Forecasting stock prices

Arie Harel and Giora Harpaz

International Review of Economics & Finance, 2021, vol. 73, issue C, 249-256

Abstract: We apply concepts form machine learning to forecast stock prices. First, we introduce the general (3 by 3) forecasting model, in which the financial markets are populated by three types of stocks: Overpriced stocks, underpriced stocks and fairly priced stocks. The objective of the financial analyst or a potential investor is to identify which stock belongs to which classification, and to take the relevant investment decision. Second, we present two different numerical examples to illustrate our forecasting models, and estimate all the relevant statistics, as well as the forecasting accuracy. Third, we introduce the Receiver Operator Curve (ROC), and demonstrate the trade-off between the sensitivity and specificity of the prediction. We also discuss the performance evaluation of forecasting stock prices.

Keywords: Forecasting; Stock prices; Accuracy; Machine learning; Receiver operator curve (search for similar items in EconPapers)
JEL-codes: C10 C51 G1 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:73:y:2021:i:c:p:249-256

DOI: 10.1016/j.iref.2020.12.033

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