Industry Costs of Equity: Incorporating Prior Information

I examine whether incorporating economically motivated prior information yields more accurate forecasts of industry costs of equity. I find that incorporating the long‐run mean of the Capital Asset Pricing Model (CAPM) parameters and the industry characteristics in the cross section produces more accurate parameter estimates, which subsequently translate into more accurate out‐of‐sample forecasts of industry costs of equity. The outperformance of this method over rolling‐window estimates becomes larger as the forecast horizon extends into the future. These findings provide evidence that the CAPM parameters have a long‐run mean‐reversion property and correlate with the industry characteristics in a systematic way.     

Varying risk premia in international bond markets

Cochrane and Piazzesi [Cochrane, J.H., Piazzesi, M., 2005. Bond risk premia. American Economic Review 95, 138–160] use forward rates to forecast future bond returns. We extend their approach by applying their model to international bond markets. Our results indicate that the unrestricted Cochrane and Piazzesi (2005) model has a reasonable forecasting power for future bond returns. The restricted model, however, does not perform as well on an international level. Furthermore, we cannot confirm the systematic tent shape of the estimated parameters found by Cochrane and Piazzesi (2005). The forecasting models are used to implement various trading strategies. These strategies exhibit high information ratios when implemented in individual countries or on an international level and outperform alternative approaches. We introduce an alternative specification to forecast future bond returns and achieve superior risk-adjusted returns in our trading strategy. Bayesian model averaging is used to enhance the performance of the proposed trading strategy.     

APPLICABILITY OF THE FAMA‐FRENCH THREE‐FACTOR MODEL IN FORECASTING PORTFOLIO RETURNS

For the model‐based estimation of the equity cost of capital, evidence shows that the common practice of using the average historical factor premiums as the estimates of the next‐period factor premiums generates inaccurate estimates. I propose an alternative way to estimate factor premiums by using the structural variables that are important predictors of future asset returns. Based on the out‐of‐sample results from a trading strategy with four in‐sample model‐selection criteria, I find that my estimation procedure performs better than the common practice even when transaction costs are considered.     

RISK PREMIUM EFFECTS ON IMPLIED VOLATILITY REGRESSIONS

This article provides new insights into the sources of bias of option implied volatility to forecast its physical counterpart. We argue that this bias can be attributed to volatility risk premium effects. The latter are found to depend on high‐order cumulants of the risk‐neutral density. These cumulants capture the risk‐averse behavior of investors in the stock and option markets for bearing the investment risk that is reflected in the deviations of the implied risk‐neutral distribution from the normal distribution. We show that the bias of implied volatility to forecast its corresponding physical measure can be eliminated when the implied volatility regressions are adjusted for risk premium effects. The latter are captured mainly by the third‐order risk‐neutral cumulant. We also show that a substantial reduction of higher order risk‐neutral cumulants biases to predict their corresponding physical cumulants is supported when adjustments for risk premium effects are made.     

Cash holdings in private firms

Evidence from a wide sample of Italian private firms shows that cash holdings are significantly related with smaller size, higher risk and lower effective tax rates, therefore supporting predictions from the trade-off model. More cash is also held by firms with longer cash conversion cycles and lower financing deficits, as predicted by the financing hierarchy theory. Reported evidence also shows that dividend payments are associated with more cash holdings, and both bank debt and net working capital represent good cash-substitutes. When controlling for macroeconomic and industry factors, some variables lose their significance, but the general findings are confirmed. Finally, cash-rich companies are found to be more profitable, to pay more dividends and to invest more in a medium-term future horizon.     

OPERATING LEVERAGE AND UNDERINVESTMENT

Using a contingent claims model, we examine the impacts of both operating leverage and financial leverage on a firm's investment decisions in the context of capacity expansion. Our model shows that quasi‐fixed operating costs could significantly mitigate the underinvestment problem for debt‐financed firms. The existing debt induces equity holders to delay equity‐financed expansion because the expanded earnings base will also benefit the debt holders by lowering the bankruptcy risk. The operating costs decrease this type of wealth transfer from equity holders to debt holders by magnifying the bankruptcy risk of the existing debt upon investment. By applying the Cox proportional hazard model on a large sample of publicly traded U.S. firms over 1966–2016, we offer empirical support for the theoretical predictions. The results are robust to various measures of operating leverage.     

Dividend Omissions and Intraindustry Information Transfers

We examine potential information transfers from companies that announce dividend omissions to their industry rivals. Specifically, we examine the abnormal stock returns and abnormal earnings forecast revisions of rivals after a company makes a dividend‐omission announcement. Our results show negative and significant abnormal stock returns and negative and significant abnormal forecast revisions for rival companies in response to the announcement, and a significant and positive relation between the two. We conclude that a dividend‐omission announcement transmits unfavorable information across the announcing company's industry that affects cash flow expectations and ultimately stock prices.     

Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach

We use Markov Chain Monte Carlo (MCMC) methods for the parameter estimation and the testing of conditional asset pricing models. In contrast to traditional approaches, it is truly conditional because the assumption that time variation in betas is driven by a set of conditioning variables is not necessary. Moreover, the approach has exact finite sample properties and accounts for errors‐in‐variables. Using S&P 500 panel data, we analyse the empirical performance of the CAPM and the Fama and French (1993) three‐factor model. We find that time‐variation of betas in the CAPM and the time variation of the coefficients for the size factor (SMB) and the distress factor (HML) in the three‐factor model improve the empirical performance. Therefore, our findings are consistent with time variation of firm‐specific exposure to market risk, systematic credit risk and systematic size effects. However, a Bayesian model comparison trading off goodness of fit and model complexity indicates that the conditional CAPM performs best, followed by the conditional three‐factor model, the unconditional CAPM, and the unconditional three‐factor model.     

Economic significance of predictability in Australian equities

This paper examines evidence of predictability in Australian equities using both statistical and economic metrics of significance. A probit‐based predictive model is used to forecast the probability that the 1 month ahead excess market return will be positive. Funds under management are then switched between equities and fixed income on the basis of this forecast. Although the statistical evidence of the model's predictive ability is mixed, the results suggest convincing evidence of an economically significant degree of return predictability. A $A1 investment in the switching strategy (market) in January 1980 grows to over $A55 ($A39) by June 2007. Although the economic significance of the switching strategy remains even in the presence of high transaction costs, robustness checks suggest that the seemingly impressive full‐sample results might be sample specific. The apparent superiority of the portfolio‐switching strategy can be traced to a handful of observations early in the study during which the predictive model provides a timely signal to exit equities. There is little evidence that the predictive model has forecasting ability across the entire sample. As such, this paper serves both to illustrate how alternate metrics of return predictability can lead to divergent conclusions, and to emphasize the importance of subjecting apparent findings of predictability to robustness checks.     

Predicting European Takeover Targets

This article extends the Palepu (1986) acquisition likelihood model by incorporating measures of a technical nature, e.g. momentum, trading volume as well as a measure of market sentiment. We use the proposed model to predict takeover targets in a large sample of European and cross‐border merger and acquisition deals and validate its performance on an in‐ and out‐of‐sample basis. The robustness of the proposed model is investigated across several dimensions. In addition we explore the ability of the model to form the basis of successful takeover timing investment strategies. The results of our empirical analysis suggest that the proposed model predicts European takeover targets with relatively high accuracy and is able to determine portfolios that earn significant returns which are not explained by conventional risk factors.     

European Momentum Strategies,Information Diffusion,and Investor Conservatism

In this paper we conduct an out‐of‐sample test of two behavioural theories that have been proposed to explain momentum in stock returns. We test the gradual‐information‐diffusion model of Hong and Stein (1999) and the investor conservatism bias model of Barberis et al. (1998) in a sample of 13 European stock markets during the period 1988 to 2001. These two models predict that momentum comes from the (i) gradual dissemination of firm‐specific information and (ii) investors’ failure to update their beliefs sufficiently when they observe new public information. The findings of this study are consistent with the predictions of the behavioural models of Hong and Stein's (1999) and Barberis et al. (1998) . The evidence shows that momentum is the result of the gradual diffusion of private information and investors’ psychological conservatism reflected on the systematic errors they make in forming earnings expectations by not updating them adequately relative to their prior beliefs and by undervaluing the statistical weight of new information.     

Corporate social responsibility and earnings forecasting unbiasedness

We investigate the relationship between corporate social responsibility (CSR) and I/B/E/S analysts’ earnings per share (EPS) forecasts using a large sample of US firms for 1992–2011. Based on literature findings, we decompose the CSR effect into four factors: accounting opacity, corporate governance, stakeholder risk, and overinvestment. We find that all of them significantly affect both the absolute forecast error on EPS and its standard deviation controlling for forecast horizon; number of analysts and forecasts; and year, industry, and broker house effects. Consistently with our ex ante hypotheses, overinvestment, stakeholder risk, and accounting opacity have a positive effect, increasing both dependent variables, while corporate governance quality has a negative effect. A crucial aspect of our findings is that high CSR quality in terms of the four factors (i.e., accounting transparency, high corporate governance quality, stakeholder risk mitigation, and absence of overinvestment) contributes to making earnings forecasts unbiased as unbiasedness is generally met in the subsample of the Top CSR quality companies and markedly violated in the subsample of the Bottom CSR companies. We also document that overinvestment and stakeholder risk are sufficient to produce this effect.     

Endogenous Life‐Cycle Housing Investment and Portfolio Allocation

This paper develops a life‐cycle portfolio allocation model to address the effects of housing investment on the portfolio allocation of households. The model employs a comprehensive housing investment structure, Epstein–Zin recursive preferences, and a stock market entry cost. Furthermore, rather than resorting to calibration we estimate the value of the relative risk aversion and elasticity of intertemporal substitution. The model shows that housing investment has a strong crowding out effect on investment in risky assets throughout the life‐cycle. We further find that the effect of the presence of housing investment on households portfolio allocation is larger than the effect of having EZ recursive preferences.     

State‐preference pricing and volatility indices

This study uses a state‐preference pricing approach to develop a state‐price volatility index (SVX), as a forecast for market future realised volatility. We show that SVX is a more efficient forecaster than CBOE VIX for 30‐day realised volatility of SPX returns, using both in‐the‐sample and out‐of‐the‐sample tests. This result is robust to different measures of realised market volatilities. We also show that SVX provides a better volatility forecast than other alternative measures, including the at‐the‐money implied volatilities and GARCH (1, 1) volatility. Our results provide a foundation for forecasting higher risk‐neutral moments using the same state prices.     

R&D Increases and Long‐Term Performance of Rivals

We examine how a firm's research and development (R&D) increases affect its intra‐industry competitors in the long run. Consistent with the R&D spillover hypothesis, when a firm unexpectedly increases its R&D spending, its intra‐industry competitors experience improvements in operating performance and analyst forecast revisions and earn positive abnormal stock returns in the long run. The industry concentration, which is related to the firm's strategic reaction, is crucial in determining the magnitude of the R&D spillover effect.     

Dispersion in analysts’ earnings forecasts and credit rating

This paper shows that the puzzling negative cross-sectional relation between dispersion in analysts’ earnings forecasts and future stock returns may be explained by financial distress, as proxied by credit rating downgrades. Focusing on a sample of firms rated by Standard & Poor's (S&P), we show that the profitability of dispersion-based trading strategies concentrates in a small number of the worst-rated firms and is significant only during periods of deteriorating credit conditions. In such periods, the negative dispersion–return relation emerges as low-rated firms experience substantial price drop along with considerable increase in forecast dispersion. Moreover, even for this small universe of worst-rated firms, the dispersion–return relation is non-existent when either the dispersion measure or return is adjusted by credit risk. The results are robust to previously proposed explanations for the dispersion effect such as short-sale constraints and leverage.     

THE EFFECT OF CROSS‐LISTING ON TRADING VOLUME: REDUCING SEGMENTATION VERSUS SIGNALING INVESTOR PROTECTION

We examine the relation between cross‐listing on the U.S. and UK regulated and unregulated exchanges and trading volume for a sample of 500 foreign firms from 34 countries. We find that the increase in trading volume is a function of both reducing segmentation and signaling investor protection. In addition, we find that home market trading volume, firm size, firm returns, and analyst forecast accuracy are the major determinants of a firm's trading volume. We also show that U.S. and UK investors trade foreign securities that originate from low‐investor‐protection countries more than they trade those from high‐investor‐protection countries, which is consistent with the bonding hypothesis.     

Drivers of holding period firm-level returns in private equity-backed buyouts

We extend the research on the drivers of holding period firm-level returns in private equity (PE)-backed buyouts by examining deal-, industry-, and macroeconomic-level drivers and their interaction. To conduct our study, we use a comprehensive and hand-collected dataset covering exited buyouts in the UK between 1995–2004, and we control for sample selection and investment risk. Our study shows that governance variables generally have a limited role in driving value creation but that use of a ratchet is positively related to both equity and enterprise value returns; we also find that leverage has a positive impact on median and top-quartile equity returns. Moreover, returns are driven by the size of the buyout and the acquisitions made during the holding period. With respect to macroeconomic and industry level factors, industry growth particularly drives buyout returns. However, the effect of industry growth is not uniform; its influence is particularly strong in insider-driven and divisional buyouts, in addition to top-quartile transactions.     

The impacts of parent’s listing status on subsidiary’s financial constraint and cost of equity capital: the case of equity carve‐outs

We find that listed parents’ carve‐outs have investment‐cash‐flow sensitivities 70 per cent lower than unlisted parents’ carve‐outs, on average. Such a finding is stronger when we consider only equity carve‐outs in technological industries. The finding suggests that listed parents are more capable of alleviating the financial constraint of their carved‐out units than private parents. Our further analysis shows that listed parents’ carve‐outs also have a lower cost of equity than their counterparts, but such difference cannot be explained by corporate transparency, as implied by analyst coverage and analysts’ forecast dispersion. Therefore, we argue that the benefits from affiliation with a listed parent to the carve‐out come mainly from the parent’s financial support rather than an increase in corporate transparency.     

Out‐of‐sample stock return predictability in emerging markets

This article builds on the widely debated issue of stock return predictability by applying a broad range of predictor variables and comprehensively considering the in‐sample and out‐of‐sample stock return predictability of ten advanced emerging markets. It compares forecasts from models with a single predictor variable, multiple predictor variables and a combination forecast approach. The results confirm the findings of Welch and Goyal (2008) for US data that only a limited number of individual predictor variables are able to deliver significant out‐of‐sample forecasts. However, a combination forecast approach provides statistically and economically significant out‐of‐sample forecast results.