Interest rate,size and book-to-market effects in Australian financial firms

The Fama–French three-factor model (1993) has been extensively used to study the pricing of nonfinancial stocks. This study provides the first examination of the pricing of Australian financial stocks using the Fama–French framework. The four-factor model (market, size, book-to-market and momentum) augmented with the level, slope and curvature of the interest rate term structure is used to examine the pricing of Australian financial stocks. The interest rate factors have not been previously considered for pricing Australian stocks within the Fama–French framework. Consistent with US evidence, we use a system-based estimation to show that the size and book-to-market factors are not priced in the cross section of the equity returns of Australian financial stocks. Momentum and term spread are priced in the equity returns of both financial and nonfinancial stocks. These findings are robust to the inclusion of control variables such as default spread, the inflation rate and a dummy variable for the global financial crisis.     

Performance of value- and size-based strategies in the Italian stock market

This paper investigates the performance of size- and value-based strategies in the Italian stock market in the period 2000–2018. Previous research argued the impossibility to define properly value-sorted portfolios due to the inaccuracy of book-to-market ratios available for Italian listed stocks. Using more accurate data, we implement portfolios sorting based on value and growth stocks, to assess the relevance of the value factor in the Italian stock market. We find that the capital asset pricing model fails to explain the cross-section of returns on the different strategies while the Fama and French three-factor model provides a better fit. The results show that all three factors are significant in explaining Italian stock returns during the sample period. Unlike previous studies, which either found no value effect at all or no clear-cut results when testing the book-to-market variable, we find that the value factor is statistically significant and the associated risk premium is of a considerable size.     

The Pricing of Italian Equity Returns

In this paper, we investigate the relationship between common risk factors and average returns for Italian stocks. Our research has identified the Italian stock market's economic variables by using the results from factor analyses and time series regressions. We study several multi‐factor models combining the relevant macroeconomic variables with the mimicking equity portfolios SMB (small minus big) and HML (high minus low) proposed by Fama and French (1993). The key question we want to ask ourselves, is whether the influential role of the size and book‐to‐market equity factors in explaining average stock returns can stand up well when competing with some macroeconomic factors. In other words, do stock returns carry some risk premium that is independent of either the market return or the economic forces that underlie the common variation in returns? Our empirical work estimates risk premiums using both traditional two‐pass procedures and one‐pass (full information) methodologies. We show that only the market index and variables linked to interest rate shifts are consistently priced in the Italian stock returns. The role of other factors, and in particular both the size and the price‐to book ratio, are crucially dependent on the estimation procedure. (J.E.L.: G11, G12).     

Drivers of expected returns in Istanbul stock exchange: Fama–French factors and coskewness

We investigate the impact of coskewness on the variation of portfolio excess returns in Istanbul Stock Exchange (ISE) over the period July 1999 to December 2005. We form portfolios according to size, industry, size and book-to-market ratio, momentum and coskewness and compare alternative asset pricing models. The traditional capital asset pricing model (CAPM) and the three-factor model of Fama and French are tested in the multivariate testing procedure of Gibbons–Ross–Shanken (1989). Coskewness is introduced as a fourth factor and its incremental effect over CAPM and Fama–French factors is examined both in multivariate tests and in cross-sectional regressions. The findings reveal that coskewness is able to explain the size premium in ISE. Hence, the basic two-moment CAPM without the coskewness factor would underestimate the expected return of size portfolios. Multivariate test results indicate that coskewness reduces the pricing bias, albeit insignificantly. Cross-sectional analysis uncovers that coskewness has a significant additional explanatory power over CAPM, especially for size and industry portfolios. However, coskewness does not have a significant incremental explanatory power over Fama–French factors in ISE.     

法玛—弗兰奇五因子模型的动态分析*

本文采用动态模型平均（dynamic model averaging,DMA）算法对法玛—弗兰奇五因子模型（Fama French five factor model,FF5模型）进行了系统性研究。基于法玛和弗兰奇（Fama and French）的数据,笔者所进行的实证分析表明：资产定价模型因子对投资组合收益的预测能力和系数是随时间动态变化的;不存在固定的因子模型能够同时解释和预测多种投资组合的收益;在对投资组合收益率进行预测时,DMA算法的预测均方误差（mean square error,MSE）显著低于固定系数的FF5模型。     

Rational functions: an alternative approach to asset pricing

This paper shows that asset prices are linear polynomials of various underlying explanatory factors and asset returns being ratios of these polynomials, are rational functions that do not add linearly when averaging. Hence, average returns should be modeled based on stock prices. However, continuous returns may be treated as approximately linear across time and modeled directly. Our new Rational Function (RF) models, empirically outperform the traditional asset pricing models like the Capital Asset Pricing Model (CAPM) and the Fama–French three and five-factor models for both average and continuous returns. Moreover, the RF theory also provides a model to estimate the asset volumes. The average change in asset volumes together with average returns provide the estimates for average change in market values of assets. Thus, the RF model approach can be used to select assets that provide either highest returns for profit maximization or highest change in market values for wealth maximization for given levels of risk.     

Testing Fama–French’s new five-factor asset pricing model: evidence from robust instruments

Fama and French (FF, 2015) propose a five-factor asset pricing model that captures size, value, profitability and investment patterns. The primary purpose here is to further investigate this new model using an improved GMM-based robust instrumental variables technique. A further purpose is to explore the relationship among the FF factors and the Pástor–Stambaugh (PS, 2003) liquidity factor. We conclude that except for the market factor, all of the factors including liquidity are not significant at even the 5% level using our GMM approach for almost all of the FF 12 sectors.     

Asset pricing with time-varying betas for stocks traded on S&P 500

This study uses a novel approach for capturing time variation in betas whose pattern is treated as a function of market returns. A two-factor model (TFM) is constructed using estimated coefficients of a nonlinear regression. The model is tested against the CAPM and the Fama and French three-factor model in the context of time series regressions. The used stocks are traded on S&P 500. The period spans from 1993 to 2011. The time series regression results depict the superiority of the TFM in explaining portfolio returns including momentum ones. We also provide evidence that the particular portfolios employed at the construction of the new model accommodate different fundamental characteristics and different risk levels.     

Panel data analysis of multi-factor capital asset pricing models

In this article, we propose MFCAPM panel models with fixed effects and test theories associated with risk exposures and anomalies postulated by Fama and French, and we assess their out-of-sample predictive performances. Based on the portfolios formed by French, we construct 10 panel models, each consisting of 10 portfolios grouped by size deciles, and another 10 panels by value deciles. In the presence of cross-section dependence, the MFCAPM panel model is estimated by the feasible generalized least squares (FGLS) method for the sample period 1963(1)-2018(9). The results show that the market, firm-size and value risk exposures are significant and robust across three-, five- and six-factor panel models. Significant time-fixed effects indicate that there are several portfolios resilient to dot.com bubble peak in 2000, while some others resilient to GFC in 2007. We estimate the models for the in-sample period 1963(1)–1999(12) and generate the out-of-sample portfolio returns for the period 2000(1)–2018(9). We find that portfolio returns forecasts generated by the six-factor panel model are superior to other MFCAPM panel models, mostly due to the momentum factor (investor behaviour) explaining large return variations and volatility exposures. The findings have implications for investors, security traders and portfolio risk managers.     

中国股票市场的二因素模型

参照Fama和French鉴定美国股票市场三因素模型的方法，研究中国股票市场的资产定价模型。在中国股票市场，账面市场效应并不存在，但与规模相关的某种系统风险因素在股票定价中起到了重要作用。由此鉴定出中国股票定价的二因素模型。     

Daily market news sentiment and stock prices

In recent years there has been a tremendous growth in readily available news related to traded assets in international financial markets. This financial news is now available through real-time online sources such as Internet news and social media sources. The increase in the availability of financial news and investor’s ease of access to it has a potentially significant impact on market stock price movement as these news items are swiftly transformed into investors sentiment which in turn drives prices. In this study, we use the Thomson Reuters News Analytics (TRNA) data set to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index constituents. We use these daily DJIA market sentiment scores to study the influence of financial news sentiment scores on the stock returns of these constituents using a multi-factor model. We augment the Fama–French three-factor model with the day’s sentiment score along with lagged scores to evaluate the additional effects of financial news sentiment on stock prices in the context of this model using Ordinary Least Square (OLS) and Quantile Regression (QR) to analyse the effect around the tail of the return distribution. We also conduct the analysis using the seven-day simple moving average (SMA) of the scores to account for news released on non-trading days. Our results suggest that even when market factors are taken into account, sentiment scores have a significant effect on Dow Jones constituent returns and that lagged daily sentiment scores are often significant, suggesting that information compounded in these scores is not immediately reflected in security prices and related return series. The results also indicate that the SMA measure does not have a significant effect on the returns. The analysis using Quantile Regression provides evidence that the news has more impact on left tail compared to the right tail of the returns.     

A panel data robust instrumental variable approach: a test of the new Fama-French five-factor model

Fama and French (FF, 2015) propose a new five-factor asset pricing model that adds profitability and investment patterns to the market, size and value variables used in FF (1992). Our purpose is to investigate this new model using an improved generalized method of moments (GMM)-based robust instrumental variables technique in a fixed-effects panel data framework. To test for measurement errors, we use a modified Hausman artificial regression. We also examine an augmented FF six-factor model that includes the Pástor–Stambaugh (PS, 2003) liquidity factor. Using the FF dataset, our GMM-based panel data approach leads us to conclude that the only consistently significant factor is the market factor.     

Luck versus skill over time: time-varying performance in the cross-section of mutual fund returns

ABSTRACT

Using returns histories spanning January 1984 to October 2014 of 5785 actively managed US closed-end equity mutual funds, we address the ‘thorny problems’ highlighted by Fama and French (The Journal of Finance, 2010, vol. 65, p. 1925) that arise due to their resampling procedure. This prevents them from capturing time variation in the parameters of equilibrium asset pricing models. These problems are addressed by combining innovative procedures which allow for testing of multiple break dates on fund-specific parameters along with cross-section bootstraps that remain valid in the presence of time-varying parameters. We find that substantial proportion – 8% – of the estimated versions of the asset pricing model have significant changes in their parameters. The effects of this time variation on the cross-section distribution of the risk-adjusted performance measure are significant and substantially increase centiles of the right tail of this distribution when compared to those produced without time-varying parameters. Our evidence regarding the lack of actively managed US equity mutual funds that generate excess returns is significantly weaker than those of Fama and French but our results do not overturn their pessimistic conclusion regarding the lack of skilled managers. We do find, unlike Fama and French, that managers generating negative returns are just unlucky but have no skill.     

What does high-dimensional factor analysis tell us about risk factors in the Australian stock market?

Estimates of the cost of equity are often sensitive to the specification of the linear factor model used in their construction. In this article, we use techniques developed for high-dimensional factor models to consider the identity of systematic risk factors in the Australian equities market. Our results support the use of neither the Capital Asset Pricing Model (CAPM) nor the Fama and French model, although they provide an explanation for the empirical performance of these models. Many other model specifications are also rejected. We find that a single-factor model with an equal-weighted market index is the best model for estimating the cost of equity in the Australian context.     

Hedging and diversification across commodity assets

ABSTRACT

We investigate the conditional cross effects and volatility spillover between equity markets and commodity markets (oil and gold), Fama and French HML and SMB factors, volatility index (VIX) and bonds using different multivariate GARCH specifications considering the potential asymmetry and persistence behaviours. We analyse the dynamic conditional correlation between the US equity market and a set of commodity prices and risk factors to forecast the transmission of shock to the equity market firstly, and to determine and compare the optimal hedge ratios from the different models based on the hedging effectiveness of each model. Our findings suggest that all models confirm the significant returns and volatility spillovers. More importantly, we find that GO-GARCH is the best-fit model for modelling the joint dynamics of different financial variables. The results of the current study have implications for investors: (i) the equity market displays inverted dynamics with the volatility index suggesting strong evidence of diversification benefit; (ii) of the hedging assets gold appears the best hedge for the US equity market as it has a higher hedge effectiveness than oil and bonds over time; and (iii) despite these important results, a better hedge may be obtained by using well-selected firm sized and profitability-based portfolios.     

The Performance of Market‐Timing Strategies of Italian Mutual Fund Investors

In this paper, we show that simple buy‐and‐hold strategies over‐perform market‐timing strategies effectively used by Italian investors in equity mutual funds. We estimate returns from market‐timing strategies using aggregate data on net flows for a large sample of equity mutual funds, available to Italian investors, that buy stocks in the following markets: Europe and the euro area, the United States and Emerging markets. In all cases, buy‐and‐hold over‐performs market‐timing with extra returns that go from 0.24 per cent per quarter (Europe and euro area) to 0.87 per cent per quarter (US market). These differences are not explained by differences in risk and risk exposure. Investors should re‐consider their investment strategies and choose cheaper, in terms of fees and simpler, in terms of portfolio allocation, passive strategies.     

Pricing of collectibles: Baedeker guidebooks

We analyze the prices of Baedeker guidebooks issued between 1828 and 1945 in the English, French and German languages. We compile a repeat sales regression index of eBay hammer prices, which reveals a common factor in Baedeker guidebook prices. We find mixed evidence on the underperformance of masterpieces (defined as the most expensive guidebooks). No significant difference is found between the average returns of masters and those of non-masters; however, under a five-factor asset-pricing model consisting of Carhart's four factors and the Pástor–Stambaugh liquidity factor, only masterpieces exhibit significant underperformance. The average price levels of the US, UK and Continental European Baedeker markets do not differ significantly, indicating that the law of one price cannot be rejected. The Baedeker market posts significant negative abnormal returns between 2005 and 2009; however, this result is not found for returns of German-language and non-master guidebooks.     

Which is the best: A comparison of asset pricing factor models in Chinese mutual fund industry

Factor models are commonly used in estimating risk-adjusted fund performance. We compare the commonly used factor models in empirical asset pricing studies and find that Fama and French (2015) five-factor model outperforms other models in the Chinese mutual fund industry and in most fund segments. The factor models we tested are more effective in explaining the return of index funds than other types. Meanwhile, we also find that the capital asset pricing model (CAPM) better controls the estimated alpha dispersion than other models. Though most multifactor models including Carhart (1997) have higher R-squared than CAPM, the cross-sectional differences between them are not statistically significant.     

The effect of debt capacity on the long-term stock returns of debt-free firms

This article examines the long-term stock market performance of debt-free firms with high and low levels of debt capacity to see whether they are different. We use Fama and French’s (1993) three-factor and Carhart’s (1997) four-factor models to examine the subsequent 1, 2, 3, 4 and 5-year stock returns of firms that stayed debt free for 3- and 5-year periods. We measure debt capacity as the expected asset liquidation value of a firm, which is proxied by the firm-level tangibility measure defined by Berger, Ofek, and Swary (1996). We find that regardless of the level of debt capacity, zero-debt firms generate positive abnormal returns in the long run after controlling for key risk factors. We also find support for the notion that preserving debt capacity in the form of higher tangibility reinforces the positive abnormal returns over and above the effect of a zero-leverage policy.     

Industry-level determinants of the linkage between credit and stock markets

This paper examines the relationship between US credit default swaps (CDS) and stock returns on an industry-wide basis across a number of investment horizons, with particular focus on the major determinants of such a relationship. Wavelet analysis is first applied to extract the CDS–stock wavelet correlation for each US industry. Then, Bayesian Model Averaging is employed to identify the key driving factors of the industry CDS–stock wavelet correlations at short- and long-term horizons. The empirical results indicate that the wavelet correlations between the industry CDS and stock returns are primarily negative over time and across time scales. Moreover, the CDS–stock correlation at longer horizons exhibits a much more stable pattern than its counterpart at shorter time frames. The results also demonstrate that the volatility of US Treasury and stock markets, as measured by the MOVE and VIX indices, respectively, the volatility of volatility, as captured by the VVIX index, and US economic policy uncertainty, as measured by the EPU index, are the most robust determinants of the correlation between CDS and stock returns at shorter and longer horizons for most US industries. In contrast, the Fama–French systematic equity factors exhibit a practically negligible explanatory power on the CDS–stock link.