Stock market returns and annuitization

I investigate the strong negative relation between recent stock returns and the annuitization of retirement savings using a novel data set with over 100,000 actual payout decisions. After controlling for several standard explanations (e.g., wealth effects), I present evidence supporting naïve beliefs and extrapolation from past returns. The effect of recent returns on annuitization dramatically increases with age, confirming that the elderly rely most heavily on recent information. My results provide insights into how beliefs are formed in old age and have implications for the design of public policies seeking to promote annuitization.     

Stock returns and product market competition: beyond industry concentration

The main purpose of this paper is to provide direct evidence that product market structure affects stock returns. This is not only through industry concentration, as found in Hou and Robinson (J Finance 61:1927–1956, 2006), but also based on firms’ product substitutability and industry market size. Furthermore, the predictive power of product substitutability and market size for stock returns is not subsumed by industry concentration. Our results highlight the multi-dimensional structure of product market competition and its impact on asset prices.     

Stock market trading activity and returns around milestones

We study the relation between daily stock market trading activity and the Dow Jones Industrial Average's (DJIA) movement around millenary milestones—numbers that end in three zeros. We find aggregate turnover to be 5% lower when the DJIA level is less than 1% away from the nearest milestone. The effect emerges as the DJIA approaches a milestone from below, and is stronger for first-time milestones compared to subsequent passages. The aggregate price impact is large, such that daily stock returns show a negative abnormal performance of − 10 basis points. Our findings suggest that millenary milestones of the DJIA play a role in some investors' decision making.     

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.     

A note on January returns in the U.S. government bond market: The term effect

This study documents a previously unobserved January effect in the market for riskless debt. Long-term government bonds have significantly lower returns in January than in the rest of the year. This January effect is opposite in sign to the January effects that have been previously documented in the markets for equity and the markets for risky debt. Tax-loss selling in the equity markets in conjunction with parking the proceeds may provide a possible explanation for the negative January effect in the market for government bonds.     

Explaining the accrual anomaly by market expectations of future returns and earnings

This study examines the accrual anomaly under the framework of the Campbell [Campbell, J.Y. (1991). A variance decomposition for stock returns. Economic Journal 101 (405), 157-179.] model. The Campbell (1991) model shows that realized asset returns are a joint function of 1) expected returns, 2) revisions in market expected future returns (i.e., return news), and 3) revisions in market expected future cash flows (i.e., cash flow news). The current study adopts the Easton [Easton, P. (2004). PE ratios, PEG ratios, and estimating the implied expected rate of return on equity capital. The Accounting Review 79 (1), 73-96.] model to estimate proxies for expected returns, return news, and cash flow news. The results show that firms with low accruals have lower expected returns than firms with high accruals, which is contradictory to prior research that argues that firms with low accruals are more risky. However, investors underestimate (overestimate) future earnings growth, a proxy for cash flow growth, for low (high) accrual firms. Further analysis demonstrates that earnings news (proxy for cash flow news) plays a major role in explaining abnormal returns associated with the accrual anomaly.     

A bifurcation model of market returns

We propose a bifurcation model of market returns to describe transitions between an ‘over-reaction’ mean regressive state and ‘under-reaction’ trend persistent states. Since July 1929, the Dow Jones Industrial Average has exhibited non-stationary state transition behavior, including: (1) mean regressive behavior during crisis situations during the Great Depression of the 1930s and again in the crisis of 2008 when the availability of credit was interrupted; (2) strongly bifurcated, or trend persistent behavior from the 1940s through 1975; and (3) more efficient behavior since 1975. The bifurcation dynamic evident in the pre-1975 era is somewhat enhanced by conditional volume and moderate volatility. The bifurcation model is used to develop a quantitative measure of the degree of market efficiency, which indicates that the market has become more efficient, i.e. less trend persistent, since 1975 with the advent of negotiated commissions and computerized trading techniques. Similar findings are presented for the S&P 500 index and the CRSP Value Weighted Index, which represent large capitalization markets. Results for the CRSP Equal Weight Index are found to be significantly less efficient and more trend persistent than the larger capitalization CRSP Value Weighted Index.     

Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors

This paper proposes a two-state Markov-switching model for stock market returns in which the state-dependent expected returns, their variance and associated regime-switching dynamics are allowed to respond to market information. More specifically, we apply this model to examine the explanatory and predictive power of price range and trading volume for return volatility. Our findings indicate that a negative relation between equity market returns and volatility prevails even after having controlled for the time-varying determinants of conditional volatility within each regime. We also find an asymmetry in the effect of price range on intra- and inter-regime return volatility. While price range has a stronger effect in the high volatility state, it appears to significantly affect only the transition probabilities when the stock market is in the low volatility state but not in the high volatility state. Finally, we provide evidence consistent with the ‘rebound’ model of asset returns proposed by Samuelson (1991), suggesting that long-horizon investors are expected to invest more in risky assets than short-horizon investors.     

A note on the returns from minimum variance investing

Disappointed with the performance of market weighted benchmark portfolios yet skeptical about the merits of active portfolio management, investors in recent years turned to alternative index definitions. Minimum variance investing is one of these popular concepts. I show in this paper that the portfolio construction process behind minimum variance investing implicitly picks up risk-based pricing anomalies. In other words the minimum variance tends to hold low beta and low residual risk stocks. Long/short portfolios based on these characteristics have been associated in the empirical literature with risk adjusted outperformance. This paper shows that 83% of the variation of the minimum variance portfolio excess returns (relative to a capitalization weighted alternative) can be attributed to the FAMA/FRENCH factors as well as to the returns on two characteristic anomaly portfolios. All regression coefficients (factor exposures) are highly significant, stable over the estimation period and correspond remarkably well with our economic intuition. The paper also shows that a direct combination of market weighted benchmark portfolio and risk based characteristic portfolios will provide a statistically significant improvement over the indirect pickup via the minimum variance portfolio.     

Stock market volatility, excess returns, and the role of investor sentiment

Using the Investors' Intelligence sentiment index, we employ a generalized autoregressive conditional heteroscedasticity-in-mean specification to test the impact of noise trader risk on both the formation of conditional volatility and expected return as suggested by De Long et al. [Journal of Political Economy 98 (1990) 703]. Our empirical results show that sentiment is a systematic risk that is priced. Excess returns are contemporaneously positively correlated with shifts in sentiment. Moreover, the magnitude of bullish (bearish) changes in sentiment leads to downward (upward) revisions in volatility and higher (lower) future excess returns.     

Stock market timing effects on individual retirement plans: All holding period returns are not ‘average’

This paper suggests a win-win approach to dealing with stock market uncertainty in the advisory role of financial planning. It gives historical examples of holding periods where the stock market returns were above and below ‘average’ as suggested by Ibbotson Associates, Inc. stock return data. Examples are used to illustrate how conservative return estimates can provide safety for individual retirement plans and more assets under management for financial advisers. Suggesting conservative planned rates of return also protects the adviser against the many ‘accountability’ lawsuits that emerge during market declines.     

International comovement of stock market returns: A wavelet analysis

The assessment of the comovement among international stock markets is of key interest, for example, for the international portfolio diversification literature. In this paper, we re-examine such comovement by resorting to a novel approach, wavelet analysis. Wavelet analysis allows one to measure the comovement in the time–frequency space. In this way, one can characterize how international stock returns relate in the time and frequency domains simultaneously, which allows one to provide a richer analysis of the comovement. We focus on Germany, Japan, UK and US and the analysis is done at both the aggregate and sectoral levels.     

Stock returns and inflation in Greece: A Markov switching approach

The paper studies the dynamic relationship between real stock returns and expected and unexpected inflation utilizing a Markov Switching vector autoregressive model (MS-VAR). The MS-VAR model has the advantage that it is able to capture the dependence structure of the series both in terms of mean and variance. Univariate and multivariate innovation decompositions are employed to separate inflation into two components, the expected and unexpected. The empirical evidence suggests that real stock returns are not related to expected and unexpected inflation and this result is independent of the method used to separate inflation into the two components. Rather, the results suggest that stock market movements are regime dependent, implying that stock market performance is not predictable.     

Sentiment and stock returns: The SAD anomaly revisited

Widely-cited research by Kamstra et al. (2003) argues that changes in mood resulting from Seasonal Affective Disorder (SAD) drive changes in investor risk aversion and cause seasonal patterns in aggregate stock returns around the world. In this paper we reexamine the so-called SAD effect by replicating and extending Kamstra et al. (2003). We study the psychological underpinnings of the SAD hypothesis and show that the time-series predictions of the SAD model do not correspond to the seasonal patterns in depression found in the general population. We also investigate the cross-sectional prediction that SAD has a greater effect on stock markets in countries where SAD is more prevalent and find no relation between the prevalence of SAD and stock returns. Finally, we document that the SAD effect is mechanically driven by an overlapping dummy-variable specification and higher returns around the turn of the year.     

Stock return volatility,operating performance and stock returns: International evidence on drivers of the ‘low volatility’ anomaly

This study highlights the link between stock return volatility, operating performance, and stock returns. Prior studies suggest that there is a ‘low volatility’ anomaly, where firms with a low stock return volatility out-perform firms with a high stock return volatility. This paper confirms that low volatility stocks earn higher returns than high volatility stocks in emerging markets and developed markets outside of North America. We also show that low volatility stocks have higher operating returns and this might explain why low volatility stocks earn higher stock returns. These results provide a partial explanation for the ‘low volatility effect’ that is independent from the existence of market anomalies or per se inefficiencies that might otherwise drive a low volatility effect. We emphasize the importance of controlling for stock return volatility when analyzing operating performance and stock performance.     

A note on asset management and market risk

The textbook view on risk in asset management companies is summarized by Hull (Risk Management and Financial Institutions, p. 372, 2007): “For an asset manager the greatest risk is operational risk.” Using evidence from various panel regression models, we show that asset management revenues carry substantial market risks, a finding that challenges not only academic risk management literature on the predominance of operative risks, but also the current industry practice of not hedging market risks that are systematically built into the revenue-generation process. For asset management companies to return to an annuity model, these risks need to be managed more actively. Shareholders do not want to be exposed to market beta by investing in asset management companies; they want to participate in these companies’ alpha generation and take advantage of their fund-gathering expertise as financial intermediaries.     

Stock market return predictability: A combination forecast perspective

Based on traditional macroeconomic variables, this paper mainly investigates the predictability of these variables for stock market return. The empirical results show the mean combination forecast model can achieve superior out-of-sample performance than the other forecasting models for forecasting the stock market returns. In addition, the performances of the mean combination forecast model are also robust during different forecasting windows, different market conditions, and multi-step-ahead forecasts. Importantly, the mean combination forecast consistently generates higher CER gains than other models considering different investors' risk aversion coefficients and trading costs. This paper tries to provide more evidence of combination forecast model to predict stock market returns.     

Time-series and cross-sectional momentum in anomaly returns

We find strong evidence of time-series and cross-sectional momentum in the long–short returns of a comprehensive sample of anomalies. Strategies that exploit such persistence deliver significant abnormal returns that are robust to the stock momentum effect, cannot be explained by traditional asset-pricing models, and are more pronounced when arbitrage capital is scarcer or market liquidity is lower. Momentum in anomaly returns dissipates but does not reverse, in the long-run. Our findings are consistent with limits-to-arbitrage and slow-moving capital causing mispricing to persist. Supporting this explanation, we find that both the level and persistence of anomaly returns are positively related to idiosyncratic volatility.     

Stock returns and the weekend effect

This paper examines two alternative models of the process generating stock returns. Under the calendar time hypothesis, the process operates continuously and the expected return for Monday is three times the expected return for other days of the week. Under the trading time hypothesis, returns are generated only during active trading and the expected return is the same for each day of the week. During most of the period studied, from 1953 through 1977, the daily returns to the Standard and Poor's composite portfolio are inconsistent with both models. Although the average return for the other four days of the week was positive, the average for Monday was significantly negative during each of five-year subperiods.     

Stock returns and the term structure

In monthly U.S. data for 1959–1979 and 1979–1983, the state of the term structure of interest rates predicts excess stock returns, as well as excess returns on bills and bonds. This paper documents this fact and uses it to examine some simple asset pricing models. In 1959–1979, the data strongly reject a single-latent-variable specification of predictable excess returns. There is considerable evidence that conditional variances of excess returns change through time, but the relationship between conditional mean and conditional variance is reliably positive only at the short end of the term structure.