A1 Refereed original research article in a scientific journal
The Effect of Bootstrap DEA in Sustainable Investment Analysis Using Exit Time
Authors: Ghasemi Doudkanlou, Mohammad; Banihashemi, Shokoofeh; Chandro, Prokash
Publisher: National Library of Serbia
Publication year: 2025
Journal: Yugoslav Journal of Operations Research
ISSN: 0354-0243
eISSN: 1820-743X
DOI: https://doi.org/10.2298/YJOR240315024G
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Open Access publication channel
Web address : https://doi.org/10.2298/yjor240315024g
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/523479546
Self-archived copy's licence: CC BY NC ND
Self-archived copy's version: Publisher`s PDF
The primary objective of this research is to develop more reliable portfolios by accurately calculating risk and return, emphasizing a secure asset weighting strategy. We employ the DEA bootstrap method and the SPP-CVaR (Stop-Profit Point-Conditional Value at Risk) methodology to achieve this objective. Previous scholarly research often lacks a robust statistical foundation for evaluating asset performance, particularly regarding sustainability, as traditional approaches rely on single data samples. Additionally, many studies fail to account for the relevance of risk and return until the investor exits the market. We introduce a new approach focusing on exit time to evaluate sustainable investments to address this gap. We employ data envelopment analysis (DEA) to assess the performance of these assets, comparing results from both the DEA bootstrap method and traditional DEA models. Our DEA models incorporate SPP-CVaR (Conditional Value at Risk) as a measure of risk and mean return as the output variable, both calculated until the investor exits the market. Traditional DEA models have limitations in statistical interpretation, so we enhance our analysis with the DEA bootstrap method. This method involves resampling data to create multiple samples, offering a distribution of performance measures for each asset and providing a more comprehensive understanding of asset performance and uncertainty. By comparing the bootstrap and results of conventional methods, we demonstrate the advantages of using statistical techniques to evaluate and compare financial assets. The SPP-CVaR is calculated by deriving and converting the risk-neutral density, simulating price paths, and identifying stop-profit points. We then analyze the exit time and price distributions to compute the SPP-CVaR for each stop-profit point. The value of this study lies in its integration of sustainability analysis with risk measures, helping investors build profitable and ethically aligned portfolios. By providing a detailed assessment of an asset's sustainability profile, our approach assists investors in making informed decisions that align with their financial and ethical goals.
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Funding information in the publication:
This research received no external funding.
