Research on Predicting Stock Market Profitability Changes Based on Machine Learning

Gaofeng Xu; Xinchen Lu

doi:10.54097/11awc370

Authors

Gaofeng Xu
Xinchen Lu

DOI:

https://doi.org/10.54097/11awc370

Keywords:

Artificial Intelligence, Machine Learning, Data Mining

Abstract

The expansion of the market in our country has led to rapid economic growth, and many companies have begun to seek foreign investment for themselves. What investors are given is an invisible but very useful thing - stocks. However, stocks are unpredictable and subject to many influences, making it difficult for investors to predict whether the stock trend will decline or rise. It is uncertain whether investors will make a profit or lose money. The development of artificial intelligence technology has played a crucial role in driving stock model prediction. With the development and gradual maturity of machine learning technology, machine learning models are used for efficient and accurate analysis of massive data in the stock market, thereby achieving data mining in the stock market, analyzing changes in stock returns, and predicting them to improve investor returns.

Downloads

Download data is not yet available.

References

[1] Cheng K , Su Y , Guan H ,et al.Mapping China's planted forests using high resolution imagery and massive amounts of crowdsourced samples[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 196:356-371.DOI:10.1016/j. isprsjprs.2023.01.005.

[2] Huang D , Zhou J , Mi B ,et al.Key-Based Data Deduplication via Homomorphic NTRU for Internet of Vehicles [J].IEEE Transactions on Vehicular Technology, 2023. DOI:10. 1109/ TVT. 2022.3205627.

[3] Agapito G , Cannataro M .An Overview on the Challenges and Limitations Using Cloud Computing in Healthcare Corporations [J]. Big Data and Cognitive Computing, 2023. DOI: 10. 3390/bdcc7020068.

[4] Carlos,Fernandez-Lozano,Rubén,et al.Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models[J].Journal of Theoretical Biology,2015.

[5] Montazeri M , Montazeri M , Montazeri M ,et al.Machine learning models in breast cancer survival prediction[J]. Technology & Health Care Official Journal of the European Society for Engineering & Medicine, 2015, 24(1):31.

[6] Faber F A , Hutchison L , Huang B ,et al.Fast machine learning models of electronic and energetic properties consistently reach approximation errors better than DFT accuracy[J]. 2017.DOI: 10. 48550/arXiv.1702.05532.

[7] Harvey C R .THE MASTERS SERIES Campbell R. Harvey, PhD: Examining Quantitative Investment Strategies[J]. 2022.

[8] Kou Z , Yu H , Peng J ,et al.Automate Strategy Finding with LLM in Quant investment[J]. 2024.

[9] Juddoo K , Malki I , Mathew S ,et al.An impact investment strategy[J].Review of Quantitative Finance and Accounting, 2023, 61(1):177-211.DOI:10.1007/s11156-023-01149-0.

[10] Kaiwen Z , Sen G , Guolei L .The impact of financial technology on China's low-carbon transformation of energy and mechanism analysis[J].Environmental Science and Pollution Research, 2024(4):31.

[11] Hakim A L , Zaerofi A , Mulyana R .Analysis of Small Medium Enterprise's Sukuk Investment Intention Through Financial Technology Securities Crowdfunding[J].Tazkia Islamic Finance and Business Review, 2023.DOI: 10.30993/ tifbr. v16i2. 301.

[12] Xuan J , Liu Y .Research on the Path of Financial Technology Enabling Wealth Management[J].Modern Economics & Management Forum, 2023, 4(3):55-57.DOI:10. 32629/memf. v4i3. 1374.