Forecasting Japanese Stock Returns with Financial Ratios and Other Variables

This paper extends the previous analyses of the forecastability of Japanese stock market returns in two directions. First, we carefully construct smoothed market price–earnings ratios and examine their predictive ability. We find that the empirical performance of the price–earnings ratio in forecasting stock returns in Japan is generally weaker than both the price–earnings ratio in comparable US studies and the price dividend ratio. Second, we also examine the performance of several other forecasting variables, including lagged stock returns and interest rates. We find that both variables are useful in predicting aggregate stock returns when using Japanese data. However, while we find that the interest rate variable is useful in early subsamples in this regard, it loses its predictive ability in more recent subsamples. This is because of the extremely limited variability in interest rates associated with operation of the Bank of Japan’s zero interest policy since the late 1990s. In contrast, the importance of lagged returns increases in subsamples starting from the 2000s. Overall, a combination of logged price dividend ratios, lagged stock returns, and interest rates yield the most stable performance when forecasting Japanese stock market returns.     

Forecasting the price of gold using dynamic model averaging

We develop several models to examine possible predictors of the return of gold, which embrace six global factors (business cycle, nominal, interest rate, commodity, exchange rate and stock price) extracted from a recursive principal component analysis (PCA) and two uncertainty and stress indices (the Kansas City Fed's financial stress index and the U.S. economic policy uncertainty index). Specifically, by comparing alternative predictive models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform linear models (such as the random walk) as well as the Bayesian model averaging (BMA) model. The DMS is the best predictive model overall across all forecast horizons. Generally, all the predictors show strong predictive power at one time or another though at varying magnitudes, while the exchange rate factor and the Kansas City Fed's financial stress index appear to be strong at almost all horizons and sub-periods. However, the forecasting prowess of the exchange rate is supreme.     

Predicting the bear stock market: Macroeconomic variables as leading indicators

This paper investigates whether macroeconomic variables can predict recessions in the stock market, i.e., bear markets. Series such as interest rate spreads, inflation rates, money stocks, aggregate output, unemployment rates, federal funds rates, federal government debt, and nominal exchange rates are evaluated. After using parametric and nonparametric approaches to identify recession periods in the stock market, we consider both in-sample and out-of-sample tests of the variables’ predictive ability. Empirical evidence from monthly data on the Standard & Poor’s S&P 500 price index suggests that among the macroeconomic variables we have evaluated, yield curve spreads and inflation rates are the most useful predictors of recessions in the US stock market, according to both in-sample and out-of-sample forecasting performance. Moreover, comparing the bear market prediction to the stock return predictability has shown that it is easier to predict bear markets using macroeconomic variables.     

Forecasting stock-market tail risk and connectedness in advanced economies over a century: The role of gold-to-silver and gold-to-platinum price ratios

We examine the predictive value of risk perceptions as measured in terms of the gold-to-silver and gold-to-platinum price ratios for stock-market tail risks and their connectedness in eight major industrialized economies using monthly data for the period 1916:02–2020:10 and 1968:01–2020:10, where we use four variants of the popular Conditional Autoregressive Value at Risk (CAViaR) framework to estimate the tail risks for both 1% and 5% VaRs. Our findings for the short sample period show that the gold-to-silver price ratio resembles the gold-to-platinum price ratios in that it is a useful proxy for global risk. Our findings for the long sample period show, despite some heterogeneity across economies, that the gold-to-silver price ratio often helps to out-of-sample forecast for both 1% and 5% stock market tail risks, particularly when a forecaster suffers a higher loss from underestimation of tail risks than from a corresponding overestimation of the same absolute size. We also find that using the gold-to-silver price ratio for forecasting the total connectedness of stock markets is beneficial for an investor who suffers a higher loss from an underestimation of total connectedness (i.e., an investor who otherwise would overestimate the benefits from portfolio diversification) than from a comparable overestimation.     

Does Residential Housing Crowd Out or Promote Households’ Stock Investment? Evidence from China

Limited participation in risky financial markets has long been a puzzle. Empirical evidence shows a strong relationship between housing and investment of risky financial assets, but with varying and conflicting results. We contribute to the literature by distinguishing housing for consumption and for investment, and by considering the role of housing price expectation when exploring households’ participation in stock markets. We find that home equity ratio and housing area play significant roles in households’ participation in stock markets. Households with higher home equity ratio or larger housing are less likely to own, and hold fewer stock assets if they do. We also find that the number of houses has a positive effect on stock investment for households with the same home equity ratio and housing size, which could be explained by credit rationing. Furthermore, housing price expectation has a negative effect on stock investment; this effect is larger for homeowners with multiple houses who are more likely to take houses for investment. Our results show insights into conflicting results of the relationship between real estate and stock investment.     

Do Decomposed Financial Ratios Predict Stock Returns and Fundamentals Better?

We investigate the prediction of excess returns and fundamentals by financial ratios, which include dividend‐price ratios, earnings‐price ratios, and book‐to‐market ratios, by decomposing financial ratios into a cyclical component and a stochastic trend component. We find both components predict excess returns and fundamentals. Cyclical components predict increases in future stock returns, while stochastic trend components predict declines in future stock returns in long horizons. This helps explain previous findings that financial ratios in the absence of decomposition find weak predictive power in short horizons and some predictive power in long horizons. We also find both components predict fundamentals.     

Financial turbulence,systemic risk and the predictability of stock market volatility

This paper adds a novel perspective to the literature by exploring the predictive performance of two relatively unexplored indicators of financial conditions, i.e. financial turbulence and systemic risk, over stock market volatility using a sample of seven emerging and advanced economies. The two financial indicators that we utilize in our predictive setting provide a unique perspective on market conditions, as they relate directly to portfolio performance metrics from both volatility and co-movement perspectives and, unlike other macro-financial indicators of uncertainty, or risk, can be integrated into diversification models within forecasting and portfolio design settings. Since the data for the two predictors are available at a weekly frequency, and our focus is to produce forecasts at the daily frequency, we use the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) approach. The results suggest that incorporating the two financial indicators (singly and jointly) indeed improves the out-of-sample predictive performance of stock market volatility models over both the short and long horizons. We observe that the financial turbulence indicator that captures asset price deviations from historical patterns does a better job when it comes to the out-of-sample prediction of future returns compared with the measure of systemic risk, captured by the absorption ratio. The outperformance of the financial turbulence indicator implies that unusual deviations in not only asset returns, but also in correlation patterns play a role in the persistence of return volatility. Overall, the findings provide an interesting opening for portfolio design purposes, in that financial indicators, which are directly associated with portfolio diversification performance metrics, can also be utilized for forecasting purposes, with significant implications for dynamic portfolio allocation strategies.     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

Predictive regression: An improved augmented regression method

This paper proposes three modifications to the augmented regression method (ARM) for bias-reduced estimation and statistical inference in the predictive regression. They are in relation to improved bias-correction, stationarity-correction, and the use of matrix formulae for bias-correction and covariance matrix estimation. The improved ARM parameter estimators are unbiased to the order of n^− 1, and always satisfy the condition of stationarity. With the matrix formulae, the improved ARM can easily be implemented for a high order model with multiple predictors. From an extensive Monte Carlo experiment, it is found that the improved ARM delivers substantial gain in parameter estimation, statistical inference, and out-of-sample forecasting in small samples. As an application, the improved ARM is applied to monthly US stock return data to evaluate the predictive power of dividend yield in univariate and bivariate predictive models.     

去伪存真:油价趋势与股票市场——来自“一带一路”35国的经验证据

油价波动深刻影响全球经济,严重时会造成全球股市动荡,甚至引发系统性金融风险。然而油价中的信息噪音严重阻碍国际油价对股票市场的预测效果。本文提出的移动平均法可有效减弱信息噪音,研究表明,本文基于移动平均法构建的油价趋势因子对“一带一路”沿线国家股票市场具有良好的样本内和样本外可预测性。进一步研究发现,国际油价波动对产油国和非产油国股票市场的影响存在非对称性。本文为国际油价冲击股票市场提供了新的有力证据,同时本文研究成果提示了油价风险,对维持我国股票市场稳定,保持金融稳定具有一定意义。     

Financial and monetary policy responses to oil price shocks: evidence from oil-importing and oil-exporting countries

In this study, we investigate the financial and monetary policy responses to oil price shocks using a Structural VAR framework. We distinguish between net oil-importing and net oil-exporting countries. Since the 80s, a significant number of empirical studies have been published investigating the effect of oil prices on macroeconomic and financial variables. Most of these studies though, do not make a distinction between oil-importing and oil-exporting economies. Overall, our results indicate that the level of inflation in both net oil-exporting and net oil-importing countries is significantly affected by oil price innovations. Furthermore, we find that the response of interest rates to an oil price shock depends heavily on the monetary policy regime of each country. Finally, stock markets operating in net oil-importing countries exhibit a negative response to increased oil prices. The reverse is true for the stock market of the net oil-exporting countries. We find evidence that the magnitude of stock market responses to oil price shocks is higher for the newly established and/or less liquid stock markets.     

Time series analysis for financial market meltdowns

There appears to be a consensus that the recent instability in global financial markets may be attributable in part to the failure of financial modeling. More specifically, it is alleged that current risk models have failed to properly assess the risks associated with large adverse stock price behavior. In this paper, we first discuss the limitations of classical time series models for forecasting financial market meltdowns. Then we set forth a framework capable of forecasting both extreme events and highly volatile markets. Based on the empirical evidence presented in this paper, our framework offers an improvement over prevailing models for evaluating stock market risk exposure during distressed market periods.     

Employee Layoffs, Shareholder Wealth and Firm Performance: Evidence from the UK

Abstract:  We examine the financial performance of UK listed companies surrounding the announcement of permanent employee layoffs. We find that poor operating and stock price performance, increased gearing, and threats from external markets for corporate control precede employee layoffs. Layoff announcements elicit a significantly negative stock price reaction, which is driven by announcements that are reactive to poor financial conditions. We also find that layoffs result in significant increases in employee productivity and corporate focus. We conclude that layoffs represent an efficient response to poor financial conditions, but that their occurrence is strongly dependent on pressure from external control markets.     

Forecasting the yield curve with linear factor models

In this work we compare the interest rate forecasting performance of a broad class of linear models. The models are estimated through a MCMC procedure with data from the US and Brazilian markets. We show that a simple parametric specification has the best predictive power, but it does not outperform the random walk. We also find that macroeconomic variables and no-arbitrage conditions have little effect to improve the out-of-sample fit, while a financial variable (Stock Index) increases the forecasting accuracy.     

Los efectos de largo plazo de la asimetría y persistencia en la predicción de la volatilidad: evidencia para mercados accionarios de América Latina

This article proposes an extension to the CGARCH model in order to capture the characteristics of short-run and long-run asymmetry and persistence, and examine their effects in modeling and forecasting the conditional volatility of the stock markets from the region of Latin America during the period from 2 January 1992 to 31 December 2014. In the sample analysis, the estimation results of the CGARCH-class model family reveal the presence of short-run and long-run significant asymmetric effects and long-run persistency in the structure of stock price return volatility. The empirical results also show that the use of symmetric and asymmetric loss functions and the statistical test of Hansen (2005) are sound alternatives for evaluating the predictive ability of the asymmetric CGARCH models. In addition, the inclusion of long-run asymmetry and long-run persistency in the variance equation improves significantly the out of sample volatility forecasts for emerging stock markets of Argentina and Mexico.     

Long-term effects of the asymmetry and persistence of the prediction of volatility: Evidence for the equity markets of Latin America

This article proposes an extension to the CGARCH model in order to capture the characteristics of short-run and long-run asymmetry and persistence, and examine their effects in modeling and forecasting the conditional volatility of the stock markets from the region of Latin America during the period from 2 January 1992 to 31 December 2014. In the sample analysis, the estimation results of the CGARCH-class model family reveal the presence of short-run and long-run significant asymmetric effects and long-run persistency in the structure of stock price return volatility. The empirical results also show that the use of symmetric and asymmetric loss functions and the statistical test of Hansen (2005) are sound alternatives for evaluating the predictive ability of the asymmetric CGARCH models. In addition, the inclusion of long-run asymmetry and long-run persistency in the variance equation improves significantly the out of sample volatility forecasts for emerging stock markets of Argentina and Mexico.     

Forecasting GDP growth with financial market data in Finland: Revisiting stylized facts in a small open economy during the financial crisis

This paper examines the ability of financial variables to predict future economic growth above and beyond past economic activity in a small open economy in the euro area. We aim to clarify potential differences in forecasting economic activity during different economic circumstances.Our results from Finland suggest that the proper choice of forecasting variables is related to general economic conditions. During steady economic growth, the preferred choice for a financial indicator is the short-term interest rate combined with past values of output growth. However, during economic turbulence, the traditional term spread and stock returns are more important in forecasting GDP growth. The time-varying predictive content of the financial variables may be utilized by applying regime-switching nonlinear forecasting models. We propose a novel application using the negative term spread and observed recession as signals to switch between regimes. This procedure yields a significant improvement in forecasting performance at the one-year forecast horizon.     

Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU?

This study mainly investigates which predictors (VIX or EPU index) are useful to forecast future volatility for 19 equity indices based on HAR framework during coronavirus pandemic. Out-of-sample analysis shows that the HAR-RV-VIX model exhibits superior forecasting performance for 12 stock markets, while EPU index just can improve forecast accuracy for 5 equity indices, implying that VIX index is more useful for most stock markets' future volatility during coronavirus crisis. The results are robust in recursive window method, alternative realized measures and sub-sample analysis; moreover, VIX index still contains the strongest predictive ability by considering kitchen sink model and mean combination forecast. Furthermore, we further discuss the predictive effect of VIX and EPU index before the coronavirus crisis. Our article provides policy makers, researchers and investors with new insights into exploiting the predictive ability of VIX and EPU index for international stock markets during coronavirus pandemic.     

Less is more? New evidence from stock market volatility predictability

The complex nature of stock market volatility has motivated researchers to apply a variety of predictors to obtain reliable predictive information for precise forecasting. This study seeks to examine the effectiveness of the novel Global Financial Uncertainty (GFU) indices, comprising of only five sub-indices, in predicting stock market volatility using the widely used mixed-data sampling (MIDAS) model. The results demonstrate the remarkable and stable predictive power of GFU, even during crises and global financial uncertainty shocks. Specifically, the financial uncertainty index from Europe plays a significant role in our analysis. Importantly, we find that the GFU index outperforms a large number of other indicators in stock volatility forecasting. The statistical and economic significance of the predictive power of GFU is remarkable. Our study provides significant insights for market participants and policymakers that highlight the need to prioritize global financial uncertainty.     

Predicting Japanese bank stock performance with a composite relative efficiency metric: A new investment tool

The paper's main objective is to predict bank stock performance one year ahead with a composite efficiency metric from relative contextual financial analysis. We bring together financial ratios, generalized data envelopment analysis and simulated annealing to rank Japanese banks on stock performance predicted from relative efficiency scores. An application of this ranking in a profitable investment strategy by designating long and short portfolios underscores the potential commercial value of the method. The method can also be used to monitor the effectiveness of ratios in forecasting stock performance and it is conducive to selecting predictive ratios when markets are changing rapidly.