor magnitude, relative to the huge winner-loser performance
difference over the ranking period. Nonetheless, monthly profit of about 0.7% is economically
significant and commands an explanation.
In this paper, we develop a model that is motivated by the evidence. We aim to
understand the roles of random shocks and investor learning in generating momentum profits.
We organize the thoughts into a model with two risky assets whose payoffs are subject to
random shocks. The representative investor learns about the shocks via noisy information. The
learning is not immediate due to noises in the information, which leads to gradual adjustment of
asset prices and appearance of underreaction. The model produces predictions about sources of
momentum and variations of momentum profits in different market conditions.
First, only idiosyncratic shocks lead to momentum. Systematic shocks that affect all
stocks do not produce momentum. Intuitively, systematic shocks are shared by all stocks and
thus do not affect cross sectional stock returns beyond risk loadings. Since momentum cannot be
explain by risk factors, it cannot be due to systematic shocks. This prediction is consistent with
earlier findings (e.g., Grundy and Martin (2001)) that momentum profit is stronger when stocks
are sorted on idiosyncratic past returns. Sorting on idiosyncratic returns better captures
idiosyncratic shocks than sorting on gross returns. This prediction is also consistent with the
finding of Hou, Peng, and Xiong (2005) that momentum is more pronounced among high R-
square stocks. Stocks with higher R-squares are likely to have experienced larger idiosyncratic
shocks which generate a larger momentum payoff.
3
4. Second, the model predicts that momentum should be more pronounced when market
confidence is higher. The intuition is that in a more confident market, investors react less to new
information, including information about random shocks. Therefore, shocks are incorporated into
price slower, which implies larger momentum profit. We empirically test this prediction and find
supportive evidence. Specifically, we find that momentum is more pronounced when volatility is
lower. Since lower volatility implies higher market confidence, this evidence supports our
prediction that a more confident market exhibits less momentum.
This finding that market confidence negatively predicts momentum can be related to the
finding of Cooper, Gutierrez and Hammed (2004) that momentum depends on market states.
They find that momentum only exists in “UP” markets. We control for market states and find
that the effect of volatility persists after controlling for market status. The explanation that
Cooper, Gutierrez and Hammed propose for their finding is that investors become more
overconfident after good market performance. Although our evidence does not contradict the
behavioral explanations, we argue that it is not necessary to introduce overconfidence to explain
momentum. After random shocks, especially as large as shown above, it is rational that investors
gradually update their opinions. As shown by Leroy (1973) and Lucas (1978), unforecastability
of asset returns is neither a necessary nor a sufficient condition of economic equilibrium.
We emphasize that the appearance of momentum exists from the stand point of
econometricians who have information that is not available to investors. From the perspective of
real time investors who have to base their decisions on information available to them, they
cannot predict momentum and contrarian in equity prices. Thus, there is no tradable strategy
available for them (Lewellen and Shanken (2002)). Investors who trade on beliefs about
fundamentals cannot exploit momentum. In addition, momentum is by nature a statistical
4
5. strategy which is not available to individual investors who usually only hold a small number of
stocks in their portfolios. The remaining question is whether portfolio managers can benefit from
a momentum strategy. Even for professional traders or money managers, there are at least three
unfavorable features of a momentum strategy: 1) high turnover and transaction costs, 2) negative
skewness, and 3) costly and risky short selling. Furthermore, it is useful to emphasize that
momentum trading is not a risk-free arbitrage opportunity. For example, it is not profitable
during the 1990-1995 and 2001-2003 periods.
Our model differs from existing rational explanations for momentum. Berk, Green and
Naik (1999) propose that momentum arises from the persistence in expected returns. Johnson
(2002) argues that since the growth rate risk carries a positive price, high growth firms tend to
have high expected returns. Sorting on past returns tends to sort firms by recent growth rates.
Momentum arises because winners have higher expected returns than losers. In theory our model
does not deny expected return as an alternative explanation for momentum. However, it is
difficult to attribute the extremely high (low) returns of the winner (loser) portfolio during the
ranking period to expected returns. Even a casual look at Figure 2 suggests that non-expected
shocks are at work. We explore unexpected shocks as a source of momentum in this study.
The model also differs from existing behavioral explanations for momentum. Daniel,
Hirschleifer, and Subrahmanyam (1998) develop a model in which investors are overconfident in
private information and this underreact to public information which produces momentum.
Barberis, Shleifer, and Vishny (1998) allow investors to suffer from the cognitive biases of
representativeness and conservatism. Investors in their model initially underreact and then
overreact when a pattern is observed in data. Hong and Stein (1999) allow investors to focus on a
subset of information. In their model, “newswatchers” focus on private information about future
5
6. fundamentals and ignore price history. “Momentum traders” look at price history only. Both
types of investors in their models have bounded rationality in the sense that they fail to take all
information into consideration. In comparison, we introduce random shocks to asset fundamental.
We assume investors are rational Bayesian learners with limited and imperfect information to
learn the true payoffs.
Our model and evidence is in spirit consistent with the arguments of Chan, Jegadeesh,
and Lakonishok (1996) that is momentum is due to slow travel of information. We contend that
instead of “slow travel of information”, momentum may be explained by “slow adjustment of
opinions”. Although seemingly identical, we contend that slow adjust of opinions is more
appealing because of two reasons. First, slow travel of information only applies to private
information. Public information reaches all information receivers immediately in today’s
financial markets. At the same time, whether momentum is due solely to private information is
unclear. In contrast, slow adjustment of opinions applies to both private and public information.
Second, our model can explain a set of accumulated evidence about momentum as discussed
above. More importantly, our model produces a new prediction that is empirically confirmed.
Therefore, our model is better in the sense of being able to explain more evidence and producing
new empirically testable predictions.
A comment on the difference between opinion and information is at demand. Varian
(1989) vividly asks: when someone conveys a probability belief to another agent, what should
the other agent respond? If he updates his posterior belief just as my probability belief, he has
interpreted my probability belief as information, or credible. If he does not update his posterior
at all, then he has interpreted my belief as opinion, or incredible. Very likely, he will partially
adjust his posterior based on my probability belief. In that case, he interprets my belief as
6
7. partially information and partially opinion. So the critical question is: when one makes a
pronouncement, is he conveying information or just conveying his opinion. Or more precisely,
how much of the pronouncement is information and how much is opinion? We can ask the same
question for any announcement. For any announcement, by a company or by a statistics bureau,
one can ask how much he can trust this announcement and how much he should update his
beliefs. Obviously all announcements, no matter how objective or numerical they seem to be, are
based on one set of methodology. The methodology itself is a set of opinions. Besides, even if
the methodology may seem to be very objective, there are always space for subjective
interpretations and discretionary judgments. At the end, we are reaching the sense that there
exists no “pure” information.
The rest of the paper is structured as follows. In Section I, we construct and solve the
model. Implications for price underreaction and momentum are derived. Testable predictions are
discussed. In Section II, we present empirical findings from our tests. Section III concludes.
I. The Model
A. Model structure
Consider a market with one safe asset and two risky assets. The safe asset pays a fixed
interest income at the end of each period. Investors can buy or sell the safe asset infinitely. For
simplicity, the interest rate is assumed to be zero. The risky assets, A and B, pay stochastic
payoffs, θ = (θ A ,θ B )T .
There are two periods and three times, t=0, 1, 2. At time 0 risk neutral investors enter the
market endowed unit of each of the risky assets. At time 1 one signal is observed for each of
7
8. risky assets about its payoff. Investors trade to reach a new equilibrium. At time 2 the asset is
liquidated after realizing the payoff.
The risky assets are subject to a random shock at time 1, after which the payoff is
x = θ + η , η = (η A ,η B )T . At time 1 a signal about each asset, s = ( s A , s B )T , is observed
s = x+ε , (1)
where ε represents a vector of noises. Plugging x into (1) give
s = θ +η + ε . (2)
Random variables, θ , η , and ε , are independent. The correlation between these two asset
payoffs is ρ = corr (θ A ,θ B ) = corr (η A ,η B ) . The noises are independent. All variables are
⎛µ ⎞
normally distributed: θ ~ N ( µθ , Σθ ) , η ~ N (0, Ση ) , ε ~ N (0, Σ ε ) , where µθ = ⎜ A ⎟ ,
⎜µ ⎟
⎝ B⎠
⎛ σ θ2 ρσ θ2 ⎞ ⎛ σ2 ρσ η2 ⎞ ⎛σ 2 0 ⎞
Σθ = ⎜ ⎟ , Ση = ⎜ η 2 ⎟ , Σε = ⎜ ε ⎟
⎜ ρσ 2 σ θ2 ⎟ ⎜ ρσ σ η2 ⎟ ⎜ 0 σ 2 ⎟ . The precisions of these variables are
⎝ θ ⎠ ⎝ η ⎠ ⎝ ε ⎠
denoted by τ θ = Σθ 1 , τ η = Ση 1 , and τ ε = Σ ε 1 , respectively.
− − −
Remark 1: The model is set up in its simplest form. Investors can buy and sell the safe
asset infinitely thus there is no wealth effect on prices of the risky assets. Two is the minimum
number of risky assets required to study cross sectional variations in risky returns such as
momentum. Two is also the minimum number of periods to study time series price behavior.
One way to interpret the liquidation at time 2 is that investors receive a decisive signal without
noise about the payoff. Extending the model into multiple periods with multiple assets and
assume investors observe a noisy signal each period does not add new insights. Investors are
8
9. assumed to be risk neutral because in this paper we do not consider risk aversion as an
explanation for price momentum or reversal. We do not model asset heterogeneity, therefore we
assume θ A and θ B , η A andη B , ε A and ε B have identical variances, respectively.
Remark 2: The timing of the model is as below. At time 0 the nature makes its first move
to pick values for θ A and θ B . At time 1 the nature makes its second move to pick values for η A ,
η B , ε A , and ε B . Investors know the joint distribution but not the value of these variables.
B. Equilibrium
For normal distribution, the posterior belief after observing the signals is given by the
standard formula (see, e.g., DeGroot (1970)).
E ( x | s ) = (τ x + τ ε ) −1 (τ x µθ + τ ε s )
Plug in (1) and rearrange, we have:
E ( x | s ) = µθ + (τ x + τ ε ) −1τ ε ~ ,
s (3)
where ~ = θ + η + ε − µθ is the information “surprises”.
s
From equation (3) it is clear that the error in initial expectation, θ − µθ , and the shock to
the payoff, η , are equivalent in the belief updating formula. This is not surprising since an error
in initial beliefs constitute a “shock” when revealed. We could have assumed that investor’s
beliefs are rational in the sense that this difference equals zero. This assumption does not change
the model.
Expanding (3) gives:
⎛ E ( x A | s) ⎞ ⎛ µ A ⎞ ⎛ ks A + (1 − k ) sB ⎞
⎜ E ( x | s ) ⎟ = ⎜ µ ⎟ + δ ⎜ (1 − k ) s + ks ⎟ ,
⎜ ⎟ ⎜ ⎟ ⎜ ⎟ (4)
⎝ B ⎠ ⎝ B⎠ ⎝ A B⎠
9
10. (1 + ρ )σ x
2
(1 − ρ 2 )σ x + σ ε2
2
where δ = , k= are constants.
(1 + ρ )σ x + σ ε
2 2
(1 − ρ )σ x + σ ε2 + ρσ ε2
2 2
Risk neutrality implies that equilibrium prices equal the expected payoff at times 0 and 1.
⎛ PA ⎞ ⎛ µ A ⎞
0
⎜ 0 ⎟ = ⎜ ⎟. (5)
⎜P ⎟ ⎜µ ⎟
⎝ B ⎠ ⎝ B⎠
⎛ PA ⎞ ⎛ µ A ⎞
1
⎛ ks + (1 − k ) s B ⎞
⎜ 1⎟ = ⎜ ⎟+δ⎜ A⎜ (1 − k ) s + ks ⎟ . (6)
⎜P ⎟ ⎜µ ⎟ ⎟
⎝ B⎠ ⎝ B⎠ ⎝ A B⎠
At time 2, the payoffs are realized and there is no uncertainty,
⎛ PA2 ⎞ ⎛θ A + η A ⎞
⎜ 2⎟=⎜
⎜ P ⎟ ⎜θ + η ⎟ . ⎟ (7)
⎝ B⎠ ⎝ B B⎠
We define return as the dollar price change during a time period, rather than the
percentage price change. This allows us to avoid dealing with the division operation in
calculating percentage returns. Equations (5), (6), and (7) jointly give
⎛ rA ⎞
1
⎛ ks + (1 − k ) sB ⎞
⎜ 1⎟ =δ⎜ A
⎜ (1 − k ) s + ks ⎟ ,
⎟ (8)
⎜r ⎟
⎝ B⎠ ⎝ A B⎠
⎛ rA ⎞ ⎛θ A + η A − µ A ⎞ ⎛ ks A + (1 − k ) sB ⎞
2
⎜ 2⎟=⎜
⎜ r ⎟ ⎜θ + η − µ ⎟ − δ ⎜ (1 − k ) s + ks ⎟ .
⎟ ⎜ ⎟ (9)
⎝ B⎠ ⎝ B B B⎠ ⎝ A B⎠
Notice worthy is that in this model all returns are unexpected. Risk neutrality implies
zero risk premium and thus zero expected returns. Returns are driven by changes in expected
payoff which constitutes surprises to the market.
10
11. C. Price underreaction
Equation (4) describes the revision in expectations as a scaled weighted average of the
information surprises. The weights on the signal of the asset and the other asset are k and 1-k,
(1 − ρ 2 )σ x + σ ε2
2
k= . Because (1 − ρ 2 )σ x + σ ε2 ≥ σ ε2 ≥ ρσ ε2 , with the equalities hold when
2
(1 − ρ )σ x + σ ε + ρσ ε
2 2 2 2
ρ = 1 , we have k ∈ [0.5, 1] . So the weight on an asset’s own signal is at least as large as the
weight on the other asset’s signal. In the special case of perfectly correlated payoffs ( ρ = 1 ), the
weights on both signals are equally split into one half. Another special case is when the assets are
independent ( ρ = 0 ). In this case k=1, which means that the beliefs about the two assets are
updated independently on its own signal without taking into consideration the other signal. In
this case the model degenerates into a single risky asset model. We assume that ρ ∈ [0,1] .
Although it is theoretically possible for ρ to be negative, equity returns are usually positively
correlated. We summarize this discussion in the following observation.
OBSERVATION 1: The weight investors put on asset’s own signal is at least one half. It
decreases with asset correlation.
(1 + ρ )σ x
2
The scaling factor, δ = , is between 0 and 1. The implication is that if the
(1 + ρ )σ x + σ ε2
2
initial expectation does not equal the true payoff at time 1, investors only partially adjust their
expectations after observing the signal. This is a natural result of Bayesian updating. It is also
straightforward that δ increases with ρ and decreases with σ ε2 . The intuition is simple. If the
assets are more correlated, essentially there is less variation in the payoff and the signals provide
11
12. better information about the assets. And when the signals are less noisier, investors put more
weight on the signal and less weight on the prior expectation. In the special case that the signals
are completely revealing ( σ ε2 = 0) , the scaling factor reaches unity.
OBSERVATION 2: If the initial expectations do not equal the true payoff, investors partially
adjust their expectations toward the true payoff. The speed of adjustment increases with asset
correlation and information quality.
An obvious yet important message from equation (4) is that if there is no difference
between investors’ initial expectation and the true payoff, on average investors’ posterior beliefs
will not be biased. That is, erroneous initial expectations or shocks are necessary for price
underreaction in this model. This partial adjustment of expectations toward the true value is
rational in the Bayesian sense. The deviation in posterior expectation from true asset value
comes from the initial error in expectations, which is partially corrected by the signal, or from
the random shock to the fundamental, which investors partially learn from the signal.
It is useful to compare this model to behavioral models. In this model we allow errors in
initial beliefs or random shocks. We assume investors are rational Bayesian learners with limited
and imperfect information to learn the true payoffs. In comparison, Daniel, Hirschleifer, and
Subrahmanyam (1998) allow investors to be overconfident in their own private information and
give themselves too much credit (too less blame) in case of success (failure). Barberis, Shleifer,
and Vishny (1998) allow the representative investor to suffer from the cognitive biases of
representativeness and conservatism. Hong and Stein (1999) allow investors to focus on a subset
12
13. of information. In their model, “newswatchers” focus on private information about future
fundamentals and ignore price history. “Momentum traders” look at price history only. Both
types of investors in their models have bounded rationality in the sense that they fail to take all
information into consideration.
Essentially, we allow investors to have errors in their expectations but do not allow
investors to make mistakes when using the Bayesian method. I also allow investors have random
errors in their expectations. But it is not easy to take advantage of such possible existence of
errors. This is completely consistent with the argument of Grossman and Stiglitz (1980). In the
end, market efficiency is not that price is correct, but no free lunch. Market efficiency does not
preclude profit from unique insight and hard work.
D. Momentum
Price underreaction is not equivalent to momentum. The former concerns the time series
autocorrelation of asset or portfolio returns. The later is a cross sectional phenomenon: past
winners continue to outperform past losers for some period of time. To have momentum, we
introduce cross sectional differences in the assets. Consider a shock to one of the stock, A.
Without loss of generality, assume η A > 0 . The expected shock to stock B is E (η B ) = ρη A .
Without loss of generality, assume that initial expectation is not biased thus the shock is the only
source for different payoff. On average stock A will be the winner and stock B will be the loser
during period 1. A momentum strategy of buying A and selling B earns expected profit of
M = [ E ( PA2 ) − E ( PA )] − [ E ( PB2 ) − E ( PB )] .
1 1
Substitute in equations (9) and organize, we have
13
14. σ ε2
M = (1 − ρ )η A . (10)
(1 − ρ )σ x + σ ε2
2
Equation (10) says that momentum profit is jointly determined by the shock, (1 − ρ )η A ,
σ ε2
and the underreaction to shock, . To understand the first term in equation (10),
(1 − ρ )σ x + σ ε2
2
(1 − ρ )η A , we decompose the total shock η A into ρη A and (1 − ρ )η A . The former represents the
part that is shared by asset B because of the inter-asset correlation, and the latter represents the
part that is idiosyncratic to asset A. From now on we label the former as systematic shock and the
latter idiosyncratic shock. Equation (10) says that only the idiosyncratic shock help generates
momentum. Intuitively, systematic shock affects both assets thus does not help generate
momentum, which is a cross sectional phenomenon. This discussion is supported by the finding
of Grundy and Martin (2001) that sorting on idiosyncratic returns produces larger momentum
profit than sorting on raw returns. This is because idiosyncratic returns better capture
idiosyncratic shocks.
σ ε2
The second term determines the size of momentum for given
(1 − ρ )σ x + σ ε2
2
idiosyncratic shock. More intuition can be obtained by decomposing the variation of asset payoff
σ x2 into ρσ x2 and (1 − ρ )σ x2 . The former captures the variation that is shared by both assets,
while the latter captures the variation that is idiosyncratic to individual assets. Therefore, we can
label ρσ x as the “systematic” variance while (1 − ρ )σ x the “idiosyncratic” variance. The second
2 2
term in equation (10) says that given an idiosyncratic shock, the strength of momentum is
determined by the ratio of the variance of noise to that of noise plus idiosyncratic asset payoff.
14
15. Several observations follow from equations (10), which makes the model subject to
empirical scrutiny. First, expected momentum profit increases with the idiosyncratic shock,
(1 − ρ )η A . If shocks to asset fundamentals can be directly identified, we should expect
momentum to be related to idiosyncratic shocks. However, this might not be possible for most
cases due to 1) shocks may simply be unobservable, and 2) the value implication of shocks may
be difficult to calculate. When shocks cannot be identified, we can infer shocks from price
changes. To the extent that more idiosyncratic shocks lead to more dispersed stock returns, we
expect momentum to be more pronounced when individual stock returns are more dispersed. In
equation (10) dispersion of stock returns is measured by the parameter ρ . It is obvious from
equation (10) that momentum decreases with ρ . This is consistent with the finding of Hou, Peng
and Xiong (2005) that momentum is more pronounced for stocks with low R-square, the returns
of which is more dispersed.
Second, momentum decreases with the variance of asset payoff σ x . Since σ x inversely
2 2
measures the confidence of prior expectations, momentum increases with market confidence.
Intuitively, when investors are more confident in their initial beliefs, they adjust their
expectations less to new information, leaving more space for momentum profit. This argument
share some similarity with the one based on overconfidence. Investors may become too confident
in their initial beliefs if they are overconfident in private information. However, overconfidence
is not necessary for this to happen.
Third, momentum profit decreases with information quality. When information is more
precise, investors learn faster and the shock is incorporated into prices faster. In the extreme case
of perfectly revealing information, σ ε2 = 0 , there will be no momentum. This implication
15
16. predicts that momentum should be more pronounced in a market when information quality is
lower. However, a caution needs to be exerted when drawing this prediction. In such a market
investors also tend to be less confident in their prior expectations because they do not have high
quality information. Since weak prior confidence lead to less momentum, the overall effect is
unclear.
So for a market in which stocks move more synchronously, momentum should be less
pronounced. If a bear market is more synchronous than a boom market, momentum should be
more pronounced in a boom market. If synchronicity displays a U shape with market returns,
momentum profit should display an inverse U shape with market returns. The evidence that
Japan does not have momentum and that in Japan stocks are very synchronous is consistent with
this argument.
II. Empirical evidence
The model produces the novel prediction that momentum should be more pronounced
when the market is more confident. In this section we empirically test this prediction in two steps.
We split the stock market of the United States from 1926 to 2007, for which period we have data,
into 5-year periods. We calculate the volatility and momentum for each subperiods and examine
whether there exists a relationship between volatility and momentum. Essentially, we consider
the subperiods as “markets” and look for volatility-momentum correlation among these
“markets”. Second, for each month, I calculate the 6-month momentum profit and correlated this
profit to the volatility prior to the formation of the momentum portfolio. The model predicts that
higher volatility lead to lower momentum profits.
16
17. A. Data and method
The data for the study are all NYSE and AMEX stocks listed on the CRSP monthly file.
Our sample period covers January 1926 to December 2007. Stocks are sorted at the end of each
month t into deciles based on their prior six month, t-5 to t, returns. The test-period profit is
calculated for t+2 to t+6. Because we need 6 months to calculate past and future returns, our first
momentum portfolio is for June 2006 and last momentum portfolio is for June 2007. We follow
the usual practice to one month between the ranking and holding period. We define each
momentum portfolio as long in the prior six month winners (highest decile) and short in the prior
six-month losers (lowest decile). We exclude stocks with a price at the end of the formation
period below $1 to mitigate microstructure effects associated with low-price stocks.
B. Raw momentum
Table I reported the momentum profit for the whole sample period of 1927-2006. To
compare with other studies, I also report momentum for three subsamples: 1927-1964, 1965-
1989, 1990-2006. The subsamples are selected as before, after, and the same as the 1965-1989
sample in the original Jagadeesh and Titman (1993) study.
For the whole sample period of 1926-2007, the average monthly profit is 0.65% per
month. This profit is highly significant. Momentum is more pronounced during the 1965-1989
period (1% per month) than during the earlier 1926-1964 period (0.43% per month) and the later
1990-2007 period (0.66% per month).
It is notice worthy that a momentum strategy has negative skewness for the whole sample
and all three subsamples. Generally, losing portfolios are more positively skewed while winning
portfolios are more negatively skewed. The momentum portfolio is significantly negatively
17
18. skewed for the whole sample and all three subsample (insert evidence about significance of
skewness). This finding is consistent with the finding of Harvey and Siddique (2000), The
momentum strategy is especially negatively skewed during the early period of 1926-1964. To the
extent that investors like positive skweness and do not like negative skewness for their portfolios,
the negative skewness of the momentum strategy helps explain why momentum profit is not
arbitraged away.
Figure 3 plots the accumulative momentum profit from July 1926. Upward slopes suggest
positive momentum profit while downward slopes suggest negative momentum profit. As can be
seen from this picture, momentum is positive for most of the times. However, momentum is
negative for the periods of 1930-1940, 1991-1994, 2000-2004, etc.
Figure 3 also plots market volatility during the 6-month portfolio ranking periods. The
clear pattern is that when volatility shoots up, momentum profit attenuates or even become
negative. For example, during the 1930s, market volatility is very high and momentum profit is
negative. The same pattern shows up in early 1990s and 2000s. This pattern provides a primitive
support to our prediction that momentum increases with market confidence.
C. Momentum across decade
Table II reports momentum within decades together with average market return and
volatility during each decade. The idea is to consider the market in different decades as different
markets. We aim to identify a correlation between volatility and momentum between these
“different markets”. Because Cooper et al (2004) find that market states help predict momentum,
we also calculate the average market return during each decade. Momentum profit is strong in
1940s, 1950s, 1960s, 1980s, and 1990s. It does not exist during 1930s and 2000s. For 1920s and
18
19. 1970s, momentum profit is significant at the 5% but not the 1% level. Table II also report the
simple average of momentum and non-momentum periods. The cutting is based on both 5% and
1% significance levels. Either case, the evidence suggests that momentum periods have higher
market returns and lower volatility. The former is consistent with Cooper et al (2004). The latter
supports our prediction.
D. Momentum conditional on market volatility
Encouraged by primitive support of our prediction, this section we conduct a more
systematic test of our prediction that market volatility negatively predicts momentum. For each
month, market volatility is calculated using daily returns during the 6-month ranking period. We
rank months on this volatility measure into quintiles. Quintile 1 includes the lowest volatility
months and quintile 5 includes the highest volatility months. Table III reports the average
momentum profits conditional on this ranking. The results are reported for the full sample of
1926-2007 and for the three subsamples of 1926-1964, 1965-1989, and 1990-2007.
The clear pattern is that momentum profit is positive and significant for low volatility
months, quintiles 1, 2, and 3. For quintiles 4 and 5, the momentum profit is insignificant, positive
or negative. The difference between quintiles 1 and 5 is highly significant. In other words,
momentum is positive and significant for 60% of months with lower volatility. Momentum profit
does not exists for the other 40% months with higher volatility. The decreasing of momentum
from low volatility to high volatility months is nearly monotonic. The result is robust across
subsamples.
Cooper et al (2004) finds that market states, defined as aggregate market returns during
the past 36 months, help determine the existence of momentum. They define “UP” markets as
19
20. months for which the past 36 month market return is positive and “DOWN” markets as months
with negative past 36-month market returns. To disentangle the effects of market states and
volatility on momentum, we sort months based on market volatility into low, medium, and high
volatility months. We independently sort months based on markets states into bad, medium and
good. Then we calculate momentum profits for the 9 groups jointly determined by market state-
volatility ranks. Table IV reports the results.
Several observations emerge from Table IV. First, after controlling for market states,
higher volatility still leads to lower momentum profit. Second, in the highest volatility group,
momentum does not exist no matter the market state is bad, medium or good. Third, after
controlling for volatility, better market state does not lead to better momentum profit. In fact, in
low volatility months higher market return leads to lower momentum. In comparison, in high
volatility months higher market returns lead to higher momentum. This reverse of pattern can
possibly due to the possibility that market state is a partial proxy for market confidence. In a
confident market (low volatility), market states does not capture additional variation in
confidence. In contrary, in a unconfident market (high volatility), market states capture further
variation in confidence.
Table IV is not conclusive due to the opposite effect of market state on momentum when
volatility is low versus when volatility is high. Table V conduct a regression analysis of
momentum on market state and volatility. Individually, market state positively predict
momentum while volatility negatively predicts momentum. The former is consistent with the
result of Cooper et al (2004). The latter is consistent with the evidence in Tables II, III and IV.
In the full sample of 1927, market state is not significant after controlling for market volatility.
On the other hand, market volatility continue to be significant after controlling for markets states.
20
21. Regression on the subsamples of 1926-1964, 1965-1989, 1990-2007 suggests that the
insignificance of market state is mainly due to the early sample period of 1926-1964. Recall that
according to Table II, momentum is negative and significant at the 10% significance level.
Overall, Tables IV and V suggests that market volatility is a better proxy for market confidence
than market state.
21
22. III. Concluding Remarks
We develop a model that aims at idiosyncratic shocks and investor learning. We
emphasize that these are two important factors, which jointly contribute to the momentum effect.
On the one hand, there exist random shocks to asset fundamentals. The shocks have an
idiosyncratic component. Alternatively, errors in investors’ expectation serve the same function.
On the other hand, investors learn about fundamentals via noisy signals. The noise prevents the
shocks from being incorporated into price quickly, giving rise to momentum.
The model implies that in the presence of the random shocks and information noise,
investor under-reaction should be observed. This implication is consistent with the documented
evidence on post event price continuation. The model further predicts that such continuation
should be more pronounced when investors are more confident in their prior beliefs. We have
documented some preliminary evidence that is consistent with this prediction.
We plan to consider two issues in future work. First, it is important to have a detailed
analysis of volatility-based forecasts of the momentum payoff. We are in the process of
performing robustness checks and conducting further tests. Second, an important direction is to
consider feasibility to extend beyond the representative agent framework. Intuitively, the
momentum profits should be intimately linked to investor trading, and hence may be associated
with interesting trading patterns. How to introduce heterogeneity among investors into the model,
which can produce trading predictions, is an inviting direction for further research.
22
23. References:
Asness, Clifford, 1997, The interaction of value and momentum strategies, Financial Analyst
Journal 53, 29–36.
Barberis, Nicholas, Andrei Shleifer, and Robert Vishny, 1998, A model of investor sentiment,
Journal of Financial Economics 49, 307–343.
Chan, Kalok, Allaudeen Hameed, and Wilson Tong, 2000, Profitability of momentum strategies
in the international equity markets, Journal of Financial and Quantitative Analysis 35,
153-172.
Chan, Louis K., Narasimhan Jegadeesh, and Josef Lakonishok, 1996, Momentum strategies,
Journal of Finance 51, 1681–1713.
Chui, Andy, Sheridan Titman, and K. C. John Wei, 2000, Momentum, ownership structure, and
financial crises: An analysis of Asian stock markets. Working paper, University of Texas
at Austin.
Conrad, Jennifer, and Gautam Kaul, 1993, Long-term overreaction or biases in computed returns?
Journal of Finance 48, 39–63.
Conrad, Jennifer, and Gautam Kaul, 1998, An anatomy of trading strategies, Review of Financial
Studies 11, 489–519.
Daniel, Kent, David Hirshleifer, and Avanidhar Subrahmanyam, 1998, Investor psychology and
security market under- and overreactions, Journal of Finance 53, 1839–1886.
Davis, James L., Eugene F. Fama, and Kenneth R. French, 2000, Characteristics, covariances
and average returns, Journal of Finance 55, 389–406.
DeBondt, Werner F. M., and Richard H. Thaler, 1985, Does the stock market overreact? Journal
of Finance 40, 793–805.
23
24. Degroot, Morris H., 1970, Optimal statistical decisions, John Wiley & Sons, Inc., Hoboken, New
Jersey
Fama, Eugene F., and Kenneth R. French, 1993, Common risk factors in the returns on stocks
and bonds, Journal of Financial Economics 33, 3–56.
Fama, Eugene F., and Kenneth R. French, 1996, Multifactor explanations of asset pricing
anomalies, Journal of Financial Economics 51, 55–84.
Fama, Eugene F., and Kenneth R. French, 1998, Value versus growth: The international
evidence, Journal of Finance 53, 1975–1999.
Grundy, Bruce D., and Spencer J. Martin, 2000, Understanding the nature of risks and the
sources of rewards to momentum investing, Review of Financial Studies, forthcoming.
Hong, Harrison, Terence Lim, and Jeremy C. Stein, 2000, Bad news travels slowly: Size, analyst
coverage, and the profitability of momentum strategies, Journal of Finance 55, 265–295.
Hong, Harrison, and Jeremy C. Stein, 1999, A unified theory of underreaction, momentum
trading and overreaction in asset markets, Journal of Finance 54, 2143–2184.
Jegadeesh, Narasimhan, and Sheridan Titman, 1993, Returns to buying winners and selling
losers: Implications for stock market efficiency, Journal of Finance 48, 65–91.
Jegadeesh, Narasimhan, and Sheridan Titman, 2000, Cross-sectional and time-series
determinants of momentum profits, Working paper, University of Illinois.
Lee, Charles, and Bhaskaran Swaminathan, 2000, Price momentum and trading volume, Journal
of Finance, forthcoming.
Loughran, Tim, and Jay R. Ritter, 1995, The new issues puzzle, Journal of Finance 50, 23–51.
Moskowitz, Tobias J., and Mark Grinblatt, 1999, Do industries explain momentum? Journal of
Finance 54, 1249–1290.
24
25. Richards, Anthony J., 1995, Comovements in national stock market returns: Evidence of predict-
ability but not cointegration, Journal of Monetary Economics 36, 631-654.
Richards, Anthony J., 1996, Winner-loser reversals in national stock market indices: Can they be
explained? Journal of Finance 52, 2130-2144.
Rouwenhorst, K. Geert, 1998, International momentum strategies, Journal of Finance 53, 267–
284.
25
26. Table I. Momentum profit
For each month from June 1926 to June 2007, NYSE/AMEX stocks in the CRSP database are ranked into
deciles based on their past 6-month returns, t-5 to t. A momentum strategy of buying winners (decile 10) and
selling losers (decile 1) is formed. Returns within deciles are equal weighted to calculate the portfolio return.
The average equal weighted monthly return for the next 6 month excluding the immediate following month,
t+2 to t+6, is reported. t values are adjusted for autocorrelation using the Newey-West method. Also reported is
the skewness of portfolio returns. The results are reported for the full sample and three subsamples.
Subsamples are selected as before, after, and the same as the 1965-1989 sample in the original study of
Jegadeesh and Titman (1993).
1926-2007 1926-1964
Return T Skewness Return T Skewness
Loser 0.0111 4.06 1.68 0.0130 2.91 1.99
2 0.0116 4.92 1.59 0.0124 3.18 1.83
3 0.0126 5.79 1.48 0.0128 3.57 1.74
4 0.0125 6.10 1.36 0.0125 3.69 1.61
5 0.0129 6.57 1.21 0.0129 3.96 1.43
6 0.0132 7.02 0.99 0.0131 4.20 1.19
7 0.0134 7.26 1.00 0.0133 4.37 1.23
8 0.0140 7.69 0.69 0.0140 4.73 0.91
9 0.0148 7.96 0.64 0.0146 4.91 0.94
Winner 0.0176 8.17 0.56 0.0173 5.10 0.86
W-L 0.0065 4.87 -2.45 0.0043 2.03 -2.95
1965-1989 1990-2007
Return T Skewness Return T Skewness
Loser 0.0075 1.95 0.23 0.0120 3.38 -0.06
2 0.0109 3.30 0.21 0.0108 3.92 -0.32
3 0.0125 4.05 0.08 0.0121 5.03 -0.57
4 0.0129 4.43 0.06 0.0118 5.45 -0.47
5 0.0131 4.78 -0.10 0.0124 6.13 -0.39
6 0.0139 5.21 -0.04 0.0124 6.55 -0.46
7 0.0140 5.33 -0.10 0.0126 6.78 -0.63
8 0.0148 5.49 -0.15 0.0130 7.00 -0.58
9 0.0156 5.47 -0.22 0.0142 7.24 -0.76
Winner 0.0174 5.16 -0.10 0.0186 7.58 -0.45
W-L 0.0100 5.21 -0.65 0.0066 3.08 -0.85
26
27. Table II. Momentum across decades
The 1926-2007 period is split into decades. The decade 1920 includes year 1926 to 1929. The decade 2000
include year 2000-2007. NYSE/AMEX stocks in the CRSP database are ranked into deciles based on their past
6-month returns. A momentum portfolio of buying the winners and selling the losers is formed and held for 6
months skipping the immediate next month. Within each decade the average momentum profit are reported.
The t values are adjusted for autocorrelation using the Newey-West method. Also reported are the average
monthly return and volatility for the value weighted market index. Simple average of momentum profit, t value,
market return, and market volatility across momentum and non-momentum decades based on the 1% and 5%
significance levels are also reported.
Decade Momentum t Market return Volatility
1920 0.0102 2.25 0.0130 0.055
1930 -0.0103 -1.72 0.0050 0.104
1940 0.0078 3.79 0.0087 0.044
1950 0.0093 6.64 0.0146 0.032
1960 0.0118 5.10 0.0073 0.036
1970 0.0064 1.99 0.0062 0.049
1980 0.0122 6.44 0.0139 0.048
1990 0.0098 3.63 0.0142 0.039
2000 0.0022 0.79 0.0033 0.042
Average 0.0066 3.21 0.0096 0.050
Momentum and non-momentum decades: 5% significance
Non-momentum -0.0041 -0.47 0.0042 0.073
Momentum 0.0096 4.26 0.0111 0.043
Momentum and non-momentum decades: 1% significance
Non-momentum 0.0021 0.83 0.0069 0.063
Momentum 0.0102 5.12 0.0117 0.040
27
28. Table III. Momentum on market volatility
For each month during June1926-June 2007, NYSE/AMEX stocks in the CRSP database are ranked into
deciles based on their past 6-month returns. A momentum portfolio of buying the winners and selling the
losers is formed and held for 6 months skipping the immediate next month. For each month we calculate
market volatility as the standard deviation of daily market return during the ranking period. Months are
independently ranked into quintiles based on this volatility measure. The average momentum profit and
autocorrelation adjusted t values within each quintile are reported. The results are reported for the full sample
and three subsamples: 1926-1964, 1965-1989, and 1990-2007.
Volatility Rank Loser Winner W-L T (Loser) T (Winner) T (W-L)
1926-2007
Low 0.0069 0.0189 0.0119 2.02 5.75 7.69
2 0.0091 0.0214 0.0122 3.44 7.96 9.34
3 0.0044 0.0137 0.0093 1.35 4.54 6.69
4 0.0139 0.0174 0.0033 2.65 4.03 1.43
High 0.0210 0.0166 -0.0044 2.48 3.06 -1.01
High-Low 0.0140 -0.0022 -0.0163 2.32 -0.51 -5.00
1926-1964
Low 0.0101 0.0191 0.0090 2.40 5.17 5.74
2 0.0139 0.0237 0.0098 3.60 5.46 4.43
3 0.0079 0.0186 0.0107 2.00 4.64 6.31
4 0.0130 0.0115 -0.0015 1.48 1.67 -0.42
High 0.0204 0.0128 -0.0076 1.36 1.31 -1.02
High-Low 0.01 -0.0063 -0.0166 0.93 -0.84 -2.88
1965-1989
Low -0.0021 0.0168 0.0189 -0.33 2.45 6.44
2 0.0007 0.0171 0.0164 0.16 3.85 7.65
3 -0.0015 0.0084 0.0099 -0.30 1.62 4.65
4 0.0203 0.0243 0.0040 2.74 4.20 1.07
High 0.0200 0.0207 0.0007 2.36 3.20 0.16
High-Low 0.0220 0.004 -0.0182 2.74 0.54 -4.38
1990-2007
Low 0.0122 0.0206 0.0083 3.68 6.25 3.77
2 0.0060 0.0167 0.0107 1.10 3.63 5.68
3 0.0068 0.0182 0.0113 1.31 4.30 3.74
4 0.0085 0.0171 0.0087 0.97 3.03 1.51
High 0.0263 0.0202 -0.0061 3.58 4.83 -1.29
High-Low 0.014 -0.0004 -0.0144 2.15 -0.09 -3.20
28
29. Table IV. Momentum on market states and volatility
For each month during June1926-June 2007, NYSE/AMEX stocks in the CRSP database are ranked into
deciles based on their past 6-month returns. A momentum portfolio of buying the winners and selling the
losers is formed and held for 6 months skipping the immediate next month. For each month we calculate
market volatility as the standard deviation of daily market return during the ranking period. Months are equally
ranked into 3 groups based on this volatility measure. For each month we also calculate market state as the
return on the value weighted market index during the past 36 months following Cooper et al (2004) and
independently rank months into 3 groups based on this market state measure. The average momentum profit
and t values within each market state-volatility group are reported.
Market Volatility
Market State Low Medium High High-Low
Bad 0.0157 0.0085 -0.0101 -0.0258
5.45 3.84 -2.15 6.22
Medium 0.0128 0.0127 0.0037 -0.0091
8.05 8.56 0.80 2.02
Good 0.0084 0.0090 0.0046 -0.0038
4.90 4.66 1.43 1.40
Good-Bad -0.0073 0.0005 0.015
-2.76 0.20 3.49
29
30. Table V. Regression of momentum on market state and volatility
For each month during June1926-June 2007, NYSE/AMEX stocks in the CRSP database are ranked into
deciles based on their past 6-month returns. A momentum portfolio of buying the winners and selling the
losers is formed. For each month we calculate market volatility as the standard deviation of daily market return
during the ranking period. For each month we also calculate market state as the return on the value weighted
market index during the past 36 months following Cooper et al (2004). Momentum profit is regressed on
market state and volatility. t values are adjusted for autocorrelation using the Newey-West method.
1926-2007 1926-1964 1965-1989 1990-2007
Intercept -0.00033 0.02 0.013 0.011 0.017 0.011
-0.09 5.39 3.67 2.07 3.51 1.97
Market State 0.72 0.44 0.34 0.82 0.67
2.38 1.71 0.91 2.69 2.50
Market Volatility -0.34 -0.26 -0.23 -0.40 -0.27
-3.22 -3.29 -2.28 -3.60 -2.45
# of months 984 984 984 468 300 216
Adj. R-square 0.064 0.089 0.107 0.091 0.149 0.121
30
31. Figure 1. Accumulative return of winners and losers post ranking. The upper blue dashed (lower black
solid) line is the accumulative return on the winner (loser) portfolio from month 1 to month 24 after portfolio
ranking. Winners (losers) are NYSE/AMEX stocks ranked into the top (bottom) 10% on the past 6 month
returns. Returns are demeaned by the average return of all NYSE/AMEX stocks.
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32. Figure 1. Accumulative return of winners and losers around ranking. The upper blue dashed (lower black
solid) line is the accumulative return on the winner (loser) portfolio from month -24 to month 24 around
portfolio ranking. Winners (losers) are NYSE/AMEX stocks ranked into the top (bottom) 10% on the past 6
month returns. Returns are demeaned by the average return of all NYSE/AMEX stocks.
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33. Figure 3. Accumulative momentum profit and market volatility. The black solid line (left axis) is the
accumulative momentum profit from June 1926 to June 2007. Momentum profit is the return on a portfolio
buying past 6 month winners and selling past 6 month losers and held for 6 month. The dashed blue line (right
axis) is the smoothed market volatility, calculated as the standard deviation of daily returns on a value-
weighted market portfolio during the 6 months ranking period.
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