In this study, we propose algorithms to predict future stock market trends based on 8 different input features, including financial technology indicators, gold prices, a gold price volatility index, crude oil price, a crude oil price volatility index, and other characteristic data using two different labeling methods with separate classification algorithms of two and three output categories, respectively including predicted stock price changes (up and down) and recommended trading actions (buy, sell, and hold), and analyze the validity of these characteristic data in terms of their ability to predict future trends. The S&P 500 (GSPC) is the target of these forecasts. Sample data from 2010 to 2018 are divided 8:2, between training and verification data, while data from 2019 are used to test the proposed approach. CNN and LSTM models are used for comparison of classification accuracy and investment returns, respectively. Bayesian optimization (BO) hyperparameters are used to improve the accuracy of the model and increase the return on investment (ROI) of the output predictions.

The purpose of this study is to verify whether using gold prices, a gold volatility index, crude oil price, and a crude oil price volatility indices as input features can enable a deep learning model accurately to predict future stock price trends, and to discuss separately the applicability of CNN and LSTM models to the abovementioned characteristics and financial indicators. We also present the results of experiments conducted to evaluate the proposed method in terms of classification accuracy and confusion matrix. In the case of three-category classification, the model takes feature data as input to outputs a predicted trading order on whether to buy, sell, or hold a given set of stocks tomorrow as well as the timing of entry and exit from each position, and also backtests the data outside the sample to find the combination of characteristics and indicators best maximizing ROI. Using this three-category method, we obtain a comprehensive ROI for a given set of individual stocks and assess whether each type of stock is suitable for the prediction model based on input features such as gold and crude oil or the fields that are suitable for the given feature.

Experimental results show that the proposed approach as able to predict whether stock price will rise or fall in the next 10 days, and the model accuracy rate can reach 67%. The results of experiments on the proposed combined CNN model with eight features, referred to as CNN8, achieved an ROI on 2019 data outside the sample period of up to 13.23%, which was superior to the 12.08% and 11.06% obtained by the models designed CNN4 (CNN with four input features) and LSTM8(LSTM with eight input features), respectively. The F1 score increased from 0.75 0.79 as a result of applying BO. The results indicate that considering the price of gold, the gold volatility index, crude oil price, and crude oil price volatility index can help obtain better ROI for companies in certain fields, such as the semiconductor, petroleum, and automotive industries, rather than merely considering financial indicators. However, for companies related to apparel, fast food, and copy processing, the input characteristics of purely financial technical indicators were found to be suitable.

在本研究中，我们提出了基于 8 种不同输入特征的预测未来股市趋势的算法，包括金融技术指标、黄金价格、黄金价格波动指数、原油价格、原油价格波动指数和其他特征数据，使用两种不同的标记方法，分别对两个和三个输出类别进行单独的分类算法，分别包括预测的股价变化（上涨和下跌）和推荐的交易行为（买入、卖出和持有），并分析这些特征数据的有效性他们预测未来趋势的能力。标准普尔 500 指数 (GSPC) 是这些预测的目标。2010 年至 2018 年的样本数据在训练和验证数据之间按 8:2 的比例划分，而 2019 年的数据用于测试所提出的方法。CNN 和 LSTM 模型分别用于比较分类精度和投资回报。贝叶斯优化 (BO) 超参数用于提高模型的准确性并增加输出预测的投资回报率 (ROI)。

本研究的目的是验证以黄金价格、黄金波动率指数、原油价格和原油价格波动率指数作为输入特征是否可以使深度学习模型准确预测未来股价走势，并分别讨论CNN 和 LSTM 模型对上述特征和财务指标的适用性。我们还介绍了在分类精度和混淆矩阵方面评估所提出方法的实验结果。在三类分类的情况下，该模型以特征数据作为输入，输出关于明天是否买入、卖出或持有一组给定股票以及每个仓位的进出时机的预测交易订单，并且还对样本外的数据进行回测，以找到最能最大化投资回报率的特征和指标的组合。使用这种三类方法，我们获得给定的一组个股的综合投资回报率，并根据黄金和原油等输入特征或适用于给定的特征。

实验结果表明，该方法能够预测未来10天股价是上涨还是下跌，模型准确率可达67%。所提出的具有八个特征的组合 CNN 模型（简称 CNN8）的实验结果，在样本周期外的 2019 年数据上实现了高达 13.23% 的 ROI，优于设计模型获得的 12.08% 和 11.06%分别是 CNN4（具有四个输入特征的 CNN）和 LSTM8（具有八个输入特征的 LSTM）。由于应用 BO，F1 分数从 0.75 增加到 0.79。结果表明，考虑到黄金价格，黄金波动率指数、原油价格和原油价格波动率指数可以帮助某些领域的公司获得更好的投资回报率，例如半导体、石油和汽车行业，而不是仅仅考虑财务指标。但是，对于服装、快餐、文案加工等相关企业，纯财务技术指标的输入特征比较合适。