Far East Journal of Experimental and Theoretical Artificial Intelligence
Volume 5, Issue 1-2, Pages 57 - 67
(May 2010)
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LOGISTIC REGRESSION MODEL IN KNOWLEDGE DISCOVERY
Yen-Ping Huang
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Abstract:
Today, many countries in the world are engaged in a massive effort in influencing practice patterns of escaping economic contraction and assigning accountability by constructing systematic mechanisms. As knowledge discovery providers play a critical role in industries and investors, many strategies have been adopted to control the factors that may influence the return of financial services offered by investors. In view of the above, this research employs data-mining technology to predict the probability of patterns for financial database. By using the significant explanatory variables obtained from logistic regression, the relationship between output and input variables can be explained. Finally, the most important factors that affect the probability of returns are the attributes of price, trading volume, turnover rate, size in circulation, and book-to-market equity. |
| Keywords and phrases: data mining, logistic regression, pattern discovery, time-series analysis. |
| Communicated by Kyong Joo Oh |
| Number of Downloads: 259 | Number of Views: 463 |
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