scholarly journals Higher moments and US industry returns: realized skewness and kurtosis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoyue Chen ◽  
Bin Li ◽  
Andrew C. Worthington
Keyword(s):  
Risk Factors ◽  
Asset Pricing ◽  
Trading Strategies ◽  
Higher Moments ◽  
Pricing Models ◽  
Firm Level ◽  
Content Type ◽  
Industry Level ◽  
Asset Pricing Models ◽  
Common Risk Factors

Purpose The purpose of this paper is to examine the relationships between the higher moments of returns (realized skewness and kurtosis) and subsequent returns at the industry level, with a focus on both empirical predictability and practical application via trading strategies. Design/methodology/approach Daily returns for 48 US industries over the period 1970–2019 from Kenneth French’s data library are used to calculate the higher moments and to construct short- and medium-term single-sort trading strategies. The analysis adjusts returns for common risk factors (market, size, value, investment, profitability and illiquidity) to confirm whether conventional asset pricing models can capture these relationships. Findings Past skewness positively relates to subsequent industry returns and this relationship is unexplained by common risk factors. There is also a time-varying effect in which the predictive role of skewness is much stronger over business cycle expansions than recessions, a result consistent with varying investor optimism. However, there is no significant relationship between kurtosis and subsequent industry returns. The analysis confirms robustness using both value- and equal-weighted returns. Research limitations/implications The calculation of realized moments conventionally uses high-frequency intra-day data, regrettably unavailable for industries. In addition, the chosen portfolio-sorting method may omit some information, as it compares only average group returns. Nonetheless, the close relationship between skewness and future returns at the industry level suggests variations in returns unexplained by common risk factors. This enriches knowledge of market anomalies and questions yet again weak-form market efficiency and the validity of conventional asset pricing models. One suggestion is that it is possible to significantly improve the existing multi-factor asset pricing models by including industry skewness as a risk factor. Practical implications Given the relationship between skewness and future returns at the industry level, investors may predict subsequent industry returns to select better-performing funds. They may even construct trading strategies based on return distributions that would generate abnormal returns. Further, as the evaluation of individual stocks also contains industry information, and stocks in industries with better performance earn higher returns, risks related to industry return distributions can also shed light on individual stock picking. Originality/value While there is abundant evidence of the relationships between higher moments and future returns at the firm level, there is little at the industry level. Further, by testing whether there is time variation in the relationship between industry higher moments and future returns, the paper yields novel evidence concerning the asymmetric effect of stock return predictability over business cycles. Finally, the analysis supplements firm-level results focusing only on the decomposed components of higher moments.

Multi-factor asset pricing models in emerging and developed markets

2019 ◽  
Vol 46 (3) ◽  
pp. 360-380
Author(s):  
Vaibhav Lalwani ◽  
Madhumita Chakraborty
Keyword(s):  
Asset Pricing ◽  
Factor Model ◽  
Five Factor Model ◽  
Superior Performance ◽  
International Studies ◽  
Pricing Models ◽  
Content Type ◽  
Developed Markets ◽  
Asset Pricing Models ◽  
Pricing Errors

Purpose The purpose of this paper is to compare the performance of various multifactor asset pricing models across ten emerging and developed markets. Design/methodology/approach The general methodology to test asset pricing models involves regressing test asset returns (left-hand side assets) on pricing factors (right-hand side assets). Then the performance of different models is evaluated based on how well they price multiple test assets together. The parameters used to compare relative performance of different models are their pricing errors (GRS statistic and average absolute intercepts) and explained variation (average adjusted R2). Findings The Fama-French five-factor model improves the pricing performance for stocks in Australia, Canada, China and the USA. The pricing in these countries appears to be more integrated. However, the superior performance in these four countries is not consistent across a variety of test assets and the magnitude of reduction in pricing errors vis-à-vis three- or four-factor models is often economically insignificant. For other markets, the parsimonious three-factor model or its four-factor variants appear to be more suitable. Originality/value Unlike most asset pricing studies that use test assets based on variables that are already used to construct RHS factors, this study uses test assets that are generally different from RHS sorts. This makes the tests more robust and less biased to be in favour of any multifactor model. Also, most international studies of asset pricing tests use data for different markets and combine them into regions. This study provides the evidence from ten countries separately because prior research has shown that locally constructed factors are more suitable to explain asset prices. Further, this study also tests for the usefulness of adding a quality factor in the existing asset pricing models.

Market anomalies, asset pricing models, and stock returns: evidence from the Indian stock market

2015 ◽  
Vol 9 (3) ◽  
pp. 306-328 ◽  
Author(s):  
Saumya Ranjan Dash ◽  
Jitendra Mahakud
Keyword(s):  
Asset Pricing ◽  
Stock Market ◽  
Pricing Models ◽  
Market Anomalies ◽  
Content Type ◽  
Asset Pricing Models ◽  
Asset Pricing Anomalies ◽  
Pricing Anomalies ◽  
Fama And French ◽  
Three Factor

Purpose – This paper aims to investigate whether the use of conditional and unconditional Fama and French (1993) three-factor and Carhart (1997) four-factor asset pricing models (APMs) captures the role of asset pricing anomalies in the context of emerging stock market like India. Design/methodology/approach – The first step time series regression approach has been used to drive the risk-adjusted returns of individual securities. For examining the predictability of firm characteristics or asset pricing anomalies on the risk-adjusted returns of individual securities, the panel data estimation technique has been used. Findings – Fama and French (1993) three-factor and Carhart (1997) four-factor model in their unconditional specifications capture the impact of book-to-market price and liquidity effects completely. When alternative APMs in their conditional specifications are tested, the importance of medium- and long-term momentum effects has been captured to a greater extent. The size, market leverage and short-term momentum effects still persist even in the case of alternative unconditional and conditional APMs. Research limitations/implications – The empirical analysis does not extend for different market scenarios like high and low volatile market or good and bad macroeconomic environment. Because of the constraint of data availability, the authors could not include certain important anomalies like net operating assets, change in gross profit margin, external equity and debt financing and idiosyncratic risk. Practical implications – Although the active investment approach in stock market shares a common ground of semi-strong form of market efficiency hypothesis which also supports the presence of asset pricing anomalies, less empirical evidence has been explored in this regard to support or repute such belief of practitioners. Our empirical findings make an attempt in this regard to suggest certain anomaly-based trading strategy that can be followed for active portfolio management. Originality/value – From an emerging market perspective, this paper provides out-of-sample empirical evidence toward the use of conditional Fama and French three-factor and Carhart four-factor APMs for the complete explanation of market anomalies. This approach retains its importance with respect to the comprehensiveness of analysis considering alternative APMs for capturing unique effects of market anomalies.

scholarly journals International Real Estate Review

2008 ◽  
Vol 11 (2) ◽  
pp. 32-46
Author(s):  
John Okunev ◽  
◽  
Patrick J. Wilson ◽  
Keyword(s):  
Asset Pricing ◽  
Real Estate ◽  
Risk Premium ◽  
Time Variation ◽  
Trading Strategies ◽  
Real Estate Investment Trust ◽  
Pricing Models ◽  
Real Estate Investment ◽  
Risk Premiums ◽  
Asset Pricing Models

This study presents further evidence of the predictability of excess equity REIT (real estate investment trust) returns . Recent evidence on forecasting excess returns using fundamental variables has resulted in diminishing returns from the 1990’s onward. Trading strategies based on these forecasts have not significantly outperformed the buy/hold strategy of the 1990’s. We have developed an alternative strategy that is based on the time variation of the risk premium of investors. Our results indicate that it is possible to outperform the buy/hold strategy by modeling the time variation of the risk premium. By modeling the dynamic behavior of the risk premium, we are able to implicitly capture economic risk premiums that are not captured by conventional multi beta asset pricing models.

scholarly journals Misspecification-Robust Inference in Linear Asset-Pricing Models with Irrelevant Risk Factors

2014 ◽  
Vol 27 (7) ◽  
pp. 2139-2170 ◽  
Author(s):  
Nikolay Gospodinov ◽  
Raymond Kan ◽  
Cesare Robotti
Keyword(s):  
Risk Factors ◽  
Asset Pricing ◽  
Robust Inference ◽  
Pricing Models ◽  
Asset Pricing Models

Pricing office rents in Sydney CBD: testing the water on automated rent reviews

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Olga Filippova ◽  
Jeremy Gabe ◽  
Michael Rehm
Keyword(s):  
Asset Pricing ◽  
Real Estate ◽  
Percentage Error ◽  
Residential Real Estate ◽  
Pricing Models ◽  
Content Type ◽  
Market Rent ◽  
Absolute Percentage Error ◽  
Asset Pricing Models ◽  
Out Of Sample

PurposeAutomated valuation models (AVMs) are statistical asset pricing models omnipresent in residential real estate markets, where they inform property tax assessment, mortgage underwriting and marketing. Use of these asset pricing models outside of residential real estate is rare. The purpose of the paper is to explore key characteristics of commercial office lease contracts and test an application in estimating office market rental prices using an AVM.Design/methodology/approachThe authors apply a semi-log ordinary least squares hedonic regression approach to estimate either contract rent or the total costs of occupancy (TOC) (“grossed up” rent). Furthermore, the authors adopt a training/test split in the observed leasing data to evaluate the accuracy of using these pricing models for prediction. In the study, 80% of the samples are randomly selected to train the AVM and 20% was held back to test accuracy out of sample. A naive prediction model is used to establish accuracy prediction benchmarks for the AVM using the out-of-sample test data. To evaluate the performance of the AVM, the authors use a Monte Carlo simulation to run the selection process 100 times and calculate the test dataset's mean error (ME), mean absolute error (MAE), mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), coefficient of dispersion (COD) and the training model's r-squared statistic (R2) for each run.FindingsUsing a sample of office lease transactions in Sydney CBD (Central Business District), Australia, the authors demonstrate accuracy statistics that are comparable to those used in residential valuation and outperform a naive model.Originality/valueAVMs in an office leasing context have significant implications for practice. First, an AVM can act as an impartial arbiter in market rent review disputes. Second, the technology may enable frequent market rent reviews as a lease negotiation strategy that allows tenants and property owners to share market risk by limiting concerns over high costs and adversarial litigation that can emerge in a market rent review dispute.

Testing International Asset Pricing Models Using Implied Costs of Capital

2009 ◽  
Vol 44 (2) ◽  
pp. 307-335 ◽  
Author(s):  
Charles Lee ◽  
David Ng ◽  
Bhaskaran Swaminathan
Keyword(s):  
Asset Pricing ◽  
World Market ◽  
Idiosyncratic Volatility ◽  
Expected Returns ◽  
International Asset Pricing ◽  
Pricing Models ◽  
Firm Level ◽  
Economic Relations ◽  
Asset Pricing Models ◽  
Market Beta

AbstractThis paper tests international asset pricing models using firm-level expected returns estimated from an implied cost of capital approach. We show that the implied approach provides clear evidence of economic relations that would otherwise be obscured by the noise in realized returns. Among G-7 countries, expected returns based on implied costs of capital have less than one-tenth the volatility of those based on realized returns. Our tests show that firm-level expected returns increase with world market beta, idiosyncratic volatility, financial leverage, and book-to-market ratios, and decrease with currency beta and firm size.

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