Valuation Ratios and Stock Return Predictability in South Africa: Is It There?

Using monthly South African data for January 1990 through October 2009, this paper, to the best of our knowledge, is the first to examine the predictability of real stock return based on valuation ratios, namely, price-dividend and price-earnings ratios. We cannot detect either short-horizon or long-horizon predictability; that is, the hypothesis that the current value of a valuation ratio is uncorrelated with future stock price changes cannot be rejected at both short and long horizons based on bootstrapped critical values constructed from linear representations of the data. We find, via Monte Carlo simulations, that the power to detect predictability in finite samples tends to decrease at long horizons in a linear framework. Although Monte Carlo simulations applied to exponential smooth-transition autoregressive models of the price-dividend and price-earnings ratios show increased power, the ability of the nonlinear framework in explaining the pattern of stock return predictability in the data does not show any promise at either short or long horizons, just as in the linear predictive regressions.     

Does Academic Research Destroy Stock Return Predictability?

We study the out‐of‐sample and post‐publication return predictability of 97 variables shown to predict cross‐sectional stock returns. Portfolio returns are 26% lower out‐of‐sample and 58% lower post‐publication. The out‐of‐sample decline is an upper bound estimate of data mining effects. We estimate a 32% (58%–26%) lower return from publication‐informed trading. Post‐publication declines are greater for predictors with higher in‐sample returns, and returns are higher for portfolios concentrated in stocks with high idiosyncratic risk and low liquidity. Predictor portfolios exhibit post‐publication increases in correlations with other published‐predictor portfolios. Our findings suggest that investors learn about mispricing from academic publications.     

International Stock Return Predictability: What Is the Role of the United States?

We investigate lead‐lag relationships among monthly country stock returns and identify a leading role for the United States: lagged U.S. returns significantly predict returns in numerous non‐U.S. industrialized countries, while lagged non‐U.S. returns display limited predictive ability with respect to U.S. returns. We estimate a news‐diffusion model, and the results indicate that return shocks arising in the United States are only fully reflected in equity prices outside of the United States with a lag, consistent with a gradual information diffusion explanation of the predictive power of lagged U.S. returns.     

Tracking Stock or Spin‐Off? Determinants of Choice

I examine the roles of valuable internal capital markets, cross-subsidization, and insider ownership as determinants of choice between tracking stock and spin-offs in corporate equity restructuring. I show that conglomerates are more likely to choose tracking stock if they want to obtain some of the benefits offered by a spin-off, without loosing the potential for valuable internal capital markets. My results suggest that the market rewards firms with valuable internal capital markets that opt for tracking stocks, and penalizes the possibility of consolidated tax treatments. The market also reacts more favorably to unanticipated tracking-stock announcements.     

Can Time‐Varying Risk of Rare Disasters Explain Aggregate Stock Market Volatility?

Why is the equity premium so high, and why are stocks so volatile? Why are stock returns in excess of government bill rates predictable? This paper proposes an answer to these questions based on a time‐varying probability of a consumption disaster. In the model, aggregate consumption follows a normal distribution with low volatility most of the time, but with some probability of a consumption realization far out in the left tail. The possibility of this poor outcome substantially increases the equity premium, while time‐variation in the probability of this outcome drives high stock market volatility and excess return predictability.     

On the Predictability of the Stock Market Volatility: Does History Matter?

This study compares the performance of the ISD, the GARCH (1,1) , the historical volatility estimates and of two lagged trading volume measures for predicting the Swiss Stock Market Index's (SMI) volatility. The ISD has a superior daily informational content than the GARCH (1,1) estimate and retains unbiased but decreasing explanatory power over up to 20 days ahead horizons. Mean and spread daily volume measures play a significant correcting role when forecasting stock market volatility over daily and longer intervals respectively and clearly dominate the GARCH (1,1) forecasts. Their significance emphasises heterogeneous horizon traders' influence on the SMI volatility time series properties     

Why Does Return Predictability Concentrate in Bad Times?

We build an equilibrium model to explain why stock return predictability concentrates in bad times. The key feature is that investors use different forecasting models, and hence assess uncertainty differently. As economic conditions deteriorate, uncertainty rises and investors' opinions polarize. Disagreement thus spikes in bad times, causing returns to react to past news. This phenomenon creates a positive relation between disagreement and future returns. It also generates time‐series momentum, which strengthens in bad times, increases with disagreement, and crashes after sharp market rebounds. We provide empirical support for these new predictions.     

Why Do Price Limits Exist in Stock Markets? A Manipulation‐Based Explanation

Numerous stock market regulators around the world impose daily price limits on individual stock price movements. We derive a simple model that shows that price limits may deter stock market manipulators. Based on our model's implications, we predict that regulators impose price limit rules for markets where the likelihood of manipulation is high. We present empirical evidence consistent with this hypothesis. Our study is the first to formally propose a manipulation‐based rationale for the existence of price limits in stock markets.     

Return Performance Surrounding Reverse Stock Splits: Can Investors Profit?

We examine the long-run return performance of over 1,600 firms with reverse stock splits. These stocks record statistically significant negative abnormal returns over the three-year period following the month of the reverse split. The sample firms experience poor operating performances over the four years that include and follow the year of the reverse split, which suggests informational inefficiencies. Because these stocks have unique financial characteristics, we also show that they would be very difficult to sell short. Thus, arbitrageurs would be restricted in their ability to earn abnormal profits, even if they correctly anticipated a price decline.     

Are Stock‐for‐Stock Acquirers of Unlisted Targets Really Less Overvalued?

Extant studies assume that targets’ private ownership mitigates acquirers’ incentives and opportunities to finance acquisitions with inflated stocks. This view stems from the observation that, although the average stock‐for‐stock acquirer's merger announcement return is negative when the target is listed, it is positive when the target is unlisted. Accordingly, extant studies often suggest that announcements of stock‐for‐stock acquisitions of unlisted targets convey favorable private information about the acquirers. However, an analysis of stock‐for‐stock acquirers’ stock performance, abnormal accruals, net operating assets, and insider trading suggests the opposite. Acquirers of unlisted targets are generally more overvalued than acquirers of listed targets.     

Real-Estate Risk Effects on Financial Institutions’ Stock Return Distribution: a Bivariate GARCH Analysis

This paper examines two relationships using the bivariate generalized autoregressive conditionally heteroskedastic (GARCH) methodology. First, the relationship between equity returns of commercial banks, savings and loans (S&Ls) and life insurance companies (LICs), and those of the real-estate investment trusts (REITs), a proxy for the real-estate sector performance. Second, the relationship between conditional volatilities of the stock returns of these financial intermediaries (FIs) and that of REITs. The former relationship allows the spillover of returns between the real-estate and the financial intermediation sector to be analyzed. The latter allows an investigation of the prevalence, direction and strength of inter-sectoral risk transmission to be carried out. Several interesting results are obtained. First, the equity returns of the FIs considered follow a GARCH process and should be modeled accordingly. Second, as found in the literature, returns on REITs should be modeled using the Fama-French multiple factor model. However, this model has to be extended to incorporate a GARCH error structure. Third, all FI returns considered are highly sensitive to REIT returns and the effects are both statistically and economically significant. This is an indication that shocks to REITs returns spillover to the former markets. Fourth, spillover of increased volatility in the real-estate sector to S&Ls and LICs is significant but not to commercial banks.     

Risk‐Sharing or Risk‐Taking? Counterparty Risk,Incentives, and Margins

Derivatives activity, motivated by risk‐sharing, can breed risk‐taking. Bad news about the risk of an asset underlying a derivative increases protection sellers' expected liability and undermines their risk‐prevention incentives. This limits risk‐sharing, creates endogenous counterparty risk, and can lead to contagion from news about the hedged risk to the balance sheet of protection sellers. Margin calls after bad news can improve protection sellers' incentives and in turn enhance risk‐sharing. Central clearing can provide insurance against counterparty risk but must be designed to preserve risk‐prevention incentives.     

Who Moves the Asia-Pacific Stock Markets—US or Japan? Empirical Evidence Based on the Theory of Cointegration

This study examines the recent debacle of the Asian-Pacific stock markets by utilizing the theory of cointegration to investigate which developing markets are moved by the markets of Japan and the United States. The empirical evidence suggests that some countries are dominated by the US, some are dominated by Japan, and the remaining countries are dominated by neither during the time period investigated. The appropriate error correction model is estimated and is used to perform out-of-sample forecasting.     

Stock market volatility: friend or foe?

Although a good deal of research effort has been allocated to understanding the time-series volatility of stock returns – as both market (or systematic) volatility and idiosyncratic (or non-systematic) volatility – the relationship of such volatility with cross-sectional volatility or dispersion of outcomes is sparse. Nevertheless, the quest to understand one must involve the quest to understand the other. In this paper, we investigate the dispersion of returns in relation to inter-temporal volatility, as well as the dynamic of dispersion of returns in generating a portfolio’s return outcome. We find that the level of such dispersion is highly significant for portfolio performance and the notion of risk.     

What Is the Expected Return on a Stock?

We derive a formula for the expected return on a stock in terms of the risk‐neutral variance of the market and the stock's excess risk‐neutral variance relative to that of the average stock. These quantities can be computed from index and stock option prices; the formula has no free parameters. The theory performs well empirically both in and out of sample. Our results suggest that there is considerably more variation in expected returns, over time and across stocks, than has previously been acknowledged.     

Marking‐to‐Market: Panacea or Pandora's Box?

Financial institutions have been at the forefront of the debate on the controversial shift in international standards from historical cost accounting to mark‐to‐market accounting. We show that the trade‐offs at stake in this debate are far from one‐sided. While the historical cost regime leads to some inefficiencies, marking‐to‐market may lead to other types of inefficiencies by injecting artificial risk that degrades the information value of prices, and induces suboptimal real decisions. We construct a framework that can weigh the pros and cons. We find that the damage done by marking‐to‐market is greatest when claims are (1) long–lived, (2) illiquid, and (3) senior. These are precisely the attributes of the key balance sheet items of banks and insurance companies. Our results therefore shed light on why banks and insurance companies have been the most vocal opponents of the shift to marking‐to‐market.     

Risk and Return in Fixed-Income Arbitrage: Nickels in Front of a Steamroller?

We conduct an analysis of the risk and return characteristicsof a number of widely used fixed-income arbitrage strategies.We find that the strategies requiring more "intellectual capital"to implement tend to produce significant alphas after controllingfor bond and equity market risk factors. These positive alphasremain significant even after taking into account typical hedgefund fees. In contrast with other hedge fund strategies, manyof the fixed-income arbitrage strategies produce positivelyskewed returns. These results suggest that there may be moreeconomic substance to fixed-income arbitrage than simply "pickingup nickels in front of a steamroller."     

Fundamentals or Managerial Discretion? The Relationship between Accrual Variability and Future Stock Return Volatility

This study extends the theoretical framework of Callen and Segal (2004) and Vuolteenaho (2002) to investigate the association between accrual variability and firm‐level stock return volatility. The empirical evidence supports our prediction that increased uncertainty in current‐period accounting accruals is associated with significantly higher volatility of future stock returns, and the results are valid for measures of both systematic and idiosyncratic volatility. When accrual variability is decomposed into fundamental and discretionary portions, we find that the positive relationship between accrual variability and future stock return volatility is dominated by the fundamental component of accrual variability. Overall, our results suggest that uncertainty reflected in accrual information is subsequently reflected in the fluctuation of future stock returns, and that the predictive content in accruals primarily reflects firms' fundamental uncertainty, rather than any effects of managerial choices and interventions in the accounting process.     

The Cross‐Section of Expected Stock Returns: What Have We Learnt from the Past Twenty‐Five Years of Research?

I review the recent literature on cross-sectional predictors of stock returns. Predictive variables used emanate from informal arguments, alternative tests of risk-return models, behavioural biases, and frictions. More than fifty variables have been used to predict returns. The overall picture, however, remains murky, because more needs to be done to consider the correlational structure amongst the variables, use a comprehensive set of controls, and discern whether the results survive simple variations in methodology .     

Style‐Related Comovement: Fundamentals or Labels?

I find that economically meaningless index labels cause stock returns to covary in excess of fundamentals. S&P/Barra follow a simple mechanical procedure to define their Value and Growth indices. In doing so, they reclassify some stocks from Value to Growth even after their book‐to‐market ratios have risen, and vice versa. Such stocks begin to covary more with the index they join and less with the index they leave. Backdated constituent data from Barra reveal no such label‐related shifts in comovement during the 10 years prior to the actual introduction of the indices in 1992.