Machine Learning Analysis of Key Features in Household Financial Decision-Making

Shenghan Zhao; Tianxiang Zhang; Ningxin Li

doi:10.54097/gapmwq55

Authors

Shenghan Zhao
Tianxiang Zhang
Ningxin Li

DOI:

https://doi.org/10.54097/gapmwq55

Keywords:

Machine Learning; Financial Decisions; Financial Risk Management; Household Investment Decision.

Abstract

This paper explores the potential and challenges of mobile Internet in household investment decisions. The rapid development of mobile Internet has brought opportunities and challenges to household asset allocation, especially in promoting greater participation in venture asset investment. This paper focuses on the application potential of machine learning in analyzing household investment behavior patterns and trends. It reveals the potential household income rules, consumption patterns and asset allocation preferences through extensive data analysis. However, machine learning faces many challenges, such as data privacy protection, algorithmic interpretation, and data acquisition costs. Finally, the paper calls for further research and exploration to deepen understanding of how technological innovation can drive intelligent and optimized household financial decisions.

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References

[1] Li, S., Lin, R., & Pei, S. (2024). Multi-modal preference alignment remedies regression of visual instruction tuning on language model. arXiv preprint arXiv:2402.10884.

[2] Li, S., & Tajbakhsh, N. (2023). Scigraphqa: A large-scale synthetic multi-turn question-answering dataset for scientific graphs. arXiv preprint arXiv:2308.03349.

[3] Liu, H., Xie, R., Qin, H., & Li, Y. (2024). Research on Dangerous Flight Weather Prediction based on Machine Learning. arXiv preprint arXiv:2406.12298.

[4] Liu, H., Shen, F., Qin, H., & Gao, F. (2024). Research on Flight Accidents Prediction based Back Propagation Neural Network. arXiv preprint arXiv:2406.13954.

[5] Haowei, Ma, et al. "CRISPR/Cas-based nanobiosensors: A reinforced approach for specific and sensitive recognition of mycotoxins." Food Bioscience 56 (2023): 103110.

[6] Li, J., Wang, Y., Xu, C., Liu, S., Dai, J., & Lan, K. (2024). Bioplastic derived from corn stover: Life cycle assessment and artificial intelligence-based analysis of uncertainty and variability. Science of The Total Environment, 174349.

[7] Lai, S., Feng, N., Sui, H., Ma, Z., Wang, H., Song, Z., ... & Yue, Y. (2024). FTS: A Framework to Find a Faithful TimeSieve. arXiv preprint arXiv:2405.19647.

[8] Wang, H., Li, J., & Li, Z. (2024). AI-Generated Text Detection and Classification Based on BERT Deep Learning Algorithm. arXiv preprint arXiv:2405.16422.

[9] Zhang, X., Xu, L., Li, N., & Zou, J. (2024). Research on Credit Risk Assessment Optimization based on Machine Learning.

[10] Huang, D., Xu, L., Tao, W., & Li, Y. (2024). Research on Genome Data Recognition and Analysis based on Louvain Algorithm.

[11] Huang, D., Liu, Z., & Li, Y. (2024). Research on Tumors Segmentation based on Image Enhancement Method. arXiv preprint arXiv:2406.05170.

[12] Xiao, J., Wang, J., Bao, W., Deng, T. and Bi, S., Application progress of natural language processing technology in financial research.

[13] Fruehwirth, Jane Cooley, Alex Xingbang Weng, and Krista MPerreira."The effect of social media use on mental health ofcollege students during the pandemic." Health Economics (2024).

[14] Jin, Y., Shimizu, S., Li, Y., Yao, Y., Liu, X., Si, H., ... & Xiao, W. (2023). Proton therapy (PT) combined with concurrent chemotherapy for locally advanced non-small cell lung cancer with negative driver genes. Radiation Oncology, 18(1), 189.

[15] Li, B., Zhang, X., Wang, X. A., Yong, S., Zhang, J., & Huang, J. (2019, April). A Feature Extraction Method for Daily-periodic Time Series Based on AETA Electromagnetic Disturbance Data. In Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence (pp. 215-219).

[16] Li, B., Zhang, K., Sun, Y., & Zou, J. (2024). Research on Travel Route Planning Optimization based on Large Language Model.

[17] Yang, J., Qin, H., Por, L. Y., Shaikh, Z. A., Alfarraj, O., Tolba, A., ... & Thwin, M. (2024). Optimizing diabetic retinopathy detection with inception-V4 and dynamic version of snow leopard optimization algorithm. Biomedical Signal Processing and Control, 96, 106501.

[18] Li, B., Jiang, G., Li, N., & Song, C. (2024). Research on Large-scale Structured and Unstructured Data Processing based on Large Language Model.

[19] Yang, J., Qin, H., Por, L. Y., Shaikh, Z. A., Alfarraj, O., Tolba, A., ... & Thwin, M. (2024). Optimizing diabetic retinopathy detection with inception-V4 and dynamic version of snow leopard optimization algorithm. Biomedical Signal Processing and Control, 96, 106501.

[20] Li, Y., Matsumoto, Y., Chen, L., Sugawara, Y., Oe, E., Fujisawa, N., ... & Sakurai, H. (2023). Smart Nanofiber Mesh with Locally Sustained Drug Release Enabled Synergistic Combination Therapy for Glioblastoma. Nanomaterials, 13(3), 414.

[21] Jin, Y., Shimizu, S., Li, Y., Yao, Y., Liu, X., Si, H., ... & Xiao, W. (2023). Proton therapy (PT) combined with concurrent chemotherapy for locally advanced non-small cell lung cancer with negative driver genes. Radiation Oncology, 18(1), 189.

[22] Nitta, H., Mizumoto, M., Li, Y., Oshiro, Y., Fukushima, H., Suzuki, R., ... & Sakurai, H. (2024). An analysis of muscle growth after proton beam therapy for pediatric cancer. Journal of Radiation Research, 65(2), 251-255.

[23] Nakamura, M., Mizumoto, M., Saito, T., Shimizu, S., Li, Y., Oshiro, Y., ... & Sakurai, H. (2024). A systematic review and meta-analysis of radiotherapy and particle beam therapy for skull base chondrosarcoma: TRP-chondrosarcoma 2024. Frontiers in Oncology, 14, 1380716.

[24] Li, Y., Mizumoto, M., Oshiro, Y., Nitta, H., Saito, T., Iizumi, T., ... & Sakurai, H. (2023). A retrospective study of renal growth changes after proton beam therapy for Pediatric malignant tumor. Current Oncology, 30(2), 1560-1570.

[25] Shimizu, S., Mizumoto, M., Okumura, T., Li, Y., Baba, K., Murakami, M., ... & Sakurai, H. (2021). Proton beam therapy for a giant hepatic hemangioma: A case report and literature review. Clinical and Translational Radiation Oncology, 27, 152-156.

[26] Kumada, H., Li, Y., Yasuoka, K., Naito, F., Kurihara, T., Sugimura, T., ... & Sakae, T. (2022). Current development status of iBNCT001, demonstrator of a LINAC-based neutron source for BNCT. Journal of Neutron Research, 24(3-4), 347-358.

[27] Shimizu, S., Nakai, K., Li, Y., Mizumoto, M., Kumada, H., Ishikawa, E., ... & Sakurai, H. (2023). Boron neutron capture therapy for recurrent glioblastoma multiforme: imaging evaluation of a case with long-term local control and survival. Cureus, 15(1).

[28] Gupta, S., Motwani, S. S., Seitter, R. H., Wang, W., Mu, Y., Chute, D. F., ... & Curhan, G. C. (2023). Development and validation of a risk model for predicting contrast-associated acute kidney injury in patients with cancer: evaluation in over 46,000 CT examinations. American Journal of Roentgenology, 221(4), 486-501.

[29] Rosner, B., Glynn, R. J., Eliassen, A. H., Hankinson, S. E., Tamimi, R. M., Chen, W. Y., ... & Tworoger, S. S. (2022). A multi-state survival model for time to breast cancer mortality among a cohort of initially disease-free women. Cancer Epidemiology, Biomarkers & Prevention, 31(8), 1582-1592.

[30] Yaghjyan, L., Heng, Y. J., Baker, G. M., Bret-Mounet, V., Murthy, D., Mahoney, M. B., ... & Tamimi, R. M. (2022). Reliability of CD44, CD24, and ALDH1A1 immunohistochemical staining: Pathologist assessment compared to quantitative image analysis. Frontiers in Medicine, 9, 1040061.

[31] Zhou, Q. (2024). Portfolio Optimization with Robust Covariance and Conditional Value-at-Risk Constraints. arXiv preprint arXiv:2406.00610.

[32] Zhou, Q. (2024). Application of Black-Litterman Bayesian in Statistical Arbitrage. arXiv preprint arXiv:2406.06706.

[33] Chen, Z., Ge, J., Zhan, H., Huang, S., & Wang, D. (2021). Pareto self-supervised training for few-shot learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 13663-13672).

[34] Zhang, Y., Qu, T., Yao, T., Gong, Y., & Bian, X. (2024). Research on the application of BIM technology in intelligent building technology. Applied and Computational Engineering, 61, 29-34.

[35] Weng A. Depression and Risky Health Behaviors[J]. Available at SSRN 4843979.