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  • 标题:Stock Performance after Securities Analyst's Rating Downgrades Using Sentiment analysis and Sequencial Pattern Mining
  • 本地全文:下载
  • 作者:Katsuhiko Okada ; Takahiro Azuma ; Masakazu Nakamoto
  • 期刊名称:人工知能学会論文誌
  • 印刷版ISSN:1346-0714
  • 电子版ISSN:1346-8030
  • 出版年度:2012
  • 卷号:27
  • 期号:6
  • 页码:355-364
  • DOI:10.1527/tjsai.27.355
  • 出版社:The Japanese Society for Artificial Intelligence
  • 摘要:Securities analysts disclose their opinion on stocks and write reports indicating how they believe the target firm will perform against the market index. Many brokerage firms issue ratings based on five scales, namely, ``strong buy'', ``buy'', ``neutral'', ``sell'' and ``strong sell''. Empirically, it is known that firms downgraded from `strong buy' to `buy' lose value regardless of the fact that the analyst's signal is still positive. We investigate characteristics of firms that lose large market value in the post-downgrade period. Using data-mining approach, we found higher pre-downgrade volatility is strongly associated with the negative return in the post-downgrade period. Among high volatility firms, small capitalization stocks and stocks with inferior sentiment are particularly vulnerable to such downgrades. The result is consistent with the hypothesis in the field of Finance, i.e. the higher the disagreement level among investors in the market more overvalued the stock remains.
  • 关键词:rating downgrades ; sentiment analysis ; sequencial pattern mining ; emerging pattern
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