Industry Stock Returns Research Articles

Maximum Predictability of Australian Industry Stock Returns

Previous studies have identified that some selected economic or financial variables contain significant power to predict portfolio returns (see, for example, Keim and Stambaugh, 1986; Fama and French 1988, 1989; Balvers, Cosimano and McDonald, 1990; Ferson and Harvey, 1993; Lo and MacKinlay, 1999). This paper aims to investigate the maximum predictability of Australian industrial stock returns. A maximally predicted portfolio (MPP) is constructed in this study to reveal whether the maximum predictability is of economic significance. Through the adoption of market timing and industry group-rotation strategies in the out-of-sample tests, the results reveal that the predictive power does not improve the naive prediction. The timing ability and profitability associated with predictability are not economically significant. When the industry momentum is examined, the results show that the group-rotation strategy can enhance the portfolio performance, but the predictability only captures a small magnitude of the rotation profit.

Dynamic investigation into the predictability of Australian industrial stock returns: Using financial and economic information

This paper employs Bayesian dynamic linear forecasting techniques to investigate the factors driving the predictability of Australian stock market. The unanticipated components of a set of economic and financial variables are chosen as the proxies for the economic risk factors that influence the industrial stock returns. The prior information is incorporated with the predictor variables and updated at each month during the sample period. The final test result reveals that the unanticipated components of term structure and short-term interest rate are the most significant variables to be priced in industry returns. The aggregate dividend-yield variable has influence on some of the industries. The industrial return's predictability is well explained by the time-varying risk premium of economic factors. The comparison between multivariate analysis and univariate analysis strongly indicates that the correlations within the industries are critical in the investigation of the predictability of returns.

Computer-Intensive Time-Varying Model Approach to the Systematic Risk of Australian Industrial Stock Returns

This paper aims to investigate the form of systematic risk of Australian industrial stock returns. We suggest using four stochastic state-space models for the analysis. The stochastic properties of systematic risk are studied by examining four classes of state-space models: random walk model, random coefficient model, ARMA(1, 1) model and mean reverting model (or moving mean model). We have found that the industrial portfolio betas are unstable. The variation of industrial portfolio beta is either random or mean-reverting. Among the nineteen industrial groups, ten of them have the mean-reverting process betas but six of them seem to have a moving long-term mean. Five of the industrial groups have the random process betas, more specifically; the betas of three of them are the random walk processes while the betas of the other two are just the random coefficients. We have also identified that the betas of five industrial groups seem to follow an ARMR(1,1) process.

Long Swings in the Dollar and the Exchange Rate Exposure of Stock Returns

Theoretical models predict that firm behavior will differ under a depreciating relative to an appreciating currency regime. Consequently, the exchange rate exposure of a firm's stock return should also depend on the currency regime. We assess these theoretical predictions by examining the exchange rate exposure of stock returns using an econometric model that allows for stock returns to switch between two different regimes. We find that the two implied stock return regimes correspond to periods of depreciation and appreciation of the dollar. Consistent with the theory, we find that exchange rate exposures of industry stock returns are different in depreciations relative to appreciations. Over half of our sample of 30 industries have a statistically significant exposure coefficient and these exposures are double the size of those estimated from single regime models. Almost all of the industries that are exposed have extensive international trade. Evidence is also presented that is consistent with theoretical predictions that the size of the exchange rate change is important in determining exposure.

Linear and Nonlinear Exchange Rate Exposure and the Price of Exchange Rate Risk

Current empirical research concludes that the effect of exchange rates on US stock returns is negligible. In contrast, we present new evidence that numerous US industries are exposed to exchange rates. The differences between our findings and those in the extant literature are a result of using a new methodological approach which takes account of exchange rate regimes based on periods of depreciation and appreciation. Within each regime we show that first, the market's own exposure to exchange rates should be taken into account before considering industry exposure, second, bilateral rates should be used as opposed to a currency basket, and third, the possible nonlinear nature of exchange rate exposure should be considered. We present new empirical evidence of important economic and statistical linear and nonlinear relationships between exchange rates and industry stock returns. The size and sign of exposure coefficients in each regime depends on the extent to which an industry imports and exports. We also present new results regarding the pricing of bilateral currency risk which we find to be statistically and economically significant. The expected return earned due to exchange rates is positive when the dollar is appreciating and negative when the dollar is depreciating.

Asymmetric Cross-sectional Dispersion in Stock Returns: Evidence and Implications

This paper documents that daily stock returns of both firms and industries are more dispersed when the overall stock market rises than when it falls. This positive relation is conceptually distinct from - and appears unrelated to - asymmetric return correlations. I argue that the source of the relation is positive skewness in sector-specific return shocks. I use this asymmetric behavior to explain a previously-observed puzzle: aggregate trading volume tends to be higher on days when the stock market rises than when it falls. The idea proposed here is that trading is more active on days when the market rises because on those days there is more non-market news on which to trade. I find that empirically, the bulk of the relation between volume and the signed market return is explained by variations in non-market volatility.

Monetary Policy, Stock Returns, and the Role of Credit in the Transmission of Monetary Policy

We use a multi-factor asset pricing model to investigate whether fluctuations in industry stock returns are due to industry stock returns are due to industry-specific shocks or to monetary and other macroeconomic factors. We find that common factors explain a substantial portion of the variation in stock returns, indicating that economic fluctuations are not due to industry-specific factors alone. We also find that disinflationary policy benefits large but not small firms. These results have mixed implications for the view that credit market frictions propagate monetary shocks.

Aggregate Disturbances or Sectoral Shifts: Canadian Evidence

This paper is an empirical investigation of the relative importance of aggregate disturbances and sectoral shifts in the explanation of unemployment cycles in Canada. Two contributions are made in this paper. First, the identification of these two shocks is accomplished by the mean and the dispersion of industrial stock returns, respectively. Secondly, detailed discussions on the interindustry employment movement are provided and a corresponding dispersion is reexamined using quarterly data over three-digit industries. All results here indicate the insignificance of sectoral shifts in Canada.

Federal Reserve Monetary Policy and Industry Stock Returns

Recent studies identify stock return patterns associated with changes in Federal Reserve monetary policy. We find that these return patterns prevail across sixteen industry stock indices. However, significant cross‐industry variation exists as the apparel industry exhibits mean annual returns that are 50% higher under an expansive Fed policy than under a restrictive policy, while the same return difference for the oil industry is only 20%. This cross‐industry variation suggests that monetary conditions may be used by investors to estimate different expected returns across industries. Furthermore, the findings support the view that monetary considerations should be considered in ex ante asset pricing models such as the CAPM.

What's Good for GM...? Using Auto Industry Stock Returns to Forecast Business Cycles and Test the Q-Theory of Investment

We examine the ability of auto industry stock returns to forecast quarterly changes in the growth rates of real GDP, consumption, and investment. We find that auto stock returns are superior to aggregate stock market returns in predicting growth rates of GDP and various forms of consumption. The superior predictive power of auto returns holds for both in-sample and out-of-sample forecasts and has not declined over time. We then apply a finding in this paper---that market returns have no explanatory power for future output or consumption growth when auto returns are included in the regression---to analyze the causal relation between the stock market and investment. We use auto returns to proxy for forecasts of future fundamentals, allowing market returns to capture the effect of the stock market on investment. We find that aggregate returns forecast equipment investment in the presence of auto returns, providing empirical support for q-theory. Results for structures investment are less convincing.(This abstract was borrowed from another version of this item.)

What's Good for GM...? Using Auto Industry Stock Returns to Forecast Business Cycles and Test the Q-Theory of Investment

We examine the ability of auto industry stock returns to forecast quarterly changes in the growth rates of real GDP, consumption, and investment. We find that auto stock returns are superior to aggregate stock market returns in predicting growth rates of GDP and various forms of consumption. The superior predictive power of auto returns holds for both in-sample and out-of-sample forecasts and has not declined over time. We then apply a finding in this paper---that market returns have no explanatory power for future output or consumption growth when auto returns are included in the regression---to analyze the causal relation between the stock market and investment. We use auto returns to proxy for forecasts of future fundamentals, allowing market returns to capture the effect of the stock market on investment. We find that aggregate returns forecast equipment investment in the presence of auto returns, providing empirical support for q-theory. Results for structures investment are less convincing.

Monetary Policy, Stock Returns, and the Role of Credit in the Transmission of Monetary Policy

We use a multi-factor asset pricing model to investigate whether fluctuations in industry stock returns are due to industry stock returns are due to industry-specific shocks or to monetary and other macroeconomic factors. We find that common factors explain a substantial portion of the variation in stock returns, indicating that economic fluctuations are not due to industry-specific factors alone. We also find that disinflationary policy benefits large but not small firms. These results have mixed implications for the view that credit market frictions propagate monetary shocks.

Industry Returns and the Fisher Effect

ABSTRACTWe investigate the cross‐sectional relation between industry‐sorted stock returns and expected inflation, and we find that this relation is linked to cyclical movements in industry output. Stock returns of noncyclical industries tend to covary positively with expected inflation, while the reverse holds for cyclical industries. From a theoretical perspective, we describe a model that captures both (i) the cross‐sectional variation in these relations across industries, and (ii) the negative and positive relation between stock returns and inflation at short and long horizons, respectively. The model is developed in an economic environment in which the spirit of the Fisher model is preserved.

Industry Returns and the Fisher Effect

We investigate the cross-sectional relation between industry-sorted stock returns and expected inflation, and we find that this relation is linked to cyclical movements in industry output. Stock returns of noncyclical industries tend to covary positively with expected inflation, while the reverse holds for cyclical industries. From a theoretical perspective, we describe a model that captures both (i) the cross-sectional variation in these relations across industries, and (ii) the negative and positive relation between stock returns and inflation at short and long horizons, respectively. The model is developed in an economic environment in which the spirit of the Fisher model is preserved.

Tax Reform's Impact on Insurance Industry Stock Returns

This article analyzes the market response of insurers' stock returns to the formulation debate, and enactment of the Tax Reform Act of 1986. Provisions of the Tax Reform Act portended increases in the effective tax rates of insurers despite the proposed lowering of marginal rates. The stock prices of insurers reacted negatively in the formulation, debate, and Committee mark-up phases of the legislative process. Significant abnormal returns were not evidenced in the enactment phase. Several provisions of the Tax Reform Act of 1986 (hereafter referred to as TRA '86) boded unfavorable consequences for insurers. The repeal of the 20 percent special life insurers' deduction, limitations of loss reserves to the present value of unpaid losses, reduction n the deductibility of unearned premiums, taxation of a portion of certain dividends (those subject to the intercorporate exclusion) and previously tax-exempt securities, and the potential tax on inside buildup, were aimed directly at the operations of insurers.(1) Stockholders' expectations could have been adversely impacted by the announcement of any one or combination of these provisions as they progressed through the legislative process. The purpose of this article is to analyze the market response of insurers' stock returns to the formulation, debate, and enactment of TRA '86. Data and Methodology The sample analyzed in this study consists of 19 large, publicly-traded insurers.(2) Each company in the final sample successfully completed the following screening process. First, a sample participant had to have an uninterrupted return series for the entire analysis period. Second, the shares of a sample company had to be actively traded so that non-synchronous price data were not an important problem. Third, a sample participant could have no major firm-specific announcements during the estimation and event intervals. The screening for firm-specific announcements entailed a search of the Wall Street Journal Index beginning six months prior to an event date and extending through the analysis period. When a relevant firm-specific event occurred, the company was eliminated as a sample observation. The well-known Fama, Fisher, Jensen, and Roll methodology is used to estimate the market's reaction to new information about TRA '86.(3) The Fama, et.al. approach facilitates the construction of statistical tests that can reveal significant departures of actual returns from those predicted by the single-index return generating model. Once a sample has been screened for return variations due to specific firm effects, the remaining residual (abnormal) returns are attributable to the event being studied. The event interval analyzed in this article extends from 60 days prior to a particular event date (ED-60) to 60 days after the event date (ED+60). Furthermore, each of the four event windows (EWs) was separated into several sub-phases around its specific event date. The reasoning behind this approach is as follows. The content of pending tax legislation begins to leak to the public prior to the scheduling of hearings and other official proclamations. Hence, adjustments of returns may begin to occur in the pre-announcement and pre-enactment phases of the event period. As investors come to anticipate specific changes in the tax code, they act on their expectations and returns begin to adjust before the event's official announcement. Return adjustments following the announcement and passage of TRA '86 were evaluated in terms of the significance of cumulative residuals over the period ED+1 to ED+60. The event windows considered in this article are listed in Table 1. The announcement of President Reagan's tax reform proposal and Chairman Rostenkowski's agreement to work with the Administration to draft a tax package are the main focuses of EW-1. Both Treasury I and II included modifications to the tax code that had important implications for insurers. …