A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market {\em per se}, whilst the latter draws more concerns on the banking sector and economic policies.Comment: 25 page

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

ESG in the financial industry: What matters for rating analysts?

This paper examines ESG rating analysts' views from Sustainalytics in order to highlight the main ESG features discussed across 11 sectors. We perform a topic modeling and a sentiment analysis to identify the content of analysts' opinions on the companies' ESG performance and to uncover the embedded sentiment associated with each ESG feature. The results of the topic modeling consist of 13 topics with a sector driven distribution. The analysis suggests that the best ESG performing financial institutions show to be actively committed to the code of best practice in governance and disclosure transparency. Whereas penalized financial entities seem to manifest less attention to ethical conduct and mis-selling. Furthermore, data privacy and security attract analysts' attention and should be closely monitored by financial entities. Finally, it is important to actively disclose ESG activities as the more information is available the better ESG commitment is reflected in analysts' views

Testing Market Response to Auditor Change Filings: a comparison of machine learning classifiers

The use of textual information contained in company filings with the Securities Exchange Commission (SEC), including annual reports on Form 10-K, quarterly reports on Form 10-Q, and current reports on Form 8-K, has gained the increased attention of finance and accounting researchers. In this paper we use a set of machine learning methods to predict the market response to changes in a firm\u27s auditor as reported in public filings. We vectorize the text of 8-K filings to test whether the resulting feature matrix can explain the sign of the market response to the filing. Specifically, using classification algorithms and a sample consisting of the Item 4.01 text of 8-K documents, which provides information on changes in auditors of companies that are registered with the SEC, we predict the sign of the cumulative abnormal return (CAR) around 8-K filing dates. We report the correct classification performance and time efficiency of the classification algorithms. Our results show some improvement over the naïve classification method

The Need for Transparency in Commodity and Commodity Derivatives Markets. ECMI Research Report No. 3, 15 December 2008

This paper argues that transparency-boosting measures specifically tailored to commodity and commodity derivatives markets are much needed. In particular, encouraging the creation of a clearing infrastructure for OTC commodity and commodity derivatives markets would be desirable. Moreover, EU regulators should consider setting up a new, more effective market abuse regime aimed at preventing manipulation in both the physical and financial commodities markets. Finally, in cooperation with the G20, EU authorities should consider the creation of an International Commodity Agency to increase transparency and restore confidence in international physical markets for commodities. The paper is structured as follows: Section 2 briefly discusses the fundamentals of commodity spot and futures markets. Section 3 presents both physical commodity markets and commodity derivative markets in their usual breakdown categories: agriculture, metals and energy. Section 4 discusses the regulations in the EU and the US concerning commodity derivatives. Section 5 advances certain policy proposals and the last section draws the conclusions