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• W2276054934 abstract "OF THE DISSERTATION Data Mining Perspectives on Equity Similarity Prediction By JOHN ROBERT YAROS Dissertation Director: Tomasz Imielinski Accurate identification of similar companies is invaluable to the financial and investing communities. To perform relative valuation, a key step is identifying a “peer group” containing the most similar companies. To hedge a stock portfolio, best results are often achieved by selling short a hedge portfolio with future time series of returns most similar to the original portfolio generally those with the most similar companies. To achieve diversification, a common approach is to avoid portfolios containing any stocks that are highly similar to other stocks in the same portfolio. Yet, the identification of similar companies is often left to hands of single experts who devise sector/industry taxonomies or other structures to represent and quantify similarity. Little attention (at least in the public domain) has been given to the potential that may lie in data-mining techniques. In fact, much existing research considers sector/industry taxonomies to be ground truth and quantifies results of clustering algorithms by their agreement with the taxonomies. This dissertation takes an alternate view that proper identification of relevant features and proper application of machine learning and data mining techniques can achieve results that rival or even exceed the expert approaches. Two representations of similarity are considered: 1) a pairwise approach, wherein a value is computed to quantify the similarity" @default.
• W2276054934 created "2016-06-24" @default.
• W2276054934 creator A5047997010 @default.
• W2276054934 date "2014-01-01" @default.
• W2276054934 modified "2023-09-23" @default.
• W2276054934 title "Data mining perspectives on equity similarity prediction" @default.
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• W2276054934 doi "https://doi.org/10.7282/t34b32xv" @default.
• W2276054934 hasPublicationYear "2014" @default.
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