The Applications and Future of Big Data Technology in Financial Investment

Mengqi Liu

doi:10.54097/wyh3eg27

Authors

Mengqi Liu

DOI:

https://doi.org/10.54097/wyh3eg27

Keywords:

Big data, data mining, high-frequency trading, stock price prediction, data ethics.

Abstract

The application of big data technology to financial investment has been a general trend and research hotspot in recent years. The rapid development of technologies such as artificial intelligence and machine learning has gradually transformed investment activities from manual decision-making to algorithmic decision-making. Big data technology can help investors and enterprises process more information, make more transactions and make more accurate judgments, also bring more profits. However, this technology also has its limitations and problems. This paper introduces the main components of big data technology and its practical application in financial investment, and analyzes the advantages and risks of this technology. Finally, this paper discusses the future development direction of big data technology in financial investment, the attitude of society towards technology development and the issues that investors, enterprises and governments need to pay attention to. This paper also mentions the importance of paying attention to and ensuring data ethics while developing technology. This paper aims to systematically summarize the current status of big data technology, provide suggestions and risk warnings for the use of big data technology in various fields, and provide a reference for the research of big data technology.

Downloads

Download data is not yet available.

References

[1] X. Jia and R. Y. K. Lau, "The control strategies for high frequency algorithmic trading," in 2018 IEEE 4th International conference on control science and systems engineering (ICCSSE), 2018: IEEE, pp. 49-52.

[2] M. Aquilina, E. Budish, and P. O’neill, "Quantifying the high-frequency trading “arms race”," The Quarterly Journal of Economics, vol. 137, no. 1, pp. 493-564, 2022.

[3] X. Cao et al., "A novel recurrent neural network based online portfolio analysis for high frequency trading," Expert Systems with Applications, vol. 233, p. 120934, 2023.

[4] P. Yu and X. Yan, "Stock price prediction based on deep neural networks," Neural Computing and Applications, vol. 32, pp. 1609-1628, 2020.

[5] H. M. Markowits, "Portfolio selection," Journal of finance, vol. 7, no. 1, pp. 71-91, 1952.

[6] Z. Zhou, M. Gao, H. Xiao, R. Wang, and W. Liu, "Big data and portfolio optimization: a novel approach integrating DEA with multiple data sources," Omega, vol. 104, p. 102479, 2021.

[7] T. Yin, C. Liu, F. Ding, Z. Feng, B. Yuan, and N. Zhang, "Graph-based stock correlation and prediction for high-frequency trading systems," Pattern Recognition, vol. 122, p. 108209, 2022.

[8] P. Soni, Y. Tewari, and D. Krishnan, "Machine Learning approaches in stock price prediction: A systematic review," in Journal of Physics: Conference Series, 2022, vol. 2161, no. 1: IOP Publishing, p. 012065.

[9] S. Wu, Y. Liu, Z. Zou, and T.-H. Weng, "S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis," Connection Science, vol. 34, no. 1, pp. 44-62, 2022.

[10] L. Nemes and A. Kiss, "Prediction of stock values changes using sentiment analysis of stock news headlines," Journal of Information and Telecommunication, vol. 5, no. 3, pp. 375-394, 2021.

[11] M. Leo, S. Sharma, and K. Maddulety, "Machine learning in banking risk management: A literature review," Risks, vol. 7, no. 1, p. 29, 2019.

[12] Y. Song and R. Wu, "The impact of financial enterprises’ excessive financialization risk assessment for risk control based on data mining and machine learning," Computational Economics, vol. 60, no. 4, pp. 1245-1267, 2022.

[13] B. Cui, "Research on Big Data Risk Control Model of Venture Capital," in Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing, 2021, pp. 173-181.

[14] L. Deng and Y. Chang, "Risk Management of Investment Projects Based on Artificial Neural Network," Wireless Communications and Mobile Computing, vol. 2022, 2022.

[15] W. Peng, "Research on the Application of Big Data in Financial Investment Risk Management and Control," in 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022), 2022: Atlantis Press, pp. 1139-1147.

[16] J. Li, "Venture financing risk assessment and risk control algorithm for small and medium-sized enterprises in the era of big data," Journal of Intelligent Systems, vol. 31, no. 1, pp. 611-622, 2022.

[17] H. Zhou, G. Sun, S. Fu, J. Liu, X. Zhou, and J. Zhou, "A big data mining approach of PSO-based BP neural network for financial risk management with IoT," IEEE Access, vol. 7, pp. 154035-154043, 2019.

[18] T. Hasso, D. Müller, M. Pelster, and S. Warkulat, "Who participated in the GameStop frenzy? Evidence from brokerage accounts," Finance Research Letters, vol. 45, p. 102140, 2022.

[19] S. Zeranski and I. E. Sancak, "Does the 'Wirecard AG' Case Address FinTech Crises?" Available at SSRN 3666939, 2020.

[20] A. Yarali, R. Joyce, and B. Dixon, "Ethics of big data: privacy, security and trust," in 2020 Wireless Telecommunications Symposium (WTS), 2020: IEEE, pp. 1-7.

[21] J. M. Puaschunder, "Big data ethics," Puaschunder, JM (2019). Journal of Applied Research in the Digital Economy, vol. 1, pp. 55-75, 2019.

[22] B. C. Stahl and D. Wright, "Ethics and privacy in AI and big data: Implementing responsible research and innovation," IEEE Security & Privacy, vol. 16, no. 3, pp. 26-33, 2018.