Factors In Asset Pricing Models Research Articles

The extreme temperature factor in asset pricing models: Evidence from Europe

Growing concern about climate change has led to increased research into the effects of climate on markets. One of the weather variables studied is temperature. The previous studies considered that the temperature influences on asset returns through changes in investor mood. There are few studies that incorporate a risk factor to analyze the effects of temperature changes on asset returns. We extract positive and negative extreme temperature changes to design three temperature factors. By a cross-section asset pricing model, we find evidence that temperature shocks (hot and cold) show a significant monthly risk premium and skewness for temperature changes.

Testing Illiquidity and Momentum Factors with Asset Pricing: Evidence from Emerging Capital Markets of South Asia

This paper explores the asset pricing factors which pose aggregate risk to the portfolio returns in the Pakistan Stock Exchange (PSX). The purpose is to test the illiquidity and momentum factors along with the well-documented three factors in asset pricing model using Fama and French (2015) and Lam and Tam (2011) for one of the emerging markets of South Asia, i.e., Pakistan, using 20-year monthly data from January 2001 to December 2020. This paper adopts the methodology of Fama and French (2015) as used in the developed market and Lam and Tam (2011) for the emerging market phenomenon, to formulate the portfolio returns based on Size, Book-to-Market(BM), and the Liquidity, and test these returns against the factors causing the return variations. The results based on the 3-factor model, liquidity-adjusted model, and the momentum-based model validate factors of excess market return (CAPM), size, value, liquidity, and momentum as significant risk factors in explaining the variations in portfolio returns of PSX stocks. The portfolio access returns for PSX stocks are adjusted for risk factors with mixed significant response holding average explanatory power. Our results are robust to the GRS F-test.

Default risk, size, and equity returns: Evidence from an emerging stock market

Although the relationship among default risk, size, and equity returns is comprehensively investigated in developed stock markets, the analysis is still lacking for Vietnam, an important emerging market in Southeast Asia. The key aim of this research is to examine the relationship among default risk, size, and equity returns in the Vietnamese stock market, and compare the explanatory power of the default-risk factor to the size factor in asset pricing models. We use an option-based model to obtain the proxy of default risk for approximately 360 listed firms in Vietnam. Empirical results show that distance-to-default is negatively related to stock returns. When size is controlled, the default effect exists in different size-ranked portfolios. In asset pricing models, the default-risk factor is more powerful in explaining Vietnamese equity returns compared to the size factor of Fama and French (1993). As a result, default risk is a significant factor in Vietnamese stock returns, consistent with the risk-based point of view.

Is Farmland a Common Risk Factor in Asset Pricing Models?

Farmland represents the largest share of the U.S. agricultural balance sheet, accounting for nearly 80% of U.S. farm assets. Motivated by the well-documented real estate risk factor and the similarities between farmland and real estate investing, this paper examines whether farmland has a risk factor, like real estate, that is affecting asset returns. The proposed farmland risk factor is proxied by the National Council of Real Estate Investment Fiduciaries farmland property index (Farmland NCREIF). Relying on quarterly data from 1991-Q1 to 2016-Q2, we employed the Generalized Method of Moments (GMM) to provide empirical evidence that even though farmland exhibits diversification benefits, it fails to be a risk factor. Instead, market frictions and/or non-risk explanations might provide a more plausible description of farmland’s high risk-adjusted return.

A Cross-Sectional Test for Missing Factors in Asset Pricing Models

A Cross-Sectional Test for Missing Factors in Asset Pricing Models

The Prior Adaptive Group Lasso with an Application to Risk Factor Selection

This paper develops and presents the prior adaptive group lasso for generalized linear models. The prior adaptive group lasso is an extension of the prior lasso developed by Jiang, He, and Zhang (2016), which allows for the use of existing information from previous or similar studies in the estimation of the lasso. We demonstrate that the estimator exhibits consistent variable selection and estimation similarly to those derived in Wang and Tian (2019) under at set of similar conditions. The performance of the prior adaptive group lasso estimator is illustrated in a Monte Carlo study. Finally, the estimator is applied in selecting the set of relevant risk factors in asset pricing models conditioning on the fact that the chosen factors must be able to price the test assets as well as the remaining factors. The empirical study shows that the prior adaptive group lasso yields a set of factors that explain the time variation in the returns while delivering 𝛼 estimates close to zero. We also show how this set of factors has evolved over time. We find that the canonical factor models of Fama and French (1993), (Carhart, 1997), (Fama and French, 2015), and (Hou, Xue, and Zhang, 2015) are insufficient to price the cross section of the test assets together with the remaining traded factors, and we find that the required number of pricing factors to include at any given time is closer to 20.

Data Envelopment Analysis and Multifactor Asset Pricing Models

Recent literature shows that market anomalies have significantly diminished, while research on market factors has largely improved the performance of asset pricing models. In this paper we study the extent to which data envelopment analysis (DEA) techniques can help improve the performance of multifactor models. Specifically, we test the explanatory power of the Fama and French three-factor model, combined with an additional factor based on DEA, on a sample of 2101 European equity funds, for the period from 2001 to 2016. Accordingly, we first form the fund portfolios that constitute our test assets and create the efficiency factor. Secondly, we estimate the prices of risk tied to the four factors using ordinary least squares (OLS) on a two-stage cross-sectional regression. Finally, we use the R-squared statistic estimated by generalized least squares (GLS), as well as the Gibbons Ross and Shanken test and the J-test for overidentifying restrictions in order to study the performance of the model, including and omitting the efficiency factor. The results show that the efficiency factor improves the performance of the model and reduces the pricing errors of the assets under consideration, which allows us to conclude that the efficiency index may be used as a factor in asset pricing models.

Circus Ring to Zoo to Museum: The Fragility of Factors in Characteristic-based Asset Pricing Models

Economically relevant factors in asset pricing models should impound information on the future path of state variables that drive asset risk premia. Imposing this condition, we investigate which publicly available characteristics predict individual stock returns during the sample period used by Fama and French (1993) i.e. their discovery period and the post-1993 or out-of-sample period. We find four characteristics have significant predictive power, in the cross-section, over and above that of their factors. In the out-of-sample period, five new characteristics become significant predictors. Similar results are seen for the Chen and Zhang (2010) model. Next, we find that characteristics that forecast stock returns before and after major economic events are very different. Finally, we find that the ability of characteristics to reflect economic uncertainty and sentiment changes in sign and magnitude over time- often vanishing altogether. Our results suggest that the search for a unique characteristic-based asset pricing model is unlikely to be fruitful given the secular variation in the relation between the sources of macroeconomic risks and firm-level characteristics.

On Comparing Asset Pricing Models

ABSTRACTRevisiting the framework of (Barillas, Francisco, and Jay Shanken, 2018, Comparing asset pricing models, The Journal of Finance 73, 715–754). BS henceforth, we show that the Bayesian marginal likelihood‐based model comparison method in that paper is unsound : the priors on the nuisance parameters across models must satisfy a change of variable property for densities that is violated by the Jeffreys priors used in the BS method. Extensive simulation exercises confirm that the BS method performs unsatisfactorily. We derive a new class of improper priors on the nuisance parameters, starting from a single improper prior, which leads to valid marginal likelihoods and model comparisons. The performance of our marginal likelihoods is significantly better, allowing for reliable Bayesian work on which factors are risk factors in asset pricing models.

Weighing asset pricing factors: a least squares model averaging approach

Empirical evidence has demonstrated that certain factors in asset pricing models are more important than others for explaining specific portfolio returns. We propose a technique that evaluates the factors included in popular linear asset pricing models. Our method has the advantage of simultaneously ranking the relative importance of those pricing factors through comparing their model weights. As an empirical verification, we apply our method to portfolios formed following Fama and French [A five-factor asset pricing model. J. Financ. Econ., 2015, 116, 1–22] and demonstrate that models accommodated to our factor rankings do improve their explanatory power in both in-sample and out-of-sample analyses.

Stock pricing in Latin America: The synchronicity effect

This paper investigated whether the stock price synchronicity level (SPSL) is a pricing factor in the Latin American scenario. To do so, the shares with the highest liquidity level listed in the stock exchange in five Latin American (LA) countries (Argentina, Brazil, Chile, Mexico and Peru) were used. The results indicated that the SPSL is associated with a positive premium. This premium was obtained by the CAPM model and by the Fama-French three- and five-factor models. There was evidence that the average SPSL increases in periods of greater turmoil in the financial markets. Moreover, it was found that the SPSLs are not associated monotonically with the efficiency levels of stock prices. Overall, the use of the SPSL factor in asset pricing models reduced the bias in estimating the stock premiums in LA.

Asset growth and stock return: evidence in the Brazilian market

Abstract Empirical evidence suggests that firms which have experienced fast growth, through increased external funding and by making capital investments and acquisitions, tend to show bad operating performance and lower stock returns, whereas firms that have experienced contraction, through divestiture, share repurchase and debt retirement, tend to show good operating performance and higher stock returns. So, this study aimed to analyze the relationship between asset growth and stock return in the Brazilian stock market, and it tested the hypothesis that asset growth is negatively related to future stock return. To do this, the methodology was divided into 3 steps: verifying 1) if asset growth anomaly exists; 2) if this relation may be explained by the investment friction hypothesis and/or by the limits-to-arbitrage hypothesis; and 3) if asset growth is a risk factor or mispricing. In addition, the analysis was carried out both at a portfolio level and an individual assets level. The sample included all the non-financial firms listed at B3 from June 1997 to June 2014. As for the main results, this study found that the asset growth effect exists, both at the portfolio level and the individual assets level, although it is sensitive to the proxy. About the effect’s materiality, this study concluded that the asset growth effect is not economically relevant, since it is not observed in big firms, regardless of the proxy used, a fact that makes it difficult to explore this effect. Another finding is that the asset growth effect may not be related to the limits-to-arbitrage hypothesis and to the financial constraint hypothesis; also, this effect may be considered a risk factor, suggesting that the investment effect documented in the Brazilian stock market may be explained by the rational asset pricing perspective. Therefore, capital market professionals should take into account the asset growth factor in asset pricing models for better investment risk assessment.

Affect and stock returns

We argue that affect plays an important role in pricing models for stocks. We exploit a novel dataset of opinions shared on a social media platform to quantify the affect associated with stocks. We show that individual stock opinions collected from a social media platform systematically differ from other risk factors and qualify as an additional factor in asset pricing models. Stocks with high affect feature smaller risk premiums.

Real Estate and Consumption Growth as Common Risk Factors in Asset Pricing Models

AbstractUsing a linear multifactor pricing model, we study the influence of equity market, the consumption growth and the return on real estate wealth on asset returns. The real estate risk factor is proxied alternatively by the National Association of Real Estate Investment Trusts (NAREIT) index, unlevered NAREIT index and National Council of Real Estate Investment Fiduciaries property index. Estimates are based on CRSP's monthly decile portfolio returns from January 1972 to December 2013 (including the Vintage REIT era and the New REIT era). Generalized method of moment results show that the real estate factor is particularly useful to explain the cross‐sectional variation of returns in the last two decades generally associated with the so‐called real estate bubble.

The Determinants of Risk Premia on the Italian Stock Market: Empirical Evidence on Common Factors in Asset Pricing Models

We study the pricing factor structure of Italian equity returns using 25 years of data. A two-step empirical analysis is provided where first we estimate an unrestricted multifactor model to test if there is any evidence of misspecification. Then, we estimate the restricted model through the Generalized Methods of Moments. We find that the market premium and the size premium are confirmed for a domestic Italian investor. On the contrary, weak evidence is found for the value premium. Finally, we highlight, that augmenting the model with a momentum factor may at least partially improve its performance. As a robustness check we control if the above results also hold for three shorter sub-periods taking into account the macroeconomic and financial conditions that characterized the Italian economy. The results are generally confirmed in the case of the size and value factors while the momentum effect shows an irregular trend playing any role in the first sub-period but becoming more important in the subsequent two.

The Determinants of Risk Premia on the Italian Stock Market: Empirical Evidence on Common Factors in Asset Pricing Models

We study the pricing factor structure of Italian equity returns using 25 years of data. A two-step empirical analysis is provided where first we estimate an unrestricted multifactor model to test if there is any evidence of misspecification. Then, we estimate the restricted model through the Generalized Methods of Moments. We find that the market premium and the size premium for stocks are confirmed for a domestic Italian investor. On the contrary, weak evidence is found for the value premium. Finally, we highlight, that augmenting the model with a momentum factor may at least partially improve its performance. As a robustness check we control if the above results also hold for three shorter sub-periods taking into account the macroeconomic and financial conditions that characterized the Italian economy. The results are generally confirmed in the case of the size and value factors while the momentum effect shows an irregular trend playing any role in the first sub-period but becoming more important in the subsequent two.

A New Asset Pricing Model Based on the Zero-Beta CAPM: Theory and Evidence

This paper utilizes Black's (1972) zero-beta CAPM to derive an alternative form dubbed the ZCAPM. The ZCAPM posits that asset prices are a function of market risk composed of two components: average market returns and cross-sectional market volatility. Market risk associated with average market returns in the CAPM market model is known as beta risk. We refer to market risk related to cross-sectional market volatility as zeta risk. Using U.S. stock returns from January 1965 to December 2015, out-of-sample cross-sectional asset pricing tests show that both market forces in the ZCAPM are significantly priced. For example, using beta-zeta sorted portfolios, the market prices of beta and zeta risks have t-values of 11.2 and 23.1, respectively, which far exceed the significance of any previous cross-sectional tests of various factors in asset pricing models. These and other empirical tests lead us to conclude that the ZCAPM represents a major breakthrough in asset pricing models.

Modelos de Precificação de Ativos e o Efeito Liquidez: Evidências Empíricas no Mercado Acionário Brasileiro

Este artigo teve por objetivo analisar se existe o prêmio de liquidez no mercado acionário brasileiro. Adicionalmente, buscou-se averiguar se a liquidez é precificada e se o retorno das ações era explicado não apenas pelo fator de risco sistemático, conforme propõe o CAPM, pelos três fatores de Fama e French (1993) e pelo fator momento de Carhart (1997), mas também pela liquidez, conforme sugerido por Amihud e Mendelson (1986). Para isso, foram usadas cinco medidas de liquidez e optou-se pelo emprego de portfólios. Dentre os modelos de precificação de ativos analisados, o CAPM mostrou-se o menos adequado na explicação dos retornos. Verificou-se que a inclusão dos fatores tamanho e BM no CAPM, do fator momento no modelo de três fatores, e da liquidez no modelo de quatro fatores melhorou seu poder explicativo dos retornos das carteiras, evidenciando uma superioridade do modelo de cinco fatores em relação aos demais modelos de precificação de ativos.

Assessing Misspecified Asset Pricing Models with Empirical Likelihood Estimators

Hansen and Jagannathan (1997) compare misspecified asset pricing models based on least-square projections on a family of admissible stochastic discount factors. We extend their fundamental contribution by considering Minimum Discrepancy (MD) projections where misspecification is measured by convex functions that can explicitly take into account higher moments of asset returns. The MD problems are solved on dual spaces with the interpretation of optimal portfolio problems based on HARA utility functions, producing a family of estimators that captures the least-square problem as a particular case. We use our proposed methodology to test the Consumption Asset Pricing Model and illustrate, under several different discrepancy functions and regions of the parametric space, the relation between the parametric proxy model, and the closest admissible SDF. On the estimation problem, not surprisingly, all MD estimators clearly reject the CCAPM model. However, some of these estimators lead to admissible SDFs that are very distinct from the one implied by the least-square solution. By their pricing implications, this rich set of optimal MD SDFs represent useful tools to diagnose missing factors in asset pricing models.

Investor Sentiment and REIT Returns

Recent advances in the field of behavioral finance have given a fillip to the use of behavioral factors in asset pricing models. This study adds to the understanding of the REIT return generating process by exploring the behavioral impact of investor sentiment on REIT returns. The results show that when investors are optimistic (pessimistic), REIT returns become higher (lower). These findings are robust when conventional control variables are considered. Empirical analysis indicates steady erosion in the importance of the default and term structure interest rate variables previously considered as important determinants of REIT returns. Previous noise trading papers that consider the impact of institutional traders conclude that institutional investors cannot arbitrage away noise trader risk. The results of this paper find an exception in the case of small REITs. Examination of REITs based on size reveals that the return generating process of small REITs differs from that of mid-size and large REITs. Analysis of the return generating process by performance shows high performance REITs are more sensitive to the independent variables in the model as compared to the low and mid performance REITs.