<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.</p>
</abstract>

Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.

Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem

Faculty of Economic Sciences, Division of Quantitative Finance

Faculty of Mathematics and Computer Science

Central European Economic Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential...

Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem

Published Online: Jan 29, 2021

Page range: 44 - 62

DOI: https://doi.org/10.2478/ceej-2021-0004

Keywords
machine learning, nvidia, stock returns, technical analysis, fundamental analysis

© 2021 Marcin Chlebus et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem

Published Online: Jan 29, 2021

Page range: 44 - 62

DOI: https://doi.org/10.2478/ceej-2021-0004

Keywordsmachine learning, nvidia, stock returns, technical analysis, fundamental analysis

© 2021 Marcin Chlebus et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
machine learning, nvidia, stock returns, technical analysis, fundamental analysis