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Climate-Focused Portfolio Approaches

Investors gain confidence that an equity or corporate bond portfolio is invested in companies that are helping to address climate risk. To support a transition to a net-zero carbon economy, sustainable investors monitor the carbon impact of the companies in their portfolios.

A carbon footprint is the total greenhouse gas emissions (GHG) caused by an individual, event, organization, service, place, product or business activity, which is expressed as carbon dioxide equivalent. A carbon footprint measures the negative impact of a company’s operations on the environment. Some providers, such as MSCI, have created a range of carbon footprint metrics that compare the carbon characteristics of a portfolio with a benchmark.

For bond investors, the most relevant metric is the weighted average carbon intensity of a portfolio, which measures the carbon footprint of a portfolio in terms of the volume of carbon dioxide emissions per value of sales. This metric is applicable across asset classes. It is simple to calculate. It does not need the market cap or sales data required for other equity ownership-related measures and can be expressed in a score for the portfolio and a score for the benchmark.

The carbon footprint investing approach aims to identify key sustainability issues. One is whether the issuing company’s strategy is aligned with recognized carbon-reduction targets. The other is how the companies are financing their transition to net-zero carbon. It is also important to stay aware of the wider picture and whether conventional analysis adequately reflects reality.

However, a carbon footprint approach does not tell the whole story of the impact of a company. It can sometimes be misleading. For one, a carbon footprint is only a snapshot in time, which cannot look forward to allowing for companies’ carbon-reduction plans. Secondly, it cannot capture the nuances of carbon use. The GHG Protocol distinguishes between direct and indirect sources of carbon emissions; the distinctions depend on whether a company emits the carbon itself as an intrinsic part of its business or as a user of energy or further up or down the supply chain. Moreover, taking bond portfolios as an example, existing carbon intensity reporting tools do not differentiate between carbon footprints from conventional bonds and those from green bonds or other ESG-labeled bonds. These structures raise capital either for specific green projects via a green bond issue, or to support firm-wide carbon-use reduction via sustainability-linked bonds targeting key performance indicators.

Using the carbon footprint approach, companies that have a relatively large carbon footprint might be overlooked even if they are making significant contributions to decarbonization through climate solutions. Many products needed to help curb global emissions over the long term require industrial commodities, such as steel, cement, lithium and cobalt, which are energy-intensive to produce. Similarly, companies with a low carbon footprint might be included even if they rely heavily on carbon offsets. Carbon offsets help make up for the GHGs that an entity produces by allowing it to buy, sponsor or fund a carbon-reduction initiative elsewhere; however, offset projects that are not certified by reliable third parties may not be as effective as advertised.

A carbon handprint, by contrast, measures the positive impact or carbon avoided by using the products of a company. Using the carbon handprint approach, a clean energy company will be judged on the amount of zero-carbon energy generated, while a resource efficiency company is ranked on its ability to save energy for other companies or entities. Products and services that address the physical effects of climate change include drought-resistant crops, coastal infrastructure to protect cities and communities, and smart irrigation systems to improve water use efficiency. Such progress is needed in many areas, such as resource efficiency solutions, clean energy solutions, transportation solutions, agriculture solutions, food waste solutions, and recycling solutions.

The three investing principles for a carbon handprint approach are: (a) search for climate solutions across regions and sectors; (b) make sure that target companies have solid fundamentals; and (c) invest in portfolios that actively engage with their portfolio companies. By assessing carbon handprints, actively engaging with management, and conducting independent research of business fundamentals, investors can obtain the information needed to measure a company’s carbon handprint accurately and convincingly, and create a portfolio of companies with superior long-term return potential that are providing solutions to the biggest climate challenges.

Using carbon handprints to invest in climate-focused companies can also help investors create differentiated portfolios. The MSCI Climate Change Index or the MSCI Climate Paris Aligned Index, are carbon footprint measures. As a result, these popular climate benchmarks look very similar to the MSCI ACWI Index. A carbon handprint index would look very different than the broader equity benchmark.

Lastly, climate-aware investors need to dig much deeper into the business and emission policies of each company to understand whether a low carbon footprint is truly indicative of a good environmental actor. Investors should also be clear about the key milestones as well as the end goal of the carbon-reduction journey of the companies.

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Uncertainty Clouds the Outlook

An anti-goldilocks economy refers to the global economy where policymakers are confronted with a stagflationary supply shock, which is negative for growth and will tend to push up inflation further. In the current market condition, inflation is “too hot,” as evidenced by the higher energy and food prices, as well as the disrupted supply chains and trade. Globally speaking, energy and food prices have been increasing significantly since late April of 2020, while supply chains and trade flows have been lowering since May of 2020. On the other hand, economic growth is “too cold,” as indicated by the tighter financial conditions since year-end 2020 and the lower business and consumer confidence due to uncertainty.

Significant uncertainty clouds the outlook; however, the baseline development-market (DM) growth remains above trend, especially in the US, assuming a 1.5% DM real GDP growth. DM inflation, as measured by core CPI inflation, peaks at elevated levels, the target of which was 2%.

The current global markets witness greater divergence and dispersion of outcomes among countries and regions: DM exposure to Russia varies across the US, the UK, and the eurozone. Italy has approximately 21% of all outstanding loans to Russia, accounting for 2.5% of all the foreign claims (FCs) of Italian banks. Austria has nearly 14% of outstanding loans, representing 3.7% of all Australian banks’ FCs. France’s loans are also approximately 21%; however, they are just 0.7% as a share of total FCs. No other European economies have Russian FCs exceeding 0.4% of their totals. The direct impact on European banks’ balance sheets from Russia exposures will be relatively low. On the emerging-market (EM) side, looking at EM commodity exporters and importers, winners and losers are highly divergent, as indicated by the commodity trade balance as a percentage of GDP.

On volatility across equities and rates, although modest in absolute terms, equity volatility, as measured by the VIX index, has increased, especially since May of 2021. Meanwhile, rate volatility, measured by the CBOE interest rate volatility index, has picked up amidst inflation concerns.

Policymakers struggle to balance higher inflation against greater uncertainty. Higher and more broad-based inflation raises the risk that inflation expectations become unanchored. Market expectations for year-end 2022 policy rates are higher across the Fed and the key central banks; however, central banks are unlikely to ride to the rescue, increasing the risk of weaker growth or even a recession. In the US, different indicators imply that it may already be in the late cycle. Elevated inflation will continue to drive rate markets.

A decrease in demand from the Fed has raised mortgage rates and cheapened valuations. As of March 22, 2022, Agency MBS spreads have widened almost 40 basis points (b.p.) since the Fed announced its intention to taper MBS purchases, back to its cheapest levels in two years. Higher rates and wider spreads have caused mortgage rates to rise, likely lowering MBS supply, extending outstanding MBS, and increasing yield potential. As of March of 2022, the conventional 30-year mortgage rate was 4.51%.

Further on credit, spreads have retraced well above their post-pandemic tights. Investment-grade credit indices have largely recovered from the COVID-19 sell-off; however, sector dispersion remains high. The recovery trade in select resilient corporate credits that have ample liquidity, particularly hardest-hit sectors such as travel and leisure, may be poised to outperform and offer further upside to a reopening of the economy.

Lastly, the financials sector, especially banks, benefit from strong balance sheets with an average investment-grade rating, which can deliver attractive yields. As of Feb 28, 2022, the average yield for the European additional Tier 1 market was 561 b.p. and that of the global high yield market was 526 b.p.

In fact, higher rates may prove to be an attractive entry point, as index yields per unit of interest rate risk are historically low. As of Feb 28, 2022, the yield-to-duration ratio of the Bloomberg US Aggregate index was 0.35, and that of the Bloomberg Global Aggregate was 0.24. Dispersed valuations reinforce the importance of active management and selection. In the current market conditions, portfolio flexibility and liquidity are the crucial.

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Rising COVID-19 Infections in China in March

The COVID-19 infections in China have increased dramatically in March and may continue to rise in the coming days. Shenzhen, Dongguan, and Changchun, have been locked down for over a week. Shanghai has also significantly tightened its COVID-19 restrictions. In China, the number of asymptomatic cases in the current outbreak is much higher, which is likely due to a combination of the milder symptoms of omicron variants and the vaccinations that reduce the severity of the infection. On the other hand, imported cases have been comparatively high. Moreover, the fact that more provinces have been affected is posing a greater challenge in controlling the outbreak.

People are speculating that China may relax its zero-COVID policy; in fact, the highest health authority in China recently urged regions experiencing severe outbreaks to control the epidemic quickly by taking strict measures. Difficulties in getting most of the population vaccinated effectively via a booster shot present further constraints around relaxing the zero-COVID policy.

Looking at indicators, traffic congestion and subway passenger volume may be affected by the imposed restrictions as well as individual risk aversion. Meanwhile, daily coal consumption provides color on industrial activity. These three measures dropped notably during the past outbreaks of COVID-19 and are showing negative effects. Purchasing Managers’ Indices further indicate that activities held up relatively well in both January and February; however, they may weaken in March. The path of economic growth in 2022 will largely depend on how quickly China controls the outbreak and how much additional policy support it has in place to counteract the hit to its growth.

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Value Discount

Value stocks outperformed through mid-February this year as investors repriced expensive growth stocks. Mounting inflation and rising interest rates are creating conditions for broader value recovery, particularly for companies with solid business fundamentals. Style return patterns have been erratic over 2021, where returns of global value stocks and growth stocks flip-flopped, and both cohorts ended the year with similar gains. During the sell-off in January 2022, value stocks outperformed growth stocks by a wide margin, as evidenced by the fact that the MSCI World Value Index fell by 0.6% through February 15, while the MSCI World Growth Index dropped by 10.1%, in US-dollar terms.

Inflation is changing the game. With investors and central banks acknowledging that inflation will stay higher for longer than initially expected, interest rates are poised to continue rising, which should benefit value stocks, even as it raises challenges for equity investors. Rising rates tend to compress valuation multiples of all stocks; growth stocks are particularly vulnerable, while value stocks are generally more resilient.

Despite their recent outperformance, value stocks still trade at a near-record discount to growth peers. As of January 31, 2022, the price-to-forward earnings ratio of the MSCI World Value was 50% lower than that of the MSCI World Growth. Investors might think this discount implies that value stocks are impaired. The three indicators of profitability, earnings expectations, and balance sheet strength, however, show that value-stock fundamentals rank near historic highs relative to growth stocks.

In recent years, multiples of growth stocks have benefited from the falling-rate environment as well as investor demand for high-flying growth companies. Many of these companies have long-term potential but weak current cash flows. This, together with fears of a prolonged economic slump, filed a massive valuation gap between growth stocks and value stocks. However, these trends are ripe for reversal. Since 2009, valuations of growth stocks have been tightly linked to the real yield of the 10-year US Treasury. As real yields fell, growth stocks valuations rose in near lockstep. Over the same period, valuations of global value stocks were relatively stable.

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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 the 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). 

By its nature, ESG assessment requires a forward-looking approach that static metrics may miss. What’s accepted practice today may be considered unacceptable tomorrow, as rules, regulations and popular opinion continue to evolve. Therefore, capturing ESG benefits and identifying risks requires an integrated approach.

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. 

Rather than outsource ESG assessments to third-party providers, investors must conduct in-depth, hands-on research and engage actively with issuers. This approach enables investors to achieve real insight into a business and its activities, and to get a proper understanding of its future prospects as well as its past.

Lastly, developing a complete picture of corporate behavior also requires engagement with management, visiting facilities and understanding the ecosystem in which a firm operates. It also needs sufficient analyst coverage and the ability to do exhaustive fundamental homework and to validate data. Broad assumptions about industries, countries and risks can lead to suboptimal conclusions, improperly understood risks or missed opportunities.

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 


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

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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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