US Stock Returns Research Articles

An Examination of Dynamic Trading Stategies in UK and US Stock Returns

Abstract: This paper examines the performance benefits of using conditioning information in mean‐variance strategies in UK and US stock returns. The paper finds that after adjusting for trading costs, there are no significant performance benefits in using conditioning information in mean‐variance strategies. This result stems from the high turnover that is required to implement dynamic trading strategies. The paper does find that after adjusting for costs, that the unconditional approach of Ferson and Siegel (2001) significantly outperforms alternative approaches of using conditioning information in mean‐variance strategies in UK stock returns.

Fama French Factors and US Stock Return Predictability

This paper investigates whether the HML, the SMB along with the short-term reversal, the long-term reversal and the momentum factors exhibit both in-sample and out-of-sample forecasting ability for the US stock returns. Our findings suggest that these factors contain significantly more information for future stock market returns than the typically employed financial variables. We also go one step further and test whether these variables can proxy for the aforementioned factors. Our results suggest that the default spread and to a lesser extent the term spread contain important information for the evolution of the factors examined.

Tower Building and Stock Market Returns

Construction starts of skyscrapers predict subsequent US stock returns. The predictive ability exceeds that of alternatives such as the prevailing historical mean, predictions based on dividend ratios and recently suggested combination forecasts. One explanation for these patterns is that tower building is indicative of over-optimism; alternatively, tower building could help to identify periods of low risk aversion. I present indirect evidence that is consistent with both explanations.

Modelling the Volatility-Return Trade-Off When Volatility May Be Nonstationary

In this paper a new GARCH–M type model, denoted the GARCH-AR, is proposed. In particular, it is shown that it is possible to generate a volatility-return trade-off in a regression model simply by introducing dynamics in the standardized disturbance process. Importantly, the volatility in the GARCH-AR model enters the return function in terms of relative volatility, implying that the risk term can be stationary even if the volatility process is nonstationary. We provide a complete characterization of the stationarity properties of the GARCH-AR process by generalizing the results of Bougerol and Picard (1992b). Furthermore, allowing for nonstationary volatility, the asymptotic properties of the estimated parameters by quasi-maximum likelihood in the GARCH-AR process are established. Finally, we stress the importance of being able to choose correctly between AR-GARCH and GARCH-AR processes: First, it is shown, by a small simulation study, that the estimators for the parameters in an ARGARCH model will be seriously inconsistent if the data generating process actually is a GARCH-AR process. Second, we provide an LM test for neglected GARCH-AR effects and discuss its finite sample size properties. Third, we provide an empirical illustration showing the empirical relevance of the GARCH-AR model based on modelling a wide range of leading US stock return series.

Interest rate sensitivity of US property/liability insurer stock returns

PurposeThe purpose of this study is to investigate interest rate sensitivity of the US property/liability (P/L) insurers stock returns using various return generating process models incorporating different interest rate changes such as actual interest rate changes, unexpected interest rate changes and orthogonalized market returns.Design/methodology/approachThe study follows the 1974 two‐index model by Stone. In the two‐index model, three different interest rate indices are tested one at a time to examine if interest rate sensitivity of the insurers stock returns, if any, is vulnerable to an interest rate index used.FindingsIt is found that the US P/L insurers' stock returns are sensitivity to interest rate changes. The impact of actual interest rate changes on the stock returns is little different from that of unexpected interest rate changes, which is consistent with findings in the banking literature. When orthogonalized market returns are used in the models in lieu of actual market returns, the statistical significance on the estimated interest rate sensitivity of the returns improves. Consistent with extant studies of financial institution's interest rate sensitivity, the paper also reports that the interest rate sensitivity of insurer stock returns is time varying.Research limitations/implicationsDue to the data availability, the period studied is between 1992 and 2001. However, the sample period does not weaken the findings of the study. In addition, future research could incorporate the insurers' balance sheet items to investigate which balance sheet items (i.e. investment in bonds, stocks and other items on asset side and reserves and other items on liability side) explain most interest rate sensitivity.Practical implicationsInvestors can adopt the findings of this study in creating or adjusting their portfolio with the US P/L insurers in it. The insurers stock returns are more sensitive to changes in long‐term interest rate when the underwriting profit increases and the stock returns are more sensitive to changes in short‐term interest rate when the underwriting profit decreases.Social implicationsAlthough generalization is difficult and the conclusion is not as convincing as it could be because only one underwriting cycle is sampled, it is still noteworthy to recognize that the insurers' interest rate sensitivity is closely related to the insurance industry's underwriting cycle or performance.Originality/valueThis is the first study reporting the association between the interest rate sensitivity of the US stock returns and the underwriting performance.

Pricing assets with higher moments: Evidence from the Australian and us stock markets

This paper investigates the importance of higher moments of return distributions in capturing the variation of average stock returns for companies listed in the leading S&P US and Australian indices. We find that Australian stocks are more negatively skewed but less leptokurtic than US stocks. As a result, we find that co-skewness plays a more important role in explaining Australian returns while co-kurtosis is consistently influential for US stock returns. We postulate that the differences in results are related to the underlying firm characteristics of the companies in the two indices, where principally the Australian firms are noticeably smaller than their US counterparts and concentrated in a smaller number industry sectors. This implies that for many smaller exchanges around the world higher moment characteristics displayed by the US market may not be applicable. We also show our results are robust to partly explaining average stock returns in the presence of size, value, and momentum effects.

Modelling skewness and kurtosis with the skewed Gauss–Laplace sum distribution

An asymmetric extension of the recently proposed (symmetric) Gauss–Laplace sum distribution for stock returns is developed, motivated by the fact that many stock return distributions display significant asymmetries. The properties of the new distribution, insofar as relevant for estimation, testing, and the modelling of skewness and kurtosis, are derived. An application to three major US stock return indices shows an excellent fit of the model, which outperforms many popular alternatives.

A comparison of mean–variance efficiency tests

We analyse the asymptotic properties of mean–variance efficiency tests based on generalised methods of moments, and parametric and semiparametric likelihood procedures that assume elliptical innovations. We study the trade-off between efficiency and robustness, and prove that the parametric estimators provide asymptotically valid inferences when the conditional distribution of the innovations is elliptical but possibly misspecified and heteroskedastic. We compare the small sample performance of the alternative tests in a Monte Carlo study, and find some discrepancies with their asymptotic properties. Finally, we present an empirical application to US stock returns, which rejects the mean–variance efficiency of the market portfolio.

The 'Fed Model' and the Changing Correlation of Stock and Bond Returns: An Equilibrium Approach

This paper presents an equilibrium model that provides a rational explanation for two features of data that have been considered puzzling: The positive relation between US dividend yields and nominal interest rates, often called the Fed-model, and the time-varying correlation of US stock and bond returns. Key ingredients are time-varying first and second moments of consumption growth, inflation, and dividend growth in conjunction with Epstein-Zin and Weil recursive preferences. Historically in the US, inflation has signalled low future consumption growth. The representative agent therefore dislikes positive inflation shocks and demands a positive risk premium for holding assets that are poor inflation hedges, such as equity and nominal bonds. As a result, risk premiums on equity and nominal bonds comove positively through their exposure to macroeconomic volatility. This generates a positive correlation between dividend yields and nominal yields and between stock and bond returns. High levels of macro volatility in the late 1970s and early 1980s caused stock and bond returns to comove strongly. The subsequent moderation in aggregate economic risk has brought correlations lower. The model is able to produce correlations that can switch sign by including the covariances between consumption growth, inflation, and dividend growth as state variables.

Is Gold a Hedge or a Safe Haven? an Analysis of Stocks, Bonds and Gold

Is gold a hedge against sudden changes in stock and bond returns, or does it instead have a subtly different property, that of being a safe haven? This paper addresses these two interlinked questions. A safe haven is defined as a security that is uncorrelated with stocks and bonds in case of a market crash. This is counterpoised against a hedge, defined as a security that is uncorrelated with stocks or bonds on average. We study constant and time-varying relationships between stocks, bonds and gold in order to investigate the existence of a hedge and a safe haven. The empirical analysis examines US, UK and German stock and bond returns and their relationship with gold returns. We find that gold is a hedge against stocks on average and a safe haven in extreme stock market conditions. This finding suggests that the existence of a safe haven enhances the stability and resiliency of financial markets since it reduces investors' losses at times when a reduction is needed the most. A portfolio analysis further shows that the safe haven property is extremely short-lived so that an investor buying gold one day after a shock loses money.

Time-varying factor models for equity portfolio construction

Most equity risk models applied in practice assume stable return correlations over time. However, there is considerable evidence suggesting that correlations among stock returns and hence, variance–covariance matrices (VCMs) become unstable over time. In this paper, we account for correlation instabilities in US stock returns and derive VCMs from time-varying factor model estimates. To do so, we use three different estimation approaches: (1) moving window least squares, (2) flexible least squares and (3) the random walk model. Our empirical results suggest that a time-varying estimation of return correlations fits the data considerably better than time-invariant estimation and thus, increases the efficiency of risk estimation and portfolio selection.

Inflation Bets or Deflation Hedges? The Changing Risks of Nominal Bonds

The covariance between US Treasury bond returns and stock returns has moved considerably over time. While it was slightly positive on average in the period 1953-2009, it was unusually high in the early 1980’s and negative in the 2000’s, particularly in the downturns of 2000-02 and 2007-09. This paper species and estimates a model in which the nominal term structure of interest rates is driven by four state variables: the real interest rate, temporary and permanent components of expected inflation, and the “nominal-real covariance” of inflation and the real interest rate with the real economy. The last of these state variables enables the model to fit the changing covariance of bond and stock returns. Log bond yields and term premia are quadratic in these state variables, with term premia determined by the nominal-real covariance. The concavity of the yield curve ― the level of intermediate-term bond yields, relative to the average of short- and long-term bond yields ― is a good proxy for the level of term premia. The nominal-real covariance has declined since the early 1980’s, driving down term premia.

Correlation Dynamics between Asia-Pacific, EU and US Stock Returns

This paper investigates the correlation dynamics in the equity markets of 13 Asia-Pacific countries, Europe and the US using the asymmetric dynamic conditional correlation GARCH model (AG-DCC-GARCH) introduced by Cappiello, Engle and Sheppard (2006). We find significant variation in correlation between markets through time. Stocks exhibit asymmetries in conditional correlations in addition to conditional volatility. Yet asymmetry is less apparent in less integrated markets. The Asian crisis acts as a structural break, with correlations increasing markedly between crisis countries during this period though the bear market in the early 2000s is a more significant event for correlations with developed markets. Our findings also provide further evidence consistent with increasing global market integration. The documented asymmetries and correlation dynamics have important implications for international portfolio diversification and asset allocation.

Are stock returns on the US used as an exogenous predictor to the Asian emerging equity markets

By using a block recursive vector autoregression model and two new out-of-sample tests, this study has found that the US stock returns have predictive ability for the four Asian emerging equity markets. The estimates from weekly data suggest that returns on S&P500 positively predict stock returns of Asian emerging markets up to three weeks.

Evidence for Nonlinear Asymmetric Causality in US Inflation, Metal, and Stock Returns

The purpose of this paper is to propose a version of causality testing that focuses on how the sign of the returns affects the causality results. We replace the traditional VAR specification used in the Granger causality test by a discrete-time bivariate noisy Mackey glass model. Our test reveals interesting and previously unexplored relationships in US economic series, including inflation, metal, and stock returns.

Estimating the uncertainty of relative risk aversion

This note reports estimates of the coefficient of relative risk aversion, using a method recently proposed by Azar (2006). In contrast to his work, the complete information of US stock return data over the period 1926 to 2002 is utilized. Moreover, a bootstrap procedure is applied to estimate the associated uncertainty. Point estimates close to 3.5 are obtained. However, ranging from 1.4 to 7.1, the 95% confidence interval is wide.

International equity flows and the predictability of US stock returns

AbstractWe examined the link between international equity flows and US stock returns. Based on the results of tests of in‐sample and out‐of‐sample predictability of stock returns, we found evidence of a strong positive (negative) link between international equity flows and contemporaneous (one‐month‐ahead) stock returns. Our results also indicate that an investor, in real time, could have used information on the link between international equity flows and one‐month‐ahead stock returns to improve the performance of simple trading rules. Copyright © 2007 John Wiley & Sons, Ltd.

Modeling Conditional Skewness in Stock Returns

In this paper, we propose a new GARCH-in-Mean (GARCH-M) model allowing for conditional skewness. The model is based on the so-called z distribution capable of modeling skewness and kurtosis of the size typically encountered in stock return series. The need to allow for skewness can also be readily tested. The model is consistent with the volatility feedback effect in that conditional skewness is dependent on conditional variance. Compared to previously presented GARCH models allowing for conditional skewness, the model is analytically tractable, parsimonious and facilitates straightforward interpretation.Our empirical results indicate the presence of conditional skewness in the monthly postwar US stock returns. Small positive news is also found to have a smaller impact on conditional variance than no news at all. Moreover, the symmetric GARCH-M model not allowing for conditional skewness is found to systematically overpredict conditional variance and average excess returns.

Crude oil shocks and stock market returns

This article examines whether nonlinear crude oil effect observed in aggregate US stock return can be explained by unexpected shocks from the crude oil market. I separate the distribution of aggregate US stock return into variance component driven by smoothly arriving news information and discrete Poisson news arriving from the crude oil market. I find that unexpected crude oil shocks have nonlinear effect on excess US stock market return. Contemporaneous and lagged returns on crude oil futures have significant negative effect on jump distribution in US stock market returns. I also investigate if the volatility of aggregate US stock return is in any way related to information released at the Organization of Petroleum Exporting Countries (OPEC) meetings. The empirical result reveals no significant feedback effect from OPEC meetings to the US stock markets.

Elusive return predictability

Investors' searches for successful forecasting models cause the data generating process for financial returns to change over time, which means that individual return forecasting models can, at best, hope to uncover evidence of ‘local’ predictability. We illustrate this point on a suite of forecasting models used to predict US stock returns, and propose an adaptive forecast combination approach. Most of the time the forecasting models perform rather poorly, but there is evidence of relatively short-lived periods with modest return predictability. The short duration of the episodes where return predictability appears to be present and the relatively weak degree of predictability even during such periods makes predicting returns an extraordinarily challenging task.