Stock Returns and Real Activity: A Century of Evidence

This paper analyzes the relation between real stock returns and real activity from 1889–1988. It replicates Fama's (1990) results for the 1953–1987 period using an additional 65 years of data. It also compares two measures of industrial production in the tests: (1) the series produced by Babson for 1889–1918, spliced with the Federal Reserve Board index of industrial production for 1919–1988, and (2) the new Miron and Romer (1989) index spliced with the Federal Reserve Board index in 1941. Fama's findings are robust for a much longer period—future production growth rates explain a large fraction of the variation in stock returns. The new Miron-Romer measure of industrial production is less closely related to stock price movements than the older Babson and Federal Reserve Board measures.     

The Comovement of US and UK Stock Markets

US and UK stock returns are highly positively correlated over the period 1918–99. Using VAR‐based variance decompositions, we investigate the nature of this comovement. Excess return innovations are decomposed into news about future dividends, real interest rates, and excess returns. We find that the latter news component is the most important in explaining stock return volatility in both the USA and the UK and that stock return news is highly correlated across countries. This is evidence against Beltratti and Shiller's (1993) finding that the comovement of US and UK stock markets can be explained in terms of a simple present value model. We interpret the comovement as indicating that equity premia in the two countries are hit by common real shocks.     

Regime Change and the Role of International Markets on the Stock Returns of Small Open Economies

We examine the influence of US, UK and German macroeconomic and financial variables on the stock returns of two relatively small, open European economies, Ireland and Denmark. Within a nonlinear framework, we allow for time variation via regime switching using a smooth transition regression (STR) model. We find that US (global) and UK and German (regional) stock returns are significant determinants of returns in both markets. Further, global information represented by oil and US asset price movements drive changes between states in each market. Significantly, the role of country‐specific domestic variables is typically confined to a single state while global and regional variables pervade all states.     

Economic and financial crises and the predictability of U.S. stock returns

We argue that the use of publicly available and easily accessible information on economic and financial crises to detect structural breaks in the link between stock returns and macroeconomic predictor variables improves the performance of simple trading rules in real time. In particular, our results suggest that accounting for structural breaks and regime shifts in forecasting regressions caused by economic and financial crises has the potential to increase the out-of-sample predictability of stock returns, the performance of simple trading rules, and the market-timing ability of an investor trading in the U.S. stock market.     

Rejoinder

This paper provides a formula for a commonly used measure of the economic value of asset return predictability. In doing this, we find that there is a strong connection between this measure and a traditional statistical measure of predictive quality. In particular, we demonstrate that the maximum amount an investor is willing to pay for predictability knowledge (the performance fee) is a simple transformation of the R ² statistic associated with the predictor equation. We illustrate the use of these results with an application to the Ibbotson US bond and equity data (and a set of pertinent predictors), and via application to the results published in Fama and French [1988. Dividend yields and expected stock returns. Journal of Financial Economics 22: 3–25], Balvers, Cosimano, and McDonald [1990. Predicting stock returns in an efficient market. Journal of Finance 45: 1109–28], Lettau and Ludvigson [2001. Consumption, aggregate wealth and expected stock returns. Journal of Finance 56: 815–49], and Santa-Clara and Yan [2010. Crashes, volatility, and the equity premium: Lessons from S&;P 500 options. Review of Economics and Statistics 92: 435–51].     

Real‐Time Risk Pricing Over the Business Cycle: Some Evidence for the UK

Abstract:   The fully‐revised data typically utilized in empirical research do not reflect the true information available to financial market participants at the time of their decision‐making. This paper uses a new real‐time macroeconomic dataset to appraise the relative importance of different vintages of data on economic variables as determinants of UK stock returns using the framework of Arbitrage Pricing Theory. We find that two factors influence expected stock returns, namely unanticipated inflation and economic uncertainty, but only when measured in real‐time. Moreover, their pricing influence is only present during phases of the business cycle when their associated risks are at their most prevalent.     

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.     

Forecasting Surrender Rates Using Elliptical Copulas and Financial Variables

A multistage stochastic model to forecast surrender rates for life insurance and pension plans is proposed. Surrender rates are forecasted by means of Monte Carlo simulation after a sequence of GLM, ARMA-GARCH, and copula fitting is executed. The model is illustrated by applying it to age-specific time series of surrender rates derived from pension plans with annuity payments of a Brazilian insurer. In the GLM process, the only macroeconomic variable used as an explanatory variable is the Brazilian real short-term interest rate. The advantage of such a variable is that we can take future market expectation through the current term structure of interest rates. The GLM residuals of each age/gender group are then modeled by ARMA-GARCH processes to generate i.i.d. residuals. The dependence among these residuals is then modeled by multivariate Gaussian and Student's t copulas. To produce a conditional forecast on a stock market index, in our application we used the residuals of an ARMA-GARCH model fitted to the Brazilian stock market index (Ibovespa) returns, which generates one of the marginal distributions used in the dependence modeling through copulas. This strategy is adopted to explain the high and uncommon surrender rates observed during the recent economic crisis. After applying known simulation methods for elliptical copulas, we proceeded backwards to obtain the forecasted distributions of surrender rates by application, in the sequel, of ARMA-GARCH and GLM models. Additionally, our approach produced an algorithm able to simulate multivariate elliptical copulas conditioned on a marginal distribution. Using this algorithm, surrender rates can be simulated conditioned on stock index residuals (in our case, the residuals of the Ibovespa returns), which allows insurers and pension funds to simulate future surrender rates assuming a financial stress scenario with no need to predict the stock market index.     

Relationship between gold and stock markets during the global financial crisis: Evidence from nonlinear causality tests

This paper investigates the nonlinear dynamic co-movements between gold returns, stock market returns and stock market volatility during the recent global financial crisis for the UK (FTSE 100), the US (S&P 500) and Japan (Nikkei 225). Initially, the bivariate dynamic relationships between i) gold returns and stock market returns and ii) gold returns and stock market volatility are tested; both of these relationships are further investigated in the multivariate nonlinear settings by including changes in the three-month LIBOR rates. In this paper correlation integrals based on the bivariate model show significant evidence of nonlinear feedback effect among the variables during the financial crisis period for all the countries understudy. Very limited evidence of significant feedback is found during the pre-crisis period. Results from the multivariate tests including changes in the LIBOR rates provide results similar to the bivariate results. These results imply that gold may not perform well as a safe haven during the financial crisis period due to the bidirectional interdependence between gold returns and, stock returns as well as stock market volatility. However, gold may be used as a hedge against stock market returns and volatility in stable financial conditions.     

A Reexamination of Real Stock Returns,Real Interest Rates,Real Activity,and Inflation: Evidence from a Large Data Set

Using the informational sufficiency procedure from Forni and Gambetti (2014) along with data from McCracken and Ng (2014), we update the results of Lee (1992) and find that his vector autoregression (VAR) is informationally deficient. To correct this problem, we estimate a factor augmented VAR (FAVAR) and analyze the differences once informational deficiency is corrected with an emphasis on the relationship between real stock returns and inflation. In particular, we examine Modigliani and Cohn's (1979) inflation illusion hypothesis, Fama's (1983) proxy hypothesis, and the “anticipated policy hypothesis.”     

Stock Volatility during the Recent Financial Crisis

This paper uses monthly returns from 1802 to 2010, daily returns from 1885 to 2010, and intraday returns from 1982 to 2010 in the USA to show how stock volatility has changed over time. It also uses various measures of volatility implied by option prices to infer what the market was expecting to happen in the months following the financial crisis in late 2008. This episode was associated with historically high levels of stock market volatility, particularly among financial sector stocks, but the market did not expect volatility to remain high for long and it did not. This is in sharp contrast to the prolonged periods of high volatility during the Great Depression. Similar analysis of stock volatility in the United Kingdom and Japan reinforces the notion that the volatility seen in the 2008 crisis was relatively short‐lived. While there is a link between stock volatility and real economic activity, such as unemployment rates, it can be misleading.     

Empirical analysis of real and financial volatilities on stock excess returns: evidence from Taiwan industrial data

This paper tests the relation between stock excess returns and risk factors measured by volatility. The sources of the volatility are based on the volatility of macroeconomic factors and time-series volatility. To model the macroeconomic fundamentals, we divide the risk into real and financial volatilities pertinent to Taiwan's economic environment. By examining the data of indusry excess returns and market excess returns, we find evidence to reject the hypothesis that the stock excess returns are independent of the real and financial volatilities.     

Effects of Economic Convergence on Stock Market Returns in Major EMU Member Countries

This study updates and extends existing literature by investigating the effects of economic convergence among major European Economic and Monetary Union (EMU) member countries on stock market returns in each respective nation. Main findings include: (1) long-term stability in the EMU appears to be attainable, but further integration of product and factor markets is needed to reinforce convergence of real sectors; (2) the UK can be considered a quasi EMU participant due to convergence of its key economic variables with those of formal EMU members; and (3) economic convergence appears to be an important contributing factor to returns from stock markets in the included EMU countries except Germany.     

Does the stock market predict real activity? Time series evidence from the G-7 countries

This paper extends one aspect of the US stock market study of Fama (1990) and Schwert (1990). We examine the relationship between industrial production (IP) growth rates and lagged real stock returns for the G-7 countries using both in-sample cointegration and error-correction models and the out-of-sample forecast-evaluation procedure of Ashley et al. (1980). The cointegration tests show a long-run equilibrium relationship between the log levels of IP and real stock prices, while the error-correction models indicate a correlation between IP growth and lagged real stock returns for all countries except Italy. The out-of-sample tests show that in several sub-periods the US, UK, Japanese, and Canadian stock markets enhance predictions of future IP.     

Stock investment strategy combining earnings power index and machine learning

We propose an intermediate-term stock investment strategy based on fundamental analysis and machine learning. The approach uses predictors from the Earnings Power Index (EPI) as input variables derived from cross-sectional and time-series data from a company’s financial statements. The analytical methods of machine learning allow us to validate the link between financial factors and excess returns directly. We then select stocks for which returns are likely to increase at the time of the next disclosed financial statement. To verify the proposed approach’s usefulness, we use company data listed publicly on the Korean stock market from 2013 to 2019. We examine the profitability of trading strategy based on ten machine-learning techniques by forming long, short, and hedge portfolios with three different measures. As a result, most portfolios, including EPI-related variables, present positive returns regardless of the period. Especially, the neural network of the two layers with sigmoid function presents the best performance for the period of 3 months and 6 months, respectively. Our results show that incorporating machine learning is useful for mid-term stock investment. Further research into the possible convergence of financial statement analysis and machine-learning techniques is warranted.     

Stock Market Returns and Economic Fundamentals in an Emerging Market: The Case of Korea

This paper investigates the feedback relationship between stock market returnsand economic fundamentals in an emerging market. Starting from an intertemporalconsumption-based CAPM (CCAPM), we obtain a restricted VAR model for stockreturns and macroeconomic variables. We then apply this model to Korea and findstatistically significant departures from the restrictions implied by CCAPM.Consequently, an unrestricted VAR model is used to analyze the variations of expectedand unexpected returns in the Korean stock market. It is shown that the expectedmarket returns vary with a set of macroeconomic variables, and that thepredictable component is substantial. Reflecting richer dynamics in the data,relative to the usual single equation modeling in the literature, the estimatedVAR model shows considerable predictive ability for both real economic activityand real returns. Using the model for a variance decomposition of unexpectedreturns, we find that, although we cannot directly observe the market's revisionof expected future dividend growth, we can estimate a large part of therevision with the news in the expected industry output growth from our VAR model.Finally, we also find that economic fundamentals can explain only a smallportion of the variation in unexpected returns in the Korean stock market.     

Asset market linkages: Evidence from financial, commodity and real estate assets

We use a general Markov switching model to examine the relationships between returns over three different asset classes: financial assets (US stocks and Treasury bonds), commodities (oil and gold) and real estate assets (US Case-Shiller index). We confirm the existence of two distinct regimes: a “tranquil” regime with periods of economic expansion and a “crisis” regime with periods of economic decline. The tranquil regime is characterized by lower volatility and significantly positive stock returns. During these periods, there is also evidence of a flight from quality - from gold to stocks. By contrast, the crisis regime is characterized by higher volatility and sharply negative stock returns, along with evidence of contagion between stocks, oil and real estate. Furthermore, during these periods, there is strong evidence of a flight to quality - from stocks to Treasury bonds.     

Valuation in US Commercial Real Estate

We consider a log‐linearized version of a discounted rents model to price commercial real estate as an alternative to traditional hedonic models. First, we verify a key implication of the model, namely, that cap rates forecast commercial real estate returns. We do this using two different methodologies: time series regressions of 21 US metropolitan areas and mixed data sampling (MIDAS) regressions with aggregate REIT returns. Both approaches confirm that the cap rate is related to fluctuations in future returns. We also investigate the provenance of the predictability. Based on the model, we decompose fluctuations in the cap rate into three parts: (i) local state variables (demographic and local economic variables); (ii) growth in rents; and (iii) an orthogonal part. About 30% of the fluctuation in the cap rate is explained by the local state variables and the growth in rents. We use the cap rate decomposition into our predictive regression and find a positive relation between fluctuations in economic conditions and future returns. However, a larger and significant part of the cap rate predictability is due to the orthogonal part, which is unrelated to fundamentals. This implies that economic conditions, which are also used in hedonic pricing of real estate, cannot fully account for future movements in returns. We conclude that commercial real estate prices are better modelled as financial assets and that the discounted rent model might be more suitable than traditional hedonic models, at least at an aggregate level.     

The Usefulness of Book-to-Market and ROE Expectations for Explaining UK Stock Returns

Abstract:  The fundamental valuation perspective on stock returns suggests that book-to-market will be positively related to returns if market value of equity equals future expected cash flows discounted at the expected return and book value proxies for future cash flows. Building on this perspective, we develop a log linear model which includes expectations of future BM and ROE in addition to current BM as explanatory variables for future stock returns. We show that these three variables explain a significant part of UK cross-sectional stock returns and that they remain highly statistically significant after including additional risk proxy variables. This supports relevance of fundamental valuation based firm characteristics for explaining stock returns and indicates their potential usefulness for predicting future stock returns.     

Macroeconomic determinants of stock market capitalization in an emerging market: fresh evidence from cointegration with unknown structural breaks

This article explores the macroeconomic determinants of stock market development in an emerging market (Pakistan) over the period of 1974–2010. We have applied Zivot–Andrews unit root test for integrating properties of the variables and the autoregressive distributed lag bounds testing for cointegration. The direction of causality between the variables is investigated by applying the vector error-correction model Granger causality approach. Our results revealed that variables are cointegrated for long run relationship. Economic growth, inflation, financial development and investment increase stock market development, but trade openness decreases it. The causality analysis confirms that stock market development is a Granger cause of economic growth, inflation, financial development, investment and trade openness. This article indicates the importance of trade openness while formulating a comprehensive financial policy.