Abstract
The purpose of this paper is to investigate whether the current account balance can help in forecasting the quarterly S&P500-based equity premium out-of-sample. We consider an out-of-sample period of 1970:Q3 to 2014:Q4, with a corresponding in-sample period of 1947:Q2 to 1970:Q2. We employ a quantile predictive regression model. The quantile-based approach is more informative relative to any linear model, as it investigates the ability of the current account to forecast the entire conditional distribution of the equity premium, rather than being restricted to just the conditional-mean. In addition, we employ a recursive estimation of both the conditional-mean and quantile predictive regression models over the out-of-sample period which allows for time-varying parameters in the forecast evaluation part of the sample for both of these models. Our results indicate that unlike as suggested by the linear (mean-based) predictive regression model, the quantile regression model shows that the (changes in the) real current account balance contains significant out-of-sample information when the stock market is performing poorly (below the quantile value of 0.3), but not when the market is in normal to bullish modes (quantile value above 0.3). This result seems to be intuitive in the sense that, when the markets are performing average to well, that is performing around the median and above of the conditional distribution of the equity premium, the excess return is inherently a random-walk and hence, no information, from a predictor (changes in the real current account balance) is able to predict the equity premium.
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Notes
The CRSP Index (which includes the NYSE, AMEX, and Nasdaq) is believed to provide a better proxy for market returns than S&P 500 Index as it is much broader than the latter (Rapach and Zhou 2013).
Complete details of the structural break tests are available upon request from the authors. Note that, with both the conditional mean-based and the quantile models estimated recursively over the out-of-sample horizon which includes all the breaks, we are able to account for possible nonlinearity that would arise in the relationship between the first-differences of the real current account and the equity premium due to such regime shifts. Hence, we provide a fair comparison across the two types of model by controlling for such nonlinearity due to structural breaks, and in the process, this allows us to gauge the possibility of the importance of nonlinearity that exists inherently at various phases of the equity market with the current account balance.
As suggested by an anonymous referee, we conducted various tests of heteroscedasticity on the residuals of the predictive regression model. However, we could not reject the null of homoscedasticity. Complete details of these results are available upon request from the authors.
The RMSFE of model j for one-step-ahead forecast is calculated as follows: \( \sqrt{\frac{1}{T-1-m}{\left({{\displaystyle {\sum}_{m=M}^{T-1}{y}_{m+l}-y}}_{j,m+1}^p\right)}^2} \), where y m+l is the true data, and \( {y}_{j,m+l}^p \) is its out of sample forecasting from model j; m is the initial forecast origin; and T is the sample size.
Qualitatively similar, though slightly weaker, forecasting gains were obtained under the quantile predictive regression model untill the quantile range of 0.30, when we used the changes in nominal current account balance instead of the real values of the same. The linear (conditional mean-based) predictive regression model continued to show no evidence of predictability using changes in nominal current account balance. In addition to using the nominal current account, our results were qualitatively same when we used the S&P500 instead of the CRSP-VW index to compute the excess returns, i.e., forecasting gains were only concentrated around the lower quantiles, with the conditional mean-based model producing no gains. Using an alternative metric for the measure of forecasting performance, namely the Mean Absolute Percentage Forecast Errors (MAPFE), also produced qualitatively similar results as that obtained from the relative RMSFE. However, we also observed a forecasting gain at the quantile level of 0.90. Finally, the significance of the MSE-F test statistic was also confirmed by the ENC-NEW statistic of Clark and McCracken (2001). Complete details of all these results are available upon request from the authors.
Following Rapach and Zhou (2013), we report the utility gains for γ = 3 since the results are qualitatively similar for other reasonable γ values.
The break dates obtained were as follows: Euro Area: 2000:Q4, 2003:Q2, 2007:Q3; Japan: 1990:Q1, 1995:Q3, 2000:Q3, 2004:Q3, and 2009:Q2; and the UK: 1975:Q1, 1995:Q2, and 2003:Q2.
References
Aye, G. C., Deale, F. W., and Gupta, R. (Forthcoming). Does Debt Ceiling and Government Shutdown Help in Forecasting the US Equity Risk Premium? Panoeconomicus.
Bai J, Perron P (2003) Computation and analysis of multiple structural change models. J Appl Econ 18(1):1–22
Balcilar M, Gupta R, Kyei C, Wohar ME (2016) Does economic policy uncertainty predict exchange rate returns and volatility? Evidence from a nonparametric causality-in-quantiles test. Open Econ Rev 27:229–250
Baharumshah A, Liew V (2006) Forecasting performance of exponential smooth transition autoregressive exchange rate models. Open Econ Rev 17:235–251
Bekiros S, Gupta R (2015) Predicting stock returns and volatility using consumption-aggregate wealth ratios: a nonlinear approach. Econ Lett 131:83–85
Bekiros S, Gupta R, Kyei C (2016a) On economic uncertainty, stock market predictability and nonlinear spillover effects. NAJEF 36:184–191
Bekiros S, Gupta R, Majumdar A (2016b) Incorporating economic policy uncertainty in US equity premium models: a nonlinear predictability analysis. Finance Research Letters. doi:10.1016/j.frl.2016.01.012
Brock WA, Scheinkman JA, Dechert WD, LeBaron B (1996) A test for independence based on the correlation dimension. Econ Rev 15(3):197–235
Campbell JY (2008) Viewpoint: estimating the equity premium. Can J Econ 41:1–21
Campbell JY, Thompson SB (2008) Predicting the equity premium out of sample: can anything beat the historical average? Rev Financ Stud 21(4):1509–1531
Cenesizoglu, T., and Timmermann A., (2008). Is the distribution of stock returns predictable? Available online at SSRN http://ssrn.com/abstract=1107185.
Cenesizoglu T, Timmermann A (2012) Do Return Prediction Models Add Economic Value? J Bank Financ 36(11):2974–2987
Clark TE, McCracken MW (2001) Test of equal forecast accuracy and encompassing for nested models. J Econ 105(1):85–110
Koenker R, Bassett Jr G (1978) Regression quantiles. Econometrica 46:33–50
McCracken MW (2007) Asymptotics for out of sample tests of granger causality. J Econ 140(2):719–752
Meligkotsidou L, Panopoulou E, Vrontos ID, Vrontos SD (2014) A quantile regression approach to equity premium prediction. J Forecast 33(7):558–576
Mercereau, B., 2003a. The role of stock Markets in Current Account Dynamics: a time series approach. The BE J Macroecon 3 (1), 1{30.
Mercereau, B., 2003b. The Role of Stock Markets in Current Account Dynamics: Evidence from the United States. IMF Working Papers 03/108, International Monetary Fund.
Rapach D, Zhou G (2013) Forecasting Stock Returns. In: Elliott G, Timmermann A (eds) Handbook of Economic Forecasting, vol Volume 2A. Elsevier, Amsterdam, pp. 328–383
Serletis A, Malliaris A, Hinich M, Gogas P (2012) Episodic nonlinearity in leading global currencies. Open Econ Rev 23:337–357
Welch I, Goyal A (2008) A comprehensive look at the empirical performance of equity premium prediction. Rev Financ Stud 21(4):1455–1508
Acknowledgments
We would like to thank two anonymous referees for many helpful comments. However, any remaining errors are solely ours.
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Gupta, R., Majumdar, A. & Wohar, M.E. The Role of Current Account Balance in Forecasting the US Equity Premium: Evidence From a Quantile Predictive Regression Approach. Open Econ Rev 28, 47–59 (2017). https://doi.org/10.1007/s11079-016-9408-x
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DOI: https://doi.org/10.1007/s11079-016-9408-x