The Fintech index has been more active in the stock market with the Fintech industry expanding. The prediction of the Fintech index is significant as it is capable of instructing investors to avoid risks and provide guidance for financial regulators. Traditional prediction methods adopt the deep neural network (DNN) or the combination of genetic algorithm (GA) and DNN mostly. However, heavy computational load is required by these algorithms. In this paper, we propose an integrated artificial intelligence-based algorithm, consisting of the random frog algorithm (RF), GA, and DNN, to predict the Fintech index. The proposed RF-GA-DNN prediction algorithm filters the key input variables and optimizes the hyperparameters of DNN. We compare the proposed RF-GA-DNN with the traditional GA-DNN in terms of convergence time and prediction accuracy. Results show that the convergence time of GA-DNN is up to 20 hours and its prediction accuracy is 97.4%. In comparison, the convergence time of our RF-GA-DNN is only about 1.5 hours and the prediction accuracy reaches 97.0%. These results demonstrate that the proposed RF-GA-DNN prediction algorithm significantly reduces the convergence time with the promise of competitive prediction accuracy. Thus, the proposed algorithm deserves to be widely recommended for predicting the Fintech index.

中文翻译：

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基于RF-GA-DNN算法的金融科技指数预测

随着金融科技行业的扩张，金融科技指数在股市中更加活跃。金融科技指数的预测意义重大，因为它能够指导投资者规避风险，为金融监管机构提供指导。传统的预测方法多采用深度神经网络（DNN）或遗传算法（GA）与DNN的结合。然而，这些算法需要大量的计算负载。在本文中，我们提出了一种基于人工智能的集成算法，由随机青蛙算法 (RF)、GA 和 DNN 组成，用于预测金融科技指数。提出的 RF-GA-DNN 预测算法过滤关键输入变量并优化 DNN 的超参数。我们在收敛时间和预测精度方面将提出的 RF-GA-DNN 与传统的 GA-DNN 进行比较。结果表明，GA-DNN 的收敛时间可达 20 小时，预测准确率为 97.4%。相比之下，我们的 RF-GA-DNN 的收敛时间仅为 1.5 小时左右，预测准确率达到 97.0%。这些结果表明，所提出的 RF-GA-DNN 预测算法显着减少了收敛时间，并保证了具有竞争力的预测精度。因此，所提出的算法值得被广泛推荐用于预测金融科技指数。这些结果表明，所提出的 RF-GA-DNN 预测算法显着减少了收敛时间，并保证了具有竞争力的预测精度。因此，所提出的算法值得被广泛推荐用于预测金融科技指数。这些结果表明，所提出的 RF-GA-DNN 预测算法显着减少了收敛时间，并保证了具有竞争力的预测精度。因此，所提出的算法值得被广泛推荐用于预测金融科技指数。