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Author: Nathaniel Light
Publisher:
ISBN:
Category :
Languages : en
Pages : 70
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Book Description
We propose a new approach for estimating expected returns on individual stocks from a large number of firm characteristics. We treat expected returns as latent variables and apply the partial least squares (PLS) estimator that filters them out from the characteristics under an assumption that the characteristics are linked to expected returns through one or few common latent factors. The estimates of expected returns constructed by our approach from twenty six firm characteristics generate a wide cross-sectional dispersion of realized returns and outperform estimates obtained by alternative techniques. Our results also provide evidence of commonality in asset pricing anomalies.
Author: Nathaniel Light
Publisher:
ISBN:
Category :
Languages : en
Pages : 70
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Book Description
We propose a new approach for estimating expected returns on individual stocks from a large number of firm characteristics. We treat expected returns as latent variables and apply the partial least squares (PLS) estimator that filters them out from the characteristics under an assumption that the characteristics are linked to expected returns through one or few common latent factors. The estimates of expected returns constructed by our approach from twenty six firm characteristics generate a wide cross-sectional dispersion of realized returns and outperform estimates obtained by alternative techniques. Our results also provide evidence of commonality in asset pricing anomalies.
Author: Xun Zhong
Publisher:
ISBN:
Category :
Languages : en
Pages : 181
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Book Description
My dissertation consists of three chapters that study various aspects of stock return predictability. In the first chapter, I explore the interplay between the aggregation of information about stock returns and p-hacking. P-hacking refers to the practice of trying out various variables and model specifications until the result appears to be statistically significant, that is, the p-value of the test statistic is below a particular threshold. The standard information aggregation techniques exacerbate p-hacking by increasing the probability of the type I error. I propose an aggregation technique, which is a simple modification of 3PRF/PLS, that has an opposite property: the predictability tests applied to the combined predictor become more conservative in the presence of p-hacking. I quantify the advantages of my approach relative to the standard information aggregation techniques by using simulations. As an illustration, I apply the modified 3PRF/PLS to three sets of return predictors proposed in the literature and find that the forecasting ability of combined predictors in two cases cannot be explained by p-hacking. In the second chapter, I explore whether the stochastic discount factors (SDFs) of five characteristic-based asset pricing models can be explained by a large set of macroeconomic shocks. Characteristic-based factor models are linear models whose risk factors are returns on trading strategies based on firm characteristics. Such models are very popular in finance because of their superior ability to explain the cross-section of expected stock returns, but they are also criticized for their lack of interpretability. Each characteristic-based factor model is uniquely characterized by its SDF. To approximate the SDFs by a comprehensive set of 131 macroeconomic shocks without overfitting, I employ the elastic net regression, which is a machine learning technique. I find that the best combination of macroeconomic shocks can explain only a relatively small part of the variation in the SDFs, and the whole set of macroeconomic shocks approximates the SDFs not better than only few shocks. My findings suggest that behavioral factors and sentiment are important determinants of asset prices. The third chapter investigates whether investors efficiently aggregate analysts' earnings forecasts and whether combinations of the forecasts can predict announcement returns. The traditional consensus forecast of earnings used by academics and practitioners is the simple average of all analysts' earnings forecasts (Naive Consensus). However, this measure ignores that there exists a cross-sectional variation in analysts' forecast accuracy and persistence in such accuracy. I propose a consensus that is an accuracy-weighted average of all analysts' earnings forecasts (Smart Consensus). I find that Smart Consensus is a more accurate predictor of firms' earnings per share (EPS) than Naive Consensus. If investors weight forecasts efficiently according to the analysts' forecast accuracy, the market reaction to earnings announcements should be positively related to the difference between firms' reported earnings and Smart Consensus (Smart Surprise) and should be unrelated to the difference between firms' reported earnings and Naive Consensus (Naive Surprise). However, I find that market reaction to earnings announcements is positively related to both measures. Thus, investors do not aggregate forecasts efficiently. In addition, I find that the market reaction to Smart Surprise is stronger in stocks with higher institutional ownership. A trading strategy based on Expectation Gap, which is the difference between Smart and Naive Consensuses, generates positive risk-adjusted returns in the three-day window around earnings announcements.
Author: Stijn Claessens
Publisher: World Bank Publications
ISBN:
Category : Rate of return
Languages : en
Pages : 28
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Book Description
Author: Martijn Cremers
Publisher:
ISBN:
Category :
Languages : en
Pages : 62
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Book Description
We examine the pricing of both aggregate jump and volatility risk in the cross-section of stock returns by constructing investable option trading strategies that load on one factor but are orthogonal to the other. Both aggregate jump and volatility risk help explain variation in expected returns. Consistent with theory, stocks with high sensitivities to jump and volatility risk have low expected returns. Both can be measured separately and are important economically, with a two-standard deviation increase in jump (volatility) factor loadings associated with a 3.5 to 5.1 (2.7 to 2.9) percent drop in expected annual stock returns.
Author: Andrew Ang
Publisher:
ISBN:
Category :
Languages : en
Pages : 56
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Book Description
We examine how volatility risk, both at the aggregate market and individual stock level, is priced in the cross-section of expected stock returns. Stocks that have past high sensitivities to innovations in aggregate volatility have low average returns. We also find that stocks with past high idiosyncratic volatility have abysmally low returns, but this cannot be explained by exposure to aggregate volatility risk. The low returns earned by stocks with high exposure to systematic volatility risk and the low returns of stocks with high idiosyncratic volatility cannot be explained by the standard size, book-to-market, or momentum effects, and are not subsumed by liquidity or volume effects.
Author: Nikolay Doskov
Publisher:
ISBN:
Category :
Languages : en
Pages : 41
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Book Description
We propose a new set of tradable aggregate risk factors that help us understand the cross-section of stock returns. We argue that the true stochastic discount factor is a combination of aggregate return factors that drive equity market returns. Hence, we consider new factors using data such as market dividend swaps and market volatility futures. In the particular case of value and size portfolios, we find that differences in expected returns can be explained by a single-factor projection of the discount factor that loads only on a dividend growth return factor constructed with market dividend swap data. Hence, value and small capitalization stocks have higher expected returns due to their exposure to dividend growth returns implying that growth risks (dividend growth news and/or expected return news associated with dividend growth) are the only source of their risk premia. A tradable dividend level factor and a volatility-based factor are also priced in the cross-section of other stock portfolios sorted on dividend yield, earnings yield and cash-flow-to-price.
Author: Wayne Ferson
Publisher: MIT Press
ISBN: 0262351307
Category : Business & Economics
Languages : en
Pages : 497
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Book Description
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.
Author: Radu Burlacu
Publisher:
ISBN:
Category :
Languages : en
Pages : 42
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Book Description
We derive a cross-sectional asset pricing measure from a noisy multi-asset rational expectations equilibrium model. The measure is based on the time-series covariance of an asset's returns and security prices. Empirically, stocks with a measure one standard deviation above and below the average have returns that differ by 0.36% the following month (4.44% per annum) which is statistically significant at the 1%-level. Results remain significant after including variables such as stock market capitalization, book-to-market ratio, and the probability of information-based trading. Our measure can be understood as a proxy for information risk and/or supply uncertainty. We show the two explanations are theoretically intertwined.
Author: Hesu Yang
Publisher:
ISBN:
Category :
Languages : en
Pages : 202
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Book Description
We provide a detailed investigation of interaction effects, calendar and time-of-day effects, and industry-aggregation returns of various cross-sectional biases in the literature using a WLS Fama-Macbeth regression methodology on daily returns in the US equity markets from 1982 to 2011 and on intraday returns from 1993 to 2007. Among our findings regarding return effects are that 1) the reversal-momentum-reversal pattern in the short-, medium-, and long-term is highly variable by month, that 2) the industry momentum effect, as initially reported in Moskowitz and Grinblatt (1999) has largely disappeared according to the given methodology, and that 3) while intraday cross-sectional return variation displays periodicity effects as described by Heston, Korajczyk and Sadka (2010), the return structure varies significantly by time of day, unlike their report. Additionally, we also find that the “linearity” of a stock's past returns, as well as the skewness of the returns, have power in predicting the cross-section of stock returns; the results for skewness provide some empirical support for the results of Barberis and Huang (2008). For the size, value, risk, and turnover factors that we test, returns are generally much stronger in January than in other months, although industry aggregates general show little predictive power (with a few exceptions), echoing the results of Asness, Porter, and Stevens (2000). Finally, we implement a testing scheme that evaluates returns to portfolios that capture some of the pricing biases, taking into account various real-world constraints and trading costs. We find that 1) there are significant risk-adjusted returns to semi-active “structured” portfolios that arbitrage the noted biases (net of trading costs, given the constraints), especially after 2002, but that 2), using a short-scale time frame for calculating IR encourages benchmark hugging and suggests a semi-passive portfolio over active portfolios.
Author: Thanos Verousis
Publisher:
ISBN:
Category :
Languages : en
Pages : 33
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Book Description
This study investigates whether the cross-sectional dispersion of stock returns, which reflects the aggregate level of idiosyncratic risk in the market, represents a priced state variable. We find that stocks with high sensitivities to dispersion offer low expected returns. Furthermore, a zero-cost spread portfolio that is long (short) in stocks with low (high) dispersion betas produces a statistically and economically significant return, after accounting for its exposure to other systematic risk factors. Dispersion is associated with a significantly negative risk premium in the cross-section (-1.32% per annum) which is distinct from premia commanded by a set of alternative systematic factors. These results are robust to a wide set of stock characteristics, market conditions, and industry groupings.
