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ESG Ratings and Methodology

The interest in ESG investing creates greater demand for ESG data, ratings, rankings, and methodologies (Abhayawansa & Tyagi, 2021). Different ESG ratings, rankings, and methodologies produce significantly different assessments of the ESG performance of companies (Abhayawansa & Tyagi, 2021). A lack of transparency about the data sources, weightings, and methodologies can also make it difficult to ensure the true ESG performance of companies was accounted for when making security selection and portfolio investment decisions (Abhayawansa & Tyagi, 2021).  

Giese et al. (2019) provided a framework for the integration of ESG into benchmarks at various strategic levels, from the top policy benchmark level to the performance benchmark of individual allocations. Giese et al. highlighted the different investment objectives that asset owners might pursue when integrating ESG and how they could reflect these in their choice of ESG benchmarks. The findings of Giese et al. (2019) revealed integrating ESG into benchmarks made sense as a framework to achieve consistency because benchmarks were not only used at different strategic levels but also across all areas of asset management, index-based, factor-based, and active management to define the underlying investable universe and to provide a yardstick for performance.

Henriksson et al. (2019) recommended an approach to integrate ESG issues into portfolios that was based on two premises. The first is that classification of firms as good or bad ESG companies should be performed using ESG items that are material in that industry (Henriksson et al., 2019). The second premise is that it is possible to overcome the sparse voluntary ESG data reported by firms by constructing an ESG good minus bad (GMB) factor and then finding those firms whose returns load significantly on this factor (Henriksson et al., 2019).

Dimson et al. (2020) examined the extent of, and reasons for, disagreement among the leading suppliers of ESG ratings, and found the weightings given to each pillar of an ESG rating varied across agencies. Dimson et al. also reviewed the investment performance of portfolios and of indexes screened for their ESG credentials. The findings of Dimson et al. (2020) indicated ESG ratings, used in isolation, were unlikely to make a material contribution to portfolio returns. Surprisingly, Dimson et al. provided evidence that ESG indexes did not outperform the parent index from which they were derived over the longest available period. While the findings of Dimson et al. indicated FTSE4GOOD and MSCI-ESG, the two indexes that most closely represented what ESG investors did, showed no evidence of underperformance. 

Unlike Dimson et al. (2020), Abhayawansa and Tyagi (2021) examined the causes of differences in the ratings and rankings generated by different agencies. The results of Abhayawansa and Tyagi (2021) indicated the divergences among raters could be attributed to differences in the definitions of ESG constructs, which was a theorization problem, and methodological differences, a commensurability problem. The results also revealed while users of ESG ratings were advised to study the definitions and methodologies before their use. Gidwani (2020) used the CSRHub data set to show that ESG ratings regressed strongly toward the mean. Specifically, Gidwani found these ratings included both data from most commercial ESG ratings firms and another 640 sources, and the observed regression persisted within the ratings data across nine years, for a sample set of more than 8,000 companies. Gidwani also found newly rated companies showed even more reversion than seasoned companies. Elsenhuber and Skenderasi (2020) indicated central banks had introduced ESG factors mainly for their pension fund investments, with the aim of further integrating sustainable investing into their own funds and in foreign exchange reserves portfolios. This is because the strategic asset allocation of the latter tends to be less diverse, and it focuses on the asset classes that does not have a conventional ESG approach.

Bahra and Thukral (2020) conducted a study to understand how different ESG scores were from traditional agency credit ratings. Specifically, Bahra and Thukral aimed to determine (a) whether E, S, and G scores were correlated; (b) whether ESG scores could enhance the investment process; and (c) whether an active, ESG-tilted corporate bond portfolio strategy generated superior performance versus a relevant benchmark that did not explicitly take ESG scores into account. Bahra and Thukral found evidence that ESG scores could be used to enhance portfolio outcomes via lower drawdowns, reduced portfolio volatility, and marginally increased risk-adjusted returns. Employing backtesting, Bahra and Thukral suggested E, S, and G scores were not related to one another and that ESG scores were additive to traditional credit ratings.

Using ESG scores of firms belonging to the MSCI World universe, Alessandrini and Jondeau (2020) measured the impact of score-based exclusion on both otherwise passive investment and smart beta strategies. The results of Alessandrini and Jondeau (2020) revealed exclusion led to improved scores of initially standard portfolios without deterioration of the risk-adjusted performance. Alessandrini and Jondeau found smart beta strategies exhibited a similar pattern, often in a more pronounced way. Moreover, the results of Alessandrini and Jondeau demonstrated exclusion also implied regional and sectoral tilts as well as undesirable risk exposures of the portfolios. Alessandrini and Jondeau showed ESG screening could substantially improve ESG scores for both otherwise passive and smart beta portfolios without reducing risk-adjusted returns.

Chen and Mussalli (2020) categorized the broad types of ESG investing in the market and introduced an ESG investment framework, which resulted in a portfolio that optimally combined the dual objectives of alpha and sustainability outperformance. Chen and Mussalli indicated it was possible for ESG factors to also generate alpha, provided materiality was taken into consideration. Branch et al. (2020) presented six quantitative ESG strategies for building or restructuring portfolios to align with investors’ ethical considerations and financial goals. In exploring these strategies, Branch et al. proposed certain practices and analyzed options for ESG portfolio construction that balanced risk and the ESG preferences of investors. The quantitative methods outlined by Branch et al. (2020) could lower tracking error but might also increase exposure to unwanted stocks or sectors. To mitigate against such exposure, Branch et al. recommended investors tap the expanding set of high-quality ESG-scored company data during portfolio construction.

Henriksson et al. (2019) suggested their approach was particularly suitable for quantitative investment approaches that invested in portfolios with large number of positions and many small active exposures, wherein vendor ESG data could be used in portfolio construction efficiently without the need to employ detailed ESG analyses of many individual firms. With such portfolios, Henriksson et al. argued it would be less about the ESG classification of an individual company than about the aggregate portfolio tilt toward good ESG and away from bad ESG at the portfolio level. Chen and Mussalli (2020) suggested in situations where the asset owner’s sustainability values and alpha generation did not align, a quantitative approach could be used to graph an ESG-efficient frontier.

Dimson et al. (2020) explained why different raters’ appraisals diverged, and whether ESG was associated with subsequent fund or index outperformance. The results of Gidwani (2020) suggested it was rare that a company maintained an especially high or low ESG rating. Conducting cross-sectional correlations, Bahra and Thukral (2020) suggested E, S, and G scores were not related to one another. The results of Bahra and Thukral (2020) also suggested ESG scores were additive to traditional credit ratings.

Anson et al. (2020) identified a sustainable beta factor that was successful in screening both companies and asset managers as green or nongreen, which was an important step in building a factor model for sustainable investing. Alessandrini and Jondeau (2020) suggested starting from initially passive multicounty portfolios, ESG screening might lead to substantial regional tilts, such as overweighting Europe and underweighting the US and emerging countries or sectoral bets, for instance in favor of information technology and against financial and energy stocks.

More recently, Sorensen et al. (2021) focused on the challenges associated with ESG investing and how quantitative approaches may address them. Sorensen et al. found as compared to fundamental methods of sustainable investing, quantitative methods had several advantages. Quantitative methods to ESG investing can build on and extend the vast analytical toolbox of modern portfolio theory to incorporate investor preference in portfolio construction (Sorensen et al., 2021). Sorensen et al. also found these quantitative methods could leverage the recent data explosion to obtain insights on many intangible sustainability metrics, and they did not have the black box label. 

Two key challenges hold many asset owners and managers back from applying ESG to investment portfolio management: (a) confusion over the differences among the vast array of sustainable and impact investing disciplines; and (b) lack of clarity on whether and how investors who serve in a fiduciary capacity can incorporate these disciplines (Hays & McCabe, 2021). Hays and McCabe (2021) introduced a taxonomy of sustainable and impact investing approaches, mapped to a set of guidelines for fiduciaries to consider in practice. Hays and McCabe indicated this framework, based on a mix of market, legal, academic, and internal risk/return research, could provide investors with guidance on applicability by ESG approach by account type, ranging from investment management accounts, both nondiscretionary and discretionary, to revocable and irrevocable trusts, to ERISA accounts. Hays and McCabe also indicated sustainable and impact investing could be split into four distinct approaches: (a) ESG integration; (b) ESG mandated; (c) thematic; and (d) high impact concessionary.

Atta-Darkua et al. (2021) used a responsible investing debate to critique two methods of responsible investment, negative screening and engagement. Atta-Darkua et al. illustrated the importance of selecting an ESG score provider by examining the differences in metrics among different providers. Focusing on the process for constructing portfolios that factored ESG principles into a strictly return-oriented model, Chen and Mussalli (2021) developed an approach based on three pillars: (a) ESG factors that might also be alpha factors; (b) a unique materiality value that linked ESG considerations to alpha; and (c) a portfolio construction framework that was informed by an investor’s ESG preferences. Chen and Mussalli indicated the key strengths of this integrated ESG modeling framework included its flexibility, relevancy, and dynamic nature.

Summary and Conclusion

There has been a wide range of research in academia and the asset management industry about the financial benefits of ESG investing. However, the equally important question about how to achieve consistency when integrating ESG and what methodologies to use has not received the same level of attention (Giese et al., 2019). As a result, ESG integration is often applied inconsistently and incompletely across portfolios (Giese et al., 2019). 

Elsenhuber and Skenderasi (2020) highlighted the most critical challenges were the lack of a commonly adopted ESG taxonomy, and the limitations on the application of various ESG approaches in some of the portfolios they managed. Dimson et al. (2020) argued data were essential for making investment decisions, and most institutions relied wholly or partly on external providers of ESG data; however, minimal correlation existed between ESG ratings from alternative agencies. Hays and McCabe (2021) argued despite significant growth in interest and inflows over the past three years, sustainable and impact investing had reached an inflection point where the industry is being held back by a lack of clarity on definitions and fiduciary applicability. 

The results of Sorensen et al. (2021) suggested quantitative methods had unique advantages for sustainable investing in the areas of portfolio construction, data application, and scaling domain knowledge. Sorensen et al. also suggested the skillful quantitative practitioner could create the optimal blend of human insight and computing power to extract sustainability insights from data. Sorensen et al. (2021) argued a thoughtful analytical system could be applied to a large universe of stocks, and quantitative methods might also be leveraged to predict popular ESG vendor ratings. Sorensen et al. highlighted subjective judgement applied to building the quantitative system was essential.

Giese et al. (2019) argued ESG ratings might be suitable for integration into policy benchmarks and financial analyses. Henriksson et al. (2019) provided evidence that showed the superiority of using material, industry specific ESG items, and the merits of expanding the ESG classification using the ESG GMB loadings. Gidwani (2020) argued ESG ratings exhibited behavior that might make them difficult to use in an investment process. Gidwani also argued ESG-based investment strategies that sought to buy the best and sell the worst might not perform as well as expected. Based on the results, Bahra and Thukral (2020) argued the contingent liabilities related to ESG issues were not necessarily factored into rating agencies’ assigned credit ratings. Chen and Mussalli (2020) argued standard methods of materiality definition, based on sectors, could be limiting and were not the optimal axis to measure materiality. Chen and Mussalli also argued ESG investing was based on investor’s sustainability values, which must be incorporated as part of ESG portfolio construction. Alessandrini and Jondeau (2020) argued although the broad conclusion of improved ESG profile without affecting risk-adjusted performance also held for smart beta portfolios, aggressive exclusion of ESG low-scoring firms might lead to some reduction in exposure to targeted factors. Employing backtesting, Bahra and Thukral (2020) suggested E, S, and G scores were not related to one another and that ESG scores were additive to traditional credit ratings. Madhavan and Sobczyk (2020) provided evidence the composition of ESG scores mattered, with environmental score most closely related to fund volatility. Hays and McCabe (2021) offered a framework to align different types of ESG investments with various investment and fiduciary mandates. 

Previous researchers that have make attempts to investigate ESG ratings and methodologies have made practical implications. Gidwani (2020) proposed investors and company managers both realize that ESG ratings were likely to change toward the mean and that this pattern did not necessarily mean that a good company was getting worse or a bad one is getting better. Gidwani further proposed both investors and corporate managers adjust their understanding of the significance of ESG ratings and their expectations about how they changed. Acknowledging the challenges faced by investors who want to do well by doing good, Branch et al. (2020) stressed the need for investors to clearly understand their goals and constraints, as well as the complexities intrinsic to trade-offs between risk control and exposure to unwanted securities. Hays and McCabe (2021) proposed a strong fiduciary framework with limits for the application within each approach be guided by a focus on rigorous risk-adjusted return analysis, clear documentation, and checks and balances on implementation and ongoing monitoring. Chen and Mussalli (2021) proposed a novel quantitative framework for optimizing both alpha and the ESG aspects of a portfolio. Abhayawansa and Tyagi (2021) argued instead of attempting to compare and contrast ratings and rankings of different agencies, investors should determine the ESG constructs that were material to their own investment strategies, and then matched them with an ESG rating or ranking product that closely resembled those constructs. 

Keywords: ESG investing, fixed-income portfolio management, portfolio theory, portfolio construction, style investing, portfolio management, multi-asset allocation, factor-based models, security analysis and valuation, risk management, equity portfolio management, performance measurement, wealth management, sustainable investing, socially responsible investing, ESG, social impact, statistical methods, and analysis of individual factors/risk premia 

References 

Abhayawansa, S., & Tyagi, S. (2021). Sustainable investing: The black box of environmental, social, and governance (ESG) ratings. The Journal of Wealth Management Summer, 24(1), 49-54. https://doi.org/10.3905/jwm.2021.1.130

Alessandrini, F., & Jondeau, E. (2020). ESG investing: From sin stocks to smart beta. The Journal of Portfolio Management Ethical Investing, 46(3), 75-94. doi:10.3905/jpm.2020.46.3.075

Anson, M., Spalding, D., Kwait, K., & Delano, J. (2020). The sustainability conundrum. The Journal of Portfolio Management March 2020, 46(4), 124-138. doi:10.3905/jpm.2020.1.132

Atta-Darkua, V., Chambers, D., Dimson, E., Ran, Z., & Yu, T. (2021). Practical applications of strategies for responsible investing: Emerging academic evidence. Practical Applications, 8(4). doi:10.3905/pa.8.4.421

Bahra, B., & Thukral, L. (2020). ESG in global corporate bonds: The analysis behind the hype. The Journal of Portfolio Management, 46(8), 133-147. doi:10.3905/jpm.2020.1.171

Branch, M., Goldberg, L., & Hand, P. (2020). Practical applications of a guide to ESG portfolio construction. Practical Applications, 7(3). doi:10.3905/pa.7.3.352

Chen, M., & Mussalli, G. (2020). An integrated approach to quantitative ESG investing. The Journal of Portfolio Management Ethical Investing, 46(3), 65-74. https://doi.org/10.3905/jpm.2020.46.3.065

Chen, M., & Mussalli, G. (2021). Practical applications of an integrated approach to quantitative ESG investing. Practical Applications, 8(3). doi:10.3905/pa.8.3.413

Dimson, E., Marsh, P., & Staunton, M. (2020). Divergent ESG ratings. The Journal of Portfolio Management, 47(1), 75-87. https://doi.org/10.3905/jpm.2020.1.175

Elsenhuber, U., & Skenderasi, A. (2020). ESG investing: The role of public investors in sustainable investing. World Bank Documents. https://documents1.worldbank.org/

Gidwani, B. (2020). Some issues with using ESG ratings in an investment process. The Journal of Investing, 29(6) 76-84. doi:10.3905/joi.2020.1.147

Giese, G., Lee, L. E., Melas, D., Nagy, Z., & Nishikawa, L. (2019). Foundations of ESG investing: How ESG affects equity valuation, risk, and performance. The Journal of Portfolio Management, 45(5), 69-83. https://doi.org/10.3905/jpm.2019.45.5.069

Giese, G., Lee, L. E., Melas, D., Nagy, Z., & Nishikawa, L. (2019). Consistent ESG through ESG benchmarks. The Journal of Index Investing, 10(2), 24-42. https://doi.org/10.3905/jii.2019.1.072

Hays, M., & McCabe, J. (2021). Sustainable and impact investing: A taxonomy of approaches and considerations for fiduciaries. The Journal of Wealth Management, 1(139). doi:10.3905/jwm.2021.1.139

Henriksson, R., Livnat, J., Pfeifer, P., & Stumpp, M. (2019). Integrating ESG in portfolio construction. The Journal of Portfolio Management, 45(4) 67-81. doi:10.3905/jpm.2019.45.4.067

Madhavan, A., & Sobczyk, A. (2020). On the factor implications of Sustainable Investing in Fixed-Income Active Funds. The Journal of Portfolio Management Ethical Investing, 46(3), 141-152. https://doi.org/10.3905/jpm.2020.46.3.141

Sorensen, E., Chen, M., & Mussalli, G. (2021). The quantitative approach for sustainable investing. The Journal of Portfolio Management, 1(267). doi:10.3905/jpm.2021.1.267

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SRI and ESG: Ratings, Methodologies, and Performance

Socially responsible investing (SRI) is a well-worn term that grew in prominence during the 1980s and 1990s, but its roots trace back two millennia, shaped by civil-rights-era thinkers, faith-based organizations, and women (Townsend, 2020). The modern SRI process stands on three pillars: (a) values-based avoidance screens; (b) proactive sustainability-focused analytics, i.e., the environmental, social, and governance (ESG) investing; and (c) corporate engagement and impact investing. As for the second pillar, ESG, the assets under management (AUM) in ESG mutual funds and exchange-traded funds (ETFs) grew from US$453 billion in 2013 to US$760 billion in 2018, excluding the private funds investing directly in sustainable infrastructure and other assets (BlackRock, 2020). 

Townsend (2020) focused on the origins and continued evolution of the first two pillars, the traditional North American model for SRI, and ESG, which first took hold in Europe. Townsend (2020) found SRI and ESG had roots in not only faith-based investing, but also in the civil rights, antiwar, and environmental movements of the 1960s and 1970s. Townsend also found the investment risks posed by climate change and poor corporate governance provided a huge catalyst in the growth of ESG investing. By contrast, Elsenhuber and Skenderasi (2020) provided an overview of ESG investing from the perspective of public investors. To evaluate the role of public investors in sustainable investing, Elsenhuber and Skenderasi noted the Bank for International Settlements conducted an informal survey among many central banks, international organizations, and asset managers between April and May in 2018. 

Pástor et al. (in press) suggested the ESG factor and the market portfolio priced assets in a two-factor model. The ESG investment industry is largest when investors’ ESG preferences differ most (Pástor et al., in press). The findings of Townsend (2020) suggested ESG data were much more widely available than those back 10 years ago, making ESG investing increasingly viable.

ESG Ratings and Methodologies

ESG investing is becoming mainstream, and the COVID-19 pandemic has amplified the momentum (Abhayawansa & Tyagi, 2021). The interest in ESG investing creates greater demand for ESG data, ratings, and rankings, spawning a proliferation of agencies offering these products (Abhayawansa & Tyagi, 2021). In fact, different ESG ratings and rankings produce significantly different assessments of the ESG performance of companies (Abhayawansa & Tyagi, 2021). 

Dimson et al. (2020) examined the extent of, and reasons for, disagreement among the leading suppliers of ESG ratings, and found the weightings given to each pillar of an ESG rating also varied across agencies. Dimson et al. also reviewed the investment performance of portfolios and of indexes screened for their ESG credentials. The findings of Dimson et al. (2020) indicated ESG ratings, used in isolation, were unlikely to make a material contribution to portfolio returns. Surprisingly, Dimson et al. provided evidence that ESG indexes did not outperform the parent index from which they were derived over the longest available period. While the findings of Dimson et al. also revealed FTSE4GOOD and MSCI-ESG, the two indexes that most closely represented what ESG investors did, showed no evidence of underperformance. 

Unlike Dimson et al. (2020), Abhayawansa and Tyagi (2021) examined the causes of differences in the ratings and rankings generated by different agencies. The results of Abhayawansa and Tyagi (2021) indicated the divergences among raters could be attributed to differences in the definitions of ESG constructs, which was a theorization problem, and methodological differences, a commensurability problem. The results also revealed while users of ESG ratings were advised to study the definitions and methodologies before their use. A lack of transparency about the data sources, weightings, and methodologies could also make it difficult to ensure that the true ESG performance of companies was accounted for when making security selection and portfolio investment decisions (Abhayawansa & Tyagi, 2021). 

Dimson et al. (2020) explained why different raters’ appraisals diverged, and whether ESG was associated with subsequent fund or index outperformance. Dimson et al. argued data were essential for making investment decisions, and most institutions relied wholly or partly on external providers of ESG data; however, minimal correlation existed between ESG ratings from alternative agencies. Anson et al. (2020) identified a sustainable beta factor that was successful in screening both companies and asset managers as green or nongreen, which was an important step in building a factor model for sustainable investing. Elsenhuber and Skenderasi (2020) indicated central banks had introduced ESG factors mainly for their pension fund investments, with the aim of further integrating sustainable investing into their own funds and in foreign exchange reserves portfolios. This is because the strategic asset allocation of the latter tends to be less diverse, and it focuses on the asset classes that does not have a conventional ESG approach. 

Investment Performance of ESG-Rated Funds

ESG investing remains a topic of keen debate, largely from the investigation of its performance over different periods (Anson et al., 2020). A natural question concerns how ESG attributes affect expected return and risk (Madhavan & Sobczyk, 2020). Unfortunately, the databases that accumulate sustainable metrics are both inconsistent and incomplete, leading to a large dispersion of results and conclusions (Anson et al., 2020). Anson et al. (2020) devised an empirical test of the value of sustainable investing that did not depend upon the choice of sustainable database or metrics used. The results of Anson et al. (2020) revealed a negative alpha associated with the sustainable funds, compared to a portfolio not constrained by a sustainable mandate. 

Inconsistent with Anson et al.’s (2020) results, Giese et al. (2019) examined three transmission channels within a standard discount cash flow model: (a) the cash-flow channel; (b) the idiosyncratic risk channel; and (c) the valuation channel. Giese et al. provided a link between ESG information and the valuation and performance of companies. Specifically, Giese et al. tested each of these transmission channels using MSCI ESG Ratings data and financial variables, and the results showed the ESG information of companies was transmitted to their valuation and performance, both through their systematic risk profile and their idiosyncratic risk profile. 

Most recently, Pástor et al. (in press) modeled investing that considered ESG criteria. The results of Pástor et al. (in press) revealed in equilibrium, green assets had low expected returns because investors enjoyed holding them and because green assets hedged climate risk. Pástor et al. found green assets nevertheless outperformed when positive shocks hit the ESG factor, which captured the shifts in customers’ tastes for green products and investors’ tastes for green holdings. 

Apart from ESG equity investments, interest in sustainable investing in fixed income has also grown tremendously (Madhavan & Sobczyk, 2020). Theoretically speaking, bond ratings for a particular issuer are broadly similar regardless of the rating agency; however, this is not the case for ESG ratings (Dimson et al., 2020). Companies with a high score from one rater often receive a middling or low score from another rater (Dimson et al., 2020).

Using quarterly holdings data, Madhavan and Sobczyk (2020) conducted a study for a broad sample of US fixed income active mutual funds to determine their performance attribution. Specifically, Madhavan and Sobczyk aimed to attribute active returns to (a) the returns to static factor exposures; (b) time-varying factor exposures; and (c) security selection. Madhavan and Sobczyk found funds with strong ESG attributes derived a significant fraction of their alpha from static factor exposures, which reflected a tilt toward higher-quality bonds that were less volatile.

The evidence of Madhavan and Sobczyk (2020) indicated a strong negative relation between a fund’s total return and its holdings-based ESG score. This holdings-based analysis of active fixed-income mutual funds provides deep insight into the relation between ESG attributes and investment performance. Madhavan and Sobczyk (2020) suggested funds whose holdings could be mapped to ESG attributes derived a significant fraction of their alpha from static factor exposures, reflecting the composition of their bond portfolios. 

Summary and Conclusion

The survey results of the Bank for International Settlements indicated public investors were increasingly being pressed to play a role in sustainable investing, but they faced various challenges in this process (Elsenhuber & Skenderasi, 2020). Among these challenges, Elsenhuber and Skenderasi (2020) found the most critical ones were the lack of a commonly adopted ESG taxonomy, and the limitations on the application of various ESG approaches in some of the portfolios they managed. A key ingredient in growing ESG investments would be achieving a common understanding across asset owners, asset managers, other market participants, and regulators of what was expected from financial products that offered exposure to sustainable investment themes (BlackRock, 2020). BlackRock (2020) argued this would require a strong system of classification that could enable asset owners to differentiate products and provided clear, transparent data regarding product attributes. 

The results of Giese et al. (2019) suggested that changes in the ESG characteristics of a company might be a useful financial indicator. Giese et al. (2019) argued ESG ratings might be suitable for integration into policy benchmarks and financial analyses. Abhayawansa and Tyagi (2021) argued instead of attempting to compare and contrast ratings and rankings of different agencies, investors should determine the ESG constructs that were material to their own investment strategies, and then matched them with an ESG rating or ranking product that closely resembled those constructs. Anson et al. (2020) suggested a sustainable factor could be identified and applied to both companies and fund managers. Anson et al. (2020) argued sustainable funds had consistent factor and sector tilts that must be accounted for as part of the portfolio construction process.

As for investment performance, the databases that accumulate sustainable metrics may be limited and present inconsistent and incomplete data, leading to a large dispersion of results and conclusions (Anson et al., 2020). On the other hand, Madhavan and Sobczyk (2020) provided evidence the composition of ESG scores mattered, with environmental score most closely related to fund volatility. Regardless of the debate on investment performance, sustainable investing nonetheless produces positive social impact by making firms greener and by shifting real investment toward green firms (Pástor et al., in press).

Keywords: ESG investing, fixed-income portfolio management, portfolio theory, portfolio construction, style investing, portfolio management, multi-asset allocation, factor-based models, security analysis and valuation, risk management, equity portfolio management, foundations and endowments, performance measurement, wealth management, sustainable investing, socially responsible investing, ESG, social impact

References 

Abhayawansa, S., & Tyagi, S. (2021). Sustainable investing: The black box of environmental, social, and governance (ESG) ratings. The Journal of Wealth Management Summer, 24(1) 49-54. https://doi.org/10.3905/jwm.2021.1.130

Anson, M., Spalding, D., Kwait, K., & Delano, J. (2020). The sustainability conundrum. The Journal of Portfolio Management March 2020, 46(4), 124-138. doi:10.3905/jpm.2020.1.132

BlackRock. (2020). Towards a common language for sustainable investing. BlackRock Public Policy/ViewPoint. https://blackrock.com/publicpolicy

Dimson, E., Marsh, P., & Staunton, M. (2020). Divergent ESG ratings. The Journal of Portfolio Management, 47(1), 75-87. https://doi.org/10.3905/jpm.2020.1.175

Elsenhuber, U., & Skenderasi, A. (2020). ESG investing: The role of public investors in sustainable investing. World Bank Documents. https://documents1.worldbank.org/

Giese, G., Lee, L. E., Melas, D., Nagy, Z., & Nishikawa, L. (2019). Foundations of ESG investing: How ESG affects equity valuation, risk, and performance. The Journal of Portfolio Management, 45(5), 69-83. https://doi.org/10.3905/jpm.2019.45.5.069

Madhavan, A., & Sobczyk, A. (2020). On the factor implications of Sustainable Investing in Fixed-Income Active Funds. The Journal of Portfolio Management Ethical Investing, 46(3), 141-152. https://doi.org/10.3905/jpm.2020.46.3.141

Pástor, Ľ., Stambaugh, R. F., & Taylor, L. A. (in press). Sustainable investing in equilibrium. Journal of Financial Economics. https://doi.org/10.1016/j.jfineco.2020.12.011

Townsend, B. (2020). From SRI to ESG: The origins of socially responsible and sustainable investing. The Journal of Impact and ESG Investing Fall, 1(1), 10-25. https://doi.org/10.3905/jesg.2020.1.1.010

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Advances in Investment Portfolio Management: November 12, 2020

Portfolio management refers to the selection of securities and their continuous shifting in the portfolio for optimizing the return and maximizing the wealth of an investor by analyzing the strengths, weaknesses, opportunities, and challenges for performing a wide range of activities related to a portfolio (Joseph & Varghese, 2017). Investing in the stock market effectively is possible only when based on scientifically sound methods for analyzing investment instruments (Wielki et al., 2019). For maximum possible gain, the portfolio needs to be optimally managed which involves selecting the best possible investment opportunities and avoiding the highly volatile and high-risk stocks (Nazarova & Levichev, 2017; Pandey et al., 2019).

Joseph and Varghese (2017) lighted up the influential factors which could affect the decisions taken in the context of a portfolio and tried to emphasize the dire need for having an investment portfolio. Sunchalin et al. (2019)studied the methodology of construction and management of the investment portfolio and discussed the models for constructing an investment portfolio as well as its practical realization. Sunchalin et al. also reviewed the methods of artificial intelligence, including neural networks and genetic algorithms, based on which the researchers built the model of forecasting the return on assets. Wielki et al. (2019) developed a method for selecting financial instruments for the formation of an investment portfolio of securities. Based on the analysis of the competitiveness of joint-stock companies by identifying the market’s capacity, the share price, and the company’s share in of the stock market turnover, Wielki et al. argued that the proposed method had the potential to become a scientific basis for effective long-term investment on the stock market.

Fussy-Based Models 

In recent times, the use of soft computing-based methods of fuzzy and neuro-fuzzy models has become increasingly popular for optimal selection and rejection of the portfolio elements to maximize the investor’s profit (Pandey et al., 2019). Since a lot of investment and trading is still carried out based on an individual or a group of decision-makers’ verbal instructions which are opaque in nature, the role of fuzzy in transforming these vague sentences into the language and the machines can understand is phenomenal where the traditional mean-variance models are lagging (Pandey et al., 2019). 

The self-learning feature of computing-based models is better than the statistical modeling-based optimization models (Pandey et al., 2019). Besides, the generation of a large amount of vague data in the digital world today has given the fuzzy-based methods a decisive edge over the statistical models, in which the problems related to volatility are dealt with probabilistic fuzzy c-means clustering and functional fuzzy rule-based models (Pandey et al., 2019). Overall, applying fuzzy-based systems for portfolio optimization and managing is a novel approach with improved performance.

Nazarova and Levichev (2017) investigated the effective management of the investment portfolio, including various types of assets. Through an integrated approach, combining the selection of assets with the help of fuzzy clustering, the Markowitz classical model, and rebalancing, Nazarova and Levichev (2017) managed to reduce the portfolio management problem to the problem of maximizing the Sharpe ratio at a given level of risk, i.e., a comprehensive model for evaluating the effectiveness of investment portfolio management with functions of profit maximization, constraints, levels of risk, and the constancy of the weighting factors. Besides, Nazarova and Levichev (2017) resulted in a mathematical model, which provided a significant increase in the effectiveness of portfolio management compared to conventional approaches. Further, Nazarova and Levichev (2017) proposed a modified algorithm for rebalancing over time, which allowed to combine all the advantages of active management with a reduction in transaction costs., and carried out the choice of control method incorporating the investment horizon. The most promising part is the development of an algorithm-based special software that can be used by both private investors and managers of investment funds.

Behavioral Finance and Portfolio Management 

Crowd psychology and cognitive biases are the outcomes of irrational behaviors (Antony, 2020). Identifying the irrationality in the behavioral patterns can reduce the market anomalies that we are facing in the stock market operations (Antony, 2020). Application of behavioral finance will help in policymaking process by designing optimal portfolio and strategies to minimize the risk by controlling the emotions of the investors (Antony, 2020). Researchers have developed the behavioral portfolio model as an extension of the capital asset pricing model (CAPM), which is a prescriptive model by incorporating behavioral biases (Antony, 2020). The behavioral portfolio model explains why the investors invest with multiple objectives such as the future requirements of family, retirement savings, and funding for meeting emergencies (Antony, 2020). 

Focusing on the factors affecting individual investors’ behavior and their portfolio, Seetharaman et al. (2017) modeled the survey with partial least squares (PLS) structural equation modeling, and supported the conventional views on the influence of the independent variables of investment objective, risk profile, and asset familiarity on the perceived extent of investor behavior. Seetharaman et al. also examined how this perceived extent of investor behavior might predict the individual choice of portfolio and its performance. As the extent of investor behavior was also an intervening variable in the study, Seetharaman et al. made an attempt to assess its mediating effect on the investment objective, risk profile, and asset familiarity in the overall model. Besides, Seetharaman et al. examined the goodness of the measures and assessed them by looking at the validity and reliability of the measures using the PLS approach and showed that the measures used exhibited both convergent and discriminant validity. Next, Seetharaman et al. proceeded to assess the reliability of the measures by looking at the Cronbach’s alpha values and composite reliability values and found that both the Cronbach’s alpha values and composite reliability values were on a par with the criteria set up by other established researchers. Seetharaman et al. also found that investment objective and asset familiarity exerted an impact on investor behavior, with asset familiarity having the strongest impact. Investor behavior, in turn, influences the choice of a portfolio of the investors. In another word, asset familiarity introduced the bias and created the confidence that the returns were guaranteed, which might prevent individuals from diversifying their portfolio, and hence there was a need to create awareness (Seetharaman et al., 2017). Seetharaman et al. provided useful insights and information regarding the factors that investment planners, financial advisers, and individuals needed to improve their choice of the portfolio and its performance, while failed to test people’s investment decisions and hence their portfolio. 

Currency Hedge

Investment management on the international financial market necessitates a special approach to foreign currency hedging (Borochkin, 2017). The majority of international investors fully eliminate the risk associated with their foreign-exchange holdings, seeking profits only from stock price differentials (Borochkin, 2017). In certain circumstances, a correlation between the local currency exchange rate and local stock index may provide additional opportunities for profit generation (Borochkin, 2017).

Borochkin (2017) conducted a study to test the hypothesis that partial currency risk-taking might reduce the total portfolio risk and increase return on international investment. Borochkin (2017) applied the global optimization approach to calculate investment portfolios for 11 countries of the world, whereby each portfolio included shares of 20–25 highly capitalized companies and assessed investment strategy efficiency based on the Sharpe Ratio, Sortino Ratio, Treynor Ratio, and Omega Ratio. Borochkin (2017) found that currency hedge position at the rate of about 14% of the total portfolio value might increase investment yield by 2% annually on the 10-year time span. Borochkin (2017) suggested that a total currency risk hedge was necessary for investment in developed and developing countries that pursued the policy of regular devaluation of their national currency. If the return on the stock market is lower than that of risk-free instruments, market regulators inside a particular country should consider that a sudden devaluation of national currency may be needed (Borochkin, 2017).

Keywords: investment portfolio, international portfolio investment, global optimization, foreign currency hedging, digital technologies, neural networks, genetic algorithm, financial investment, investment decisions, investor factors

References 

Antony, A. (2020). Behavioral finance and portfolio management: Review of theory and literature. Journal of Public Affairs, 20(2), e1996. https://doi.org/10.1002/pa.1996

Borochkin, A. A. (2017). Investment portfolio forex risk hedging in the international stock market. Economic Analysis: Theory and Practice, 6(465), 1022-1042. https://doi.org/10.24891/ea.16.6.1022

Joseph, A., & Varghese, J. (2017). A study on factors affecting investment decision making in the context of portfolio management. PESQUISA, 3(1), 52-56. http://www.pesquisaonline.net

Nazarova, V., & Levichev, I. (2017). Development of the model of improving the effectiveness of investment portfolio. HSE Economic Journal, National Research University Higher School of Economics, 21(3), 451-481. https://ideas.repec.org/i/a.html

Pandey, M., Singh V., Verma, N. K. (2019). Fuzzy based investment portfolio management. Applying Fuzzy Logic for the Digital Economy and Society, 73-95. https://doi.org/10.1007/978-3-030-03368-2_4

Seetharaman, A., Niranjan, I., Patwa, N., & Kejriwal, A. (2017). A study of the factors affecting the choice of investment portfolio by individual investors in Singapore. Accounting and Finance Research, 6(3), 153. doi:10.5430/afr.v6n3P153 

Sunchalin, A. M., Ivanyuk, V.A., Sunchalina, A. L. (2019). Investment portfolio management and forecasting the return on assets based on artificial intelligence methods (neural analysis and genetic algorithm. 1st International Scientific Conference “Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth” (MTDE 2019). https://doi.org/10.2991/mtde-19.2019.54

Trindade, B. C., Reed, P. M., & Characklis, G. W. (2019). Deeply uncertain pathways: Integrated multi-city regional water supply infrastructure investment and portfolio management. Advances in Water Resources, 134, 103442. https://doi.org/10.1016/j.advwatres.2019.103442

Wielki, J., Stopochkin, A., & Sytnik, I. (2019). Investment portfolio management based on the study of the competitiveness of joint-stock companies. Quality-Access to Success, 20(S1), 387-392. https://www.srac.ro/calitatea/en/index.html

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Market Recap Monday, December 24, 2018

On global equities

Volatility in global equity markets persisted in November, fears of rising interest rates, multiple trade conflicts, the end of QE, and related uncertainty about the global economic outlook weighed on sentiment and set the market on edge. Additionally, U.S. crude ended last week down 11% at $45.59, posting the worst performance since January 2016; the falling crude oil prices added to concerns.

Swiss equities have held up relatively well in the latest sell-off due to their strong tilt to defensive sectors. Although the dividend yield is attractive, as compared to other European countries, the valuation of the overall Swiss equity market is less compelling, which is well above its 10-year average. Historically, the Switzerland Stock Market reached an all-time high of 9611.61 in January 2018 and a record low of 1287.60 in January 1991. Inflation data for November seen falling 0.1% MoM and rising 1.0% YoY.

In emerging markets (EM), while corporate earnings forecasts have gone down over the past months, actual corporate earnings look to be stable and more reasonable. Additionally, the U.S. dollar has not strengthened further relative to Asian currencies. Moreover, valuations of EM equities are attractive – EM equities are around 11.5x, as measured by trailing P/E ratio, relative to 15x of their developed markets (DM) counterparts. That said, EM equities are traded at close to a 25% discount to DM equities. All these developments are modestly positive for EM equities.   

On fixed income

The fundamentals of EM sovereign bonds in USD are solid, their yield of around 7% is compelling, the carry of the EM sovereign bonds is attractive, and EM sovereign and corporate yields have widened to attractive levels. As spreads move wider, the dispersion and the risk of negative returns is likely to decrease. EM sovereign not only offers an attractive yield-duration combination but also portfolio diversification. While EM local currency posted the biggest losses, followed by EM sovereign bonds.

Hedge funds during market volatility

Hedge funds are a useful source of return and stability in a multi-asset portfolio, especially during times of market volatility. They can offer superior risk-return compared to many other asset classes and access to uncorrelated investment opportunities, which provides downside protection and diversification benefits. The current global environment of heightened stock dispersion, low cross-asset correlation, rising interest rates, moderately higher volatility, and diverging monetary and economic policies are supporting the performance of the asset class.

Strategies That Chinese Small and Medium-Sized Enterprises Use to Attract Venture Capital

Abstract

Small and medium-sized enterprises (SMEs) contribute to China’s economic growth and help maintain social stability. However, SME business leaders have cited access to finance as an obstacle of SMEs’ survival and success. The purpose of this multiple case study was to identify main strategies SME entrepreneurs and business leaders used to attract venture capital (VC) investments to achieve financial sustainability and business expansion. Data were collected from a purposive sample of 23 entrepreneurs and leaders from 4 SMEs in China and an analysis of organizational artifacts. The resource-based view theory served as the primary conceptual framework. The data analysis process entailed using coding techniques to identify keywords, narrative segments, and concepts. Member checking ensured the credibility and trustworthiness of the data interpretation and analysis. The process led to 4 themes including developing a unique and pioneering business model, assembling a management team with industry experience, indicating use of raised capital in investing in technology, and engaging with superior principal endorsements during the fund-raising efforts. The implication for positive social change included the potential to enhance the capability of SME entrepreneurs and business leaders to obtain VC funding to support their businesses, which can increase economic development and improve the social stability of local communities in China. The findings from the study may contribute to the development of the SME sector in China and benefit their owners, business leaders, employees, future entrepreneurs, the local community, as well as economy of China.