Investor psychology and return seasonalities in the cross section PDF Download

Author: Yuting Meng DiGiovanni
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Author: Malcolm Baker
Publisher:
ISBN:
Category : Investments
Languages : en
Pages : 36

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We examine how investor sentiment affects the cross-section of stock returns. Theory predicts that a broad wave of sentiment will disproportionately affect stocks whose valuations are highly subjective and are difficult to arbitrage. We test this prediction by studying how the cross-section of subsequent stock returns varies with proxies for beginning-of-period investor sentiment. When sentiment is low, subsequent returns are relatively high on smaller stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks, extreme-growth stocks, and distressed stocks, consistent with an initial underpricing of these stocks. When sentiment is high, on the other hand, these patterns attenuate or fully reverse. The results are consistent with predictions and appear unlikely to reflect an alternative explanation based on compensation for systematic risk.

Author: Farid Bavi
Publisher:
ISBN:
Category :
Languages : en
Pages :

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The thesis tried to find seasonality effect and evidence of Momentum strategy with the use of cross sectional method in six different markets including Australia, Frankfurt, Shanghai, XETRA, UK, and Swiss stock markets during the period of 1995-2017. The studied confirmed that, there is no effect when we calculate the earnings over the course of the months. But there is an economically significant effect when measuring the cross-section of expected stock returns across seasonal month.

Author: Lawrence E. Lifson
Publisher: John Wiley & Sons
ISBN: 9780471183396
Category : Business & Economics
Languages : en
Pages : 218

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Dieses maßgebende Buch aus der Schmiede der Harvard-Universität stützt sich auf Erkenntnisse und Ideen führender Experten aus den Bereichen Investment und Psychologie. Die Beiträge stammen u.a. von Abby Joseph Cohen, stellvertretender Vorstandsvorsitzender bei Goldman-Sachs, Samuel Hayes von der Harvard Business School und von Mark Hubert, Kolumnist beim Forbes Magazine. Diskutiert werden u.a. die psychologische Aspekte von Risiko, Massenpsychologie und geschlechtsbedingten Unterschieden im Investmentgeschäft. Mit einem Vorwort von John Train, dem Autor zahlreicher Bestseller zur Geldanlage. (11/97)

Author: Antti Ilmanen
Publisher: John Wiley & Sons
ISBN: 1119990726
Category : Business & Economics
Languages : en
Pages : 102

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This comprehensive reference delivers a toolkit for harvesting market rewards from a wide range of investments. Written by a world-renowned industry expert, the reference discusses how to forecast returns under different parameters. Expected returns of major asset classes, investment strategies, and the effects of underlying risk factors such as growth, inflation, liquidity, and different risk perspectives, are also explained. Judging expected returns requires balancing historical returns with both theoretical considerations and current market conditions. Expected Returns provides extensive empirical evidence, surveys of risk-based and behavioral theories, and practical insights.

Author: Stijn Claessens
Publisher: World Bank Publications
ISBN:
Category : Rate of return
Languages : en
Pages : 28

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Author: Wenjie Ding
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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We extend the noise trader risk model of Delong et al. (J Polit Econ 98:703-738, 1990) to a model with multiple risky assets to demonstrate the effect of investor sentiment on the cross-section of stock returns. Our model formally demonstrates that market-wide sentiment leads to relatively higher contemporaneous returns and lower subsequent returns for stocks that are more prone to sentiment and difficult to arbitrage. Our extended model is consistent with the existing empirical evidence on the relationship between sentiment and cross-sectional stock returns. Guided by the extended model, we also decompose investor sentiment into long- and short-run components and predict that long-run sentiment negatively associates with the cross-sectional return and short-run sentiment positively varies with the cross-sectional return. Consistent with these predictions, we find a negative relationship between the long-run sentiment component and subsequent stock returns and positive association between the short-run sentiment component and contemporaneous stock returns.

Author: Victor Krug Kovacs Borges
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: IMCA
Publisher: John Wiley & Sons
ISBN: 1118912411
Category : Business & Economics
Languages : en
Pages : 1152

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The complete body of knowledge for CIMA candidates and professionals The 2015 Certified Investment Management Analyst Body of Knowledge + Test Bank will help any financial advisor prepare for and pass the CIMA exam, and includes key information and preparation for those preparing to take the test. CIMA professionals integrate a complex body of investment knowledge, ethically contributing to prudent investment decisions by providing objective advice and guidance to individual and institutional investors. The CIMA certification program is the only credential designed specifically for financial professionals who want to attain a level of competency as an advanced investment consultant. Having the CIMA designation has led to more satisfied careers, better compensation, and management of more assets for higher-net-worth clients than other advisors. The book is laid out based on the six domains covered on the exam: I. Governance II. Fundamentals (statistics, finance, economics) III. Portfolio Performance and Risk Measurements IV. Traditional and Alternative Investments V. Portfolio Theory and Behavioral Finance VI. Investment Consulting Process

Author: Steven L. Heston
Publisher:
ISBN:
Category :
Languages : en
Pages : 37

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This paper introduces seasonality into a model of expected stock returns. We confirm previous findings that there is no evidence for cross-sectional variation in expected stock returns when we restrict the means to be constant throughout the year. Yet, we show there is substantial variation when considering each month of the year separately. Applying a seasonal structure we estimate an annualized standard deviation of 13.8%. There is strong evidence stocks have distinct expected returns in January, February, ... December. The estimated seasonal variation in expected returns is positive in every calendar month and especially high during October, December, and January. This structure is independent of industry, size, and earnings announcements. These results support the inclusion of seasonal structure into asset-pricing models.