Using Accounting Data To Predict Firm Level And Aggregate Stock Returns
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Predicting Firm Level Stock Returns
Author | : David G. McMillan |
Publisher | : |
Total Pages | : 34 |
Release | : 2017 |
Genre | : |
ISBN | : |
This paper examines the predictive ability of several stock price ratios, stock return dispersion and distribution for individual firm level stock returns. Analysis typically focusses on market level returns, however, for the asset pricing model that underlies predictability to hold, firm-level predictability should also be present. In addition, we examine the economic content of predictability by considering whether the predictive coefficient has the theoretically correct sign and whether it is related to future output growth. Movement in stock returns should reflect investor expectations regarding future economic conditions. While stock returns are often too noisy to act as predictors for future economic behaviour, factors that predict stock returns should equally have predictive power for output growth. In our analysis, we use the time-varying predictive coefficient to predict output growth, as the coefficient reflects the sensitivity of stock returns to the predictor variable and thus can be regarded as investors' confidence in the predictive relation. The results suggest that several stock price ratios have predictive power for individual firm stock returns, exhibit the correct coefficient sign and has predictive power for output growth. Each of these ratios has a measure of fundamentals dividend by the stock price and has a positive predictive relation with stock returns and output growth. This implies that as investors expect future economic conditions to improve and earnings and dividends to rise, so expected stock returns will increase. This supports the stock return predictive relation that arises through the cash flow channel.
Statistics of Random Processes II
Author | : R.S. Liptser |
Publisher | : Springer Science & Business Media |
Total Pages | : 348 |
Release | : 2013-04-17 |
Genre | : Mathematics |
ISBN | : 1475742932 |
Stock Returns and Volatility
Author | : Gregory R. Duffee |
Publisher | : |
Total Pages | : |
Release | : 2001 |
Genre | : |
ISBN | : |
It has been previously documented that individual firms' stock return volatility rises after stock prices fall. This paper finds that this statistical relation is largely due to a positive contemporaneous relation between firm stock returns and firm stock return volatility. This positive relation is strongest for both small firms and firms with little financial leverage. At the aggregate level, the sign of this contemporaneous relation is reversed. The reasons for the difference between the aggregate- and firm-level relations are explored.
Empirical Asset Pricing
Author | : Wayne Ferson |
Publisher | : MIT Press |
Total Pages | : 497 |
Release | : 2019-03-12 |
Genre | : Business & Economics |
ISBN | : 0262039370 |
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Knowledge-Based Systems
Author | : Rajendra Akerkar |
Publisher | : Jones & Bartlett Publishers |
Total Pages | : 375 |
Release | : 2009-08-25 |
Genre | : Computers |
ISBN | : 1449662706 |
A knowledge-based system (KBS) is a system that uses artificial intelligence techniques in problem-solving processes to support human decision-making, learning, and action. Ideal for advanced-undergraduate and graduate students, as well as business professionals, this text is designed to help users develop an appreciation of KBS and their architecture and understand a broad variety of knowledge-based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters is designed to be modular, providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material presented and to simulate thought and discussion. A comprehensive text and resource, Knowledge-Based Systems provides access to the most current information in KBS and new artificial intelligences, as well as neural networks, fuzzy logic, genetic algorithms, and soft systems.
Why Does Aggregate Insider Trading Predict Future Stock Returns
Author | : Hasan Nejat Seyhun |
Publisher | : |
Total Pages | : 46 |
Release | : 1991 |
Genre | : Insider trading in securities |
ISBN | : |
An Exploration of Two Accounting-based Models for Earnings Misstatements and Their Implications for Stock Returns
Author | : Suzie Noh |
Publisher | : |
Total Pages | : 59 |
Release | : 2014 |
Genre | : |
ISBN | : |
Using two popular accounting-based models for earnings manipulation (i.e., the Beneish M-Score and the Dechow F-Score) and the financial data of public companies from 2004 to 2012, 1 find that the M-Score (F-Score) predicts less (more) earnings overstatements during the recent financial crisis in 2007-2008 than other sample years. However, a detailed investigation at the industry level reveals that this does not hold in all industries. I further show that the potential misstating firms flagged by the M-Score tend to under-perform the market both at the aggregate and the industry level, and some of those flagged by the F-Score under-perform at the industry level. Finally, by running Fama-French three-factor regressions at the aggregate level, I provide evidence that the firms flagged by the MScore generally yield negative risk-adjusted stock returns. The evidence suggests public availability of financial statements alone does not ensure that all the elements of financial statements are fully integrated into prices in a timely manner. Overall, this study provides substantial support for the use of quantitative accounting analysis in equity trading.
Skewness in Stock Returns
Author | : Rui A. Albuquerque |
Publisher | : |
Total Pages | : 71 |
Release | : 2014 |
Genre | : |
ISBN | : |
Aggregate stock market returns display negative skewness. Firm stock returns display positive skewness. The large literature that tries to explain the first stylized fact ignores the second. This article provides a unified theory that reconciles the two facts by explicitly modeling firm-level heterogeneity. I build a stationary asset pricing model of firm announcement events where firm returns display positive skewness. I then show that cross-sectional heterogeneity in firm announcement events can lead to conditional asymmetric stock return correlations and negative skewness in aggregate returns. I provide evidence consistent with the model predictions.