scholarly journals New Methods for the Cross-Section of Returns

2020 ◽  
Vol 33 (5) ◽  
pp. 1879-1890 ◽  
Author(s):  
G Andrew Karolyi ◽  
Stijn Van Nieuwerburgh

Abstract The cross-section and time series of stock returns contains a wealth of information about the stochastic discount factor (SDF), the object that links cash flows to prices. A large empirical literature has uncovered many candidate factors—many more than seem plausible—to summarize the SDF. This special volume of the Review of Financial Studies presents recent advances in extracting information from both the cross-section and the time series, in dealing with issues of replication and false discoveries, and in applying innovative machine-learning techniques to identify the most relevant asset pricing factors. Our editorial summarizes what we learn and offers a few suggestions to guide future work in this exciting new era of big data and empirical asset pricing.

2020 ◽  

This paper examines the relationship between financial constraints and the stock returns explaining the pricing of stock through financially constrained and unconstrained firms in Pakistan. Three proxies; total assets, tangible to total assets and cash holding to total assets ratios) have been used for financial constraints and the study tried to investigate that either the investors are compensated for taking the extra risk or not in Pakistan Stock Exchange (PSX). We find that the financially constrained firms don’t earn higher returns when their capital structure is heavy with liquid assets and their cash flows are more than the unconstrained firms in PSX. Moreover, the time series results showed that the risk-adjusted returns of the most constrained firms give the mix and somewhat negative and significant and insignificant results for the Pakistani firms listed in PSX sorted based on tangible to total assets and Cash holding to total asset ratios. Keywords: Asset Pricing, Financial constraints, risk-adjusted performance of portfolios


2016 ◽  
Vol 6 (2) ◽  
pp. 72-78
Author(s):  
Kung-Cheng Ho ◽  
Shih-Cheng Lee ◽  
Po-Hsiang Huang ◽  
Ting-Yu Hsu

Financial distress has been invoked in the asset pricing literature to explain the anomalous patterns in the cross-section of stock returns. The risk of financial distress can be measured using indexes. George and Hwang (2010) suggest that leverage can explain the distress risk puzzle and that firms with high costs choose low leverage to reduce distress intensities and earn high returns. This study investigates whether this relationship exists in the Taiwan market. When examined separately, distress intensity is found to be negatively related to stock returns, but leverage is found to not be significantly related to stock returns. The results are the same when distress intensity and leverage are examined simultaneously. After assessing the robustness by using O-scores, distress risk puzzle is found to exist in the Taiwan market, but the leverage puzzle is not.


2016 ◽  
Vol 121 (1) ◽  
pp. 28-45 ◽  
Author(s):  
Ray Ball ◽  
Joseph Gerakos ◽  
Juhani T. Linnainmaa ◽  
Valeri Nikolaev

2019 ◽  
Vol 12 (4) ◽  
pp. 165 ◽  
Author(s):  
Zaremba

The last three decades brought mounting evidence regarding the cross-sectional predictability of country equity returns. The studies not only documented country-level counterparts of well-established stock-level anomalies, such as size, value, or momentum, but also demonstrated some unique return-predicting signals such as fund flows or political regimes. Nonetheless, the different studies vary remarkably in terms of their dataset and methods employed. This study aims to provide a comprehensive review of the current literature on the cross-section of country equity returns. We focus on three particular aspects of the asset pricing literature. First, we study the choice of dataset and sample preparation methods. Second, we survey different aspects of the methodological approaches. Last but not least, we review the country-level equity anomalies discovered so far. The discussed cross-sectional return patterns not only provide new insights into international asset pricing but can also be potentially translated into effective country allocation strategies.


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