Domain adaptation using stock market prices to refine sentiment dictionaries

As part of a larger project where we are examining the relationship and influence of news and social media on stock price, here we investigate the potential links between the sentiment of news articles about companies and stock price change of those companies. We describe a method to adapt sentiment word lists based on news articles about specific companies, in our case downloaded from the Guardian. Our novel approach here is to adapt word lists in sentiment classifiers for news articles based on the relevant stock price change of a company at the time of web publication of the articles. This adaptable word list approach is compared against the financial lexicon from Loughran and McDonald (2011) as well as the more general MPQA word list (Wilson et al., 2005). Our experiments investigate the need for domain specific word lists and demonstrate how general word lists miss indicators of sentiment by not creating or adapting lists that come directly from news about the company. The companies in our experiments are BP, Royal Dutch Shell and Volkswagen

The Sound of Slurs: Bad Sounds for Bad Words

An analysis of a valenced corpus of English words revealed that words that rhyme with slurs are rated more poorly than their synonyms. What at first might seem like a bizarre coincidence turns out to be a robust feature of slurs, one arising from their phonetic structure. We report novel data on phonaesthetic preferences, showing that a particular class of phonemes are both particularly disliked, and overrepresented in slurs. We argue that phonaesthetic associations have been an overlooked source of some of the more peculiar, arational aspects of slurs. We conclude by drawing broader morals about the evolution of the lexicon

Magnitude effects for experienced rewards at short delays in the escalating interest task

A first-person shooter video game was adapted for the study of choice between smaller sooner and larger later rewards. Participants chose when to fire a weapon that increased in damage potential over a short interval. When the delay to maximum damage was shorter (5 – 8 s), people showed greater sensitivity to the consequences of their choices than when the delay was longer (17 – 20 s). Participants also evidenced a magnitude effect by waiting proportionally longer when the damage magnitudes were doubled for all rewards. The experiment replicated the standard magnitude effect with this new video game preparation over time scales similar to those typically used in nonhuman animal studies and without complications due to satiation or cost

Does equity analyst research lack rigour and objectivity?:Evidence from conference call questions and research notes

Doubts have been raised about the rigour and objectivity of sell-side analysts’ research due to institutional structures that promote pro-management behaviour. However, research in psychology stresses the importance of controlling for biases in individuals’ inherent cognitive processing behaviour when drawing conclusions about their propensity to undertake careful scientific analysis. Using social cognition theory, we predict that the rigour and objectivity evident in analyst research is more pronounced following unexpected news in general and unexpected bad news in particular. We evaluate this prediction against the null hypothesis that analyst research consistently lacks rigour and objectivity to maintain good relations with management. Using U.S. firm earnings surprises as our conditioning event, we examine the content of analysts’ conference call questions and research notes to assess the properties of their research. We find that analysts’ notes and conference call questions display material levels of rigour and objectivity when earnings news is unexpectedly positive, and that these characteristics are more pronounced in response to unexpectedly poor earnings news. Results are consistent with analysts’ innate cognitive processing response counteracting institutional considerations when attributional search incentives are strong. Exploratory analysis suggests that studying verbal and written outputs provides a more complete picture of analysts’ work

Learning tone and attribution for financial text mining

Attribution bias refers to the tendency of people to attribute successes to their own abilities but failures to external factors. In a business context an internal factor might be the restructuring of the firm and an external factor might be an unfavourable change in exchange or interest rates. In accounting research, the presence of an attribution bias has been demonstrated for the narrative sections of the annual financial reports. Previous studies have applied manual content analysis to this problem but in this paper we present novel work to automate the analysis of attribution bias through using machine learning algorithms. Previous studies have only applied manual content analysis on a small scale to reveal such a bias in the narrative section of annual financial reports. In our work a group of experts in accounting and finance labelled and annotated a list of 32,449 sentences from a random sample of UK Preliminary Earning Announcements (PEAs) to allow us to examine whether sentences in PEAs contain internal or external attribution and which kinds of attributions are linked to positive or negative performance. We wished to examine whether human annotators could agree on coding this difficult task and whether Machine Learning (ML) could be applied reliably to replicate the coding process on a much larger scale. Our best machine learning algorithm correctly classified performance sentences with 70% accuracy and detected tone and attribution in financial PEAs with accuracy of 79%

Towards a Multilingual Financial Narrative Processing System

Large scale financial narrative processing for UK annual reports has only become possible in the last few years with our prior work on automatically understanding and extracting the structure of unstructured PDF glossy reports. This has levelled the playing field somewhat relative to US research where annual reports (10-K Forms) have a rigid structure imposed on them by legislation and are submitted in plain text format. The structure extraction is just the first step in a pipeline of analyses to examine disclosure quality and change over time relative to financial results. In this paper, we describe and evaluate the use of similar Information Extraction and Natural Language Processing methods for extraction and analysis of annual financial reports in a second language (Portuguese) in order to evaluate the applicability of our techniques in another national context (Portugal). Extraction accuracy varies between languages with English exceeding 95%. To further examine the robustness of our techniques, we apply the extraction methods on a comprehensive sample of annual reports published by UK and Portuguese non-financial firms between 2003 and 2015

In Search of Meaning:Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse

We critically assess mainstream accounting and finance research applying methods from computational linguistics (CL) to study financial discourse. We also review common themes and innovations in the literature and assess the incremental contributions of work applying CL methods over manual content analysis. Key conclusions emerging from our analysis are: (a) accounting and finance research is behind the curve in terms of CL methods generally and word sense disambiguation in particular; (b) implementation issues mean the proposed benefits of CL are often less pronounced than proponents suggest; (c) structural issues limit practical relevance; and (d) CL methods and high quality manual analysis represent complementary approaches to analyzing financial discourse. We describe four CL tools that have yet to gain traction in mainstream AF research but which we believe offer promising ways to enhance the study of meaning in financial discourse. The four approaches are named entity recognition, summarization, semantics and corpus linguistics