Predictability of the Aggregate Danish Stock Market

Individual predictor variables have historically been popular in academia for forecasting stock markets returns. Researchers in finance have long tried to predict these stock returns, especially on the American market, but there is still no clear agreement if returns are even predictable, or to what extent. Given the curiosity in predictability, this thesis pursues finding empirical evidence using different theoretically suggested forecasting methodologies for the predictability of the Danish stock market, using data from 1973 to 2014. More specifically it will emphasise recent methodologies to overcome the drawback of only using individual predictive regressions. On a theoretical basis two different frameworks for assessing predictability will be presented; the constant expected return model versus the time-varying return model, and the hypothetical relationship between 10 predictor variables and the equity premium will be inspected. Besides utilizing bivariate regression forecast, the paper will also feature the following multivariate forecasting approaches; combination forecast, sum-of-parts, diffusion index and complete subset regression. A thorough analysis of the aforementioned relationships will be carried out on the Danish equity market, and assessed in regards to the statistical and economic performance. Specifically, the paper will produce three investment criteria which a successful and reliable method should accomplish: 1) achieve positive utility gains, 2) derive no dominating fraction of predictive power from a single period and 3) have a consistent and positive upward trend in predictive power. This should sort out the sub-optimal methods, and produce models which are ideally useful on a future ex-ante basis. An out-of-sample forecasting period of 1990-2014 will be separated into business cycles, and economically motivated restrictions from Campbell and Thompson (2008) will be imposed. The forecasting models, representing the time-varying expected return model, will be evaluated relative to the historical average forecast, representing the constant expected return model. A hypothesis test using the MSFE-adjusted statistic proposed by Clark and West (2007) will be conducted, and rejecting the null would imply time-varying expected returns could exist and, more specifically, that stock return predictability would be possible to a certain extent. The bivariate forecasts proved to have poor predictive power relative to the historical average. The German short-term interest rate fulfilled, as the only individual predictor variable, all the investment criteria, significantly explaining 2% more than the benchmark in the overall period, while the meanvariance investor would be willing to pay a yearly certainty equivalent return of 2.51% for having this forecast accessible. The combination forecasts and complete subset regression both fulfilled all the investment criteria, but the best complete subset regression (k = 5) enjoyed the highest predictive power of 2.21% with a certainty equivalent return of 2.38%, consequently representing the best forecasting method in the thesis. The origin of predictability was primarily found in recession periods for both approaches, which is similar to other academic literature, and the models have notably improved over the last seven years. The findings generally displayed superiority of the multivariate approach over the simple bivariate method, as these enjoyed higher relative predictive power, while reducing exposure to potential biases. On the basis of time-varying risk premium from fluctuating risk aversion, the results suggest predictability of the Danish stock market.