Framework to assist investment portfolio generation for financial sector

Carmo, Breno Barros Telles do; Medeiros, Pablo Picasso Morais de; Gonçalo, Thomas Edson Espíndola; Correia, Gabriela Colaço

doi:10.5585/exactaep.2021.18687

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…B.B.T. Carmo et al [30] presented a PROMETHEE V method with linear programming for helping investors with various options and scope of customization for investment portfolios. N. Eriotis et al [31] investigated and presented a report on the investment portfolios spread over a period of one and a half decades in…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Weighted Hybrid Recommendation System using Sharpe Ratio for a Profitable Diversified Investment Portfolio

Saini

Vaz

2022

jour

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Identifying where to invest and how much to invest can be very challenging for common people who have limited knowledge in the domain. Portfolio managers are financial professionals who spend a lot of time and effort to help investors in investing funds and implementing investment strategies, but not all can afford to consult them. The study aims to develop a weighted hybrid recommendation system that recommends an optimized investment portfolio based on the investor’s preferences regarding risk and return. Generally, investors usually ask investment for advice from friends or relatives with similar risk preferences or if they are interested in a particular item, the investors ask for the experience of someone who already has invested in the same item. Therefore, the methodology considers the investor’s past behavior and the past behavior of the nearest neighbor investors with similar risk preferences. Using user-based collaborative filtering the number of stocks is recommended using Pearson correlation based on the investor’s income, then using another user-based collaborative filtering the number of stocks is recommended based on the investor’s age. Weights are assigned to the recommended number of stocks generated based on income and age and their weighted average is finally considered. Finally, the feasibility of the proposed system was assessed through various experiments. Based on the received results, the authors conclude that the proposed weighted hybrid approach is robust enough for implementation in the real world. The novelty of the paper lies in the fact that none of the existing approaches make use of more than one type of weighted recommendation algorithm. Additionally, the final results obtained this way have been never further fortified with the highest Sharpe ratio and minimum risk for the investor. This combination of hybrid and Sharpe ratios has never been explored before.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Weighted Hybrid Recommendation System using Sharpe Ratio for a Profitable Diversified Investment Portfolio

Saini

Vaz

2022

jour

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Market Risk Analysis with Value at Risk Models using Machine Learning in BIST-30 Banking Index

Demirdöğen

2024

Adam Akademi Sosyal Bilimler Dergisi

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Market risk is one of the most critical risks for banks and portfolio managers. According to Basel criteria, Value at Risk (VaR) calculations should be conducted at regular intervals. VaR calculations can be performed using various methods, and the approaches and variables added to the model can vary significantly. Developments in machine learning and deep learning methods have increased the diversity of VaR calculations, enabling the construction of more accurate and complex models. In this study, a portfolio was created using the stocks of 4 major banks in BIST30 (AKBNK, GARAN, ISCTR, YKBNK) with the help of Monte Carlo simulation and Random Forest. Calculations were made for 126 periods with a 10-day interval using 5 years of daily data. Predictions were made for the last 4 periods using 3 different Value at Risk (VaR) methods (historical, parametric, and Monte Carlo). Independent variables such as VIX (fear index), USD/TL, Gold/TL, and Brent/TL were used. The suitability of the variables was tested with machine learning regularization methods, including Ridge, Lasso, and Elastic Net regression models. Random Forest was again used to measure the impact of independent variables on stocks' weights. For each VaR model, stock weight distributions were determined for the last 4 periods, and the realized VaR results were compared. As a result of the findings, the parametric VaR method provided the best result for the first period, while the historical VaR method provided the closest result for the other three periods. When comparing the findings with the actual results, it was observed that the findings were more optimistic, and even the closest results did not come within 30% of the actual value. The reason for the difference being greater than expected could be attributed to the fact that the value of bank stocks has been below their value in the last two years and the sharp movements in the stock market in the selected last 4 periods, independent of individual stocks.

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Framework to assist investment portfolio generation for financial sector

Abstract: The research themes are focused on sustainability, life cycle assessment, decision-making, healthcare operations, transportation and engineering education.

Cited by 2 publications

References 24 publications

A Novel Weighted Hybrid Recommendation System using Sharpe Ratio for a Profitable Diversified Investment Portfolio

A Novel Weighted Hybrid Recommendation System using Sharpe Ratio for a Profitable Diversified Investment Portfolio

Market Risk Analysis with Value at Risk Models using Machine Learning in BIST-30 Banking Index

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