Does LSTM neural network improve factor models' predictions of the European stock market?
|Author:||Mgr. Jiří Zelenka|
|Year:||2021 - summer|
|Leaders:|| doc. PhDr. Jozef Baruník Ph.D.
|Work type:|| Economic Theory
|Awards and prizes:|
|Abstract:||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 market. 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 better predictions given the same input as OLS but their forecasting error is not
significantly lower according to the Diebold-Mariano test.