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Does LSTM neural network improve factor models' predictions of the European stock market?
Zelenka, Jiří ; Baruník, Jozef (advisor) ; Čech, František (referee)
This thesis wants to explore the forecasting potential of the multi-factor models to predict excess returns of the aggregated portfolio of the European stock mar- ket. These factors provided by Fama and French and Carhart are well-known in the field of asset pricing, we also add several financial and macroeconomic factors according to the literature. We establish a benchmark model of ARIMA and we compare the forecasting errors of OLS and the LSTM neural networks. Both models take the lagged excess returns and the inputs. We measure the performance with the root mean square error and mean absolute error. The results suggest that neural networks are in this particular task capable of bet- ter predictions given the same input as OLS but their forecasting error is not significantly lower according to the Diebold-Mariano test. JEL Classification C45, C53, C61, E37, G11, G15 Keywords Stocks, European market, Neural networks, LSTM, Factor Models, Fama-French, Predic- tions, RMSE Title Does LSTM neural network improve factor mod- els' predictions of the European stock market?
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