Current ESRC Research Grant (January 1st 2018-December 31st 2020):

 


"Investigating Structural Change in Predictive Regressions with Applications to Forecasting Stock Returns"
(Award Number ES/R00496X/1).

Investigating Structural Change in Predictive Regressions with Applications to Forecasting Stock Returns

Principal Investigator:  Robert Taylor, Essex Business School, University of Essex

Summary

A vast body of empirical research involving econometric methods has been undertaken investigating stock return predictability. These methods have largely been based on predictive regression models and have investigated a wide array of financial and macroeconomic variables as putative predictors for returns. Popular variables investigated for their predictive ability for returns have included valuation ratios such as the dividend-price ratio, earnings-price ratio, book-tomarket ratio and various interest rate spreads. Overall, these empirical studies have tended to find either no or only relatively weak statistical evidence of in-sample predictability in stock returns. Yet for many variables there are good theoretical reasons to expect them to provide some form of predictive power for returns. For example, a finding that the price-dividend ratio has some predictive power for returns is consistent with orthodox financial theory, because if the dividend-price ratio is time-varying then according to the present value model it must forecast either the dividend growth rate or returns to some extent. A more general explanation for predictability is that expected returns vary with expected business conditions for which those variables found to have some predictive ability are good proxies. The overwhelming majority of empirical studies of stock return predictability have assumed that the coefficients in the prediction regression models do not change over the available sample data. However, market efficiency arguments suggest that if stock returns are predictable then it is likely to be a temporary rather than a permanent phenomenon. More specifically, if exploiting the predictive power of a regression model can be used to generate abnormal trading profits, then in an efficient market the model will be exploited by large numbers of investors thereby causing the predictive power of the relevant predictor to be eliminated. If a variable begins to have predictive power for stock returns, then a short window of predictability might exist before investors learn about the new relationship between that variable and returns, but market efficiency implies that it will eventually disappear. In such cases, standard predictive regression models based on the full sample of available data will have almost no power to detect these short predictive regimes. It therefore seems reasonable to consider the possibility that the predictive relationship might change over time, so that over a long span of data one may observe windows of time during which predictability occurs and others where it does not.

This project will develop the rigorous econometric methods that are needed to: (i) be capable of effectively detecting the presence of structural change in the coefficients of predictive regression models when applied to the full sample of data, and (ii) use methods of sub-sample data analysis designed to be effective in uncovering such windows of predictability and to enable real-time monitoring for early detection of these windows of predictability as they occur. The project will additionally investigate the role and impact of the forecasting horizon (either short, eg quarterly or monthly, or longer) being considered when using these methods. The theoretical developments will be conducted using large sample econometric theory and will involve state-of-the-art bootstrap methods. The practical relevance of the theoretical results will be explored using simulation experiments. Clear guidance will also be given to empirical researchers through worked examples and applications of the techniques to key international equity data sets. The empirical aspects of the project will investigate both traditional predictors (such as those discussed above) as well as more recently considered so-called technical analysis indicators (where only price or volume data is used to predict returns).

Papers, Computer Code and Data-sets

"A Bootstrap Stationarity Test for Predictive Regression Invalidity", by Iliyan Georgiev, David Harvey, Stephen Leybourne and Robert Taylor,  published in the Journal of Business and Economic Statistics.  Also published as Essex Finance Centre Working Paper number 28, 2018, University of Essex (also published in the RePEc working paper series and available from https://econpapers.repec.org/paper/esyuefcwp/21006.htm ) pdf of working paper including supplementary appendix.   The published journal version is available from the publisher's website under a Gold Open Access agreement at https://doi.org/10.1080/07350015.2017.1385467 or   https://amstat.tandfonline.com/doi/full/10.1080/07350015.2017.1385467#.WpV4LnykKUk    A copy of slides of a research presentation based on the research in this paper given at the EFiC 2017 conference can be obtained here.   A Gauss program to implement the methods developed in this paper is available here which is currently set up to run on the data sets used in the paper here, here, here, and here.   This program can be run as freeware by using the OxGauss facility in the freeware version of the Ox package available from http://www.doornik.com/products.html .

"Testing for Parameter Instability in Predictive Regression Models", by Iliyan Georgiev, David Harvey, Stephen Leybourne and Robert Taylor, published in the Journal of EconometricsAlso published as Essex Finance Centre Working Paper number 29, 2018, University of Essex (also published in the RePEc working paper series)  pdf of working paper including supplementary appendix The publisher's journal formatted version of the paper can be obtained free of charge under a Gold Open Access agreement from   https://doi.org/10.1016/j.jeconom.2018.01.005   A Gauss program to implement the methods developed in this paper is available here which is currently set up to run on the data sets used in the paper here.   This program can be run as freeware by using the OxGauss facility in the freeware version of the Ox package available from http://www.doornik.com/products.htm

 "Detecting Regimes of Predictability in the U.S. Equity Premium" by David Harvey, Stephen Leybourne, Robert Sollis and Robert Taylor.  Working paper available here and also available from the RePEc working paper series here.  A copy of slides  of a research presentation based on the research in this paper given at the 2018 EC^2 conference in Rome can be obtained here.

"Testing for Episodic Predictability in Stock Returns" by Matei Demetrescu, Iliyan Georgiev, Paulo M. M. Rodrigues and Robert Taylor.  Working paper available here and also available from the RePEc working paper series here. A copy of slides  of a research presentation based on the research in this paper given at the 2019 Econometric Society European Meeting held in Manchester in August 2019 can be obtained here.

Events

Two one-day workshops based around the theme of the project will be run. 

The first workshop, titled “Predictive Regression Models: Theory and Applications to Returns”,  will take place on Monday 9th September 2019 at Wivenhoe House Hotel, University of Essex.  Speakers will include: Amit Goyal, Matei Demetrescu, Tassos Magdalinos, Ekaterini Panopoulou, Peter Boswijk, Jean-Yves Pitarakis and Paulo Rodrigues.  The programme can be downloaded here.  Attendance is free, but places are strictly limited and advance registration is required to attend the event.  This can be done on Eventbrite by following this link.    My slides

 

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