11/19/2023 0 Comments Nasdaq max drawdown![]() In accordance to the efficient market hypothesis theory ( Fama, 1970), the stock prices have fully reflected all valuable and pertinent information. Time-series chart of the closing prices of the five major global stock indexes between 2019.05 (The closing price is normalized to the maximum and minimum). Based on this, we conducted in-depth investigation on two major aspects, namely, finding more data that could reflect external stock market features and further enhancing the performance of the time-series forecasting model. ![]() The poor handling mechanism of most stock market forecasting models for unforeseen events limits the predictive power of the models during this period, which has prompted the investigation of stock market forecasting models that can handle COVID-19 pandemic events ( Ronaghi et al., 2022, Štifanić et al., 2020). Consequently, the global stock market has experienced a significant decline, as is evident. Besides, COVID-19 has had a huge impact on the stock market, and Fig. 1 demonstrates a time series chart of the closing prices of the five major global stock indexes from January 2019 to May 2020. Experiments indicate that our model achieves superior performance both in terms of predicting the accuracy of the China CSI 300 Index during the COVID-19 period and in terms of sing market trading.Īs the stock market and artificial intelligence technology develop rapidly, a new generation of quantitative trading tools on the basis of machine learning has performed well in stock prediction tasks ( Giudici et al., 2022, Ma et al., 2022, Shah et al., 2022, Yan et al., 2020), and numerous quantitative stock trading researchers have gained huge profits from the stock market, which is prospering. Moreover, a temporal convolutional neural network with a multi-scale attention mechanism is designed (MA-TCN) alongside a Multi-View Convolutional-Bidirectional Recurrent Neural Network with Temporal Attention (MVCNN-BiLSTM-Att), adjusting the model to account for the changing status of COVID-19 and its impact on the stock market. Besides, it introduces a multi-task learning framework to extract global and local features of the stock market. ![]() ![]() This paper presents a framework for multitasking learning-based stock market forecasting (COVID-19-MLSF), which can extract the internal and external features of the stock market and their relationships effectively during COVID-19.The innovation comprises three components: designing a new market sentiment index (NMSI) and COVID-19 index to represent the external characteristics of the stock market during the COVID-19 pandemic. Existing research has struggled to extract more effective stock market features during the COVID-19 outbreak using a single time-series neural network model. Additionally, internal and external characteristics coexist in the stock market. The sudden outbreak of COVID-19 has dramatically altered the state of the global economy, and the stock market has become more volatile and even fallen sharply as a result of its negative impact, heightening investors’ apprehension regarding the correlation between unexpected events and stock market volatility. ![]()
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