Factors Affecting Forecast Accuracy of Individual Stocks: SVR Algorithm Under CAPM Framework

The research was carried out with two objectives, including applying the algorithm under the Capital Asset Pricing Model framework (CAPM) to predict individual stocks' return rates and determine the factors affecting the difference in Error for each stock. This study experimented on the Ho Chi...

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Veröffentlicht in:2022 International Conference for Advancement in Technology (ICONAT) S. 1 - 6
Hauptverfasser: Khoa, Bui Thanh, Huynh, Tran Trong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 21.01.2022
Schlagworte:
Capital Asset Pricing Model framework (CAPM)
forecast accuracy
individual stocks
Neural networks
Prediction algorithms
Predictive models
Pricing
Reactive power
return
Security
Support Vector Regression algorithm (SVR)
Urban areas
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Zusammenfassung:The research was carried out with two objectives, including applying the algorithm under the Capital Asset Pricing Model framework (CAPM) to predict individual stocks' return rates and determine the factors affecting the difference in Error for each stock. This study experimented on the Ho Chi Minh City Stock Exchange (HOSE) in the period from 12/2012 to 9/2020 with two stages; in which stage 1 is used to determine the optimal parameters in the Vector Regression algorithm (SVR), and stage 2 is used to test the predictive efficiency by rolling window method. The study pointed that the predictive model using SVR is more effective than CAPM; moreover, the study finds that the specific risk factors (VAR), the overall risk (SD), and the accuracy of CAPM (RMSECAPM) are the factors affecting the difference in the forecast error of the SVR model for individual stocks
DOI:10.1109/ICONAT53423.2022.9725916