CFA Institute

Mind the Gender Gap, Edition 3

For sustainable economic growth and the creation of a more equitable society, workforce diversity, wherein people have equitable access to career opportunities regardless of gender, is a critical input. Increasing women’s participation in the workforce tends to accelerate both economic prosperity and social development for a nation. Along with our earlier work on the subject, this report is CFA Institute and CFA Society India’s contribution to raising awareness about gender disparity and encouraging conversations within the industry to bridge this gap. This third edition of “Mind the Gender Gap” (the first edition was published in March 2023 and the second in December 2024) aims to direct attention toward the subject, serving as a resource for generating dialogue among policymakers, regulators, and industry. In this context, while the capital market regulator is responsible for framing regulations such as the Business Responsibility and Sustainability Reporting (BRSR) framework, it is essential to determine how these measures are being implemented on the ground. In May 2021, Securities and Exchange Board of India (SEBI) released the BRSR framework, a comprehensive set of sustainability disclosures covering environmental, social, and governance issues. In this report, we analyze the BRSR disclosure data for 300 companies over three reporting periods: fiscal year (FY) 2022–23, FY 2023–24, and FY 2024–25. Our sample selection methodology is designed to provide comprehensive representation, encompassing approximately 70% of total market capitalization of listed companies in India. This approach ensures that the study includes the most significant companies while covering the broader market across different sectors and industries. This report is designed for both regulators and investors as an input into more effective, evidence-based regulatory decisions and as an effective tool for an investor’s evaluation process. By tracking trends over time and examining how reported data translate into practice, this third edition aims to drive impact toward meaningful gender inclusion. For example, despite strong growth in the total workforce over this period, the representation of women in the workforce for our sample declined between FY 2022–23 and FY 2024–25, indicating that inclusion has not kept pace with expansion. When we analyze gender participation at the senior level in companies, we find that women’s participation at the board of directors (BoD) level remains between 18% and 19% throughout FY 2022–23, FY 2023–24, and FY 2024–25. The weakest representation for women, however, is among Key Managerial Personnel (KMP): For every seven male KMP, we found less than one female KMP. Almost two-thirds of the sample companies have no female KMP. Additionally, female directors earn significantly less than their male counterparts, with male directors’ remuneration being 3.6 times that of female directors. And, this pay gap has widened during the last three years. Some sectors, such as Information Technology, Financials, and Consumer Discretionary, have higher female representation in the workforce, typically ranging between 23% and 34%, compared with other sectors such as Communication Services, Energy, Industrials, Materials, Real Estate, and Utilities, where female representation ranges between 4% and 15%. Lower still are Utilities, Materials, and Energy, with only 4%–6% female participation in the workforce, and they also have some of the widest pay gaps at the senior level. Overall, between FY 2022–23 and FY 2024–25, total employment for our sample companies grew by more than 1 million, but female representation constituted only around 18% of this incremental addition. Several areas have scope for significant improvement. For example, companies must improve disclosures related to remuneration. Additional granularity on data provided pertaining to employees, such as based on hierarchy or roles performed by them along with clear definitions of what those job levels mean, will significantly improve quality analysis and actionable insights. We have observed that the definition of KMP greatly varies from company to company, and this variation may lead to inconsistent results. We recommend that guidelines be issued on classification of KMP and who should be included in that category. This standardization would make the comparison more consistent and useful, both for analysis and for possible corrective measures to reach pay parity. Beyond board diversity, there is a need to improve diversity within senior management. SEBI has already mandated that companies have at least one female independent director on company boards. It is now essential to think and discuss at the board level how to increase women’s representation in KMP, which has been lagging and has the smallest amount of female representation. Additionally, regarding remuneration disclosure, we recommend further granularity within BoD and KMP at job levels to understand the significant difference in remuneration between men and women. In the context of education, a clear gap exists between the number of women enrolled in higher education and the opportunities available for them in the workforce. For example, according to data released by the Indian government in 2024, women now constitute 43% of total enrollment in STEMM (Science, Technology, Engineering, Mathematics, and Medicine) streams at the higher education level.[1] Similarly, according to the All India Survey on Higher Education, a 2021–22 report from India’s Ministry of Education, female enrollment in higher education in India reached an all-time high of 20.7 million, with women constituting 48% of total enrollment.[2] The report also highlights that although total (male and female combined) PhD enrollment has increased 81.2% during the period between 2014–15 and 2021–22, female PhD enrollment has more than doubled during the same period. Women now constitute 46% of total new enrollments. Indian companies are making progress in disclosing useful information on gender participation in the workforce through BRSR in their annual reports. We believe such disclosure is the first step, and a critical one, to making real progress on gender parity, where much work remains. Our analysis also suggests that disclosures remain uneven, however—particularly for senior leadership categories such as BoD and KMP, where definitions and methodology vary across firms. The report’s findings highlight the need for more consistent reporting practices to enable meaningful comparison and accountability. In a country where women face significant barriers both inside and outside the workplace, we hope our follow-up report, along with our

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Chapter 7: Natural Language Processing

Brown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, et al. 2020. “Language Models Are Few-Shot Learners.” In NIPS‘20: Proceedings of the 34th International Conference on Neural Information Processing Systems, 1877–901. doi:10.48550/arXiv.2005.14165. Chen, Yifei, Bryan T. Kelly, and Dacheng Xiu. 2024. “Expected Returns and Large Language Models.” Working paper (23 August). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4416687. Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. “Attention Is All You Need.” In NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems, 6000–10. source

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Geopolitical Risk and Portfolio Oversight

We used LCTD for this illustration because it offers: A diversified, developed market equity portfolio Sector weights broadly similar to global ex US benchmarks A modest tilt towards lower carbon and transition ready companies The five largest weights are HSBC at 1.9% (Banks), AML at 1.7% (Semiconductors), AstraZeneca at 1.7% (Pharma), Iberdrola at 1.4% (Utilities) and Allianz at 1.3% (Insurance). All issuer-level references that follow use these real names and weights, drawn directly from the public holdings file. Industry Breakdown and Vulnerability Each security is mapped to one of 12 Fed industries (e.g., machinery, computers, depository institutions). For each industry we compute: Portfolio weight (%) Estimated GPR beta (sensitivity to the GPR factor) Impact score for the June 23 spike, translated into basis points of expected effect on the portfolio’s return for that event Based on the sign of the impact score and economic reasoning, industries are classified as: Vulnerable (expected to be hurt by the shock), or Resilient (expected to benefit or provide ballast). For the June 23 spike and the LCTD portfolio, the overlay estimates: Total negative impact: ≈ 33.8 bps Total positive impact: ≈ +15.3 bps Net GPR impact: ≈ 18.4 bps In other words, conditional on a shock of this severity, the portfolio is tilted modestly toward GPR-sensitive industries, with an expected drag of roughly 18 basis points compared with a GPR-neutral configuration. The vulnerability composition is summarized as: 39% of portfolio weight in vulnerable industries 61% in non-vulnerable or resilient industries five of 12 industries classified as vulnerable by the model Exhibit 3: Industry-Level GPR Impact for the June 23, 2025, Spike source

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Rethinking Variable Importance in Machine Learning

We study which firm characteristics drive the economic value of machine learning portfolios. Three results stand out. First, in-sample variable importance overfits and provides little reliable guidance, highlighting the need for out-of-sample evaluation using economic criteria. Second, conventional models are dominated by microcaps, which inflate returns and concentrate gains in costly-to-trade stocks; excluding microcaps is essential for meaningful inference. Third, some predictors carry negative importance and consistently degrade performance; removing them improves risk-adjusted returns and clarifies which characteristics matter. These findings show that only with economic restrictions can machine learning deliver robust asset pricing insights. source

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The Performance of Small Business Investment Companies

Gregory W. Brown, Wendy Y. Hu, David T. Robinson, and William M. Volckmann II A large-sample analysis shows SBIC funds outperform non-SBIC peers across IRR, MOIC, and PME. Performance is strongest for funds using moderate SBA leverage and larger fund sizes, with equity strategies showing greater variability than debt funds. source

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America’s Debt – A New Infrastructure?

At roughly 128% debt-to-GDP, the United States sits alongside France, Italy, and the United Kingdom — not in isolation. Japan stands out at over 230% debt-to-GDP, yet faces no immediate funding stress. Why? Because foreign dependence — not absolute debt — is the real constraint. China: roughly 102% debt-to-GDP, with about 3% foreign-held Japan: roughly 230% debt-to-GDP, with about 12% foreign-held United States: roughly 128% debt-to-GDP, with about 22% foreign-held The United States is unusual: it carries a large debt load, yet remains overwhelmingly domestically financed. That composition matters far more than the headline number. The foreign debt also reduced in percentage from 2019 to 2025, as seen in the following figure. source

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The Best Defensive Strategies: Two Centuries of Evidence

We examine downside protection—or defensive—strategies over more than 220 years of global financial history, covering many years in which traditional equity–bond portfolios suffer and across a wide range of economic scenarios and historical regimes. Traditional defensive equity factors—low-risk, quality, and value—consistently provide effective downside protection, whereas gold and put options prove less drawdown or cost-effective. Our long-run evidence shows that multi-asset defensive strategies, particularly a return-enhanced version of the defensive absolute return (DAR) portfolio introduced by Cavaglia et al. (2022) and trend-following, provide the most effective downside protection. DAR and trend-following are complementary across tests by diversifying each other across stages of drawdowns. Investors can improve the defensive properties and improve total portfolio outcomes of traditional portfolios by considering the deep sample evidence on defensive strategies provided in this paper. source

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