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Migration Motif:A spatial-temporal pattern mining approach for financial markets
| Content Provider | Semantic Scholar |
|---|---|
| Author | Fuhry, David |
| Copyright Year | 2009 |
| Abstract | A recent study by two prominent finance researchers, Fama and French, introduces a new framework for studying risk vs. return: the migrationof stocks across size-value portfolio space. Given the financial events of 2008, this first attempt to disentangle the relationships between migration behavior and stock returns is especially timely. Their work, however, derives results only for market segments, not individual co mpanies, and only for one-year moves. Thus, we see a new challenge for financial data mining: how to capture and categorize the migration of individual companies, and how such b e avior affects their returns. We propose a novel data mining approach to study the multi-ye ar movement of individual companies. Specifically, we address the question: “H ow does one discover frequent migration patterns in the stock market?” We present a new tra jectory mining algorithm to discovermigration motifsin financial markets. Novel features of this algorithm are it s handling of approximate pattern matching through a graph theor etical method, maximal clique identification, and incorporation of temporal and spatial c onstraints. We have performed a detailed study of the Nasdaq, NYSE, and AMEX stock markets, o ver a 43-year span. We successfully find migration motifs that confirm existing fina nce theories and other motifs that may lead to new financial models. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://etd.ohiolink.edu/!etd.send_file?accession=kent1239139458&disposition=attachment |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
