2026-09-19T09:28:41Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/1630462025-10-07T04:14:05Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Lo, Andrew Wu, Kelly Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2025-10-06T17:41:13Z 2025-10-06T17:41:13Z 2025-05 2025-06-23T14:04:16.804Z https://hdl.handle.net/1721.1/163046 Investments in women’s health remain significantly underrepresented despite mounting evidence of their societal and economic value. This thesis presents a data-driven framework for evaluating and quantifying the financial performance of publicly traded companies focused on women’s healthcare. The research introduces a novel financial index quantifying women’s health constructed with various features through management infrastructure. To assess the viability and impact of investing in this sector, the index is used as the basis for simulating portfolio strategies under a long-term buy-and-hold strategy. A key methodological contribution is the application of unsupervised learning, particularly KMeans and Spectral Clustering, to identify firms with similar ESG and financial profiles. These clusters reveal latent structures overlooked by traditional industry classifications and support the design of a women’s health-focused investment portfolio. To enhance interpretability, t-stochastic neighbor embedding distributed (t-SNE) is used to visualize the high-dimensional feature space and clustering outcomes. Backtesting confirms the potential for these clustered portfolios to deliver competitive, risk-adjusted returns while aligning with gender-focused investment objectives. Overall, this research advances gender-lens investing by integrating machine learning with financial modeling, offering a scalable methodology for thematic index construction and classification in the women’s healthcare sector. MNG Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ A Quantitative Analysis of Women’s Health Investments Thesis application/pdf Master Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="fb00bb12-3f9a-4f97-b8a4-27d3289fb213"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>A Quantitative Analysis of Women’s Health Investments</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2025-05</PublicationDate> <Authors> <Author> <DisplayName>Wu, Kelly</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>Investments in women’s health remain significantly underrepresented despite mounting evidence of their societal and economic value. This thesis presents a data-driven framework for evaluating and quantifying the financial performance of publicly traded companies focused on women’s healthcare. The research introduces a novel financial index quantifying women’s health constructed with various features through management infrastructure. To assess the viability and impact of investing in this sector, the index is used as the basis for simulating portfolio strategies under a long-term buy-and-hold strategy. A key methodological contribution is the application of unsupervised learning, particularly KMeans and Spectral Clustering, to identify firms with similar ESG and financial profiles. These clusters reveal latent structures overlooked by traditional industry classifications and support the design of a women’s health-focused investment portfolio. To enhance interpretability, t-stochastic neighbor embedding distributed (t-SNE) is used to visualize the high-dimensional feature space and clustering outcomes. Backtesting confirms the potential for these clustered portfolios to deliver competitive, risk-adjusted returns while aligning with gender-focused investment objectives. Overall, this research advances gender-lens investing by integrating machine learning with financial modeling, offering a scalable methodology for thematic index construction and classification in the women’s healthcare sector.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>