Covid-19和俄罗斯-乌克兰战争期间预测模型的性能分析

IF 0.4 Q4 BUSINESS, FINANCE Public Finance Quarterly-Hungary Pub Date : 2023-06-30 DOI:10.35551/pfq_2023_2_7
László Vancsura, Tibor Bareith
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引用次数: 0

摘要

在我们的论文中,我们研究了人工智能在2010年1月1日至2022年9月16日期间如何有效地用于预测全球主要股票市场的股票市场趋势。2019冠状病毒病和俄乌战争对资本市场产生了强烈影响,因此本研究是在一个高度动荡的环境中进行的。在三个时间间隔上进行分析,使用两种不同复杂度的机器学习算法(决策树,LSTM)和参数统计模型(线性回归)。对所得结果的评价基于平均绝对百分比误差(MAPE)。在我们的研究中,我们表明预测模型在高波动期比线性回归表现更好。另一个重要发现是,预测模型在俄乌战争后的表现优于新冠疫情爆发后的表现。股票市场价格预测可以在基本面分析和技术分析中发挥重要作用,可以纳入算法交易的决策标准,也可以单独用于自动交易。
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Analysis of the performance of predictive models during Covid-19 and the Russian-Ukrainian war
In our paper, we investigate how effectively artificial intelligence can be used to predict stock market trends in the world’s leading equity markets over the period 01/01/2010 to 09/16/2022. Covid-19 and the Russian-Ukrainian war have had a strong impact on the capital markets and therefore the study was conducted in a highly volatile environment. The analysis was performed on three time intervals, using two machine learning algorithms of different complexity (decision tree, LSTM) and a parametric statistical model (linear regression). The evaluation of the results obtained was based on mean absolute percentage error (MAPE). In our study, we show that predictive models can perform better than linear regression in the period of high volatility. Another important finding is that the predictive models performed better in the post-Russian-Ukrainian war period than after the outbreak of Covid-19. Stock market price forecasting can play an important role in fundamental and technical analysis, can be incorporated into the decision criteria of algorithmic trading, or can be used on its own to automate trading.
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来源期刊
CiteScore
0.90
自引率
40.00%
发文量
30
期刊最新文献
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