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.     

On the efficiency of the gold market: Results of a real-time forecasting approach

Using a real-time forecasting approach, we study whether publicly available information on a large set of financial and macroeconomic variables help in forecasting out-of-sample monthly excess returns on investing in gold. The real-time forecasting approach accounts for the fact that an investor must reach an investment decision in real time under uncertainty concerning the optimal forecasting model. The real-time forecasting approach also accounts for the possibility that the optimal forecasting model may change over time. We account for transaction costs and show that using forecasts implied by the real-time forecasting approach to set up a simple trading rule does not necessarily lead to a superior performance relative to a buy-and-hold strategy, implying that the gold market is informationally efficient with respect to the predictor variables that we study in this research.     

Real‐Time Forecasting and Political Stock Market Anomalies: Evidence for the United States

Using monthly data from 1953 to 2003, we apply a real‐time modeling approach to investigate the implications of U.S. political stock market anomalies for forecasting excess stock returns in real‐time. Our empirical findings show that political variables, chosen on the basis of widely used model‐selection criteria, are often included in real‐time forecasting models. However, political variables do not contribute systematically to improving the performance of simple trading rules. For this reason, political stock market anomalies are not necessarily an indication of market inefficiency.     

USING NEURAL NETS TO COMBINE INFORMATION SETS IN CORPORATE BANKRUPTCY PREDICTION

We demonstrate that the use of a neural network (NN) model to combine information from corporate financial statements and equity markets provides improved predictive estimates of the probability of corporate bankruptcy. Using performance measures, based on the receiver operating characteristic curve, the forecast combinations from the NN models are demonstrated to outperform the forecasts derived from a forecast combination generated using a logistic regression approach. This result provides support for the use of forecast combinations generated from NN models in the estimation of corporate bankruptcy probabilities as it outperforms the standard approach of forming a hybrid forecasting model which includes all the explanatory variables. Copyright © 2012 John Wiley & Sons, Ltd.     

A re-evaluation of auditors’ opinions versus statistical models in bankruptcy prediction

Existent empirical evidence on the relative performance of auditors’ going concern opinions versus statistical models in predicting bankruptcy is mixed. This study attempts to add new reliable evidence on this important issue by conducting the comparison based upon an improved statistical model. The improved statistical model incorporates some new developments advocated by recent bankruptcy prediction research (e.g., Shumway, 2001). First, the following non-traditional variables are added: a composite measure of financial distress, industry failure rate, abnormal stock returns, and market capitalization. Secondly, a hazard model is employed. The prediction ability of the hazard model with incorporation of non-financial-ratio variables is superior to that of auditors’ going concern opinions in the holdout sample. This suggests that a well-developed statistical model could serve as a decision aid for auditors to better make going-concern judgments. Further analyses reveal some evidence that industry failure rate does not have a significant impact upon auditors’ going concern judgments as it should be; auditors could improve their going concern judgments by considering industry-level information in addition to firm-specific information. Finally, we find that auditors’ opinions do have incremental contribution beyond stock-market information and industry failure rate in predicting bankruptcy.

The Brazilian bankruptcy law experience

In early 2005, the Brazilian Congress approved a new bankruptcy law. The new legislation increased creditor protection and improved the efficiency of the bankruptcy system. This paper evaluates the empirical consequences of a bankruptcy reform on a poorly developed credit market. Using data from Brazilian and non-Brazilian firms, we estimated, using two different models, the effect of the bankruptcy reform on contractual and non-contractual debt variables. In general, both models yielded similar results. Concerning contractual debt variables, we found a significant increase in the total amount and the long-term debt and a reduction in the cost of debt. For the non-contractual debt variable, we found no effect in the loans’ ownership structure.     

Empirical comparison of hazard models in predicting SMEs failure

This study aims to shed light on the debate concerning the choice between discrete-time and continuous-time hazard models in making bankruptcy or any binary prediction using interval censored data. Building on the theoretical suggestions from various disciplines, we empirically compare widely used discrete-time hazard models (with logit and clog-log links) and the continuous-time Cox Proportional Hazards (CPH) model in predicting bankruptcy and financial distress of the United States Small and Medium-sized Enterprises (SMEs). Consistent with the theoretical arguments, we report that discrete-time hazard models are superior to the continuous-time CPH model in making binary predictions using interval censored data. Moreover, hazard models developed using a failure definition based jointly on bankruptcy laws and firms’ financial health exhibit superior goodness of fit and classification measures, in comparison to models that employ a failure definition based either on bankruptcy laws or firms’ financial health alone.     

Multi-period credit default prediction with time-varying covariates

In credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time-varying covariates like balance sheet or stock market variables. If the prediction horizon covers multiple periods, this leads to the problem that the future evolution of these covariates is unknown. Consequently, some authors have proposed a framework that augments the prediction problem by covariate forecasting models. In this paper, we present simple alternatives for multi-period prediction that avoid the burden to specify and estimate a model for the covariate processes. In an application to North American public firms, we show that the proposed models deliver high out-of-sample predictive accuracy.     

The Taylor Rule and Forecast Intervals for Exchange Rates

In this paper, we examine the Meese–Rogoff puzzle from a different perspective: out‐of‐sample interval forecasting. While most studies in the literature focus on point forecasts, we apply semiparametric interval forecasting to a group of exchange rate models. Forecast intervals for 10 OECD exchange rates are generated and the performance of the empirical exchange rate models are compared with the random walk. Our contribution is twofold. First, we find that in general, exchange rate models generate tighter forecast intervals than the random walk, given that their intervals cover out‐of‐sample exchange rate realizations equally well. Our results suggest a connection between exchange rates and economic fundamentals: economic variables contain information useful in forecasting distributions of exchange rates. We also find that the benchmark Taylor rule model performs better than the monetary, PPP and forward premium models, and its advantages are more pronounced at longer horizons. Second, the bootstrap inference framework proposed in this paper for forecast interval evaluation can be applied in a broader context, such as inflation forecasting.     

A horse race of models and estimation methods for predicting bankruptcy

We use a comprehensive set of performance metrics to analyze the improvement in the classification power and prediction accuracy of various bankruptcy prediction models after adding governance variables and/or varying the estimation method used. In a sample covering bankruptcies of U.S. public firms in the period 2000 to 2015, we find that the addition of governance variables significantly improves the performance of all bankruptcy prediction models. We also find that the additional explanatory power provided by governance measures improves the further the firm is from bankruptcy, which suggests that governance variables may provide earlier and more accurate warning of the firm's bankruptcy potential. Our findings show that the performance of any bankruptcy prediction model is significantly affected by the estimation method used. We find that regardless of the bankruptcy model, hazard analysis provides the best classification and out-of-sample forecast accuracy among the parametric methods. Furthermore, non-parametric methods such as neural networks, data envelopment analysis or classification and regression trees appear to provide comparable and sometimes superior classification accuracy to hazard analysis. Lastly, we use the dynamic panel generalized methods of moments model to address concerns raised in prior studies about the susceptibility of similar studies to endogeneity issues and find that our findings continue to hold.     

The predictability of aggregate returns on commodity futures

This paper provides evidence that aggregate returns on commodity futures (without the returns on collateral) are predictable, both in-sample and out-of-sample, by various lagged variables from the stock market, bond market, macroeconomics, and the commodity market. Out of the 32 candidate predictors we consider, we find that investor sentiment is the best in-sample predictor of short-horizon returns, whereas the level and slope of the yield curve have much in-sample predictive power for long-horizon returns. We find that it is possible to forecast aggregate returns on commodity futures out-of-sample through several combination forecasts (the out-of-sample return forecasting R² is up to 1.65% at the monthly frequency).     

Forecasting volatility of the U.S. oil market

We examine the information content of the CBOE Crude Oil Volatility Index (OVX) when forecasting realized volatility in the WTI futures market. Additionally, we study whether other market variables, such as volume, open interest, daily returns, bid-ask spread and the slope of the futures curve, contain predictive power beyond what is embedded in the implied volatility. In out-of-sample forecasting we find that econometric models based on realized volatility can be improved by including implied volatility and other variables. Our results show that including implied volatility significantly improves daily and weekly volatility forecasts; however, including other market variables significantly improves daily, weekly and monthly volatility forecasts.     

STOCK MARKET PERCEPTION OF INDUSTRIAL FIRM BANKRUPTCY

The objectives of this study are to determine (1) when the stock market first perceives the impending bankruptcy of a potentially bankrupt firm and (2) what firm-specific factors explain the interval between the perception time and the eventual date of bankruptcy (i.e., market lead time). A computational methodology based on the Hillmer-Yu technique is used to determine the month in which a structural change in the mean and variance of monthly stock return occurs for a potentially bankrupt firm. This parametric change month or the “market perception time” is computed for a sample of 47 industrial firms. The range of market lead times cautions against the common assumption of a uniform event period in event studies. The lead time interval (for both the mean and variance of monthly market return) of poteintially bankrupt firms is found to be positively related to the firm's earnings per share at the time of stock market perception of eventual bankruptcy. Neither the firm's asset size nor systematic risk appear to be significant indicators of lead time interval. Also, change in investment at market perception time is positively related to percentage change in the market lead times. This suggests that innovations in the investment variable are a source of new information to the security market in assesing the probability of future bankruptcy of a firm.     

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.     

Modelling and forecasting liquidity supply using semiparametric factor dynamics

We model the dynamics of ask and bid curves in a limit order book market using a dynamic semiparametric factor model. The shape of the curves is captured by a factor structure which is estimated nonparametrically. Corresponding factor loadings are modelled jointly with best bid and best ask quotes using a vector error correction specification. Applying the framework to four stocks traded at the Australian Stock Exchange (ASX) in 2002, we show that the suggested model captures the spatial and temporal dependencies of the limit order book. We find spill-over effects between both sides of the market and provide evidence for short-term quote predictability. Relating the shape of the curves to variables reflecting the current state of the market, we show that the recent liquidity demand has the strongest impact. In an extensive forecasting analysis we show that the model is successful in forecasting the liquidity supply over various time horizons during a trading day. Moreover, it is shown that the model's forecasting power can be used to improve optimal order execution strategies.     

Predicting bankruptcy using the discrete-time semiparametric hazard model

The usual bankruptcy prediction models are based on single-period data from firms. These models ignore the fact that the characteristics of firms change through time, and thus they may suffer from a loss of predictive power. In recent years, a discrete-time parametric hazard model has been proposed for bankruptcy prediction using panel data from firms. This model has been demonstrated by many examples to be more powerful than the traditional models. In this paper, we propose an extension of this approach allowing for a more flexible choice of hazard function. The new method does not require the assumption of a parametric model for the hazard function. In addition, it also provides a tool for checking the adequacy of the parametric model, if necessary. We use real panel datasets to illustrate the proposed method. The empirical results confirm that the new model compares favorably with the well-known discrete-time parametric hazard model.     

Innovate or die: Corporate innovation and bankruptcy forecasts

We investigate the relationship between a firm’s innovation performance and its probability of bankruptcy. Estimating the discrete hazard model with a comprehensive set of bankruptcies spanning the period of 1980–2009, we find several previously neglected innovation-based variables are important determinants of bankruptcy probability, especially for firms belonging to technology-intensive industries. R&D productivity demonstrates persistent significance across different prediction horizons while the predictive power of patent count becomes larger and more significant at longer prediction horizons. We also find that a firm’s organization capital intensity correlates positively with future bankruptcy.     

Performance,stock selection and market timing of the German equity mutual fund industry

We investigate the performance of the German equity mutual fund industry over 20 years (monthly data 1990–2009) using the false discovery rate (FDR) to examine both model selection and performance measurement. When using the Fama–French three factor (3F) model (with no market timing) we find that at most 0.5% of funds have truly positive alpha-performance and about 27% have truly negative-alpha performance. However, the use of the FDR in model selection implies inclusion of market timing variables and this results in a large increase in truly positive alpha funds. However, when we use a measure of “total” performance, which includes the contribution of both security selection (alpha) and market timing, we obtain results similar to the 3F model. These results are largely invariant to different sample periods, alternative factor models and to the performance of funds investing in German and non-German firms — the latter casts doubt on the ‘home-bias’ hypothesis of superior performance in ‘local’ markets.     

Differences in the valuation of earnings and book value: Regulation effects or industry effects?

This paper uses a valuation framework on a sample of firms from four European countries (France, Germany, Netherlands, and United Kingdom) to examine how income, accruals, and book value of equity are perceived by the respective capital markets. Our model includes adjustments for industry effects and taking into account the linear information dynamics of the accounting variables posited in the Ohlson model. Consistent with previous researchers, we find that both earnings and book value of equity have valuation implications and that there is significant dispersion in the country-specific and industry-specific valuation multiples. However, when using accounting variables to forecast market values we find that industry-specific valuation multiples reduce forecasting error more than country-specific ones.     

Modelling the behaviour of the new issue market

This paper analyses the time series behaviour of the initial public offering (IPO) market using an equilibrium model of demand and supply that incorporates the number of new issues, average underpricing, and general market conditions. Model predictions include the existence of serial correlation in both the number of new issues and the average level of underpricing, as well as interactions between these variables and the impact of general market conditions. The model is tested using 40 years of monthly IPO data. The empirical results are generally consistent with predictions.