A data-driven prediction method for multi-period portfolio optimization using the real options approach

Abdollah Arasteh

Finance Research Letters, 2025, vol. 80, issue C

Abstract: Financial portfolio optimization balances risk and returns. Traditional multi-period models ignore financial time series dynamics and volatility by assuming normally distributed returns and static predictions. Many models ignore unequal estimation penalties, making them difficult. Different distribution models and uncertainty management in finance are sought to fill this gap. We test t-distributions and kernel estimators and add probabilistic risk criteria to the multi-period capital portfolio selection algorithm. Real options manage uncertainty in complex environments and provide accurate forecasts with strong decision-making tools despite volatile financial data. Modern theory applied to empirical applications improves dynamic financial system portfolio optimization and adaptive approaches.

Keywords: Multi-period portfolio optimization; Probabilistic risk measure; Piecewise linear interpolation; ARIMA-GARCH models; Real options theory (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:80:y:2025:i:c:s1544612325006634

DOI: 10.1016/j.frl.2025.107403

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