Equity Portfolios Research Articles

A Study on Optimal Equity Portfolio in the Indian Capital Market

This study evaluates optimal equity portfolio construction in the Indian capital market by applying the Sharpe Single Index Model. Data from 20 companies in the NIFTY 50 index are used to analyze risk-return relationships and identify companies for portfolio inclusion. The study concludes that constructing an optimal portfolio with selected stocks, such as Hero MotoCorp and Bharat Petroleum, based on their risk-adjusted returns, offers a strategy for maximizing returns with controlled risk

Gender difference in equity portfolio diversification

This study provides compelling evidence that individual investors consistently maintain under-diversified portfolios, with female investors exhibiting significantly lower levels of diversification than their male counterparts. Analyzing a comprehensive dataset of retail investor trades, this study provides the first detailed examination of gender differences in trading activity, performance, and portfolio diversification. Performance, and diversification are systematically related to investor characteristics such as portfolio size, income, education, language, age, and investment experience. The result that increasing diversification improves performance for all investors corresponds with the fact that female investors are found to diversify less at the same time as they show a slightly lower aggregated performance than male investors.

Tax Morale, Governance Quality And Tax Compliance

This study aims to investigate the relationship between tax morale, governance quality, and tax compliance in Nigeria. The study used the library research approach, which involved examining books, academic journals, and conference papers. According to the evaluation, tax compliance in Nigeria remains below standard, resulting in low tax morale, which is influenced by people's perceptions of government services, a lack of justice, and trust. The study also discovers that tax morale has a significant impact on tax noncompliance, such as a decrease in foreign portfolio equity investment inflows for tax avoidance, self-employed tax payers, and perceptions of higher tax fines and difficulty evading taxes. The study also discovers a favourable link between government quality (excellent governance, views of government accountability and provisions) and tax compliance. Studies have shown a considerable association between tax morale, governance quality, and tax compliance. The study recommends that government put in place efficient and effective tax collection policies, through providing people with accessible ways of paying their taxes, simplifying the process and reduction of taxes imposed on lower income individuals.

The capital-on-capital cost in solvency II risk margin

AbstractThis work contributes to the literature on time consistent valuation of insurance liabilities and to the ongoing discussion on revisions of risk margin (RM) calculation, by formally defining the concept of capital-on-capital cost. We describe the capital-on-capital as the amount required to cover unexpected variations in future regulatory capitals from the current time to liabilities maturity. That is, the capital-on-capital cost is the RM component dedicated to cover the risk of future RMs and not to cover variations of the best estimate of liabilities cash-flows. We mathematically formalize the capital-on-capital cost as the difference between two alternative time consistent liabilities valuation formula. The first is obtained through backward iteration of the one-period market-consistent valuation operator, by iterating the solvency capital requirement (SCR) risk measure. We propose a second alternative valuation formula for liabilities, based on a new time consistent dynamic formulation of the SCR risk measure, called additive-SCR (ASCR). The ASCR represents the expected total capital requirement from current time to liabilities maturity. We prove that the second valuation formula, based on ASCR, is time consistent, unless it is not based on iteration of the one-period SCR risk measure. Finally, we apply the proposed approach to a portfolio of long term equity linked life-insurance contracts. In particular, we estimate the capital-on-capital cost by calculating the difference between the two valuation formulas. Numerical results show that for liabilities with long maturities the capital-on-capital cost is a non negligible component of the RM.

Non‐causal and non‐invertible ARMA models: Identification, estimation and application in equity portfolios

The mixed causal‐non‐causal invertible‐non‐invertible autoregressive moving‐average (MARMA) models have the advantage of incorporating roots inside the unit circle, thus adjusting the dynamics of financial returns that depend on future expectations. This article introduces new techniques for estimating, identifying and simulating MARMA models. Although the estimation of the parameters is done using second‐order moments, the identification relies on the existence of high‐order dynamics, captured in the high‐order spectral densities and the correlation of the squared residuals. A comprehensive Monte Carlo study demonstrated the robust performance of our estimation and identification methods. We propose an empirical application to 24 portfolios from emerging markets based on the factors: size, book‐to‐market, profitability, investment and momentum. All portfolios exhibited forward‐looking behavior, showing significant non‐causal and non‐invertible dynamics. Moreover, we found the residuals to be uncorrelated and independent, with no trace of conditional volatility.

A Study of Hierarchical Risk Parity in Portfolio Construction

<p style="text-align: justify;"><span style="font-family: 'times new roman', times, serif; font-size: 14pt;">The construction and optimization of a portfolio is a complex process that has been a historically active research area in finance. For portfolios with highly correlated assets, the performance of traditional risk-based asset allocations methods such as the mean-variance (MV) model is limited when numerous assets are correlated. A novel clustering-based asset allocation method, called Hierarchical Risk Parity (HRP), provides an opportunity to mitigate these limitations in portfolio construction. HRP utilizes the hierarchical relationships between the covariance of assets in a portfolio to determine weight distributions, eliminating the need for the inversion of the covariance matrix that is required by most traditional risk-based asset allocation methods. This research examines the viability of Hierarchical Risk Parity (HRP) method in portfolio construction of a US equity portfolio and compares the performances of HRP to traditional asset allocation methods exemplified by the mean-variance (MV) method. The results of this research show that the performance of the HRP method is comparable to the performance of the MV method. Given these findings, HRP provides an advantageous approach for portfolio construction in practical scenarios where correlated assets are present in the portfolios.</span></p>

Explainable machine learning for financial risk management: two practical use cases

We explore the potential of machine learning (ML) models applied in two financial risk management areas, i.e., credit risk management and financial risk hedging, through two practical use cases. This comparative study starts with the issue of explainability in complex ML models used in peer-to-peer lending for credit risk management. The first use case examines the limitations of using Kernel-SHAP with dependent features and evaluates different methods for estimating these dependencies using the Lending Club dataset. Our results suggest that accounting for feature dependence improves the understanding and robustness of prediction explanations. The second use case investigates a dynamic method for hedging foreign exchange risk in international equity portfolios, emphasizing the importance of accurate forecasts of currency returns. The analysis demonstrates that predictions yielded by ML models can significantly enhance the hedging of portfolios against currency risk. These findings highlight the transformative potential of advanced ML models in financial risk management, showcasing their capability to improve financial risk measurement and management. Further, our study outlines future research directions to advance this field.

A guide to 130/30 loss harvesting

We analyze the effect of leverage on tax-managed public equity strategies for different types of investors by running back-tests on hypothetical portfolios. We found the average 130/30 long/short portfolio funded with cash generates 2.7 times more capital losses than the average long-only portfolio over the first 10 years. For an investor with a sufficient supply of short- and long-term gains, these additional losses translate to average pre-liquidation tax alpha (“TA”) of 4.41% per year. Liquidation reduces TA by about half, while a lack of short-term capital gains to offset reduces TA by approximately 40%. In combination, those investor characteristics may render a long-only loss harvesting portfolio or a low-cost ETF more desirable than a long/short tax-managed equity portfolio after costs and fees are taken into consideration. Leverage can also be used to revitalize an ossified portfolio whose loss-generating capacity has diminished.

The performance of private equity portfolio companies during the COVID-19 pandemic

We study the performance of PE-backed companies during the COVID-19 pandemic. Our findings suggest that, on average, PE-backed firms were more resilient compared to closely matched industry peers during the pandemic. However, this outperformance is of a smaller magnitude than during the pre-pandemic non-crisis period, suggesting that the outperformance is driven by investor selection of target firms ex ante, rather than active support mechanisms. The outperformance during the pandemic is found to be insignificant among firms which were the most vulnerable at the onset of the pandemic, and firms in the most exposed industries. These more vulnerable firms appear to have been less active in obtaining additional financing during the pandemic, and consequently, suffered a significantly higher incidence of distress. However, non-PE-backed firms in distress had a higher incidence of liquidation, while PE-owned firms more often negotiated formally with creditors to continue trading. Our analysis shines light on the role of PE investors during a large, exogenous shock, and suggests that, in the case of the pandemic, their adept target selection may help to explain the outperformance more so than their actions to protect vulnerable firms in a crisis.

A Heuristic for Fat-Tailed Stock Market Returns

Large negative stock and equity portfolio rates of return occur more frequently than they should under the Gaussian normal distribution. They tend to be kurtotic, roughly as if they were drawn from Student T-distributions with 2 to 5 degrees of freedom. A very easy adjustment to help assess the probability of losses is to work with a transformed Z score, Z ′ = − 1.25 − log ( − Z ) . For example, one should expect a stock return that is 17 standard deviations below the mean under the empirical distribution as often as one would expect to see a draw that is Z ′ = | – 1.25 − log ( 17 ) ≈ − 4 | standard deviations below the mean under the idealized normal distribution.

Practical Applications of Decomposition of Greenhouse Gas Emissions Associated with an Equity Portfolio

Practical Applications of Decomposition of Greenhouse Gas Emissions Associated with an Equity Portfolio

Examining time–frequency quantile dependence between green bond and green equity markets

In the context of the rapidly growing demand for green investments and the need to combat climate change, this study contributes to the emerging literature on green investments by exploring the time–frequency connectedness between green bonds (GBs) and green equities. Specifically, we examine the degree of connection between GBs and green equities, the extent to which these markets influence each other, and which one is the primary net transmitter versus the net receiver of shocks under diverse market conditions. To accomplish these objectives, we use the wavelet-based Quantile-on-Quantile (QQ), dynamic conditional correlation (DCC), portfolio implications, and Quantile VAR approaches. The results show that GBs and green equities have a strong positive connection, depending on time and frequency domains. However, a negative association between GBs and green equities is observed during periods of crisis, highlighting GBs’ ability to hedge green equity portfolios. The portfolio strategies demonstrate that investors require to invest in the Green Economy equity and S&P GB portfolio to reach the highest level of hedging effectiveness. The findings further imply that the Global Water Equity Index transmits the highest spillover to other green assets, while the Green Economy Equity Index receives the most spillover from other assets. The pairwise volatility connectivity reveals that most pairs have minimal quantile dependence, indicating the potential for diversification across the GB and green equity pairs. These findings have significant implications for investors and policymakers concerned with green investments and climate change mitigation.

Hedging global currency risk: A dynamic machine learning approach

We propose a dynamic method to hedge foreign exchange risk of international equity portfolios. The method is based on the currency return forecasts derived from a set of alternative machine learning models, built on the main factor components of the currency return time series. To illustrate our method, we take the perspective of a US portfolio manager investing in a global equity portfolio of developing and developed economies. The analysis of several model performance indicators, back-tested on data from 27 October 2008 to 30 December 2022, allows to conclude that accurate predictions of global currency factor returns, such as those obtained with non linear machine learning models, can improve currency risk hedging of global equity portfolios.

Risk exposure in ESG-driven portfolios: A wavelet study within the tail-concerned insurance sector

This paper employs wavelet analysis methodology to examine the pairwise time-frequency connectedness and lead-lag relationships between ESG rating changes and insurance equity portfolios' risk exposure. Focusing on the influence of ESG rating changes rather than absolute levels, it contributes to existing research, shedding light on the nuanced dynamics within the insurance sector. The findings underscore the importance of incorporating ESG considerations into portfolio risk assessments for policymakers and portfolio managers alike. Notably, the study reveals the impact of ESG upgrades on portfolio risk exposure, suggesting potential higher returns but also higher total volatility. Moreover, disaggregate risk (total, downside and upside) analysis uncovers insights into environmental, social, and governance factors, emphasizing the relevance of navigating beyond the total volatility and the overall ESG ratings. These insights inform adaptive risk management strategies aligned with evolving ESG standards, contributing to sustainable and risk-conscious economic and investment decisions within the insurance sector.

Crises and Contagion in Equity Portfolios

We examine the international impact of recent financial crises on contagion dynamics within international equity portfolios. First, we highlight the importance of macroeconomics for portfolio weighting for each region, and then we examine contagion via a structural regime-switching model and a contagion test. We also examine sources of contagion using regime variables, crisis events, and macroeconomic variables. In particular, we study the Argentine debt crisis, the US financial crisis, and the EU sovereign debt crisis. The macroeconomic variables include changes in market capitalization, trade integration, GDP growth, inflation rate, and interest rate. We also employ two classifications, one relating to the portfolio weighting scheme and another one that considers implied global and regional betas. The empirical findings reveal the existence of financial contagion for all the crises that we investigate. Both methods produce similar results. Stronger contagion is evident for global rather than regional betas. Europe is the region with the highest level of contagion and the one mostly affected by the crises. As far as macroeconomic variables are concerned, they are very important in two ways. They statistically significantly explain contagion, while they also reveal contagion under various portfolio weighting schemes. Both methods suggest that the Argentinian crisis mainly contributes to contagion. The research implications suggest that asset allocation and portfolio management should consider both the global and the regional aspects of contagion as differences can occur.

Practical Applications of How Many Is Too Few? Revisiting the Number of Names in Equity Portfolios

Practical Applications of How Many Is Too Few? Revisiting the Number of Names in Equity Portfolios

Embodied CO2 emissions of equity portfolios for Chinese asset managers

The 2015 Paris Agreement has set out the climate change target of limiting global warming to 1.5 °C, which poses a serious challenge to countries to reduce emissions. As the world’s largest carbon emitter, promoting the realization of the “dual-carbon” goal is the key to realizing China’s green transformation and high-quality development. Chinese asset managers play active roles in the capital market as an important channel of asset allocation. Currently, the vast majority of Chinese asset managers hold high percentages of high-carbon industries in their portfolios, and lack quantitative data of their carbon footprints embodied in equity investments, which faces huge carbon-related risks. Therefore, it’s an urgent need to comprehensively and scientifically measure financed emissions of Chinese asset managers, which is of great significance for asset managers’ carbon risk management and sustainable investment. This paper develops a detailed inventory of carbon emissions for equity portfolios managed by Chinese asset managers from 2010 to 2020, which stands as a pivotal reference for in-depth analysis of emission characteristics.

Response of Nigeria’s Stock Market to Portfolio Investment Flux: Asymmetric Evidence

The study investigates the nonlinear effects of foreign portfolio investment on the Nigerian stock market. Quarterly data was sourced from CBN statistical bulletin spanning from first quarter of 2005 to last quarter of 2022. Econometric pretest was conducted. Results from the NARDL model show significant persistence in the All Share Index (ASI), where past values strongly influence current values, aligning with momentum and short-term dynamics studies. Bond Portfolio Foreign Investment (BPFI) has a slightly positive but not robust effect on ASI, serving more as a stabilizer than a driver. Equity Portfolio Foreign Investment (EPFI) lacks an immediate impact but may affect ASI with a delay, illustrating the complex influence of foreign equity on market indices. The analysis of market capitalization (MCAP) reveals that while immediate increases in BPFI boost MCAP, lagged increases may cause reversals, and EPFI’s effects suggest initial destabilization followed by stabilization. These results reflect the transient and sometimes destabilizing effects of foreign capital flows in emerging markets. The study recommends that policymakers develop strategies to manage both short-term and long-term impacts of foreign investments. This could include controlling the pace of capital flows and creating incentives for long-term investments to reduce market volatility and enhance stability.

Unraveling the dynamics: macroeconomic drivers of equity market development in Sub-Saharan Africa

PurposeThe aim of this paper is to contribute to empirical research by identifying the key macroeconomic drivers of equity market development in Sub-Saharan Africa (SSA) and to ascertain if banking sector development complements equity market development in the SSA region.Design/methodology/approachThe study employed the dynamic panel data approach using the pool mean group (PMG). The sample covered is twenty-seven (27) SSA countries between the period 2000 to 2020.FindingsThe result suggests that banking sector development, economic growth, migrant remittance and trade openness are the key drivers of equity market development in the SSA region. The study also revealed that banking sector development complements equity market development in the SSA region.Originality/valueThe use of robust measure in measuring equity market development (i.e. ratio of portfolio equity to gross domestic product) in ascertaining the macroeconomic drivers of equity market development. Likewise, exploring whether banking sector development complements equity market development in the SSA region makes the paper more unique, especially using the ratio of bank credit to bank deposit as a measure banking sector development.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2024-0005

Banking on innovation: Listed and non-listed equity investing, evidence from société générale de Belgique, 1850–1934

Société Générale de Belgique was the world's first universal bank. It pioneered another innovation: investing in non-listed equity. We use hand-collected data to show that the bank earned significant positive risk-adjusted returns from 1850 to 1934. This offset its flat return on the listed equity portfolio and underperforming bond portfolio. Other Belgian universal banks followed this strategy. As such, we argue that this innovation laid the groundwork for other financial institutions to invest in listed and non-listed assets.