...Slow Diffusion of Information and Price Momentum in Stocks: Evidence from Options Markets Zhuo Chen∗ Andrea Lu† September 6, 2014 Abstract This paper investigates the source of price momentum in the equity market using information from options markets. The empirical results provide direct evidence of the gradual information diffusion model in Hong and Stein (1999). Consistent with their theory, we show that a successful identification of stocks’ information diffusion stage helps explain momentum profits. We are able to enhance momentum profits by longing winner stocks with higher growth (and shorting loser stocks with larger drop) in call options implied volatility. Our empirical strategy generates a risk-adjusted alpha of 1.8% per month for a hedged winner-minus-loser portfolio over the 1996–2011 period, during which the simple momentum strategy fails to perform. The results are stronger and clearer if we use call options compared with put options, which are consistent with managers’ tendency to reveal good news and hide bad news. Our results are robust to transaction costs, choice of options’ moneyness, elimination of implied volatility persistence, and choice of options’ time-to-maturity. Finally, our results are not driven by existing stock-level characteristics, such as size, trading volume, and analyst coverage. JEL Classification: G10, G11, G12, G13 Keywords: Momentum, Implied Volatility PBC School of Finance, Tsinghua University. Email: chenzh@pbcsf.tsinghua.edu.cn...
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...Portfolio Outperform Value- and Price-Weighted Portfolios March 2012 Yuliya Plyakha Raman Uppal Goethe University Frankfurt EDHEC Business School Grigory Vilkov Goethe University Frankfurt Abstract We compare the performance of equal-, value-, and price-weighted portfolios of stocks in the major U.S. equity indices over the last four decades. We find that the equal-weighted portfolio with monthly rebalancing outperforms the value- and price-weighted portfolios in terms of total mean return, four factor alpha, Sharpe ratio, and certainty-equivalent return, even though the equal-weighted portfolio has greater portfolio risk. The total return of the equal-weighted portfolio exceeds that of the value- and price-weighted because the equal-weighted portfolio has both a higher return for bearing systematic risk and a higher alpha measured using the four-factor model. The nonparametric monotonicity relation test indicates that the differences in the total return of the equal-weighted portfolio and the value- and price-weighted portfolios is monotonically related to size, price, liquidity and idiosyncratic volatility. The higher systematic return of the equal-weighted portfolio arises from its higher exposure to the market, size, and value factors. The higher alpha of the equal-weighted portfolio arises from the monthly rebalancing required to maintain equal weights, which is a contrarian strategy that exploits reversal and idiosyncratic volatility of the stock returns;...
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...a result the universe of equity funds investing in these developing economies has been in continuous expansion. In this paper we propose a set of asset class specific predictive variables for emerging markets and exploit them in order to identify those funds that outperform the market in different phases of the economic cycle. We employ a comprehensive survivorship-bias free universe of global and regional emerging market funds and use a Bayesian framework that incorporates predictability in manager skills (stock selection and benchmark timing skills), fund risk loadings and benchmark returns by exploiting ex-ante business cycle related state variables. Our results provide empirical evidence of return predictability and the economic value of active management in emerging markets. ∗ I would like to thank Allan Timmermann for his guidance and support. I am also grateful to James Hamilton, Bruce N. Lehmann, Ross Valkanov and Debbie Watkins for their helpful comments. I also benefited from discussions with Ben Gillen. Finally, I want to thank Russ Wermers for providing me with the mutual fund dataset. 1 1 Introduction During the last decades the mutual fund industry has been continuously growing and gaining importance in global financial markets. As of end of 2007, total worldwide mutual funds’ assets amounted to 26.2 trillion dollar, with the US accounting for 46% of the market. As the industry evolved, mutual funds have been gaining the interest of academics...
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...I. JUDGMENT and DECISION BIASES In the first section of the survey, psychological effects which are relevant for securities markets are defined. These effects mostly derive from common roots. Since it is almost impossible to analyze all the given data, rules of thumbs are preferred such as algorithmic, heuristics and mental modules. Heuristics must be applied to appropriate problems and cases so that they can be effective. There have been debates between both economists and psychologists on how heuristics do. Most of the economists believe in the fact that errors are independent across individual investors. The idea ends up with the equilibrium point and correction. However people mostly share similar heuristics gained from experience. Human mind is not designed solely to make good decisions but also to experience pleasantness. Individuals believe that they are better than they actually are. Also decisions are affected by feelings and mood of decision maker individuals. People can learn from past experiences and failures. However learning is hard and self-deception makes people realize their success more than failures and losses. Many (though not all) of the cognitive biases are stronger for individuals with low cognitive ability or skills than for those with high ability or skills, consistent with biases being genuine errors. A. Heuristic Simplification A.1 Attention/Memory/Ease-of-processing effects Lack of full attention, limitations on memory...
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...Table of Contents 1. Introduction…………………………………………………………………………....3 2. Equity Analysis………………………………………………………………………..3 3. Recommendation……………………………………………………………………....6 4. JLG Equity Analysis Template………………………………………………………7 5. Value Line Report……………………………………………………………………12 INTRODUCTION PepsiCo is a world leader in convenient snacks, foods, and beverages, with revenues of more than $39 billion and over 185,000 employees. PepsiCo owns some of the world's most popular brands, including Pepsi-Cola, Mountain Dew, Diet Pepsi, Lay's, Doritos, Tropicana, Gatorade, and Quaker(http://phx.corporate-ir.net/phoenix.zhtml?c=78265&p=irol-homeProfile&t=&id=&). Their brands are available worldwide through a variety of go-to-market systems, including direct store delivery (DSD), broker-warehouse, and food service and vending. PepsiCo was founded in 1965 through the merger of Pepsi-Cola and Frito-Lay. Tropicana was acquired in 1998 and PepsiCo merged with the Quaker Oats Company, including Gatorade, in 2001(http://phx.corporate-ir.net/phoenix.zhtml?c=78265&p=irol-homeProfile&t=&id=&). I’ve selected PepsiCo as my investment and Value Line report was the key factor in my decision. EQUITY ANALYSIS Equity analysis includes analysis of traditional and value-based metrics. Traditional metrics include expected growth rates, price multiples, projected ROE, fundamental stock return and residual income. Expected growth rates and price multiples combined with relative valuation measures...
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...Kong stock market. Our results show that liquidity is an important factor for pricing returns in Hong Kong after taking well-documented asset pricing factors into consideration. The results are robust to adding portfolio residuals and higher moment factor in the factor models. The results are also robust to seasonality, and conditional-market tests. We also compare alternative factor models and find that the liquidity four-factor model (market excess return, size, book-to-market ratio, and liquidity) is the best model to explain stock returns in the Hong Kong stock market, while the momentum factor is not found to be priced. Ó 2011 Elsevier B.V. All rights reserved. Article history: Received 10 June 2010 Accepted 17 January 2011 Available online 22 January 2011 JEL classification: G12 G15 Keywords: Liquidity Asset pricing Hong Kong stock market Factor model Fama French three factors Higher moment Momentum 1. Introduction Investors face liquidity risk when they transfer ownership of their securities. Therefore, investors consider liquidity to be an important factor when making their investment decisions. Amihud and Mendelson (1986) find a positive return-illiquidity relation. Since that study, many other researchers continue to investigate the return-illiquidity (liquidity) relation, but evidence over the past two decades is generally inconsistent and...
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...technical analysis tools play an important role for the security evaluation. According to Penman (2010), investors estimate the stock future prices and trends by collecting and estimate the past prices and information. However, there are some conflict points on the momentum strategies performance, and it is a technical tool with multiple economy factors needs to be considered into. Why do momentum strategies exist? Refer to both behavioural and market-based argumentations. Momentum strategies are the stock analysis stool exists in the financial evaluation process, also in funds and currency investment. According to Chan, Jegadeesh, and Lakonishok J (1996) said, "it is a strategy that buying stocks in a high returns over the past three to twelve months, and selling those that had the poor returns over the same period." In the other words, the outperform stock will remain well but the underperform stock will continually worse (Fama & French, 1992). From the views from market- based argumentation, massive of evidence find that the momentum strategies are profitable for financial investment. For example, Aharoni, Ho and Zeng (2012) had a test in the profitability of momentum strategies in Australia stock market, which be found that the...
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...Betting Against Beta Andrea Frazzini and Lasse H. Pedersen* This draft: October 9, 2011 Abstract. We present a model with leverage and margin constraints that vary across investors and time. We find evidence consistent with each of the model’s five central predictions: (1) Since constrained investors bid up high-beta assets, high beta is associated with low alpha, as we find empirically for U.S. equities, 20 international equity markets, Treasury bonds, corporate bonds, and futures; (2) A betting-against-beta (BAB) factor, which is long leveraged lowbeta assets and short high-beta assets, produces significant positive risk-adjusted returns; (3) When funding constraints tighten, the return of the BAB factor is low; (4) Increased funding liquidity risk compresses betas toward one; (5) More constrained investors hold riskier assets. * Andrea Frazzini is at AQR Capital Management, Two Greenwich Plaza, Greenwich, CT 06830, e-mail: andrea.frazzini@aqr.com; web: http://www.econ.yale.edu/~af227/ . Lasse H. Pedersen is at New York University, AQR, NBER, and CEPR, 44 West Fourth Street, NY 10012-1126; e-mail: lpederse@stern.nyu.edu; web: http://www.stern.nyu.edu/~lpederse/. We thank Cliff Asness, Aaron Brown, John Campbell, Kent Daniel, Gene Fama, Nicolae Garleanu, John Heaton (discussant), Michael Katz, Owen Lamont, Michael Mendelson, Mark Mitchell, Matt Richardson, Tuomo Vuolteenaho and Robert Whitelaw for helpful comments and discussions as well as seminar participants...
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...process: 1. Alpha generation by running a regression model to identify winner and loser stocks with a qualitative implementation plan. 2. Optimize portfolio by neutralizing size, industry and sector exposures while emphasizing security selection. 3. Utilize a proprietary risk model based off multi-factor regression to take a disciplined approach by taking calculated risks. * Stock Selection model is based on factor groupings. The four broad factors are: Quality, Momentum, Valuation and Growth. Some of the factors include: Earnings quality, P/E, earnings momentum and ROE. * This multifactor alpha engine ranks the U.S. equities universe. CEP goes long the top ranked stocks while shorting the bottom ranked stocks. * An additional source of alpha is their Market Timing Model. This factor timing model identifies the right time to emphasize or de-emphasize certain factors. There are three market phases: expansion, downturn and rebound. In the case of a market rebound, the portfolio is tilted more towards value...
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...Question 1 For 4 Dow Jones stocks for a 15 year period, compute quarterly realized betas from daily data. Find firm specific and macroeconomic variables that help explain quarterly beta. Answer 1.1 Factors Introduction Factor model survey the sensitivity of a stock return as a function of one or more factors. There are single-factor and multi-factor models. In factors model, based on the type of factors used, it can be classified to economic and fundamental factor models. Economic factor models use macroeconomic and financial markets variables as factors, while fundamental factor models use firm-specific microeconomic variables, such as financial indicators. In recent research shows that the change in macroeconomic factors could be reflected in the change of systematic risk which impacts a stock’s expected return (Humpe & Macmillan 2007). Macroeconomic factors included industry production index, CPI, GDP, unemployment rate, inflation rate, risk premium, default premium, business cycle index and so on. From Chen (1986) notable study which uses variables include industrial production, inflation, risk premium, term structure, market index, consumption and oil prices to found out that industrial production, unanticipated change in the risk premium, unanticipated inflation, and, a slightly weaker, the unanticipated change in term structure, are the most important factors affecting expected stock returns. The 15 macroeconomic variables used as factors in our model are...
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...JAPANESE CANDLESTICK CHARTING TECHNIQUES ~-Y?~L&B~E!% L ?ABWt "Candles Exhaust Themselves to Give Light to Men" JAPANESE CANDLESTICK CHARTING TECHNIQUES A Contemporary Guide to the Ancient Investment Techniques of the Far East STEVE NISON NEW YORK INSTITUTE OF FINANCE NewYork London Toronto Sydney Tokyo Singapore Library of Congress Cataloging-in-Publication Data Nison, Steve. Japanese candlestick charting techniques : a contemporary guide to the ancient investment technique of the Far East I Steve Nison. p. cm. Includes bibliographical references and index. ISBN 0-13-931650-7 1. Stocks-Charts, diagrams, etc. 2. Investment analysis. I. Title. HG4638.N57 1991 90-22736 332.63'22-dc20 CIP This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional service. If legal advice or other expert assistance is required, the services of a competent professional person should be sought. From a Declaration of Principles Jointly Adopted by a Committee of the American Bar Association and a Committee of Publishers and Associations 01991 by Steve Nison All rights reserved. No part of this book may be reproduced in any form or by any means without permission in writing from the publisher. New York Institute of Finance Simon & Schuster Printed in the United States of America 1 0 9 8 7 Acknowledgements ...
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...Beta (finance) From Wikipedia, the free encyclopedia Jump to: navigation, search In finance, the Beta (β) of a stock or portfolio is a number describing the volatility of an asset in relation to the volatility of the benchmark that said asset is being compared to. This benchmark is generally the overall financial market and is often estimated via the use of representative indices, such as the S&P 500.[1] An asset has a Beta of zero if its returns change independently of changes in the market's returns. A positive beta means that the asset's returns generally follow the market's returns, in the sense that they both tend to be above their respective averages together, or both tend to be below their respective averages together. A negative beta means that the asset's returns generally move opposite the market's returns: one will tend to be above its average when the other is below its average.[2] It measures the part of the asset's statistical variance that cannot be removed by the diversification provided by the portfolio of many risky assets, because of the correlation of its returns with the returns of the other assets that are in the portfolio. Beta can be estimated for individual companies using regression analysis against a stock market index. Contents[hide] * 1 Definition * 1.1 Security market line * 2 Choice of benchmark * 3 Investing * 4 Academic theory * 5 Multiple beta model * 6 Estimation of beta * 7 Extreme and interesting cases * 8 Criticism...
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...exponential moving average of Accumulation/Distribution and a 10 period EMA of AD * Buy when the oscillator moves above the zero line * Sell when it moves below zero * If the security makes a new high or low that is not confirmed by the chaikin oscillator, a potential reversal is pending * Ease of Movement * Reduces each period’s Rice and Volume to a single value that represents the ease at which prices are moving upward or downward * Buy when EMV crosses above the zero line, indicating ease of upward price movement * Sell when EMV crosses below the zero line, Indication ease of downward price movement * Force Index * Combines price changes and Volume into a single value that attempts to represent the magnitude of the force driving a rally or decline * When the smoothed Force index crosses the zero line, it indicates a change in trend and can be used as a buy/sell signal * Linear regression forecast * Calculates a “line of best fit” at each date, then plots the price value of that line at the specified point in time * Similar in display and interpretation to a moving average * MACD * Moving Average Convergence/Divergence is a price oscillator based on the difference between two moving averages * Sell when the MACD move below the MACD Signal line * Buy when the MACD moves above the MACD Signal Line * Another approach ...
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...existence of a momentum effect. They attribute this effect to the fact that investors underreact to the release of firm-specific information. In detail, they compared the performance of stocks that have performed well in the past with those that have performed poorly, in 3-12 months’ time. Their theory is if stock prices either overreact or underreact to information consistently then profitable trading strategies that select stocks based on their past returns will exist. Previous studies in this area had shown that over a 3 to 5 year horizon, stocks that had performed poorly over the previous 3 to 5 years achieved higher returns than stocks that performed well over the same period. Their study covered the period from 1965 to 1989, involved ranking each company at the beginning of each month by its returns over the last ‘J’ months. Based on these rankings, ten equally weighted decile portfolios are constructed, with the portfolio comprised of those companies with the strongest historical returns and the portfolio with the lowest historical returns. The timeframes for both ‘J’ and ‘K’ horizons were 3, 6, 9 and 12 months. They constructed portfolios based on companies’ 3 month historical returns, and looked at their performance over the subsequent 3, 6, 9 and 12 months. The paper focused largely on the ‘zero cost’ portfolios, representing the difference in performance between the ‘winners’ and ‘losers’. It is important to note that momentum strategy appears...
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...CONCORDIA UNIVERSITY John Molson School of Business - Department of Finance Portfolio Management - FINA 411/2/A, C Course Outline – Fall 2014 Instructor: Dr. Abraham I. Brodt Office: MB 12.215 Tel: 848-2424-2997 Fax: 848-4500 E-mail: ABrodt@jmsb.concordia.ca [SUBJECT: FINA 411 …….] Classes: FINA 411/2A Mondays 11:45 - 14:30 [MB1.437] FINA 411/2C Wednesdays 11:45 - 14:30 [MB5.255] Office Hours: Mondays and Wednesdays 15:30 -- 16:30 [Please e-mail me first to confirm] and by appointment COURSE DESCRIPTION: This course focuses on modern investment theory and its application to the management of entire portfolios. It will consist of lectures, discussions of cases and articles, and video presentations. Topics include: a) construction of optimal asset portfolios using techniques such as the single index model, b) extensions of the capital asset pricing model: theory and tests; example, the zero-beta model, c) criteria for evaluation of investment performance, d) active vs. passive portfolio management, e) investment strategies. The Formula Growth Investment Centre Lab will be used to demonstrate the use of specialized investment software. Computer exercises are assigned to illustrate the application of the theory. Prerequisites: FINA 380 or 385; FINA 390 or 395. LEARNING OBJECTIVES To understand the theory and practice of Portfolio Management for Individuals and Institutions, e.g. Endowments, Mutual Funds, Pension Plans, etc. ...
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