University of South Florida (USF) M3 Publishing
Abstract
Investment is an artistic science. The study discusses deep learning-based techniques for asset allocation. Deep learning is a subset of machine learning. The majority of the population lacks either the skills or the time to self-analyze the different financial investment options available to them and therefore they seek the help of the portfolio managers who make trading decisions on behalf of their clients depending on their risk appetite. Besides, portfolio managers analyze different assets, compare the strengths and weaknesses of each option before making a decision about which equities are suitable, to optimally balance profit and risks. This makes portfolio management a fairly complex process which eventually becomes one of the primary deterrents for a common person. Artificial intelligence can be a useful technological aid in determining profits and risks. Deep learning is the subset of machine learning and artificial intelligence. There is an essential usage of deep learning algorithms for accurately predicting the risk appetite of users by doing personality and demographic assessments on multiple levels to assist that particular individual in investing decisions. The research study proposes to decentralize the artificial intelligence-based portfolio management and create a shift of power from institutions towards the masses has been done.
DOI
https://www.doi.org/10.5038/9781955833035
Recommended Citation
Sengupta, S., Priyam, P., & Vaish, A. (2021). Decentralized approach to deep-learning based asset allocation. In C. Cobanoglu, & V. Della Corte (Eds.), Advances in global services and retail management (pp. 1–8). USF M3 Publishing. https://www.doi.org/10.5038/9781955833035
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
