Sentiment analysis algorithms and applications: A survey

Investor Psychology and Security Market Under- and Overreactions

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

The quality of the interpretation of the sentiment in the online buzz in the social media and the online news can determine the predictability of financial markets and cause huge gains or losses. That is why a number of researchers have turned their full attention to the different aspects of this problem lately. However, there is no well-rounded theoretical and technical framework for approaching the problem to the best of our knowledge. We believe the existing lack of such clarity on the topic is due to its interdisciplinary nature that involves at its core both behavioral-economic topics as well as artificial intelligence. We dive deeper into the interdisciplinary nature and contribute to the formation of a clear frame of discussion. We review the related works that are about market prediction based on online-text-mining and produce a picture of the generic components that they all have. We, furthermore, compare each system with the rest and identify their main differentiating factors. Our comparative analysis of the systems expands onto the theoretical and technical foundations behind each. This work should help the research community to structure this emerging field and identify the exact aspects which require further research and are of special significance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Nassirtoussi%20-%20Text%20Mining%20for%20Market%20Prediction%20-%202014.pdf

Review: Text mining for market prediction: A systematic review

An Empirical Analysis of the Dynamic Relation between Investment-Grade Bonds and Credit Default Swaps

We examine whether stock price prediction based on textual information in financial news can be improved as previous approaches only yield prediction accuracies close to guessing probability. Accordingly, we enhance existing text mining methods by using more expressive features to represent text and by employing market feedback as part of our feature selection process. We show that a robust feature selection allows lifting classification accuracies significantly above previous approaches when combined with complex feature types. This is because our approach allows selecting semantically relevant features and thus, reduces the problem of over-fitting when applying a machine learning approach. We also demonstrate that our approach is highly profitable for trading in practice. The methodology can be transferred to any other application area providing textual information and corresponding effect data.

Automated news reading: Stock price prediction based on financial news using context-capturing features

We examine whether stock price effects can be automatically predicted analyzing unstructured textual information in financial news. Accordingly, we enhance existing text mining methods to evaluate the information content of financial news as an instrument for investment decisions. The main contribution of this paper is the usage of more expressive features to represent text and the employment of market feedback as part of our word selection process. In our study, we show that a robust Feature Selection allows lifting classification accuracies significantly above previous approaches when combined with complex feature types. That is because our approach allows selecting semantically relevant features and thus, reduces the problem of over-fitting when applying a machine learning approach. The methodology can be transferred to any other application area providing textual information and corresponding effect data.

Automated News Reading: Stock Price Prediction Based on Financial News Using Context-Specific Features

Extreme events (such as natural disasters, political upheaval, economic crises) typically have a strong impact on crude oil markets and related price fluctuations and may eventually emerge to global oil crises. This study attempts to early detect such events based on the predictive power of online news messages. Text mining algorithms are used to turn unstructured news into actionable information and to determine which news can be regarded as relevant for the oil market. Over 45 million news messages have been examined. A decision support system is constructed which uses an indicator metric to set off an alarm based on information gathered from current and historic news stories. Regression analyses statistically attest the predictive power of online news messages and thus demonstrate the potential of the early warning system. The effect on the price of crude oil is statistically significant.

Early Warning of Impending Oil Crises Using the Predictive Power of Online News Stories

Information availability plays an important role in the efficient resource allocation of electronic markets and e-commerce. Most of this information is of qualitative nature containing essential facts that are, however, difficult to decode. Currently, the information processing capabilities of human agents facing such qualitative news is mostly unknown. Accordingly, it is crucial to understand how different decision makers process qualitative information. In this paper we show that sentiment-analysis facilitates research in qualitative information processing. We use a capital market example to demonstrate how investors and analysts perceive novel information. We find that their interpretation is different from one another: investors rapidly translate novel information into transactions, whereas analysts take more time to respond. We further observe that analysts emphasize different parts of information than investors, and are less put-off by complex information. The approach can be applied to other electronic-markets and the e-commerce industry where individuals react upon textual information.

https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1142&context=icis2012

Information Processing in Electronic Markets: Measuring Subjective Interpretation Using Sentiment Analysis

This paper investigates whether news momentum can predict medium-term stock index developments. News momentum can be built by aggregating tone of news over the past weeks. We find that news momentum can predict future stock price developments and establish profitable trading strategies that beat buy-and-hold and momentum benchmarks. Trades are issued for significant changes in momentum between current and prior weeks. We ensure stability of our results by using two different news data sets and by analyzing both different investment horizons and aggregation times for our news momentum. Compared to intraday news trading, medium-term momentum trading allows higher investment volumes and can contribute to complex investment decisions also incorporating other qualitative and quantitative factors.

https://www.computer.org/csdl/pds/api/csdl/proceedings/download-article/12OmNzwZ6wi/pdf

Michael Liebmann

Papers

Automated news reading: Stock price prediction based on financial news using context-capturing features

Automated News Reading: Stock Price Prediction Based on Financial News Using Context-Specific Features

Early Warning of Impending Oil Crises Using the Predictive Power of Online News Stories

Information Processing in Electronic Markets: Measuring Subjective Interpretation Using Sentiment Analysis

Reading All the News at the Same Time: Predicting Mid-term Stock Price Developments Based on News Momentum