Financial Sentiment Analysis for Risk Prediction

International Joint Conference on Natural Language Processing, pages 802–808, Nagoya, Japan, 14-18 October 2013. Financial Sentiment Analysis for Risk Prediction Chuan-Ju Wang‡, Ming-Feng Tsai†, Tse Liu†, Chin-Ting Chang‡ †Department of Computer Science & Program in Digital Content and Technology National Chengchi University Taipei 116, Taiwan {mftsai, g10120}@cs.nccu.edu.tw ‡Department of Computer Science University of Taipei Taipei 100, Taiwan cjwang@utaipei.edu.tw g10116006@go.utaipei.edu.tw Abstract This paper attempts to identify the impor- tance of sentiment words in financial re- ports on financial risk. By using a finance- specific sentiment lexicon, we apply re- gression and ranking techniques to ana- lyze the relations between sentiment words and financial risk. The experimental re- sults show that, based on the bag-of-words model, models trained on sentiment words only result in comparable performance to those on origin texts, which confirms the importance of financial sentiment words on risk prediction. Furthermore, the learned models suggest strong correlations between financial sentiment words and risk of com- panies. As a result, these findings are of great value for providing us more insight and understanding into the impact of finan- cial sentiment words in financial reports. 1 Introduction Sentiment analysis is the task of finding the atti- tudes of authors about specific objects. In recent years, because of the explosion of sentiment infor- mation from social web sites (i.e., Twitter and Face- book), blogs, and online forums, sentiment analysis has become one of the popular research areas in computational linguistics, such as (Narayanan et al., 2009; Mohammad and Turney, 2010). The growing importance of Sentiment Analy- sis applied to finance brings forth many research and practical issues to minds like “Why Sentiment Analysis is important?” In finance, there have been several studies (Loughran and McDonald, 2011; Price et al., 2012; Garca, 2013) using textual anal- ysis to examine the sentiment of numerous news items, articles, financial reports, and tweets about public companies. Then, the examined sentiments can be used to reflect the correlations with other fi- nancial measures, such as stock returns and volatil- ities. For most sentiment analysis algorithms, as mentioned in (Feldman, 2013), the sentiment lexi- con is the most important resource. In (Loughran and McDonald, 2011), the Harvard Psychosocio- logical Dictionary, a common dictionary for gen- eral sentiment analysis, is extended to be a finance- specific sentiment lexicon. In this study, we attempt to use the finance- specific sentiment lexicon to model the relations between sentiment information and financial risk. In specific, we formulate the problem as two differ- ent prediction tasks: regression and ranking. For the regression task, we aim to use sentiment infor- mation to predict a company’s future risk, which is usually characterized by its real-value volatility. Instead of predicting the real-value volatility, in the ranking task, we try to employ sentiments to rank companies according to their relative risk levels. From the two tasks, we observe that, trained on the finance-specific sentiment lexicon only, both the regression models and ranking models can ob- tain comparable performance to those trained on original texts, even though the word …

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