PC Regression, Vector Autoregression, and Recurrent Neural Networks: How do they compare when predicting stock index returns for building efficient portfolios?
Abstract: This thesis examines the statistical and economic performance of modeling and predicting equity index returns by application of various statistical models on a set of macroeconomic and financial variables. By combining linear principal component regression, vector autoregressive models, and LSTM neural networks, the authors find that while a majority of the models display high statistical significance, virtually none of them successfully outperform classic portfolio theory on efficient markets in terms of risk-adjusted returns. Several implications are also discussed based on the results.
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