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  • Refining the Valuation of Seasoned Equity Offerings by Creating a more Accurate Replicated Portfolio Using Machine Learning Techniques

Refining the Valuation of Seasoned Equity Offerings by Creating a more Accurate Replicated Portfolio Using Machine Learning Techniques

Student: Aksyutkin Andrey

Supervisor: Alexander Tarasov

Faculty: Faculty of Economic Sciences

Educational Programme: Joint HSE-NES Undergraduate Program in Economics (Bachelor)

Final Grade: 7

Year of Graduation: 2024

This paper measures the effect of an additional capital allocation or seasoned equity offering for stocks of publicly traded U.S. companies. Using linear regression, the most similar replicating portfolio for each company is found, against which the performance of the stock of interest is compared. This approach refines existing estimates of the effect of additional capital allocation. A significant effect is found, and for the first time we consider in detail the experience of creating a replicating portfolio by training the model on historical trading data.

Full text (added June 4, 2024)

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