Forecasting with neural network during covid-19 crisis

Abstract

The thesis concerns the topic of forecasting using Neural Networks, particu- larly 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 be- tween 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. JEL Classification C53, C81 Keywords Financial Time Series, Forecasting, Neural Net- works, ARIMA, GARCH Title Forecasting with Neural Network during Covid- 19 Crisis Author's e-mail tiep.luud@gmail.com Supervisor's e-mail barunik@fsv.cuni.cz

Teze se zab˝vá hlavn forecastingem pomocí neuronov˝ch sítí, p esn ji ohledn forecastingu v˝nos a volatility b hem volatilního období Covid-19. Teze pouûívá denní upravená data od 1. ledna 2000 do 1. lenda 2021 indexu S&P a index praûské burzy PX. Porovnání bylo provedeno mezi ist˝m ekonomet- rick˝m model, modelem na bázi neuronov˝ch sítí a hybridním modelem. Hy- bridní model byl zkonstruovám jako s dodate nou featurou estimovan˝ch hod- not econometrického modelu. K tomu se také provedla anal˝za pom ru rizika a v˝nosnosti na bázi predikovan˝ch hodnot. Testované obdobní pro vöechny modely bylo od 1. ledna 2020 do 1. ledna 2021. Na tomto období se provedli predikce hodnot a st ední kvadratická chyba kaûdého modelu byla vypo ítaná a porovnána. Záv rem je, ûe hybridní modely podali lepöí v˝kon neû ekono- metrick˝ model i model neuronov˝ch síti. Dále predicke hybridního modelu poskytnuli lepöí pom r rizika a v˝nosnosti neû ostatní. Klasifikace JEL C53, C81 Klí ová slova Finan ní asová ada, Forecasting, Neu- ronové sít , ARIMA, GARCH Název práce Forecasting s Neuronovou síti b hem Covid-19 Krize E-mail autora tiep.luud@gmail.com E-mail vedoucího práce barunik@fsv.cuni.cz