Work detail

Forecasting with neural network during covid-19 crisis

Author: Mgr. Tiep Luu Danh
Year: 2021 - summer
Leaders: doc. PhDr. Jozef Baruník Ph.D.
Work type: Finance, Financial Markets and Banking
Language: English
Pages: 93
Awards and prizes:
Abstract: The thesis concerns the topic of forecasting using Neural Networks, particularly the return and volatility forecasting in the volatile period of Covid-19.
The thesis uses adjusted close daily data from Jan 1, 2000, until Jan 1, 2021,
of the S&P index and Prague Exchange Stock index (PX). The comparison
was between the vanilla econometrical model, a neural network model, and a
hybrid neural network model. Hybrid neural networks were constructed with
an additional feature column of the fitted econometrical model. Additionally
to comparing the prediction, a risk-return trade-o analysis of the forecasted
series was conducted. The test period for all models was from Jan 1, 2020, until
Jan 1, 2021, where predictions were made. During the test period, MSE between predicted and true values was extracted and compared. The results are
that the hybrid model outperformed both econometrical as well as only neural
networks models. Furthermore, the risk-return trade-o forecast provided by
the hybrid model fares better than the other ones


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Patria Finance