UTILIZATION OF MACHINE LEARNING FOR EMPIRICAL ASSET PRICING IN EMERGING MARKETS

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dc.contributor.authorAdilov, Dastan
dc.date.accessioned2024-12-20T05:25:39Z
dc.date.available2024-12-20T05:25:39Z
dc.date.issued2024-12-12
dc.description.abstractWe perform Principal Component Regression (PCR) analysis to predict cross-sectional stock returns in emerging market economies. As a benchmark comparison, we employ OLS models and demonstrate predictive power of the machine learning based PCR model. We utilize 64 firm characteristics to determine the most significant predictors for the emerging market countries as well as for individual countries. The results, demonstrate predictive power of the PCR model over the linear regression model, showing consistent results in both the country-specific analysis and in the overall analysis of the emerging market. The most important set of predictors throughout the analysis proved to be book-to-market, sales-to-price, leverage (lev), cash flow-to-price (cfp), dividends (dy), and gross profitability (gma).
dc.identifier.citationAdilov, D. (2024). UTILIZATION OF MACHINE LEARNING FOR EMPIRICAL ASSET PRICING IN EMERGING MARKETS. Nazarbayev University Graduate School of Business
dc.identifier.urihttps://nur.nu.edu.kz/handle/123456789/8356
dc.language.isoen
dc.publisherNazarbayev University Graduate School of Business
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United Statesen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/
dc.subjectPrincipal component regression
dc.subjectEmerging markets
dc.subjectMachine learning
dc.subjectStock returns
dc.subjectType of access: Open
dc.titleUTILIZATION OF MACHINE LEARNING FOR EMPIRICAL ASSET PRICING IN EMERGING MARKETS
dc.typeMaster`s thesis

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