Over the past two decades, researchers have used big data and machine learning (ML) methods to provide insight relevant for equity valuation. Many of these studies either use or inform on accounting variables. In a paper published in KeAi’s The Journal of Finance and Data Science, Doron Nissim, a Professor of Accounting at Columbia Business School in the U.S., has reviewed a selection of these studies and identified several crucial areas that have received relatively little research attention to date. In the review, Professor Nissim elaborates on two of these: (1) the use of big data and ML methods to measure intangible assets; and (2) the incorporation of economic, financial and accounting structure in implementing ML algorithms.
Review identifies gaps in our understanding of how machine learning can aid stock valuation
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