Since the declaration of COVID-19 as a pandemic, the world stock markets have suffered huge losses prompting investors to limit or avoid these losses. The stock market was one of the businesses that were affected the most. At the same time, artificial neural networks (ANNs) have already been used for the prediction of the closing prices in stock markets. However, standalone ANN has several limitations, resulting in the lower accuracy of the prediction results. Such limitation is resolved using hybrid models. Therefore, a combination of artificial intelligence networks and particle swarm optimization for efficient stock market prediction was reported in the literature. This method predicted the closing prices of the shares traded on the stock market, allowing for the largest profit with the minimum risk. Nevertheless, the results were not that satisfactory. In order to achieve prediction with a high degree of accuracy in a short time, a new improved method called PSOCoG has been proposed in this paper. To design the neural network to minimize processing time and search time and maximize the accuracy of prediction, it is necessary to identify hyperparameter values with precision. PSOCoG has been employed to select the best hyperparameters in order to construct the best neural network. The created network was able to predict the closing price with high accuracy, and the proposed model ANN-PSOCoG showed that it could predict closing price values with an infinitesimal error, outperforming existing models in terms of error ratio and processing time. Using S&P 500 dataset, ANN-PSOCoG outperformed ANN-SPSO in terms of prediction accuracy by approximately 13%, SPSOCOG by approximately 17%, SPSO by approximately 20%, and ANN by approximately 25%. While using DJIA dataset, ANN-PSOCoG outperformed ANN-SPSO in terms of prediction accuracy by approximately 18%, SPSOCOG by approximately 24%, SPSO by approximately 33%, and ANN by approximately 42%. Besides, the proposed model is evaluated under the effect of COVID-19. The results proved the ability of the proposed model to predict the closing price with high accuracy where the values of MAPE, MAE, and RE were very small for S&P 500, GOLD, NASDAQ-100, and CANUSD datasets.

中文翻译：

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COVID-19影响下的股市重心ANN-PSOCoG粒子群优化模型

自从宣布COVID-19大流行以来，世界股票市场遭受了巨大损失，促使投资者限制或避免了这些损失。股市是受影响最大的业务之一。同时，人工神经网络（ANN）已用于预测股票市场的收盘价。但是，独立的人工神经网络有几个局限性，导致预测结果的准确性较低。使用混合模型可以解决这种限制。因此，文献报道了将人工智能网络与粒子群算法相结合以进行有效的股票市场预测的方法。这种方法可以预测在股票市场上交易的股票的收盘价，从而以最小的风险获得最大的利润。尽管如此，结果并不令人满意。为了在短时间内以较高的准确度实现预测，本文提出了一种新的改进方法，称为PSOCoG。为了设计神经网络以最小化处理时间和搜索时间并最大化预测的准确性，有必要精确地识别超参数值。为了构建最佳的神经网络，已使用PSOCoG选择最佳的超参数。所创建的网络能够高精度地预测收盘价，而所提出的模型ANN-PSOCoG表明，它可以以最小的误差来预测收盘价，在错误率和处理时间方面均优于现有模型。使用S＆P 500数据集，在预测准确度方面，ANN-PSOCoG优于ANN-SPSO，SPSOCOG约占17％，SPSO约占20％，ANN约占25％。在使用DJIA数据集时，ANN-PSOCoG的预测准确度优于ANN-SPSO约18％，SPSOCOG约24％，SPSO约33％，ANN约42％。此外，在COVID-19的影响下对提出的模型进行了评估。结果证明了所提出的模型具有高精度预测收盘价的能力，其中对于S＆P 500，GOLD，NASDAQ-100和CANUSD数据集，MAPE，MAE和RE的值非常小。在COVID-19的影响下对提出的模型进行了评估。结果证明了所提出的模型具有高精度预测收盘价的能力，其中对于S＆P 500，GOLD，NASDAQ-100和CANUSD数据集，MAPE，MAE和RE的值非常小。在COVID-19的影响下对提出的模型进行了评估。结果证明了所提出的模型具有高精度预测收盘价的能力，其中对于S＆P 500，GOLD，NASDAQ-100和CANUSD数据集，MAPE，MAE和RE的值非常小。