Equilibrium "Anomalies"

Many empirical “anomalies” are actually consistent with the single beta capital asset pricing model if the empiricist utilizes an equity‐only proxy for the true market portfolio. Equity betas estimated against this particular inefficient proxy will be understated, with the error increasing with the firm's leverage. Thus, firm‐specific variables that correlate with leverage (such as book‐to‐market and size) will appear to explain returns after controlling for proxy beta simply because they capture the missing beta risk. Loadings on portfolios formed on relative leverage and relative distress completely subsume the powers of the Fama and French (1993) returns to small minus big market capitalization (SMB) portfolios and returns to high minus low book‐to‐market (HML) portfolios factors in explaining cross‐sectional returns.     

Nonlinearity and Flight‐to‐Safety in the Risk‐Return Trade‐Off for Stocks and Bonds

We document a highly significant, strongly nonlinear dependence of stock and bond returns on past equity market volatility as measured by the VIX. We propose a new estimator for the shape of the nonlinear forecasting relationship that exploits variation in the cross‐section of returns. The nonlinearities are mirror images for stocks and bonds, revealing flight‐to‐safety: expected returns increase for stocks when volatility increases from moderate to high levels while they decline for Treasuries. These findings provide support for dynamic asset pricing theories in which the price of risk is a nonlinear function of market volatility.     

Buyout Transactions in the German‐speaking Region: Determinants of Abnormal Performance and Unlevered Returns

This is one of the first comprehensive studies of drivers of private equity performance in the German‐speaking region known as the DACH, made up of Germany, Austria, and Switzerland. It contributes three things to private equity research: First, it explains how operational value drivers affect operational performance (operational alpha) and unlevered rates of return. Second, it whether the same relationships hold across different kinds of private equity business models (those with either organic or inorganic growth strategies; or whether PE investments are small‐cap or mid‐to‐large‐cap). Third, it distinguished between the periods before and after the global financial crisis of 2008. The authors found that (1) annualised benchmark‐adjusted EBITDA margin growth (i.e. improvement in EBITDA margin) is the most significant determinant in abnormal operational performance and unlevered returns, regardless of the business model; (2) private equity firms executing a buy‐and‐build strategy generate lower unlevered returns than those executing an organic growth strategy when the benchmark company is clearly outperformed, most likely because of limited PE managerial resources; (3) mid‐to‐large‐cap private equity firms generate higher unlevered returns and operational alphas than small‐cap private equity firms when the benchmark company is clearly outperformed, because, we believe, larger companies have a higher fixed cost leverage than smaller ones; and we have found that (4) buyout transactions exited during or after the financial crisis yield higher operational alphas but lower unlevered returns compared to buyout transactions exited before the crisis, when the portfolio company underperforms its benchmark company.     

IS THE BOOK‐TO‐MARKET RATIO A MEASURE OF RISK?

We develop a leverage‐based alternative to traditional asset pricing models to investigate whether the book‐to‐market ratio acts as a proxy for risk. We argue that the book‐to‐market ratio should act as a proxy because of the expected relations between (1) financial risk and measures of capital structure based on the market value of equity and (2) asset risk and measures of capital structure based on the book value of equity. We find no relation between average stock returns and the book‐to‐market ratio in all‐equity firms after controlling for firm size, and an inverse relation between average stock returns and the book‐to‐market ratio in firms with a negative book value of equity.     

Systematic risk,the tradeoff of leverage and IPO first-day returns

In this paper, we investigate IPO first-day returns in French market. Our focus is to assess the relationship between equity risk, corporate leverage and IPO initial returns. Based on data of 254 French IPOs, traded on Euronext/Alternext markets over the period 2006 and 2016, we find that estimated beta and idiosyncratic volatility are strongly and negatively related to book and market net gearing ratios. We also find that the interaction terms between equity risk measures and corporate leverage ratios are inversely related to IPO first-day returns. In addition, we highlight that industry and macroeconomic environment variables are significant predictors of equity initial returns. Robustness check of our findings indicates less relevant results for corporate leverage when it is estimated as independent variable.

     

Announcement Effects and Asymmetric Volatility in Industry Stock Returns: Evidence from Taiwan

This study employs financial econometric models to examine the asymmetric volatility of equity returns in response to monetary policy announcements in the Taiwanese stock market. The meetings of the board of directors at the Central Bank of the Republic of China (Taiwan) are considered for testing the announcement effects. The asymmetric generalized autoregressive conditional heteroskedasticity (GARCH) model and the smooth transition autoregression with GARCH model are used to measure equity returns' asymmetric volatility. We conclude that the asymmetric volatility of countercyclical equity returns can be identified. Our findings support the leverage effect of stock price changes for most industry equity returns in Taiwan.     

Is unlevered firm volatility asymmetric?

Asymmetric volatility refers to the stylized fact that stock volatility is negatively correlated to stock returns. Traditionally, this phenomenon has been explained by the financial leverage effect. This explanation has recently been challenged in favor of a risk premium based explanation. We develop a new, unlevering approach to document how well financial leverage, rather than size, beta, book-to-market, or operating leverage, explains volatility asymmetry on a firm-by-firm basis. Our results reveal that, at the firm level, financial leverage explains much of the volatility asymmetry. This result is robust to different unlevering methodologies, samples, and measurement intervals. However, we find that financial leverage does not explain index-level volatility asymmetry. We show that this difference between index-level asymmetry and firm-level asymmetry is driven by the asymmetry of the unlevered covariance component of index volatility.     

Correlated implied volatility with jump and cross section of stock returns

I derive the option‐implied volatility allowing for nonzero correlation between price jump and diffusive risk to examine the information content of implied diffusive, jump risks and their implied covariance in the cross‐sectional variation of future returns. This study documents a strong predictive power of realized volatility and correlated implied volatility spread (RV ? IV_C) in the cross section of stock returns. The difference of realized volatility with the implied diffusive volatility (RV ? σ_C), jump risk (RV ? γ_C) and covariance (RV ? ICov) can forecast future returns. These RV ? σ_C and RV ? γ_C anomalies are robustly persistent even after controlling for market, size, book‐to‐market value, momentum and liquidity factors.     

Accounting Ratios and the Cross‐section of Expected Stock Returns

Under clean‐surplus accounting, the log return on a stock can be decomposed into a linear function of the contemporaneous log return on equity, the contemporaneous log dividend–price ratio (if the stock pays a dividend), and both the contemporaneous and lagged values of the log book‐to‐market equity ratio. This paper studies the implications of this decomposition for the cross‐section of conditional expected stock returns. The empirical analysis reveals that the log accounting ratios capture cross‐sectional variation in both the conditional mean and conditional variance of log stock returns, which is consistent with the decomposition. It also brings fresh insights to the relation between firm size (market equity) and conditional expected stock returns. The evidence indicates that the conditional median return increases with firm size, while the conditional return skewness decreases with firm size. Empirically, the skewness effect outweighs the median effect, leading to the well‐documented inverse relation between size and average returns. The results of out‐of‐sample tests suggest that investors could use the information provided by the observed values of the log accounting ratios to formulate more effective portfolio strategies.     

Why are stock returns and volatility negatively correlated?

The literature documents that low stock returns are associated with increased volatility, but two competing explanations have proved difficult to disentangle. A negative return increases leverage, making equity value more volatile. However, an increase in volatility that persists causes stock prices to drop. We follow Bekaert and Wu [Bekaert, G., Wu, G., 2000. Asymmetric volatility and risk in equity markets. Review of Financial Studies 13, 1–42.] in controlling for leverage, but distinguish between volatility regimes that persist from less persistent changes using GARCH. For post-World War II returns on the value-weighted portfolio of all NYSE stocks, we find that changes in the volatility regime are reflected in stock returns but not in GARCH.     

ASYMMETRIC COVARIANCE,VOLATILITY, AND THE EFFECT OF NEWS

We propose that covariance (rather than beta) asymmetry provides a superior framework for examining issues related to changing risk premiums. Accordingly, we investigate whether the conditional covariance between stock and market returns is asymmetric in response to good and bad news. Our model of conditional covariance accommodates both the sign and magnitude of return innovations, and we find significant covariance asymmetry that can explain, at least in part, the volatility feedback of stock returns. Our findings are consistent across firm size, firm leverage, and temporal and cross‐sectional aggregations.     

The Cross-Section of Volatility and Expected Returns

We examine the pricing of aggregate volatility risk in the cross‐section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book‐to‐market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility.     

Default Risk in Equity Returns

This is the first study that uses Merton's (1974) option pricing model to compute default measures for individual firms and assess the effect of default risk on equity returns. The size effect is a default effect, and this is also largely true for the book‐to‐market (BM) effect. Both exist only in segments of the market with high default risk. Default risk is systematic risk. The Fama–French (FF) factors SMB and HML contain some default‐related information, but this is not the main reason that the FF model can explain the cross section of equity returns.     

The Australian asset‐pricing debate

Utilising a comprehensive data set for Australian firms, we examine a range of competing asset‐pricing models, including the four‐ and five‐factor models where the equity‐risk premium is augmented by size, value, momentum and liquidity premia, and find that none of the models tested appears to adequately explain the cross section of Australian returns. A model accounting for Australia's integration with the US equity market appears to be the best of the competing models we study. Our argument that a model recognising Australia's integration with the USA is supported when we apply the portfolio and factor construction methodology suggested by Brailsford et al. (2012a,b).     

Anomalies and stock returns: Australian evidence

Prior research has identified the existence of several cross‐sectional patterns in equity returns, commonly referred to as effects. This paper tests for the existence of a number of well‐known effects using data from the Australian equities market. Specifically, we investigate the size effect, book‐to‐market effect, earnings‐to‐price effect, cashflow‐to‐price effect, leverage effect and the liquidity effect. An additional aim of this paper is to investigate the capability of the Fama–French model in explaining any observed effects. We document a size, book‐to‐market, earnings‐to‐price and cashflow‐to‐price effect but fail to find evidence of a leverage or liquidity effect. Although our findings indicate that the Fama–French model can partially explain some of the observed effects, we conclude that its performance is less than satisfactory in Australia.     

Idiosyncratic Risk,Investor Base,and Returns

Using four different proxies for a firm's investor base we demonstrate that idiosyncratic risk premiums are larger for neglected stocks and smaller or economically insignificant for visible stocks. Since neglected stocks have greater idiosyncratic volatility (IV), the total IV risk premium (price × quantity) for neglected stocks will be greater than that of visible stocks. Additionally, we find a positive size effect and negative beta effect after controlling for IV. Overall, our results provide strong support for Merton's theory that market segmentation induced by incomplete information is an important component of the influence of IV in the cross‐section of returns.     

Assessing Project Risk

Finding the appropriate discount rate, or cost of capital, for evaluating investment projects requires an accurate estimate of project risk. This can be challenging because project risk cannot be estimated directly using the CAPM, but must instead be inferred from a set of traded securities, typically the equity betas of comparable firms in the same industry. These equity betas are then unlevered to undo the effect of comparable companies' financial leverage and obtain estimates of “asset” betas, which are then used to estimate project risk. The authors show that asset betas estimated in this way are likely to overestimate project risk. The equity returns of companies are risky not only because of their existing projects but also because of their growth opportunities. Such growth opportunities often include embedded “real options,” such as the option to delay, expand, or abandon a project. Because such real options are similar to leveraged positions in the underlying project, a company's growth opportunities are typically riskier than its existing projects. Therefore, to properly assess project risk, analysts must also unlever the asset betas derived from comparable company stock returns for the leverage contributed by their growth options. The authors derive a simple method for unlevering asset betas for growth options leverage in order to properly assess project risk. They then show that standard methods for assessing project risk significantly overestimate project costs of capital—by as much as 2–3% in industries such as healthcare, pharmaceuticals, communications, medical equipment, and entertainment. Their method should also be applied to stock return volatility to derive project volatility, an important input for determining the value of a firm's growth opportunities and the appropriate time for investing in these opportunities.     

Idiosyncratic Volatility Covariance and Expected Stock Returns

Given that the idiosyncratic volatility (IDVOL) of individual stocks co‐varies, we develop a model to determine how aggregate idiosyncratic volatility (AIV) may affect the volatility of a portfolio with a finite number of stocks. In portfolio and cross‐sectional tests, we find that stocks whose returns are more correlated with AIV innovations have lower returns than those that are less correlated with AIV innovations. These results are robust to controlling for the stock's own IDVOL and market volatility. We conclude that risk‐averse investors pay a premium for stocks that pay well when AIV is high, consistent with our model.     

Stock Price Dynamics and Firm Size: An Empirical investigation

We show that after controlling for the effects of bid-ask spreads and trading volume the conditional future volatility of equity returns is negatively related to the level of stock price. This “leverage effect” is stronger for small, as compared to large, firms. We also document that while the essential characteristics of the relations between stock price dynamics and firm size are stable, the strengths of the relationships appear to change over time.     

What Makes a Stock Risky? Evidence from Sell‐Side Analysts' Risk Ratings

We examine the determinants and the informativeness of financial analysts' risk ratings using a large sample of research reports issued by Salomon Smith Barney, now Citigroup, over the period 1997–2003. We find that the cross‐sectional variation in risk ratings is largely explained by variables commonly viewed as measures of risk, such as idiosyncratic risk, size, book‐to‐market, and leverage. In addition, earnings‐based measures of risk, such as earnings quality and accounting losses, also contribute to explaining the cross‐sectional variation in the risk ratings. Finally, we document that the risk ratings can be used to predict future return volatility after controlling for other predictors of future volatility. We conclude that analysts play an important role as providers of information about investment risk.