@article {250,
	title = {Towards building ranking models with annual reports},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2010},
	pages = {338 - 343},
	abstract = {The textual content of company annual reports has proven to contain predictive indicators for the company future performance. This paper addresses the general re search 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) the feasibility of build ing ranking models with annual reports to rank future firm performance and 2) the effect of integrating meta seman tic features to help improve and support our prediction. We compare models built with different ranking algorithms and document models. We evaluate our models with a simulated 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 pro vides potential for explaining the textual factors behind the prediction.},
	keywords = {Annual reports, Documentation models, Information retrieval, Ranking models},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960663639\&partnerID=40\&md5=391fa47605e7e864c26988ad0b95ab63},
	author = {Qiu, X.Y.}
}
