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We use monthly observations on general stock price indices, over January 2001–August 2013, in order to assesssimple stochastic time series models in terms of out-of-sample forecasts. Specifically, we examine the relativestrength of out-of-sample forecasts of a random walk, with and without drift, against that of a non-linearsegmented trends model where the switch between states is governed by a Markov chain. The forecastingperformance of these processes is assessed by the root mean squared error of short- and long-term out-of-sampleforecasts, varying from 1- to 12-month horizons. We obtain compelling evidence in favor of the Markovswitching process in forecasting stock prices over short and medium-term horizons and across all countriesconsidered. These results are most likely due to risk averse behavior of investors which has been amplified bythe recent financial crisis.

Random Walk or Switching Regimes in Stock Prices? Evidence from Out-of-Sample Forecasts