基于LSTM的马来西亚股票回购预测

Muhammad Zahid bin Hilmi, A. Mahmood, A. Moin, Toni Anwar, S. Sutrisno
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引用次数: 0

摘要

股票回购是公司回购其已发行股票以减少公开市场股票数量的一种策略。通过回购,间接增加了公司的股份比例和每股收益。本研究旨在探讨股票回购策略的趋势,并设计一个简单的股票市场价格走势预测模型。本研究发现,使用长短期记忆(LSTM)作为预测算法表明,可以使用相关的股票指标,即MACD和RSI来预测股票市场的价格走势,这些指标对股票市场的价格走势有影响。研究还发现,与“High”、“Low”和“Close”参数相比,基于MAE、MSE和RMSE的“Open”参数的值最低。
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Share Buyback Prediction using LSTM on Malaysian Stock Market
Share buyback is a strategy for companies to repurchase their outstanding shares to reduce the number of shares from the open markets. With buyback, it indirectly increases the shares proportion and earning per shares (EPS) of a company. The aim of this study is to investigate the trend of share buyback strategy, and to design a simple prediction model for stock market price movement before initiating any buyback action. This study finds the use of Long Short-Term Memory (LSTM) as prediction algorithm has demonstrated that stock market price movement can be predicted using associated stock indicators, namely MACD and RSI which have an impact to the stock market price movement. The study also finds that the "Open" parameter based on the MAE, MSE and RMSE have been found to be the lowest value as compared to "High", "Low" and "Close" parameters.
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