Portfolio Risk Research Articles

Optimal Portfolio Analysis of Listed Companies in IDX 30

Investors face the dual considerations of return and risk when making investment decisions. Therefore, proper analysis is crucial, especially during the COVID-19 crisis, to achieve maximum returns while minimizing risk. This research used three portfolio optimization models, the Mean-Variance Model, the Mean-Absolute Deviation Model and the Value-at-Risk Model, to construct a stock portfolio. The findings indicated that the Mean-Variance Model can yield an expected return of 16.55% and a portfolio risk of 258.66%. The result from the Mean-Absolute Deviation Model was that the target return is 16%, along with a portfolio risk of 282.43%. Keywords: Portfolio Optimization, Mean-Variance, Mean Absolute Deviation, Value at Risk, R Language, IDX30

Portfolio risk and return between energy and non-energy stocks

This paper aims to examine the potential for portfolio returns by adding together conventional and energy stocks with varying proportions. We examine the risk and return characteristics of a portfolio comprising energy and non-energy stocks from twenty countries. The period for daily data ranges from 2nd July 1999 to 2nd July 2021. We use multiscale Sharpe and VaR ratios to examine the risk and returns behaviour of a portfolio with varying composition between energy and non-energy stocks across different investment periods. Our results highlight optimal returns for the equally weighted portfolio during normal and crisis periods except COVID-19 during which more proportion of conventional stocks is preferred. Risk estimates advocate an equally weighted portfolio for all periods however risk varies with the holding period. These results carry useful investment implications during short- and long-run holdings of conventional and energy stocks in a portfolio.

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

Risk inherence jeopardises Information System (IS) and Information Technology (IT) Project Portfolio Management (PPM) to realise thestrategic objectives. Previous studies have mainly provided Artificial Intelligence (AI) and statistical models to predict the overall riskof IS/IT project portfolio, whereas neuro-fuzzy models were rarely used. This paper proposes a Sugeno Adaptive Neuro-Fuzzy InferenceSystem (ANFIS) model based on Fuzzy Factor Analysis (FFA) named ANFIS-OPR to predict the overall risk of IS/IT project portfolio fromhistorical IS/IT project risk data. The ANFIS-OPR inputs are the relevant factor loadings resulting from the FFA application on the IS/IT projects risks set to cope with the curse of dimensionality. Then, the Sugeno ANFIS model is adopted to give strategic interpretability to the predicted IS/IT project portfolio overall risk by implementing the IS/IT Project Management Office (PMO) expert knowledge, represented by fuzzy rules, on the relationship between IS/IT project portfolio strategic alignment and the IS/IT projects risks. The ANFISOPR outputs are the predicted Overall Portfolio Risk (OPR) and Root Mean Square Error (RMSE). The paper also presents an IS/IT PMO case study that shows the proposed ANFIS-OPR efficacy, which predicted the OPR values closely to the OPR estimates with an accepted RMSE of 0.108. The proposed ANFIS-OPR is a novel intelligent decision-making tool that enables the IS/IT PMO to monitor the OPR, considering its linkage with strategic alignment; thus, contingency plans can be carried out appropriately while ensuring that the IS/IT project portfolio is strategically aligned.

Green Assets and Global Portfolio Tail Risk? A Stress-Testing exercise under multiple asset classes under distinct market phases

Despite a burgeoning literature in the sphere of cryptocurrencies and green assets, yet, as of date, the literature fares poorly in terms of a holistic assessment of all asset classes, let alone stress testing such global portfolio risk under various market conditions. Our paper fulfills such a gap in the literature. Findings reveal that, irrespective of bearish or bullish market phases, green assets should be incorporated to mute down portfolio tail risk. Robustness tests performed either via different distribution type or CVaR analysis do not materially alter the main findings of our paper. The confluence of two forces substantially boosts tail risk, namely, passive investment strategy coupled with a crypto-augmented base model. Overall, our paper advocates the inclusion of green assets not as a choice but as an obligation to portfolio managers in view of curtailing both VaR and CVaR risk levels, all geared towards hitting two birds with one stone-simultaneously buttressing a greener world while effectively mitigating global portfolio tail risk.

Penentuan Portofolio Saham Optimal Menggunakan Metode Markowitz Sebagai Dasar Keputusan Investasi

A stock portfolio is a combination of two or more securities invested over a specific period and under certain conditions. This study analyzes the combination of stocks that can be formed into an optimal portfolio using the Markowitz method. The Markowitz method is employed to maximize returns and minimize the risks associated with a portfolio. This method uses a mathematical formulation that allows for adjustments based on risk tolerance levels and expected returns to achieve an optimal portfolio. The data used in this study comprises the monthly closing prices of stocks from five selected companies, namely ICBP, BBCA, TLKM, BBNI, and INCO, for the period from June 2019 to December 2022. The findings indicate a recommended portfolio with the lowest risk, known as the Minimum Variance Portfolio (MVP). The MVP comprises the following proportions: ICBP 36.10%, BBCA 36.28%, TLKM 17.84%, INCO 8.39%, and BBNI 1.39%. This combination of stock proportions yields an expected return of 8.58% and a portfolio risk of 21.52%.Keywords: Markowitz method, Minimum Variance Portfolio, Portfolio Optimization.

Operational efficiency in the presence of undesirable byproducts: an analysis of Indian banking sector under traditional and market-based banking framework

PurposeIn the backdrop of an increase in market-based banking activities, this paper aims to study operational efficiency of Indian banking sector during 2009–2010 through 2017–2018 considering Capital Gain and Gain from Forex Market (as desirable outputs) and Slippage (as undesirable byproducts) simultaneously, along with Advances – a desirable output considered in the traditional banking performance assessment literature. This enables to have an assessment of performance (as captured by the measured efficiency scores) of Indian Banks following an alternative viewpoint about the banking activities. The authors also explain such efficiency scores in terms of bank-specific factors, banking industry competition scenario and interest rate channel.Design/methodology/approachUsing data envelopment analysis (DEA) method, the authors estimate six alternatives but interlinked operational efficiency scores (TES) of the Indian domestic commercial banks. In the second stage, they explain such TES in terms of bank-specific factors, banking industry competition scenario and interest rate channel.FindingsThe authors observe that the private sector banks as a group outperform those under public ownership. Moreover, although the private sector banks could maintain somewhat consistency in their operational efficiency performance over the sample period, public sector banks clearly show a declining tendency. The second stage econometric estimation results show that the priority sector lending has a negative effect on efficiency. Interestingly, the authors get varying results for the relationship between maturity and efficiency score depending on banks’ strategies on stressed assets management. Furthermore, the analyses result that banks are not so efficient in managing relatively larger-volume loans. It is also observed that banks’ efficiency positively depends on the Credit-to-Deposit (CD) ratio. It is found that the overall operational efficiency of the banks to manage their credit risk portfolio improves with a reduction in the lending rate (LR). However, the interaction of lending activities and capital market shows that with the increase in LR, corporate borrowers may switch to capital market to explore for desired funds, which may induce the banking sector to investment in capital markets and create a positive market sentiment.Originality/valueLiterature, although scanty, is there dealing stressed assets of a bank as some undesirable byproducts of its operational and business activities. However, such literature mostly done within the traditional framework of banking business activities and modern market-based business activities are almost absent in the literature. The authors have done it in the present study.

Do ESG ratings inhibit corporate leverage manipulation? The moderating effects of internal and external supervision

Purpose This paper aims to investigate the relationship between corporate environmental, social and governance (ESG) ratings and leverage manipulation and the moderating effects of internal and external supervision. Design/methodology/approach The authors draw on a sample of Chinese non-financial A-share-listed firms from 2013 to 2020 to explore the effect of ESG ratings on leverage manipulation. Robustness and endogeneity tests confirm the validity of the regression results. Findings ESG ratings inhibit leverage manipulation by improving social reputation, information transparency and financing constraints. This effect is weakened by internal supervision, captured by the ratio of institutional investor ownership, and strengthened by external supervision, captured by the level of marketization. The effect is stronger in non-state-owned firms and firms in non-polluting industries. The governance dimension of ESG exhibits the strongest effect, with comprehensive environmental governance ratings and social governance ratings also suppressing leverage manipulation. Practical implications Firms should strive to cultivate environmental awareness, fulfil their social responsibilities and enhance internal governance, which may help to strengthen the firm’s sustainability orientation, mitigate opportunistic behaviours and ultimately contribute to high-quality firm development. The top managers of firms should exercise self-restraint and take the initiative to reduce leverage manipulation by establishing an appropriate governance structure and sustainable business operation system that incorporate environmental and social governance in addition to general governance. Social implications Policymakers and regulators should formulate unified guidelines with comprehensive criteria to improve the scope and quality of ESG information disclosure and provide specific guidance on ESG practice for firms. Investors should incorporate ESG ratings into their investment decision framework to lower their portfolio risk. Originality/value This study contributes to the literature in four ways. Firstly, to the best of the authors’ knowledge, it is among the first to show that high ESG ratings may mitigate firms’ opportunistic behaviours. Secondly, it identifies the governance factor of leverage manipulation from the perspective of firms’ subjective sustainability orientation. Thirdly, it demonstrates that the relationship between ESG ratings and leverage manipulation varies with the level of internal and external supervision. Finally, it highlights the importance of governance in guaranteeing the other two dimensions’ roles by decomposing overall ESG.

The Effect of Sectoral Diversification on Return and Risk in Portfolio Management: An Application in Borsa Istanbul

This paper investigates sectoral diversification within Borsa Istanbul, aiming to elucidate its impact on portfolio risk and return dynamics. Spanning the timeframe from 2020 to 2022, the study meticulously analyzes stocks from pivotal sectors, including banking, energy, and iron and steel. Employing a robust methodology, the research harnesses Monte Carlo simulations to generate many hypothetical portfolios, subsequently evaluating them on the Efficient Frontier to identify optimal risk-return trade-offs. Key performance metrics, such as the Sharpe Ratio, Sortino Ratio, and Maximum Drawdown, further enrich the analysis, providing a granular view of portfolio behaviors. The significance of this study lies in its bridging of theoretical constructs of diversification with the tangible realities of an emerging market like Borsa Istanbul. Our main findings underscore the potential benefits of sectoral diversification while highlighting the complexities inherent in portfolio construction. The insights gleaned offer valuable guidance for investors, emphasizing the delicate balance between risk mitigation and return optimization in a diversified portfolio.

Enterprise risk management and corporate tax planning

AbstractThis study examines the impact of enterprise risk management (ERM) programs on corporate tax planning. ERM is a holistic approach to managing an enterprise's entire portfolio of risks. We hand‐collect data on ERM adoption for a sample of Standard & Poor's 500 firms from 1993 to 2016. We empirically document that firms with ERM programs have lower cash effective tax rates than firms without ERM. Additionally, we find that the relation between ERM and tax avoidance is stronger among firms with more business segments. Finally, our results suggest ERM adoption offsets an increase in opacity and tax uncertainty typically associated with tax avoidance strategies. Overall, we provide evidence that ERM allows firms to exploit tax avoidance opportunities through enhanced coordination and communication.

Optimal Portfolio Using Roy’s Safety-First Method on Primary Consumer Goods Sector Stocks

Before carrying out investment activities, investors need to form an optimal investment portfolio. This study aims to form an optimal portfolio in primary consumer goods sector stocks that sell the basic needs of the community so that stocks in the sector tend to be stable. The method used in forming the optimal portfolio is Roy's Safety-first method. The portfolio formed produces 6 combinations of stocks consisting of WIIM, DSNG, MRAT, CAMP, SIMP, and MBTO stocks respectively with a proportion of funds of 44.05%, 16.38%, 18.61%, 15.06%, 4.32%, and 1.59% with an expected return portfolio of 3.10% and a portfolio risk of 1.65%.

Investment modeling between energy futures and responsible investment

This research examines the hedging and diversification ability of energy futures (crude oil, heating oil, natural gas, gasoline, carbon) concerning investment in four different segments of responsible investment. Wavelet coherence and DCC-GARCH are used to inspect the association and volatility transmission between them. Conditional correlation, optimum portfolio weights and hedge ratios are also calculated for real-time data and decomposed series to analyze the optimum portfolio structure. Our analysis reveals that energy futures are more efficient as a hedge to responsible investment during the short-run period. Natural gas is the perfect hedge against all the segments of responsible investment. Each segment of energy futures can hedge investments in sustainability stocks and green bonds. Carbon and Gasoline are not the perfect hedge for ESGL and GCEI but can be used as a cheap hedge. Our study will help responsible investors to hedge their portfolio risk using related assets, countering their familiarity bias.

Optimising Portfolio Risk by Involving Crypto Assets in a Volatile Macroeconomic Environment

Optimising Portfolio Risk by Involving Crypto Assets in a Volatile Macroeconomic Environment

Research on the Optimal Strategy of Investment Portfolio Based on Markowitz Model

Nowadays, investment is becoming increasingly common. Under the globalization of the economy, investors are given more investment opportunities and choices. Investors need to select excellent assets and allocate the selected asset portfolio on weight during the investment process. The mean-variance model proposed by Markowitz plays an important guiding role in investment and risk management. This model can effectively evaluate investors portfolio risk and return decisions and significantly impact their decision-making choices. This study uses the data of the annual average return and variance from 11 risky assets and 1 risk-free asset in about 20 years to construct an investment portfolio in a fixed group based on the Markowitz Model. The study calculates the changes in the allocation of the maximum Sharpe ratio and the minimum variance portfolios under three different constraints and one condition with the addition of risk-free assets, respectively, and then analyzes changes in the outcome of data. The capital allocation lines are introduced to analyze the outcome of an investment portfolio with risk-free assets. Compared with the changes from three constraints, reasons are explained why these changes happen under three constraints. Then, the above limitations are taken as a premise, and the study proposes recommendations for the optimal investment portfolio selection for different investors according to the investment cycle, the preference of investors based on the situation of different investors, and market constraints. The study guides investors future investment decisions.

Diversifying and hedging REIT portfolios with cryptocurrencies: Evidence from global and regional REIT indices

Hedging the financial risk of portfolios of securitized real estate assets is daunting because of the unique nature of the underlying assets and because no direct market exists to trade on property-related derivatives. In this paper, we conduct a global study of the diversification and risk mitigation benefits of cryptocurrencies for REIT stocks. Specifically, we examine the dynamic relationship between Bitcoin and REIT indices obtained from multiple countries and regions of the world. We also explore whether portfolios of REIT stocks that are hedged with Bitcoin futures obtain significant improvement in portfolio risk mitigation and return performance. Furthermore, given that Bitcoin and REIT stocks are prone to large volatility swings, we examine the nature of the tail dependences in the multivariate distributions of our REIT indices universe and Bitcoin using Archimedean copula functions. Lastly, we compare the market risk of Bitcoin-hedged REIT portfolios to the unhedged portfolios of REIT stocks using value-at-risk. We find region-specific benefits from using Bitcoin to hedge REIT exposure. Furthermore, we show that using Bitcoin to dynamically hedged REIT portfolios had minimal impact on the bivariate and multivariate joint distributions of REIT portfolios. We also find evidence of region-specific reductions in the market risk exposure of dynamically hedged REIT portfolios relative to unhedged and naively hedged REIT portfolios. We posit that Bitcoin could improve the mean-variance frontiers of REIT investors in countries with higher market friction and lower global integration.

Debt Derisking

We examine how corporate bond fund managers manipulate portfolio risk in response to incentives. We find that liquidity risk concerns drive the allocation decisions of underperforming funds, whereas tournament incentives are of secondary importance. This leads laggard fund managers to trade off yield for liquidity while holding the exposure to other sources of risk constant. The documented derisking is stronger for managers with shorter tenure and is reinforced by a more concave flow to performance sensitivity and by periods of market stress. Derisking meaningfully supports ex post laggard fund returns. Flexible net asset values (swing pricing) may, however, reduce derisking incentives and create moral hazard. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03406 .

Can gold-backed cryptocurrencies have dynamic hedging and safe-haven abilities against DeFi and NFT assets?

Given that the interconnections of NFT and DeFi digital assets with other stablecoins still not sufficiently studied, this paper is two-fold. We first examine the dynamic conditional correlation between gold-backed cryptocurrencies and NFTs and DeFi assets during the period 02/11/2021-05/01/2023. We thereafter assess the diversification potential of gold-backed cryptocurrencies against NFTand DeFi. To this end, we use the time-varying Student's copula to investigate the cross-markets linkages among different assets in dynamic fashion. We afterwards compute the optimal hedging ratios and effectiveness index to better explore the effectiveness of portfolio risk management. Our findings clearly show that the degree of dependence between gold-backed cryptocurrencies and NFT and DeFi tokens tends to vary over time. Our results also display that gold-backed cryptocurrencies act as suitable hedging (or diversifying) assets during normal times. Nevertheless, such assets can be considered as robust safe havens during the 2022 bear market for the most of NFT and DeFi assets. More specifically, PAXG and PMGT are found to be the best safe haven instruments for both NFT and DeFi tokens. DGX also serves as safe-haven assets for some NFT and DeFi assets, but with a lower risk-mitigation capacity compared to PAXG and PMGT. In most cases, DGX tends to act as a strong diversifier. Its hedging feature is only recorded for the NFT Protocol (xNFT) and the DeFi token Chainlink (LINK). Our results are of particular interest to investors and portfolio managers who search for safe havens to mitigate the risk of their NFT and DeFi portfolios.

Evaluation of the performance of Spanish family businesses portfolios

PurposeThe primary objective of this study is to analyze the performance and risk characteristics of portfolios composed of Spanish family businesses (FBs) when sustainability and quality factors are taken into account. By comparing different portfolio compositions against a benchmark, the study aims to provide insights into the impact of these factors on portfolio performance.Design/methodology/approachThis study employs an empirical approach to evaluate the performance and risk of portfolios consisting of Spanish family businesses (FBs) by incorporating sustainability and quality factors. It compares the results of various portfolios against a benchmark, utilizing GARCH models and the extended six-factor model of Fama and French for the period 2018–2023.FindingsThe findings reveal that investing in Spanish family businesses (FBs) yields higher returns compared to the index, with portfolios incorporating quality factors demonstrating superior performance. However, the inclusion of sustainability factors negatively affects portfolio performance. These results highlight the significance of considering sustainability and quality factors in portfolio construction and investment decisions.Originality/valueThis study contributes to the existing literature by examining the performance and risk implications of incorporating sustainability and quality factors into portfolios of family businesses. The findings offer valuable insights for investors and managers interested in constructing portfolios or developing financial products that balance risk and return effectively.

The dynamic connectedness between collateralized loan obligations and major asset classes: a TVP-VAR approach and portfolio hedging strategies for investors

Motivated by the increasing demand for alternative assets that can contribute to reducing portfolio risk, this paper examines the volatility spillovers between collateralized loan obligations (CLOs) and various in-demand investment instruments, including equities, bonds, crude oil, commodities, gold, bitcoin, shipping and real estate. The applied methodology comprehends the time-varying parameter vector autoregressive (TVP-VAR) modification of the classical spillover approach, for the period from January 1, 2012, to August 31, 2023. The empirical findings show moderate levels of dynamic connectedness; albeit several external shocks strengthened the interconnection among the assets. Moreover, we compare the ability of CLOs for hedging, during the overall sample period and multiple subperiods, by estimating hedge ratios and optimal portfolio weights, in order to inform investors about feasible portfolio adjustments. Our results indicate that CLOs constitute an effective hedging tool, irrespective of the period covered, as the short position in their volatility provides high hedging effectiveness for investors holding long positions in the volatility of all the remaining assets.

Investment Portfolio Management System Using Machine Learning

Abstract: Portfolio management is the concept of determining the proportions of various assets to be held in a portfolio in order to maximize return while minimizing risk exposure. Investment banking and financial management both depend heavily on portfolio optimization. Choosing the greatest feasible combinations of several portfolios to construct an optimal portfolio is an exponentially complex challenge in terms of computation. It's commonly believed that public opinion and financial markets are intertwined. Recently, a variety of machine learning algorithms have been employed to anticipate short-term financial markets with positive outcomes. On the other hand, historical returns don't seem to fit the normal distribution theory. But sentiment analysis performs better when combined with long short-term memory networks and historical data. In this project, we want to use AI/ML to predict portfolio risk and provide insights into how stocks will perform. We will train our model using datasets obtained from the Yahoo Financial API that include historical data from the top 100 companies (NIFTY 100) in the NSE and BSE from 2010 to 2021.

Element Aggregation for Estimation of High-Dimensional Covariance Matrices

This study addresses the challenge of estimating high-dimensional covariance matrices in financial markets, where traditional sparsity assumptions often fail due to the interdependence of stock returns across sectors. We present an innovative element-aggregation method that aggregates matrix entries to estimate covariance matrices. This method is designed to be applicable to both sparse and non-sparse matrices, transcending the limitations of sparsity-based approaches. The computational simplicity of the method’s implementation ensures low complexity, making it a practical tool for real-world applications. Theoretical analysis then confirms the method’s consistency and effectiveness with its convergence rate in specific scenarios. Additionally, numerical experiments validate the method’s superior algorithmic performance compared to conventional methods, as well as the reduction in relative estimation errors. Furthermore, empirical studies in financial portfolio optimization demonstrate the method’s significant risk management benefits, particularly its ability to effectively mitigate portfolio risk even with limited sample sizes.