Size, book-to-market ratio and macroeconomic news

Little is known about the reactions of daily returns on portfolios with different characteristics to unexpected changes in macroeconomic conditions. This paper fills this void by analyzing the reactions of daily returns on portfolios formed on size and book-to-market ratio to news about a wide range of macroeconomic variables. Returns on different portfolios not only react to different news but also react differently to the same news. Reactions of portfolios to macroeconomic news also change over the business cycle. Results are strongest for news about Employees on Nonfarm Payrolls in expansions. Both at daily and monthly frequencies, large and growth firms react differently to employment news from small and value firms in expansions but not in recessions. Differences in the sensitivities of expected future cash flows to employment news in expansions can help explain differences in the observed reactions.     

Stock market bubbles, inflation and investment risk

This paper proposes an autoregressive regime-switching model of stock price dynamics in which the process creates pricing bubbles in one regime while error-correction prevails in the other. In the bubble regime the stock price depends negatively on inflation. In the error-correction regime it depends on the price-dividend ratio. We find that the probability of regime-switch depends on exogenous inflation and lagged price. The model is consistent with Shleifer and Vishny's theoretical noise trader and arbitrageur model and Modigliani's inflation illusion phenomenon. The results emphasize the importance of inflation and the price-dividend ratio when assessing investment risk.     

Stock market crashes,firm characteristics,and stock returns

A number of studies have investigated the causes and effects of stock market crashes. These studies mainly focus on the factors leading to a crash and on the volatility and co-movements of stock market indexes during and after the crash. However, how a stock market crash affects individual stocks and if stocks with different financial characteristics are affected differently in a stock market crash is an issue that has not received sufficient attention. In this paper, we study this issue by using data for eight major stock market crashes that have taken place during the December 31, 1962–December 31, 2007 period with a large sample of US firms. We use the event-study methodology and multivariate regression analysis to study the determinants of stock returns in stock market crashes.     

Bubbles in the dividend–price ratio? Evidence from an asymmetric exponential smooth-transition model

Recent stock price movements have led to a re-examination of the present value model. An increasing belief is that although dividends and prices are indeed cointegrated, they may exhibit non-linear dynamics in the process of reversion. This paper implements an empirical model designed to capture two possible explanations for such non-linearity, namely transaction costs and noise traders. Utilising data from a number of countries we show that the dynamics of the log dividend yield are, first, characterised by an inner random walk regime, where the benefits of engaging in trade do not outweigh the costs and so the process moves randomly. Second, a reverting outer regime where the dynamics of reversion differ between positive and negative deviations, such that price rises greater than the level supported by dividends exhibit a greater degree of persistence than price falls relative to dividends.     

Institutional investors,intangible information,and the book-to-market effect

This paper establishes a robust link between the trading behavior of institutions and the book-to-market effect. Building on work by Daniel and Titman (2006), who argue that the book-to-market effect is driven by the reversal of intangible returns, I find that institutions tend to buy (sell) shares in response to positive (negative) intangible information and that the reversal of the intangible return is most pronounced among stocks for which a large proportion of active institutions trade in the direction of intangible information. Furthermore, the book-to-market effect is large and significant in stocks with intense past institutional trading but nonexistent in stocks with moderate institutional trading. This influence of institutional trading on the book-to-market effect is distinct from that of firm size. These results are consistent with the view that the tendency of institutions to trade in the direction of intangible information exacerbates price overreaction, thereby contributing to the value premium.     

Stock returns and inflation revisited: An evaluation of the inflation illusion hypothesis

Several hypotheses have been proposed to explain the stock return–inflation relation. The Modigliani and Cohn’s inflation illusion hypothesis has received renewed attention. Another hypothesis is the two-regime hypothesis. We reexamine these hypotheses using long sample data of the US and international data. We find that the inflation illusion hypothesis can explain the post-war negative stock return–inflation relation, but it is not compatible with the pre-war positive relation. Using a structural VAR identification method, we show that there are two regimes with positive and negative stock return–inflation relations not only in each period of the US but also in every developed country we consider. This seems inconsistent with the inflation illusion hypothesis that predicts only a negative relation.     

Models of the yield curve and the curvature of the implied forward rate function

We examine several alternative models of the UK gilt yield curve using daily data for the period 12 July 1996-10 February 2010. We select the best models according to two criteria: low out of sample errors in pricing bonds and low curvature of the implied forward rate curve function. We suggest additions to some of the models that significantly improve their performance. Some of the new models out perform those typically used by the central banks. In particular this paper suggests that the model used by the Canadian Central Bank which both outperforms other models and is particularly easy to estimate, is well suited to the UK gilt market.     

Political regimes,business cycles,seasonalities, and returns

This paper provides a method for testing for regime differences when regimes are long-lasting. Standard testing procedures are generally inappropriate because regime persistence causes a spurious regression problem – a problem that has led to incorrect inference in a broad range of studies involving regimes representing political, business, and seasonal cycles. The paper outlines analytically how standard estimators can be adjusted for regime dummy variable persistence. While the adjustments are helpful asymptotically, spurious regression remains a problem in small samples and must be addressed using simulation or bootstrap procedures. We provide a simulation procedure for testing hypotheses in situations where an independent variable in a time-series regression is a persistent regime dummy variable. We also develop a procedure for testing hypotheses in situations where the dependent variable has similar properties.     

Stock returns,aggregate earnings surprises,and behavioral finance

We study the stock market's reaction to aggregate earnings news. Prior research shows that, for individual firms, stock prices react positively to earnings news but require several quarters to fully reflect the information in earnings. We find a substantially different pattern in aggregate data. First, returns are unrelated to past earnings, suggesting that prices neither underreact nor overreact to aggregate earnings news. Second, aggregate returns correlate negatively with concurrent earnings; over the last 30 years, for example, stock prices increased 5.7% in quarters with negative earnings growth and only 2.1% otherwise. This finding suggests that earnings and discount rates move together over time and provides new evidence that discount-rate shocks explain a significant fraction of aggregate stock returns.     

Stock returns and macro risks: Evidence from Finland

Deviations from the CAPM have generally been observed for the stock markets. One of many alternative approaches is using macro variables as systematic risks. We tested with a number of macro risks for the explanation of Finnish industry returns for a period from 1993:03 until 2008:07. The evidence suggests macro risks explain larger cross-sectional variations in average industry returns than the market factor alone and same is reported with the Hansen and Jagannathan (1997) specification measure. The changes in expected returns with a positive shock in the exchange rate risk and unanticipated inflation remain economically persistent for the post euro period, arguably a sign for the regulatory impact of the coordinated policies from European central bank (ECB). The robustness checks show the prevalence of macro risks, and market risk cannot be ignored altogether.     

Multifrequency news and stock returns

Equity prices are driven by shocks with persistence levels ranging from intraday horizons to several decades. To accommodate this diversity, we introduce a parsimonious equilibrium model with regime shifts of heterogeneous durations in fundamentals, and estimate specifications with up to 256 states on daily aggregate returns. The multifrequency equilibrium has higher likelihood than the Campbell and Hentschel [1992. No news is good news: an asymmetric model of changing volatility in stock returns. Journal of Financial Economics 31, 281–318] specification, while producing volatility feedback 10 to 40 times larger. Furthermore, Bayesian learning about volatility generates a novel trade-off between skewness and kurtosis as information quality varies, complementing the uncertainty channel [e.g., Veronesi, 1999. Stock market overreaction to bad news in good times: a rational expectations equilibrium model. Review of Financial Studies 12, 975–1007]. Economies with intermediate information best match daily returns.     

Stock market momentum, business conditions, and GARCH option pricing models

This paper examines the forecasting performance of GARCH option pricing models from a market momentum perspective, and the possible impacts of financial crises and business conditions are also examined. The empirical results demonstrate that market momentum impacts the forecasting performance of GARCH option pricing models. The EGARCH model performs better under downward market momentum, while the standard GARCH performs better under upward market momentum. In addition, parsimonious models generally outperform richly parameterized ones. The above findings are robust to financial crises, and the results further demonstrate that business conditions influence the forecasting performance of GARCH option pricing models.     

Composition of wealth,conditioning information,and the cross-section of stock returns

Value stocks covary with aggregate consumption more than growth stocks during periods when financial wealth is low relative to consumption. However, the conditional value premium does not exhibit such countercyclical behavior. Consequently, a one-factor conditional consumption-based asset pricing model can be rejected without making any arbitrary assumptions on the dynamics of the price of risk or the conditional moments. Empirical evidence is somewhat more consistent with a consumption-based model augmented with an aggregate wealth growth factor, which can be motivated by either recursive preferences or relative wealth concerns.     

Predicting stock returns

This paper studies whether incorporating business cycle predictors benefits a real time optimizing investor who must allocate funds across 3,123 NYSE-AMEX stocks and cash. Realized returns are positive when adjusted by the Fama-French and momentum factors as well as by the size, book-to-market, and past return characteristics. The investor optimally holds small-cap, growth, and momentum stocks and loads less (more) heavily on momentum (small-cap) stocks during recessions. Returns on individual stocks are predictable out-of-sample due to alpha variation, whereas the equity premium predictability, the major focus of previous work, is questionable.     

Variations in effects of monetary policy on stock market returns in the past four decades

Stock prices are sensitive to monetary policy. However, the sensitivities are not stable over time. A drastic change in monetary policy can alter effects of monetary policy on stock returns. This study finds that stock prices can be affected by current changes, unexpected changes, or near-future changes in the funds/discount rates, due to different policy goals or targets in different periods. Specifically, this study provides empirical evidence that monetary policy influences the stock market in different ways in the 1960s, the 1970s, the Volcker and Greenspan periods.     

Stock returns and the Miller Modigliani valuation formula: Revisiting the Fama French analysis

Fama and French (2006) use the dividend-discount model to develop the role of expected profitability, expected investment, and the book-to-market ratio as predictors of stock returns. One reported empirical result is anomalous. The valuation model establishes that the comparative static relation between expected returns and expected investment is negative, yet it appears to be positive and insignificant. We show that the posited valuation relations apply at the firm level, and not at the per share level at which they were tested. Once the variables are measured at the firm level, all the Fama French predictions are validated.     

Profitability,investment and average returns

Valuation theory says that expected stock returns are related to three variables: the book-to-market equity ratio (B_t/M_t), expected profitability, and expected investment. Given B_t/M_t and expected profitability, higher expected rates of investment imply lower expected returns. But controlling for the other two variables, more profitable firms have higher expected returns, as do firms with higher B_t/M_t. These predictions are confirmed in our tests.     

The intertemporal relation between expected returns and risk

This paper explores the time-series relation between expected returns and risk for a large cross section of industry and size/book-to-market portfolios. I use a bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model to estimate a portfolio's conditional covariance with the market and then test whether the conditional covariance predicts time–variation in the portfolio's expected return. Restricting the slope to be the same across assets, the risk-return coefficient is highly significant with a risk–aversion coefficient (slope) between one and five. The results are robust to different portfolio formations, alternative GARCH specifications, additional state variables, and small sample biases. When conditional covariances are replaced by conditional betas, the risk premium on beta is estimated to be in the range of 3% to 5% per annum and is statistically significant.     

Payout yield,risk, and mispricing: A Bayesian analysis

We develop a simple parametric model in which hypotheses about predictability, mispricing, and the risk-return tradeoff can be evaluated simultaneously, while allowing for time variation in both risk and expected return. Most of the return predictability based on aggregate payout yield is unrelated to market risk. We consider a range of Bayesian prior beliefs about the risk-return tradeoff and the extent to which predictability is driven by mispricing. The impact of these beliefs on an investor's certainty-equivalent return when choosing between a market index and riskless T-bills is economically significant, in both ex ante and out-of-sample analyses.     

Idiosyncratic risk and the cross-section of expected stock returns

Theories such as Merton [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510] predict a positive relation between idiosyncratic risk and expected return when investors do not diversify their portfolio. Ang, Hodrick, Xing, and Zhang [2006. The cross-section of volatility and expected returns. Journal of Finance 61, 259–299], however, find that monthly stock returns are negatively related to the one-month lagged idiosyncratic volatilities. I show that idiosyncratic volatilities are time-varying and thus, their findings should not be used to imply the relation between idiosyncratic risk and expected return. Using the exponential GARCH models to estimate expected idiosyncratic volatilities, I find a significantly positive relation between the estimated conditional idiosyncratic volatilities and expected returns. Further evidence suggests that Ang et al.'s findings are largely explained by the return reversal of a subset of small stocks with high idiosyncratic volatilities.