Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies

Loke, Zi Xuan; Goh, Say Leng; Kendall, Graham; Abdullah, Salwani; Sabar, Nasser R.

doi:10.1109/access.2023.3263198

Cited by 9 publications

(2 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Significantly, the survey highlights a growing interest in hybrid methodologies, particularly noticeable since 2018. The findings presented by Loke et al (2023) emphasize the importance of acknowledging and addressing the emerging trends and gaps in this field. This expansion has resulted in noteworthy improvements in the outcomes obtained ( Jang & Seong, 2023 ).…”

Section: Theoretical Aspectsmentioning

confidence: 95%

“…An increase in research efforts directed at formulating investment portfolios has been observed, driven by the proliferation of accessible data and the introduction of innovative methodologies. In light of these advancements, a comprehensive survey by Loke et al (2023) delineates the developments in the Portfolio Optimization Problem (POP) from 2018 to 2022. The paper categorizes contemporary solution techniques, highlighting key areas, including metaheuristics, mathematical optimization, hybrid approaches, matheuristics, and machine learning.…”

Section: Theoretical Aspectsmentioning

confidence: 99%

See 1 more Smart Citation

Management of investment portfolios employing reinforcement learning

Santos,

Garruti,

Barboza

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

Investors are presented with a multitude of options and markets for pursuing higher returns, a task that often proves complex and challenging. This study examines the effectiveness of reinforcement learning (RL) algorithms in optimizing investment portfolios, comparing their performance with traditional strategies and benchmarking against American and Brazilian indices. Additionally, it was explore the impact of incorporating commodity derivatives into portfolios and the associated transaction costs. The results indicate that the inclusion of derivatives can significantly enhance portfolio performance while reducing volatility, presenting an attractive opportunity for investors. RL techniques also demonstrate superior effectiveness in portfolio optimization, resulting in an average increase of 12% in returns without a commensurate increase in risk. Consequently, this research makes a substantial contribution to the field of finance. It not only sheds light on the application of RL but also provides valuable insights for academia. Furthermore, it challenges conventional notions of market efficiency and modern portfolio theory, offering practical implications. It suggests that data-driven investment management holds the potential to enhance efficiency, mitigate conflicts of interest, and reduce biased decision-making, thereby transforming the landscape of financial investment.

show abstract

Section: Theoretical Aspectsmentioning

confidence: 95%

Section: Theoretical Aspectsmentioning

confidence: 99%

Management of investment portfolios employing reinforcement learning

Santos,

Garruti,

Barboza

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

Harnessing Cognitively Inspired Predictive Models to Improve Investment Decision-Making

Carandente,

Sperlí

2024

Cogn Comput

View full text Add to dashboard Cite

In the last years, researchers and practitioners have focused on defining portfolio optimization approaches. This task aims to identify a suitable distribution of assets for maximizing profits and minimizing risks, also offering protection against unexpected market behaviors. Nevertheless, the state-of-the-art approaches encounter significant limitations due to the complex nature of the task: (1) forecasting of non-stationary, non-linearity and volatile stock price; (2) budget allocation over different stocks satisfying multi-objective objective function; (3) risk costs can significantly affect the effectiveness of the designed approaches. In this paper, we propose a cognitively inspired framework for portfolio optimization by integrating deep learning-based stock forecasting for maximizing the revenue and portfolio diversification and Shape Ratio for minimizing the risk. Furthermore, the cognitively inspired forecasting module relies on the LSTM-based approach which combines historical financial data and technical indicators. Hence, this approach addresses the portfolio optimization task with the aim of designing more and more cognitive agents that perform autonomous actions for supporting decision-making. To make these agents cognitive, we further integrate stock forecasting into the portfolio optimization model, also investigating the main factors affecting both stock forecasting and portfolio optimization tasks. The proposed framework has been evaluated in two stages on a real-world dataset, composed of four years of information about stocks from six different areas. Firstly, we compare the proposed forecasting models based on LSTM and GRU, pointing out that the former achieves higher effectiveness results although the latter has a shorter training time. Finally, the proposed framework has been compared with different baselines, obtaining a net difference of $168 at the maximum. Finally, we compare the proposed approach w.r.t. several baselines in terms of total revenue, also providing an ablation analysis to investigate how stock prediction might support investors in dealing with portfolio optimization task.

show abstract