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- W4366374293 abstract "If the market is efficient, with stock prices accurately reflecting the true risk of an investment, then the issue becomes simpler. While this is true, investors may have a window of opportunity to discover a successful investing strategy if the market is inefficient. The primary goal of this research is to use the Support Vector Machine (SVM) algorithm to predict daily cycles of price increases for the ten largest-cap companies trading on the Hanoi Stock Exchange (HNX) over the Covid-19 timeframe (January 1st, 2019, to December 1st, 2022). Study how the model performs when trained and tested with a moving window. The outcome was an impressive average accuracy of 81.68 percent for the predicting model." @default.
- W4366374293 created "2023-04-21" @default.
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- W4366374293 date "2023-03-03" @default.
- W4366374293 modified "2023-10-01" @default.
- W4366374293 title "Predicting Stock Price Movement for The Inefficient Market: Case of Hanoi Stock Exchange (HNX)" @default.
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- W4366374293 doi "https://doi.org/10.1109/inocon57975.2023.10101296" @default.
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