Digital Optimization of Portfolio with Market Restrictions

Kulian, Victor R.; Yunkova, Olena; Korobova, Marina V.

doi:10.1109/atit49449.2019.9030534

Cited by 1 publication

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“…To validate the results, the authors gave a numerical simulation in stock portfolio. In [19], the authors studied the problem of optimal portfolio diversification where they proposed a framework to help financial advisors determine the structure of optimal portfolio by mathematically modeling the dynamics of the markets and the stocks. In [20], the authors addressed the portfolio optimization challenge using the minimax principle.…”

Section: Introductionmentioning

confidence: 99%

A Generative Adversarial Network for Financial Advisor Recruitment in Smart Crowdsourcing Platforms

2022

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Financial portfolio management is a very time-consuming task as it requires the continuous surveying of the market volatility. Investors need to hire potential financial advisors to manage portfolios on their behalf. Efficient hiring of financial advisors not only facilitates their cooperation with investors but also guarantees optimized portfolio returns and hence, optimized benefits for the two entities. In this paper, we propose to tackle the portfolio optimization problem by efficiently matching financial advisors to investors. To this end, we model the problem as an automated crowdsourcing platform to organize the cooperation between the different actors based on their features. The recruitment of financial advisors is performed using a Generative Adversarial Network (GAN) that extrapolates the problem to an image processing task where financial advisors’ features are encapsulated in gray-scale images. Hence, the GAN is trained to generate, based on an investor profile given as an input, the ’ideal’ financial advisor profile. Afterwards, we measure the level of similarity between the generated ideal profiles and the existing profiles in the crowdsourcing database to perform a low complexity, many-to-many investor-to-financial advisor matching. In the simulations, intensive tests were performed to show the convergence and effectiveness of the proposed GAN-based solution. We have shown that the proposed method achieves more than 17% of the average expected return compared to baseline approaches.

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Section: Introductionmentioning

confidence: 99%