Distillation of News Flow Into Analysis of Stock Reactions

Abstract

News carry information of market moves. The gargantuan plethora of opinions, facts and tweets on financial business offers the opportunity to test and analyze the * This research was supported by the Deutsche Forschungsgemeinschaft through the SFB 649 'Economic Risk', Humbold-Universität zu Berlin. We like to thank the Research Data Center (RDC) for the data used in this study. We would also like to thank the International Research Training Group (IRTG) 1792.1 influence of such text sources on future directions of stocks. It also creates though the necessity to distill via statistical technology the informative elements of this prodigious and indeed colossal data source. Using mixed text sources from professional platforms, blog fora and stock message boards we distill via different lexica sentiment variables. These are employed for an analysis of stock reactions: volatility, volume and returns. An increased (negative) sentiment will influence volatility as well as volume. This influence is contingent on the lexical projection and different across GICS sectors. Based on review articles on 100 S&P 500 constituents for the period of October 20, 2009 to October 13, 2014 we project into BL, MPQA, LM lexica and use the distilled sentiment variables to forecast individual stock indicators in a panel context. Exploiting different lexical projections, and using different stock reaction indicators we aim at answering the following research questions: (i) Are the lexica consistent in their analytic ability to produce stock reaction indicators, including volatility, detrended log trading volume and return? (ii) To which degree is there an asymmetric response given the sentiment scales (positive v.s. negative)? (iii) Are the news of high attention firms diffusing faster and result in more timely and efficient stock reaction? (iv) Is there a sector specific reaction from the distilled sentiment measures? We find there is significant incremental information in the distilled news flow. The three lexica though are not consistent in their analytic ability. Based on confidence bands an asymmetric, attention-specific and sector-specific response of stock reactions is diagnosed.

Forex exchange rate forecasting using deep recurrent neural networks

“…Past price movements and transformation of these methods, for example, in the form a technical indicators (Lo et al 2000), represent the main input to such forecasting models. More recently, research has started to examine auxiliary data sources such as financial news (Feuerriegel and Prendinger 2016;Zhang et al 2015), tweets (Oliveira et al 2017), and, more generally textual data (Junqué de Fortuny et al 2014;Khadjeh Nassirtoussi et al 2014).…”

Sentiment-Driven Stochastic Volatility Model: A High-Frequency Textual Tool for Economists

“…The timestamp, the date, the contributor, the symbols, the title, and the complete text are all extracted using a self-written automatic web scraper and stored at our Research Data Center of the IRTG 1792 at Humboldt-Universität zu Berlin. For more details, please refer to Zhang et al (2016).…”

“…The volatility, however, is latent and requires selecting the proxies (Andersen et al, 2002). Given that news and sentiment drive the financial markets presented in the current literature (Zhang et al (2016)), we aim to propose a continuous-time framework in which the sentiment will drive volatility and returns.…”

LASSO-Driven Inference in Time and Space

“…Moreover, unsupervised learning approaches are employed to extract sentiment variables from the articles. Two sentiment dictionaries: the BL option lexicon (Hu and Liu, 2004) and the LM financial sentiment dictionary (Loughran and McDonald, 2011) were used in Zhang et al (2016). For each article i (published on day t), the average proportion of positive/negative words using BL or LM lexica -P os BL j,i,t , N eg BL j,i,t , P os LM j,i,t , N eg LM j,i,t -are considered as the text sentiment variables.…”

“…We refer to Zhang et al (2016) for more details about the data gathering and processing pro- Table D.1 in Appendix D in the supplementary materials.…”