Machine Learning for Stock Prediction by Different Models

Liurui Shi ()
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Liurui Shi: University College London, Department of Mathematics

A chapter in Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022), 2022, pp 318-323 from Springer

Abstract: Abstract Machine learning is a big and popular topic in recent years and is applied wildly in the field of finance to assist researchers in analyzing the tendency of financial assets in the global market as well as the local market. However, predicting stocks or a portfolio is a challenging task due to the uncertainties and randomness of the financial market. Different models have different structures and therefore they have different performances in reducing the uncertainties in the financial field. This paper investigates the impact of Covid-19 on the accuracy of different machine learning techniques and analyzes the effect of walk-forward validation on the stock prediction. The experimental result indicates that the ARIMA model with the use of walk-forward validation has the performance for forecasting the stock price and walk-forward validation improves the accuracy of forecasting and reduces the errors of the models compared to simple time series splitting. So the technique of walk-forward validation is useful to be implemented in the stock price prediction to maximize the capital gain and minimize the analytical error due to uncertainties.

Keywords: Covid-19; Forecasting; ARIMA; Accuracy; Walk-forward validation (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-036-7_48

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DOI: 10.2991/978-94-6463-036-7_48

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