Optimization of investment portfolios in renewable energy using advanced financial modeling techniques
Price water house Coopers (PwC), Lagos Nigeria.
Review
International Journal of Multidisciplinary Research Updates, 2022, 03(02), 040-058.
Article DOI: 10.53430/ijmru.2022.3.2.0054
Publication history:
Received on 17 July 2022; revised on 25 August 2022; accepted on 28 August 2022
Abstract:
The growing emphasis on sustainability and decarbonization has driven significant investment into renewable energy markets. Optimizing investment portfolios in renewable energy is crucial for maximizing returns, minimizing risks, and ensuring alignment with global climate goals. This study explores the application of advanced financial modeling techniques to optimize renewable energy portfolios, leveraging data-driven approaches and machine learning algorithms to address the unique challenges of this emerging sector. The research employs a combination of traditional portfolio optimization models, such as the Markowitz Modern Portfolio Theory (MPT), and advanced methods like Monte Carlo simulations, scenario analysis, and machine learning-based predictive analytics. By incorporating real-time market data, historical price trends, and risk metrics, these models aim to identify optimal asset allocations across diverse renewable energy segments, including solar, wind, hydropower, and bioenergy. Additionally, sustainability metrics, such as carbon reduction potential and Environmental, Social, and Governance (ESG) scores, are integrated into the optimization framework to align investment strategies with ethical and environmental objectives. Results indicate that incorporating machine learning techniques, such as reinforcement learning and neural networks, enhances the accuracy of price forecasts and risk assessments. This enables investors to dynamically adjust portfolio strategies based on changing market conditions. Furthermore, integrating ESG metrics improves long-term portfolio performance by reducing exposure to regulatory and reputational risks. The study provides actionable insights for institutional investors, fund managers, and policymakers on constructing robust renewable energy portfolios that balance financial returns and sustainability objectives. It underscores the importance of advanced financial modeling in addressing the complexities of renewable energy investments, including high capital costs, policy uncertainties, and fluctuating market dynamics. This research contributes to the growing field of sustainable finance by offering a comprehensive framework for optimizing renewable energy investment portfolios. Future studies will explore the integration of blockchain for enhancing transparency and real-time tracking of asset performance in renewable energy markets.
Keywords:
Renewable Energy; Portfolio Optimization; Financial Modeling; Machine Learning; Markowitz Theory; Monte Carlo Simulations; ESG Metrics; Sustainable Finance; Risk Assessment; Investment Strategies
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0