Structural Breaks and Predictive Regression Models of Aggregate U.S. Stock Returns

In this article we examine the structural stability of predictiveregression models of U.S. quarterly aggregate real stock returnsover the postwar era. We consider predictive regressions modelsof S&P 500 and CRSP equal-weighted real stock returns basedon eight financial variables that display predictive abilityin the extant literature. We test for structural stability usingthe popular Andrews SupF statistic and the Bai subsample procedurein conjunction with the Hansen heteroskedastic fixed-regressorbootstrap. We also test for structural stability using the recentlydeveloped methodologies of Elliott and Müller, and Baiand Perron. We find strong evidence of structural breaks infive of eight bivariate predictive regression models of S&P500 returns and some evidence of structural breaks in the threeother models. There is less evidence of structural instabilityin bivariate predictive regression models of CRSP equal-weightedreturns, with four of eight models displaying some evidenceof structural breaks. We also obtain evidence of structuralinstability in a multivariate predictive regression model ofS&P 500 returns. When we estimate the predictive regressionmodels over the different regimes defined by structural breaks,we find that the predictive ability of financial variables canvary markedly over time.     

Evaluating stock returns with time-varying risk aversion driven by trend deviations from the consumption-to-wealth ratio: An analysis conditional on income levels

Based on the cointegrating relationship between consumption and wealth, we estimate the long run consumption-to-wealth ratio for each of five consumer income quintiles as well as national data for benchmarking purposes. Short run deviations from the consumption-to-wealth ratio for each quintile are examined for their ability to forecast changes in future consumption, income, housing values, and especially stock returns. We demonstrate that these trend deviations when combined with consumption growth in a multifactor model, significantly improve the ability of the dividend-to-price ratio to forecast future market returns over short and intermediate horizons for consumers in the highest-income quintile. This paper contributes to the financial economic literature by showing that the highest-income consumers are forecasting future stock returns with the help of the persistence in the dividend-to-price ratio and are modifying their consumption accordingly.     

Transmission of Stock Returns and Volatility between the U.S. and Japan: Evidence from the Stock Index Futures Markets

In this paper, we examine the nature of transmission of stock returns and volatility between the U.S. and Japanese stock markets using futures prices on the S&P 500 and Nikkei 225 stock indexes. We use stock index futures prices to mitigate the stale quote problem found in the spot index prices and to obtain more robust results. By employing a two-step GARCH approach, we find that there are unidirectional contemporaneous return and volatility spillovers from the U.S. to Japan. Furthermore, the U.S.'s influence on Japan in returns is approximately four times as large as the other way around. Finally, our results show no significant lagged spillover effects in both returns and volatility from the Osaka market to the Chicago market, while a significant lagged volatility spillover is observed from the U.S. to Japan. This revised version was published online in August 2006 with corrections to the Cover Date.     

剩余收益模型与股票未来回报

本文采用Hou et al.(2012)公司基本面盈余预测模型并结合剩余收益模型对上市公司的内在价值进行估计,并分析内在价值与市价比率(V/P)与股票未来回报之间的关系。我们发现基于V/P分组的投资组合,在未来一至三年规模调整的持有超额回报套利分别达到15.2%、37.9%和55.9%;在控制了市账比等因素以后,V/P对股票未来回报仍然具有显著的预测作用。本文的研究克服了以往文献中运用证券分析师盈余预测进行剩余收益模型估值的内在局限,并提供了我国资本市场背景下切实可行的基于剩余收益模型估值的投资组合策略。     

Is U.S. economic policy uncertainty priced in China's A-shares market? Evidence from market,industry, and individual stocks

This paper is motivated by Bali, Brown, and Tang (2017) who find U.S. economic policy uncertainty (EPU) is priced in the cross-section of U.S. stock returns, and uses weekly data from March 2006 to April 2016 to study whether shocks in U.S. EPU also influence prices of China's A-shares from a market, industry, and individual stock perspective. Our methodology relies on an ARMA (1,1) model to extract shocks in the U.S. EPU series and a GARCH (1,1) model to examine how returns of China's A-shares respond to these shocks after controlling for business conditions proxied by term and credit spread in China. Generally, we find that shocks in U.S. EPU significantly and negatively explain returns of Chinese A-shares with a lag of one week. In addition, the market index containing small and growth stocks is more sensitive to shocks in U.S. EPU than the index containing big and value stocks. Furthermore, we find that firms in manufacturing, information technology, and media industries in China are more sensitive to shocks in U.S. EPU, while firms in agriculture and real estate industries respond less to shocks in U.S. EPU. Finally, China's A-shares which decline more in response to shocks in U.S. EPU have higher returns, smaller market capitalization, weaker operating profitability, higher asset growth, and better past year's cumulative returns. Overall, our findings show that investors in the Chinese A-shares market require a premium to hold stocks that are sensitive to shocks in U.S. economic policy uncertainty.     

On the predictability of Chinese stock returns

We examine stock return predictability in China. We take 18 firm-specific variables that have been documented to predict cross-sectional stock returns in the U.S. and examine their relation with stock returns in China for the sample period from 1995 to 2007. We find relatively weak predictability for Chinese stocks. Only five firm-specific variables predict returns in the Chinese market. Tests on U.S. stock returns find that more predictors can explain cross-sectional stock return variation. We test two explanations for the cause of weak returns predictability in China. First, perhaps return predictors in China are less heterogeneously distributed than they are in the U.S. Second, stock prices are less informative in China than they are in the U.S. We find support for both explanations.     

Consumption, Aggregate Wealth, and Expected Stock Returns

This paper studies the role of fluctuations in the aggregate consumption–wealth ratio for predicting stock returns. Using U.S. quarterly stock market data, we find that these fluctuations in the consumption–wealth ratio are strong predictors of both real stock returns and excess returns over a Treasury bill rate. We also find that this variable is a better forecaster of future returns at short and intermediate horizons than is the dividend yield, the dividend payout ratio, and several other popular forecasting variables. Why should the consumption–wealth ratio forecast asset returns? We show that a wide class of optimal models of consumer behavior imply that the log consumption–aggregate wealth (human capital plus asset holdings) ratio summarizes expected returns on aggregate wealth, or the market portfolio. Although this ratio is not observable, we provide assumptions under which its important predictive components for future asset returns may be expressed in terms of observable variables, namely in terms of consumption, asset holdings and labor income. The framework implies that these variables are cointegrated, and that deviations from this shared trend summarize agents' expectations of future returns on the market portfolio.     

International nonlinear causality between stock markets

In this paper, we test for linear and nonlinear Granger causality between the French, German, Japanese, UK and US daily stock index returns from 1973 to 2003. We find a strong contemporaneous linear dependence between European countries and a directional linear dependence from the US towards the other markets. Besides, linear causality increases after 1987, a finding consistent with the expected effects of financial liberalization of the 1980s and the 1990s. Above all, we document the presence of bidirectional nonlinear causality between daily returns. To check for spurious nonlinear causality, we filter out heteroskedasticity using a FIGARCH model. The dramatic decrease in the number of significant nonlinear causality lags confirms that heteroskedasticity played a major part in the previous findings. We then check if a few structural breaks can explain the remaining nonlinear causality. We find that a large number of nonlinear relationships vanish when we control for structural breaks, whereas linear causality remains.     

Symmetric and asymmetric nonlinear causalities between oil prices and the U.S. economic sectors

This study investigates the causal dynamics of the U.S. sector price changes and oil price changes using the symmetric nonlinear and asymmetric nonlinear causality tests. We find a unidirectional causality from each sector to the oil market using the Granger and MWald linear causality tests. However, the symmetric nonlinear and asymmetric nonlinear causality for negative price changes tests yield unidirectional causality from the oil to the sector price changes which sharply contrast the evidence using the linear models. We find bidirectional causality using the asymmetric nonlinear test for positive price changes, suggesting temporal, dual and nonlinear information flow during bull markets. Our results from the nonlinear and asymmetric causality tests remain robust after accounting for structural breaks. The empirical findings unravel nonlinear interactions between sector price and oil price changes as well as the importance of signs of changes in the interacting variables, implying oil returns may need to be priced when forecasting sector returns.     

Resurrecting the size effect: Evidence from a panel nonlinear cointegration model for the G7 stock markets

Firm size is known to be an important factor affecting stock returns. This study proposes a panel threshold cointegration model to investigate the impact of the size effect on stock returns for the panel of G7 countries: Canada, France, Germany, Italy, Japan, the U.K., and the U.S. over the period 1991:1–2012:12. The empirical analysis is based upon the nonlinear cointegration framework using the asymmetric ARDL cointegration methodology (Shin et al., 2011). This methodological approach permits a much richer degree of flexibility in the dynamic adjustment process toward equilibrium, than in the classical linear model. Our findings indicate the presence of asymmetric adjustment around a unique long-run equilibrium. In particular, the empirical analysis provides evidence of asymmetric effects between stock returns and the size effect, while controlling for the book-to-market ratio and the price-to-earnings ratio.     

Why Do Markets Move Together? An Investigation of U.S.-Japan Stock Return Comovements

This article explores the fundamental factors that affect cross-country stock return correlations. Using transactions data from 1988 to 1992, we construct overnight and intraday returns for a portfolio of Japanese stocks using their NYSE-traded American Depository Receipts (ADRs) and a matched-sample portfolio of U. S. stocks. We find that U. S. macroeconomic announcements, shocks to the Yen/Dollar foreign exchange rate and Treasury bill returns, and industry effects have no measurable influence on U.S. and Japanese return correlations. However, large shocks to broad-based market indices (Nikkei Stock Average and Standard and Poor's 500 Stock Index) positively impact both the magnitude and persistence of the return correlations.     

ROBUSTNESS OF THE HEADQUARTERS‐CITY EFFECT ON STOCK RETURNS

Recent studies report that U.S. firms headquartered near each other experience positive comovement in their stock returns, a finding suggestive of local biases in equity trading activity. We investigate the robustness of these findings and find that including additional pricing factors in models for monthly stock returns materially reduces the magnitude of the headquarters‐city effect in stock returns. Additionally, we find that an implicit null hypothesis of zero local return comovement is inappropriate as there is positive comovement between a stock's return and returns on portfolios of stocks from nonheadquarters cities, on average. Nevertheless, results benchmarked against estimates based on resampling methods indicate a significant and robust headquarters‐city effect in stock returns.     

Nonlinear relationship between monetary policy and stock returns: Evidence from the U.S.

This paper studies the relationship between monetary policy and stock market return in the U.S. using nonlinear econometric models. It first employs a univariate Markov-switching model on each of the three stock indices and three monetary policy variables, displaying significant regime-switching patterns and common movements. This paper then uses a Markov-switching dynamic bi-factor model to simultaneously extract two latent common factors from stock indices and monetary policy variables to represent monetary policy changes and stock market movements separately. The smoothed probabilities of regimes demonstrate that expansionary monetary policy regimes follow economic recessions, but bear stock markets usually occur before economic recessions. The maximum likelihood estimation results show that expansionary monetary policy such as a decrease in the federal funds rate raises stock returns, but stock returns don't directly influence monetary policy decision.     

U.S. monetary policy indicators and international stock returns: 1970-2001

It is documented in the literature that U.S. and many international stock returns series are sensitive to U.S. monetary policy. Using monthly data, this empirical study examines the short-term sensitivity of six international stock indices (the Standard & Poor 500 [S&P] Stock Index, the Morgan Stanley Capital International [MSCI] European Stock Index, the MSCI Pacific Stock Index, and three MSCI country stock indices: Germany, Japan, and the United Kingdom) to two major groups of U.S. monetary policy indicators. These two groups, which have been suggested by recent research to influence stock returns, are based on the U.S. discount rate and the federal funds rate. The first group focuses on two binary variables designed to indicate the stance in monetary policy. The second group of monetary indicators involves the federal funds rate and includes the average federal funds rate, the change in the federal funds rate, and the spread of the federal funds rate to 10-year Treasury note yield. Dividing the sample period (1970-2001) into three monetary operating regimes, we find that not all policy indicators influence international stock returns during all U.S. monetary operating periods or regimes. Our results imply that the operating procedure and/or target vehicle used by the Federal Reserve Board (Fed) influences the efficacy of the policy indicator. We suggest caution in using any monetary policy variable to explain and possibly forecast U.S. and international stock returns in all monetary conditions.     

Predictability of stock market returns: New evidence from developed and developing countries

Existing studies have concentrated on predicting stock market returns mainly in the U.S. market. We focus on the predictive power of the average correlation, an indicator of co-movements of returns on industry portfolios, and estimate a number of regression models using more than 10,000 monthly observations from 18 developed and 9 developing markets. We find that the average correlation is an effective predictor in most markets. This predictive power is not driven by the state of the economy. It is also not sensitive to subsample periods, although it is stronger in the most recent period and weaker during financial crises.     

U.S. Banking Deregulation, Small Businesses, and Interstate Insurance of Personal Income

We estimate the effects of deregulation of U.S. banking restrictions on interstate personal income insurance for the period 1970 to 2001. Interstate income insurance occurs when personal income reacts less than one‐to‐one to state‐specific output shocks. We find that insurance improved after banking deregulation, with a larger effect in states where small businesses are more important and on proprietors' income than on other components of personal income. Our explanation centers on the role of banks as a prime source of small business finance and on the close intertwining of the personal and business finances of small business owners.     

A comprehensive investigation on the predictive power of economic policy uncertainty from non-U.S. countries for U.S. stock market returns

This study uses economic policy uncertainty (EPU) indices for ten developed countries, three diffusion models, and five combination methods to forecast excess returns in the U.S. stock market. It shows empirically that, over the period January 1997 to January 2022, non-U.S. EPU indices have better predictive power for U.S. equity market excess returns than the U.S. EPU index itself. This illustrates how economic information from international markets can affect the U.S. stock market. This finding challenges the extensively recognized view that the U.S. is where important market signals are initially transmitted to other markets, suggesting that this belief is incomplete. Our outcomes are robust to a battery of tests covering model selection, model specification, forecast horizons, and the pandemic period, and their economic values are assessed. The findings are essential for the financial field to confront future fierce situations and crises.     

Does local political support influence financial markets? A study on the impact of job approval ratings of political representatives on local stock returns

Using data on job approval ratings of governors, U.S. senators, and the president, we find that firms located in states with high approval ratings outperform firms located in states with low approval ratings by .64% per month. Furthermore, this relationship is stronger when investors are actively involved in politics, when local politicians are closer to the center of political power, for small firms that have a larger proportion of local investors, and for financially strong areas where investors are ready to execute investments in local stocks. Overall, our study shows that investors’ political sentiment is important in determining stock returns.     

The impact of hedging on the market value of equity

We examine the annual stock performance of firms that disclose the use of derivatives to hedge over the period 1995 to 1999. We find that only 21.6% of publicly traded U.S. corporations in our sample hedged with derivative instruments over this period and their use is concentrated in the larger companies. Similar to other studies we find that when derivatives are used, interest rate and currency securities are used much more frequently than commodity products. Our sample of 1308 companies that hedge outperforms other securities by 4.3% per year on average over our sample period. This result is robust to several alternative methods of estimating abnormal returns. When we segment performance by the type of hedge used, however, we find that the over-performance is due entirely to larger firms that hedge currency. We find no abnormal returns for firms hedging either interest rates or commodities. The abnormal returns in firms hedging currency is robust to alternative models that seek to control for exchange rate fluctuations and global equity returns; however, we find no significant abnormal returns to currency hedgers when using an augmented model that controls for the role of intangible assets.     

U.S. stock market crash risk, 1926–2010

This paper examines how well alternate time-changed Lévy processes capture stochastic volatility and the substantial outliers observed in U.S. stock market returns over the past 85 years. The autocorrelation of daily stock market returns varies substantially over time, necessitating an additional state variable when analyzing historical data. I estimate various one- and two-factor stochastic volatility/Lévy models with time-varying autocorrelation via extensions of the Bates (2006) methodology that provide filtered daily estimates of volatility and autocorrelation. The paper explores option pricing implications, including for the Volatility Index (VIX) during the recent financial crisis.