Abstract
During these unprecedented times, online portfolio management equipped with the right set of tools and guided financial service is a way to financial stability. Wealth management and personalized investments rely solely on the investor's decisions. Most of these decisions are a product of the state of the financial market, and the ability of the investor to perceive and manage risks that come as a part of investing in the market. The increasing complexity of the investment environment has created a need for better quality financial advice. It is essential to assess the client’s risk tolerance to cater to the latter’s needs based on this assessment. This work uses a systematic mapping methodology to encompass 21,400 works related to personalized investments, the factors that decide the financial risk tolerance (FRT) and risk-taking behaviour (FRB), and the role machine learning can play to carve out the picture of the state of the art. This review revealed that 4 survey papers and 11 articles can help us identify areas of research to build personalized online portfolio management tools with clients’ risk tolerance assessments. The results indicate that financial institutions are investing heavily in data-driven approaches to risk management and customer behaviour prediction, but middle and lower-income households have largely been left out of this process.
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Acknowledgements
Robert Merkle from Beacon networking for life, Ira Laefsky, from Ira M Laefsky & Associates, Pammi Bhullar from Edelman Financial Engines, Hardware directors Aleksandar Zoranovic and Bratislav Veljkovic at Advanced Brain Monitoring. Jesse Gardner, Professors Raghu Sangwan, Mohamad Kassab, Satish Srinivasan, and Big Data Lab Penn State Great Valley.
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Mishra, R., Haridas, A.C., Khunduru, N., Chundru, A., Mahbub, S., Ramljak, D. (2022). Online Portfolio Management: A Survey of Data-Driven Approaches. In: Qiu, R., Chan, W.K.V., Chen, W., Badr, Y., Zhang, C. (eds) City, Society, and Digital Transformation. INFORMS-CSS 2022. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-15644-1_27
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