Nonparametric retrospection and monitoring of predictability of financial returns


Anatolyev, Stanislav (2009) "Nonparametric retrospection and monitoring of predictability of financial returns", Journal of Business and Economic Statistics, Vol. 27, No. 2, pp. 149160


We develop and evaluate sequential testing tools for a class of nonparametric tests for predictability of financial returns that includes, in particular, the directional accuracy and excess profitability tests. Our sequential methods consider in a unified framework both retrospection of a historical sample and monitoring newly arriving data. To this end, we focus on linear monitoring boundaries that are continuations of horizontal lines corresponding to retrospective critical values, elaborating on both two-sided and one-sided testing. We run a simulation study and illustrate the methodology by testing for directional and mean predictability of returns in young stock markets in Eastern Europe.

Paper in RePEc:

Journal of Business and Economic Statistics, Vol. 27, No. 2, pp. 149160

Paper in accepted version:


Data used in the paper:

Returns from Eastern European stock markets

Presented at:

2006 North American Summer Meeting of Econometric Society, University of Minnesota, Minneapolis, USA, June 22-25, 2006
2006 Econometric Society European meeting, University of Vienna, Austria, August 24-28, 2006
XX New Economic School research conference, Moscow, Russia, November 9-11, 2006

Cited by:

Kian-Ping Lim, Weiwei Luo & Jae H. Kim (2013) "Are US stock index returns predictable? Evidence from automatic autocorrelation-based tests", Applied Economics, vol. 45, pp. 953-962.

Kian-Ping Lim & Chee Wooi Hooy (2013) "Non-linear predictability in G7 stock index returns", Manchester School, vol. 13, pp. 620637.

Kian-Ping Lim & Robert Brooks (2011) "The evolution of stock market efficiency over time: a survey of the empirical literature", Journal of Economic Surveys, vol. 25, pp. 69-108.

Luca Fanelli & Giulio Palomba (2011) "Simulation-based tests of forward-looking models under VAR learning dynamics", Journal of Applied Econometrics, vol. 26, pp. 762-782.