NDLI: Learning to rank firms with annual reports

Content Provider	IEEE Xplore Digital Library
Author	Xin Ying Qiu
Copyright Year	2009
Description	Author affiliation: Luter School of Business, Christopher Newport University, Newport News, VA 23606 (Xin Ying Qiu)
Abstract	The textual content of company annual reports has proven to contain predictive indicators for the company future performance. This paper addresses the general research question of evaluating the effectiveness of applying machine learning and text mining techniques to building predictive models with annual reports. More specifically, we focus on these two questions: 1) can the advantages of the ranking algorithm help achieve better predictive performance with annual reports? and 2) can we integrate meta semantic features to help support our prediction? We compare models built with different ranking algorithms and document models. We evaluate our models with a simulated investment portfolio. Our results show significantly positive average returns over 5 years with a power law trend as we increase the ranking threshold. Adding meta features to document model has shown to improve ranking performance. The SVR & Meta-augemented model outperforms the others and provides potential for explaining the textual factors behind the prediction.
Starting Page	1
Ending Page	6
File Size	284749
Page Count	6
File Format	PDF
ISBN	9781424442539
DOI	10.1109/ICDIM.2009.5356781
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Text mining Machine learning algorithms Data security Investments Machine learning Companies Predictive models Economic forecasting Data mining Portfolios
Content Type	Text
Resource Type	Article

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