Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 2944-2949· 0 citations· 16 references
Abstract
This paper explores the effectiveness of integrating Artificial Neural Network (ANN) based return forecasts into three portfolio optimization frameworks: Equal-Weight (EW), Mean-Variance (MV), and Black-Litterman (BL), under varying market regimes, using 67 stocks listed on Thailand’s Market for Alternative Investment (MAI) as a case study. Portfolios are constructed using ANN predictions and tested across pre-COVID stable conditions (2019) and a volatile period (2020–2024), with an additional evaluation of rebalanced portfolios on 2024 data. Results indicate that BL achieves the highest risk-adjusted returns under stable conditions, while EW outperforms significantly during high volatility, driven largely by outlier stock performance during the COVID-19 recovery. Rebalancing with updated ANN forecasts did not guarantee improved performance, highlighting both the promise and limitations of machine learning-enhanced optimization in emerging markets.
The rapid growth of retail investors in Indonesia, from 2.48 million in 2019 to over 20 million by 2025, underscores an urgent need for empirically grounded portfolio optimization frameworks adoptable into practical tools such as robo-advisory systems. This study applies the Markowitz Mean-Variance model to construct a...
Irfan Andi Pramudya, Intan Shaferi· The International Conference...· 0 citations
This article assesses the performance of various stock market portfolios using a space-filling mixture design and a model for the logratios in the portfolio allocation. Traditional portfolio analysis relies on historical correlations between various returns and adopts various optimization models. However, these methods...
R. Khattree, Md Shakhawat Alam· Journal of the Indian Societ...· 0 citations
Background: Indonesia's capital market has experienced a sustained increase in investor participation, creating a stronger need for systematic and implementable portfolio construction methods. This study evaluates estimation risk in mean-variance optimization by comparing traditional Markowitz optimization with two mea...
Enggal Dwi Mulyaningtyas, R. Rokhim· Economic Military and Geogra...· 0 citations
A portfolio optimization framework that combines machine learning-based stock price prediction with Modern Portfolio Theory (MPT) and provides a systematic decision-support approach for combining predictive analytics with portfolio optimization is developed.
Subhash Naidu B, R. N. Kulkarni· International Journal of Cre...· 0 citations
The global stock market is a critical mechanism for the allocation of scarce financial resources to productive economic activities. However, investors continuously face the dual challenge of minimising risk while simultaneously maximising returns. This tension becomes particularly acute during catastrophic events such...
D. Arhinful, I. Ampofi, Ebenezer Asiedu· American Journal of Applied...· 0 citations
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