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1. 1. PC Regression, Vector Autoregression, and Recurrent Neural Networks: How do they compare when predicting stock index returns for building efficient portfolios?

   University essay from KTH/Optimeringslära och systemteori

   Author : David Hallberg; Erik Renström; [2019]
   Keywords : PC Regression; Vektorautoregression; och Återkopplande Neurala Nätverk: En jämförelse mellan deras förmåga att prognostisera aktieindexavkastning för att konstruera effektiva portföljer; Huvudkomponentregression; vektorautoregression; LSTM; återkopplande neurala nätverk; portföljteori; portföljoptimering; maskininlärning; makroekonomi; finans; aktieavkastning; aktieindex;

   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. READ MORE