01635nas a2200181   4500008004100000245005600041210005600097260000900153300001400162490000600176520105100182653001901233653002501252653002601277653001901303100001401322856011701336       2010                        eng d00aTowards building ranking models with annual reports0 aTowards building ranking models with annual reports  c2010  a338 - 3430 v83 aThe 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.10aAnnual reports10aDocumentation models10aInformation retrieval10aRanking models1 aQiu, X.Y.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960663639&partnerID=40&md5=391fa47605e7e864c26988ad0b95ab63