| Summary: | According to several previous studies, neural network-based stock price predictors perform better for plunging patterns of stock prices than normal stock price patterns. Focusing on this issue, this study proposes a novel method that uses a neural network-based stock price predictor to predict the upward trend-reversal of the plunging market itself. To achieve more consistent prediction results for plunging patterns, newly designed input features are added to improve the performance of traditionally used neural network-based predictors. The statistics of the prediction scores for past plunging markets and analyzed, and the results are used to predict the upward trend-reversal in the plunging market that occurred during the test period. We demonstrate the superiority of the proposed method through the simulation results of 3-year trading on KOSDAQ, a representative stock market in South Korea.
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