TCN-based Futures Prediction Using Financial Indices, Bargain Chips, and Forum Messages

Min-Te Sun, Kotcharat Kitchat, Li-Chung Hsieh
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Abstract

Traditional research on the stock and/or futures price prediction mostly uses the past stock/future prices and technique indicators, such as KD, RSI, and MACD, as features. Very few studies consider the forum messages or bargaining chips as stock and/or futures price prediction features. In this research, discussion messages from both PTT and CMoney forums are converted into daily sentimental vectors using the retrained BERT. The daily sentimental vector as well as three bargaining chips are then used as features to train the GRU and TCN models. The experiment results show that the TCN performs better than the GRU-based RNN model in terms of MAE, MAPE, RMSE, and accuracy. In addition, both of the bargaining chips and forum messages are verified to be useful in the futures price prediction. The market simulations based on the historical futures price show that a simple investment strategy using the TCN models with techniques, bargaining chips, and forum messages can earn more than 7 times of the investment in the period of one year.
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基于tcn的期货预测,利用金融指数、交易筹码和论坛信息
传统的股票和/或期货价格预测研究大多使用过去的股票/未来的价格和技术指标,如KD、RSI和MACD作为特征。很少有研究将论坛信息或议价筹码作为股票和/或期货价格预测的特征。在本研究中,使用重新训练的BERT将来自PTT和CMoney论坛的讨论信息转换为日常情感向量。然后使用每日情感向量以及三个讨价还价筹码作为特征来训练GRU和TCN模型。实验结果表明,TCN在MAE、MAPE、RMSE和准确率方面都优于基于gru的RNN模型。此外,还验证了议价筹码和论坛留言对期货价格预测的有效性。基于历史期货价格的市场模拟表明,使用TCN模型结合技术、议价筹码和论坛信息的简单投资策略可以在一年内获得7倍以上的投资收益。
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