Portfolio Optimization Techniques Research Articles

Cryptocurrency Portfolio Allocation under Credibilistic CVaR Criterion and Practical Constraints

The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the market’s unique dynamics and unpredictability. In response to these challenges, this paper introduces a novel portfolio optimization model tailored for the cryptocurrency market, leveraging a credibilistic CVaR framework. CVaR was chosen as the primary risk measure because it is a downside risk measure that focuses on extreme losses, making it particularly effective in managing the heightened risk of significant downturns in volatile markets like cryptocurrencies. The model employs credibility theory and trapezoidal fuzzy variables to more accurately capture the high levels of uncertainty and volatility that characterize digital assets. Unlike traditional probabilistic approaches, this model provides a more adaptive and precise risk management strategy. The proposed approach also incorporates practical constraints, including cardinality and floor and ceiling constraints, ensuring that the portfolio remains diversified, balanced, and aligned with real-world considerations such as transaction costs and regulatory requirements. Empirical analysis demonstrates the model’s effectiveness in constructing well-diversified portfolios that balance risk and return, offering significant advantages for investors in the rapidly evolving cryptocurrency market. This research contributes to the field of investment management by advancing the application of sophisticated portfolio optimization techniques to digital assets, providing a robust framework for managing risk in an increasingly complex financial landscape.

Investment management approaches: A comprehensive review of equity trading simulators, methodological challenges, and future directions

This paper provides a comprehensive review of equity trading simulators, focusing on their performance in assuring pre-trade compliance and portfolio investment management. A systematic search was conducted that covered the period of January 2000 to May 2023 and used keywords related to equity trade simulators, portfolio management, pre-trade compliance, online trading, and artificial intelligence. Studies demonstrating the use of simulators and online platforms specific to portfolio investment management, written in English, and matching the specified query were included. Abstracts, commentaries, editorials, and studies unrelated to finance and investments were excluded. The data extraction process included data related to challenges in modern portfolio trading, online stock trading strategies, the utilization of deep learning, the features of equity trade simulators, and examples of equity trade simulators. A total of 32 studies were included in the systematic review and were approved for qualitative analysis. The challenges identified for portfolio trading included the subjective nature of the inputs, variations in the return distributions, the complexity of blending different investments, considerations of liquidity, trading illiquid securities, optimal portfolio execution, clustering and classification, the handling of special trading days, the real-time pricing of derivatives, and transaction cost models (TCMs). Portfolio optimization techniques have evolved to maximize portfolio returns and minimize risk through optimal asset allocation. Equity trade simulators have become vital tools for portfolio managers, enabling them to assess investment strategies, ensure pre-trade compliance, and mitigate risks. Through simulations, portfolio managers can test investment scenarios, identify potential hazards, and improve their decision-making process.

Analyzing the Relationship between Real Estate Investments and Portfolio Diversification

Real estate has long been considered an attractive investment option for individuals and institutions seeking to build wealth and diversify their portfolios. Unlike traditional investment vehicles such as stocks and bonds, real estate offers unique characteristics that can potentially enhance returns and mitigate risk. This analysis aims to explore the role of real estate investments in portfolio diversification and assess their potential impact on overall portfolio performance. Portfolio diversification is a fundamental principle in investment management, as it helps to spread risk across different asset classes and mitigate the impact of market fluctuations on a portfolio's overall value. By including assets with low or negative correlations, investors can reduce the volatility of their portfolios and potentially achieve higher risk-adjusted returns. Real estate investments, including direct property ownership, real estate investment trusts (REITs), and other real estate-related securities, have traditionally exhibited low correlations with other asset classes, such as equities and bonds. This low correlation can be attributed to the unique characteristics of real estate, including its tangible nature, the presence of rental income streams, and the potential for capital appreciation. Furthermore, real estate investments can provide a hedge against inflation, as property values and rental rates tend to increase during periods of rising prices. This feature makes real estate an attractive diversification option, particularly for investors seeking to protect their portfolios from the eroding effects of inflation. This analysis will delve into the historical performance of real estate investments, examine their risk and return characteristics, and evaluate their potential contribution to portfolio diversification. By examining empirical data and leveraging portfolio optimization techniques, we aim to provide insights into the optimal allocation of real estate investments within a diversified portfolio.

Optimizing Portfolio Construction Using Nifty India Manufacturing Index: A Risk-Return Analysis with Python

This paper presents a comprehensive analysis of portfolio construction strategies aimed at maximizing risk-adjusted returns for investors. Utilizing historical data on stock returns and risks, a meticulous selection process was employed to identify 15 stocks with superior risk-return profiles. These stocks were chosen based on their outperformance relative to the dataset's average measures, prioritizing returns higher than the dataset average and risks lower than average risk levels. The selected stocks, representing a diverse range of manufacturing-related businesses, formed the cornerstone of an optimal portfolio designed to minimize sector-specific risks while maximizing growth potential. Through rigorous portfolio optimization techniques, including Markowitz's mean-variance optimization, the study identified an optimal portfolio allocation with a return of 50.22% and volatility of 13.33%. Insights from the study offer valuable perspectives for investors seeking realistic and sustainable wealth accumulation strategies. The research also identifies avenues for future exploration, including the integration of alternative asset classes, behavioral finance insights, and advancements in risk management technologies. This study demonstrates the effectiveness of Python in financial analysis and portfolio optimization while furthering the theory of portfolios and providing valuable guidance for making investment decisions. Keywords: Returns, Risk, Volatility, Optimal Portfolio Construction, Manufacturing Sector, Markowitz Portfolio Theory,

Optimizing Portfolio Allocation in the Indian Defense Sector: A Python-Based Approach

This paper presents a detailed analysis of portfolio construction strategies aimed at maximizing risk-adjusted returns for investors, utilizing Python-based methodologies. By leveraging historical data on stock returns and risks, a meticulous selection process identified 5 stocks with superior risk-return profiles, outperforming the dataset's average measures with higher returns and lower risks. The selected stocks from the defense industry formed the foundation of an optimal portfolio designed to minimize sector-specific risks while maximizing growth potential. Through rigorous portfolio optimization techniques, including Markowitz's mean-variance optimization, an optimal portfolio allocation was identified with a return of 62.40% and volatility of 27.74%. Insights from the study provide valuable perspectives for investors seeking realistic and sustainable wealth accumulation strategies. The research also highlights future avenues for exploration, such as integrating alternative asset classes, incorporating behavioral finance insights, and leveraging advancements in risk management technologies. This study contributes to advancing portfolio theory and offers practical guidance for investment decision-making.This study demonstrates the effectiveness of Python in financial analysis and portfolio optimization while furthering the theory of portfolios and providing valuable guidance for making investment decisions. Keywords: Returns, Risk, Volatility, Optimal Portfolio Construction, Defense Sector, Markowitz Portfolio Theory,

Constructing Portfolio Optimization: Analysis in Indonesia Non-Cyclical Industry (Markowitz Approach and Skewness and Kurtosis)

Purpose: The purpose of this research is to examine the application of portfolio optimization in the context of real-world financial issues, particularly in light of the challenges posed by the COVID-19 pandemic. Traditional portfolio optimization strategies, such as those proposed by Markowitz, often rely on the assumption of normally distributed returns, which may not accurately capture the risks associated with extreme events like the COVID-19 crisis. This study aims to shed new light on portfolio optimization methods by exploring various approaches and considering the implications of non-normally distributed returns on portfolio construction. Methods: This research employs a quantitative approach to analyze portfolio optimization techniques in the face of non-normally distributed returns. Using data from financial markets impacted by the COVID-19 pandemic, the study investigates different portfolio construction methods, including risk-free rate for equally weighted portfolios, optimal risk portfolios, minimum variance weights, and maximum expected returns. Various risk metrics such as variance, Sharpe ratio, and standard deviation are considered to evaluate portfolio performance under different constraints. Results and discussion: The empirical findings highlight the limitations of traditional portfolio optimization techniques, particularly in accurately assessing and managing risk in the presence of non-normally distributed returns. Assets exhibit heavily tailed returns, leading to an underestimation of risk when using standard approaches. The study identifies that certain assets offer high returns but also entail significant risks, necessitating a nuanced approach to portfolio construction. By considering stable distribution models and optimizing for both maximum expected return and minimum variance weight, investors can build more profitable and diversified portfolios while managing risk effectively. Implications of the research: The research findings have important implications for investors and financial practitioners, particularly in navigating uncertain market conditions such as those brought about by the COVID-19 pandemic. By recognizing the limitations of traditional portfolio optimization methods and embracing more sophisticated approaches that account for non-normally distributed returns, investors can make more informed decisions and better manage portfolio risk. These insights can inform the development of robust investment strategies tailored to mitigate the impact of extreme events on portfolio performance. Originality/value: This research contributes to the literature by offering a fresh perspective on portfolio optimization under the backdrop of the COVID-19 pandemic. By systematically evaluating different portfolio construction methods and considering the implications of non-normally distributed returns, the study advances understanding in the field of financial risk management. The identification of stable distribution models and the emphasis on balancing maximum expected return with minimum variance weight provide practical guidance for investors seeking to build resilient and profitable portfolios in turbulent market environments. Overall, this research underscores the importance of adapting portfolio optimization strategies to address the realities of contemporary financial markets.

Portfolio selection problem: main knowledge and models (A systematic review)

The challenge in portfolio optimization lies in creating a collection of assets that attains a target expected returnwhile mitigating risk. This problem is often framed as an optimization task, specifically Mean Variance Optimization (MVO). MVO involves formulating an objective function, typically quadratic, that is contingent on the composition of the portfolio, and linear constraints that represents the portfolio's asset allocation restriction. Several improvements have been proposed, such as adding constraints to MVO or using alternative risk measures. As a result, even though MVO model remains the most widely studied type of portfolio optimization, different types of portfolio optimization models, risk/return measurements and constraints have been suggested and used since its invention. In this work, we delve into the various risk and return measures, constraints, and mathematical models commonly used in portfolio optimization.We discuss the key risk measures employed in portfolio optimization, including the Sharpe ratio, beta, maximum drawdown and others, We explore the constraints commonly applied in portfolio optimization. Furthermore, we delve into the mathematical models utilized in portfolio optimization. Then, we emphasize the interplay between risk and return measures, constraints, and mathematical models in portfolio optimization. By providing a comprehensive overview of risk and return measures, constraints, and mathematical models, this work aims to enhance the understanding of portfolio optimization techniques and facilitate informed decisionmaking in the field of investment management. To illustrate different knowledge and models, several experiments were conducted on well-known real data portfolios.

Does Islamic investing modify portfolio performance? Time-varying optimization strategies for conventional and Shariah energy-ESG-utilities portfolio

This paper aims to assess the performance of Islamic portfolios vis-à-vis their conventional counterparts across two distinct periods: the pre-COVID-19 era and the COVID-19 era. Departing from prior studies, this study makes a novel contribution by employing an extensive array of 18 portfolio optimization techniques to construct optimal portfolios for conventional stock indices encompassing energy, utilities, and environmental, social, and governance (ESG) dimensions, as well as their Islamic equivalents. Performance comparisons are made utilizing three risk-adjusted performance measures, namely the Sharpe ratio, the Omega ratio, and the Sortino ratio. Our findings reveal that Shariah portfolios outperform conventional portfolios across all performance measures and risk-aversion levels when the most effective optimization methods are employed, both during pre-crisis and crisis periods. Additionally, our analysis highlights certain methods, namely EWMA, GM, DCC, and SHC, which produce portfolios exhibiting superior performance relative to alternative methods, as assessed by risk-adjusted metrics. Furthermore, Islamic portfolios demonstrate higher average returns compared to their conventional counterparts. Notably, incorporating ESG-related stocks into energy and utilities assets significantly enhances average returns, underscoring the potential of ESG investments. Collectively, our findings have noteworthy implications for investors, as they emphasize the role of Islamic stocks as effective diversifiers, yielding favorable resource allocation opportunities during times of crisis as well as stability. However, investors should exercise caution in selecting the optimal portfolio optimization method, as substantial performance disparities exist among different approaches.

Assessing the investment viability of Indonesia's upstream electric vehicle (EV) sector stocks amidst the COVID-19 pandemic

Amidst the global push for sustainability, the burgeoning electric vehicle (EV) industry has driven increased demand for batteries, placing the Indonesian nickel ore sector in a pivotal position due to its vast reserves. This study thoroughly examines the investment landscape of this sector, utilizing advanced portfolio optimization techniques to analyze four major nickel ore mine firms in Indonesia. Through Monte Carlo simulations, the study evaluates the optimal portfolios of risky assets, comparing their performance before and during the COVID-19 pandemic. Findings reveal a significant shift in portfolio composition during the Pandemic, reflecting investors' response to global disruptions by diversifying their holdings. Notably, the Sharpe Ratio, a risk-adjusted return measure, demonstrates an impressive increase in return relative to risk during the Pandemic, emphasizing the sector's resilience and attractiveness for investment, especially in times of economic uncertainty like the COVID-19 pandemic. The transformation in portfolio weights and the corresponding increase in risk-adjusted returns highlight the sector’s potential as a lucrative investment avenue, especially during periods of global economic uncertainty like the COVID-19 pandemic.

Portfolio Optimization Using Markowitz Model on Sri-Kehati Index

This thesis investigates the portfolio optimization process using the Markowitz model on the SRI-KEHATI index, an esteemed sustainable investment index. The study aims to explore the potential advantages of incorporating environmental, social, and governance (ESG) factors into portfolio construction. By leveraging historical financial data and reliable ESG metrics, this research develops optimized portfolios that strike a balance between risk and return while adhering to the sustainability criteria of the SRIKEHATI index. The methodology encompasses the collection of credible ESG data and financial information for the constituents of the SRIKEHATI index. The Markowitz model is subsequently employed to analyze the risk and return characteristics of each asset within the portfolio. Through the application of optimization algorithms, the study seeks to identify the optimal asset allocation that maximizes risk-adjusted returns, taking into account the ESG criteria outlined by the SRI-KEHATI index. The outcomes of this research provide valuable insights into the effectiveness of portfolio optimization techniques within the realm of sustainable investing. By considering both financial metrics and ESG factors, investors can construct portfolios that align with their sustainability objectives while optimizing risk and return. The findings shed light on the performance of the optimized portfolios and compare them with conventional approaches, thereby demonstrating the potential benefits of integrating ESG considerations into portfolio decision-making. Additionally, this study examines the practical implications associated with implementing sustainable portfolio strategies based on the SRI-KEHATI index. Overall, this thesis contributes to the expanding body of knowledge on sustainable investing and portfolio optimization, specifically focusing on the SRI-KEHATI index. It provides valuable insights for investors, asset managers, and policymakers interested in sustainable investment strategies. Furthermore, it offers a framework for incorporating ESG considerations into the portfolio construction process using the Markowitz model, thereby aiding in the development of more robust and sustainable investment portfolios.

Bayesian optimization of ESG (Environmental Social Governance) financial investments

Financial experts seek to predict the variability of financial markets to ensure investors’ successful investments. However, there has been a big trend in finance in the last few years, which are the ESG (Economic, Social and Governance) criteria, due to the growing importance of investments being socially responsible, and because of the financial impact companies suffer when not complying with them. Consequently, creating a stock portfolio should consider not only its financial performance but compliance with ESG criteria. Portfolio optimization (PO) techniques previously applied to ESG portfolios, are all closed-form analytical ones. But the real world is rather a black box with unknown analytical expressions. Thus, in this paper we use Bayesian optimization (BO), a sequential state-of-the-art design strategy to optimize black-boxes with unknown analytical and costly-to-compute expressions, to maximize the performance of a stock portfolio under the presence of ESG criteria soft constraints incorporated into the objective function. And we compare it to two other black-box techniques widely applied for the optimization of ‘conventional portfolios’ (non-ESG ones): the metaheuristics Genetic algorithm (GA) and Simulated Annealing (SA). Although BO has many theoretical advantages over GA and SA, it has never been applied to PO. Thus, this paper investigates whether BO can be used in the ESG PO framework as an alternative and compares it with GA and SA. This is the research gap to which this paper responds. To show the empirical performance of BO, we carry out four illustrative experiments and find evidence of BO outperforming the baselines. Thus we add another different optimization approach to the world of ESG investing: a black-box non-heuristic optimization approach through BO. Our study is the first paper that leverages BO and ESG scores into a PO technique. This paper opens the door to many new research lines in (ESG) portfolio optimization.

Semi-Metric Portfolio Optimization: A New Algorithm Reducing Simultaneous Asset Shocks

This paper proposes a new method for financial portfolio optimization based on reducing simultaneous asset shocks across a collection of assets. This may be understood as an alternative approach to risk reduction in a portfolio based on a new mathematical quantity. First, we apply recently introduced semi-metrics between finite sets to determine the distance between time series’ structural breaks. Then, we build on the classical portfolio optimization theory of Markowitz and use this distance between asset structural breaks for our penalty function, rather than portfolio variance. Our experiments are promising: on synthetic data, we show that our proposed method does indeed diversify among time series with highly similar structural breaks and enjoys advantages over existing metrics between sets. On real data, experiments illustrate that our proposed optimization method performs well relative to nine other commonly used options, producing the second-highest returns, the lowest volatility, and second-lowest drawdown. The main implication for this method in portfolio management is reducing simultaneous asset shocks and potentially sharp associated drawdowns during periods of highly similar structural breaks, such as a market crisis. Our method adds to a considerable literature of portfolio optimization techniques in econometrics and could complement these via portfolio averaging.

Portfolio Optimization Techniques for Cryptocurrencies

This article addresses the shortcomings of the existing literature regarding cryptocurrency portfolio construction. First, we address the effectiveness of time-series models that capture stylized features. We perform a comparison study on various methods for estimating distributions for asset returns, including normal, historical, and GARCH models within a CVaR setting. The goal of this comparison is to determine the financial benefits of constructing portfolios based on estimated distributions that consider stylized features of crypto return series. Next, we create and compare various prediction models for cryptocurrencies and integrate them with mean-variance optimization to base performance on portfolio management metrics, such as Sharpe ratio and level of diversification, rather than statistical metrics like accuracy and R² on which the literature solely focuses. We determine it is unclear which optimization approach (CVaR or Robust MVO) leads to better crypto portfolios, and so, to address this, we compare optimization procedures on out-of-sample data through a thorough cross-validation of hyperparameters for each technique. We then compare the resulting risk-optimal portfolios from each technique. The results show that a CVaR approach with a GARCH simulation and a decision tree prediction model with robust mean-variance optimization yield portfolios of similar risk. We also show that using statistical metrics to evaluate models may not always yield the best financial performance.

Risk measures and portfolio analysis in the paradigm of climate finance: a review.

Climate change brings with it new risks for the finance sector, which in turn provides new opportunities to mitigate this risk, emanating from climate change. To invest sustainably and move away from firms that have disproportionately high carbon footprints, investors need suitable risk measures and appropriate portfolio management approaches. In this paper, we conduct a review of the mathematical models used to measure carbon risk. Subsequently, we review portfolio optimization models based on modern portfolio theory and the incorporation of risk measures into portfolio optimization strategies. We find that there is a lack of consensus about the existence of a carbon premium or an equity greenium in stock prices. We also find that the literature on portfolio optimization techniques is comparatively nascent.

A brief review of portfolio optimization techniques

A brief review of portfolio optimization techniques

Dynamic portfolio optimization with inverse covariance clustering

Market conditions change continuously. However, in portfolio investment strategies, it is hard to account for this intrinsic non-stationarity. In this paper, we propose to address this issue by using the Inverse Covariance Clustering (ICC) method to identify inherent market states and then integrate such states into a dynamic portfolio optimization process. Extensive experiments across three different markets, NASDAQ, FTSE and HS300, over a period of ten years, demonstrate the advantages of our proposed algorithm, termed Inverse Covariance Clustering-Portfolio Optimization (ICC-PO). The core of the ICC-PO methodology concerns the identification and clustering of market states from the analytics of past data and the forecasting of the future market state. It is therefore agnostic to the specific portfolio optimization method of choice. By applying the same portfolio optimization technique on a ICC temporal cluster, instead of the whole train period, we show that one can generate portfolios with substantially higher Sharpe Ratios, which are statistically more robust and resilient with great reductions in the maximum loss in extreme situations. This is shown to be consistent across markets, periods, optimization methods and selection of portfolio assets.

Technical note: Modified variance incorporating high-order moments in risk measure with Gram-Charlier returns

This paper introduces a new risk measure for portfolio choice and compares its performance with two related metrics, namely the behavioral variance and the modified variance by using a Taylor’s expansion. The methodology for our proposal naturally incorporates investor attitudes to risk related to skewness and kurtosis by assuming a Gram-Charlier return distribution. The so-obtained risk measures represent a more reliable description of portfolio risk and encompass the cases where high-order moments are not relevant characteristics (i.e. under normality). Our results show the outperformance of our proposal for different risk tolerance parameters considering the minimum variance and Sharpe ratio criteria by employing random portfolio optimization technique for 11 sets of stocks.

An Application of Portfolio Mean-Variance and Semi-Variance Optimization Techniques: A Case of Fiji

In this paper, we apply the Markowitz portfolio optimization technique based on mean-variance and semi-variance as measures of risk on stocks listed on the South Pacific Stock Exchange, Fiji. We document key market characteristics and consider monthly returns data from SEP-2019 to FEB-2022 (T = 30) of 17/19 listed companies on the stock exchange to construct various portfolios like 1/N (naïve), maximum return, and market and minimum-variance with and without short-selling constraints. Additionally, we compute each stock’s beta using the market capitalization-weighted stock price index data. We note that well-diversified portfolios (market portfolio and minimum-variance portfolio) with short-selling constraints have relatively higher expected returns with lower risk. Moreover, well-diversified portfolios perform better than the naïve and maximum portfolios in terms of risk. Moreover, we find that both the mean-variance and the semi-variance measures of risk yields a unique market portfolio in terms of expected returns, although the latter has a lower standard deviation and a higher Sharpe ratio. However, for the minimum-variance portfolios and market portfolios without short selling, we find relatively higher returns and risks using the mean-variance than the semi-variance approach. The low beta of individual stock indicates the low sensitivity of its price to the movement of the market index. The study is an initial attempt to provide potential investors with some practical strategies and tools in developing a diversified portfolio. Since not all the portfolios based on mean-variance and the semi-variance analyses are unique, additional methods of investment analysis and portfolio construction are recommended. Subsequently, for investment decisions, our analysis can be complemented with additional measures of risk and an in-depth financial statement/company performance analysis.

Petroleum exploration portfolios generated with different optimization approaches: Lessons for decision-makers

Petroleum explorers recognize the value of portfolio management, but many have not seen it effectively applied in the industry or do not understand how subsurface assessments by geoscientists link to portfolio analysis and decision-making by managers. Here, we constructed a fictitious but realistic inventory of 50 prospects distributed across plays of various maturity and then generated portfolios with rank and cut, efficient frontier and portfolio filtering approaches. Different analysis and optimization techniques have varying degrees of use and success in aligning portfolio selections with specific strategic goals and available drilling budget. Portfolios ranked and cut by mean risked volumes, economic probability of success (PoS) and expected monetary value can deliver good exploration results. However, they are sub-optimal when compared with similar efficient frontier portfolios optimized for expected total recoverable resource volume and its standard deviation. Portfolios ranked and cut by geological PoS values can deliver the greatest geological success rate among all portfolios, but not much resources and monetary value when the low-risk prospects have relatively small success-case volumes. Portfolio filtering helps find suitable alternative portfolios. Better portfolios include prospects from the emerging play setting characterized by greatest geological and economic PoS values and success-case volumes, but exclude high-risk prospects from frontier play settings. The formal portfolio optimization approaches are blind to the fact that companies have to drill high-risk frontier prospects to de-risk plays and provide access to prospects in emerging plays. Portfolio analysis and optimization techniques must be coupled with good understanding of exploration business and long-term strategy to deliver excellent sustainable exploration results.

Assessment of the mediating effect of benchmarking workspace usage on portfolio optimization and client satisfaction: Indian information technology companies

PurposeMany corporates in India are constantly adapting real estate benchmarks to reduce the workspace maintenance cost. However, anecdotally benchmarking the experience of clients while designing the workspace maintenance policies is not adequately taken into consideration in India. The focus of this study is on benchmarking workspace usage based on client usability.Design/methodology/approachThe research is descriptive in nature. A structured questionnaire was sent to Information Technology (IT) companies in India to collect data through SurveyMonkey. Stratified sampling was used to collect a sample of 697 respondents which was also verified using G* software. The data collected was analysed using descriptive statistics and partial least square–structured equation modeling (PLS-SEM) to investigate the mediating effect of benchmarking the workspace usage on portfolio optimization and client satisfaction.FindingsThe structural model results obtained through the bootstrapping technique show that benchmarking workspace usage for real estate management positively impacts client satisfaction in the Indian IT workspace. The findings of this study support the full mediation effect (97%) and indicate that benchmarking practices are necessary for developing strategies for optimal portfolio asset utilization and are essential to survive in the current competitive business environment.Research limitations/implicationsThe findings of this study were influenced by the feedback from the top 100 IT clients in India. The research findings vary according to the cost-benefit analysis of adopting benchmarking measures in small and medium-sized IT companies which still benchmark the workspace usage based on cost-saving measures. Also, very sparse research has been conducted in the workspace management domain of IT firms, so the results of this study can further be used as a reference to explore this area.Practical implicationsThe study provides useful insights into how benchmarking in the workspace management domain of the CRE industry can be applied to address portfolio-related challenges, divergent client needs and improve workspace usability following energy-efficient policies. Practitioners can use this study as a guide to develop more effective workspace management policies.Social implicationsThis study may guide other firms to benchmark their current workspace usage and evaluate the impact of their workspace management policies based on the theoretical framework of value-added balanced benchmarking criteria.Originality/valueThis research adds value to the limited literature available on the impact of technology-enabled portfolio optimization techniques through benchmarking which can reduce workspace usage and enhance the usability of the workspace.