Author(s)

Ke Ma ¹

Affiliation(s)

¹ Department of Economics, University of California, Santa Cruz, CA, 95064, USA

Corresponding Author

Ke Ma

ABSTRACT

In the financial market, extreme events (such as financial crisis, market crash, black swan events, etc.) often trigger sharp fluctuations in the market, resulting in abnormal changes in asset prices and a sharp decline in market liquidity. The randomness and high impact of these events make the traditional trend prediction model based on historical data and statistical analysis ineffective and unable to accurately predict the future trend of the market. Effective trend prediction when extreme events occur in financial markets has become an important topic in the financial field. Traditional forecasting methods are faced with severe challenges due to the high nonlinearity, volatility and complexity of the market under extreme events. We address the challenge of trend prediction under extreme events in financial markets by adopting a machine learning approach. We found that machine learning model has obvious advantages in market trend prediction during extreme events, especially the application potential of machine learning method in forecasting accuracy and risk management ability, which can provide effective solutions for the prediction and response to extreme events in financial markets, and can more effectively deal with the complex non-linear relationship of the market during extreme events.

KEYWORDS

Financial markets, Trend forecasting, Extreme events, Machine learning

CITE THIS PAPER

Ke Ma, Research on Trend Prediction Challenges and Machine Learning Coping Strategies under Extreme Events in Financial Markets. Information Systems and Economics (2024) Vol. 5: 82-87. DOI: http://dx.doi.org/10.23977/infse.2024.050411.

REFERENCES

[1] B Liu. Research on Financial market trend prediction based on Machine learning algorithm [J]. Modern Electronic Technology, 2022, 45(9):5.
[2] Li M. Prediction of financial market trend by Machine Learning [J]. Data Analysis and Knowledge Discovery, 2020, 004(008):P. 118-119.
[3] Hongbing Ouyang, Kang Huang, Yan Hongju. LSTM neural network based financial time series prediction [J]. Journal of management science in China, 2020 (4): 9. DOI: CNKI: SUN: ZGGK. 0.2020-04-003.
[4] Bitong Zi, Pinyi Zhang. Financial time series forecasting model based on ARIMA - LSTM [J]. Journal of statistics and decision, 2022 (11): 5. DOI: 10.13546 / j.carol carroll nki tjyjc. 2022.11.029.
[5] Menggen Chen, Taoping Ren. New normal economic CPI prediction model building and empirical comparison [J]. Journal of research in the world, 2020 (2): 6. DOI: 10.13778 / j.carol carroll nki. 11-3705 / c. 2020.02.001.
[6] Xiuyi Zhao, Chuang Deng. China's systemic financial risk and its impact on financial cycle and economic cycle [J]. Economic Review, 2022(4):114-129.  
[7] Zhang L. Theoretical analysis of the correlation between China's financial system and macroeconomic [J]. Technical Economics and Management Research, 2019(4):5. DOI:10.3969/j.issn.1004-292X.2019.04.015.

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