Learning to Predict the Stock Market Dow Jones Index Detecting and Mining Relevant Tweets

Stock market analysis is a primary interest for finance and such a challenging task that has always attracted many researchers. Historically, this task was accomplished by means of trend analysis, but in the last years text mining is emerging as a promising way to predict the stock price movements. Indeed, previous works showed not only a strong correlation between financial news and their impacts to the movements of stock prices, but also that the analysis of social network posts can help to predict them. These latest methods are mainly based on complex techniques to extract the semantic content and/or the sentiment of the social network posts. Differently, in this paper we describe a method to predict the Dow Jones Industrial Average (DJIA) price movements based on simpler mining techniques and text similarity measures, in order to detect and characterise relevant tweets that lead to increments and decrements of DJIA. Considering the high level of noise in the social network data, w e also introduce a noise detection method based on a two steps classification. We tested our method on 10 millions twitter posts spanning one year, achieving an accuracy of 88.9% in the Dow Jones daily prediction, that is, to the best our knowledge, the best result in the literature approaches based on social networks

The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

Exploring the value of big data analysis of Twitter tweets and share prices

Over the past decade, the use of social media (SM) such as Facebook, Twitter, Pinterest and Tumblr has dramatically increased. Using SM, millions of users are creating large amounts of data every day. According to some estimates ninety per cent of the content on the Internet is now user generated. Social Media (SM) can be seen as a distributed content creation and sharing platform based on Web 2.0 technologies. SM sites make it very easy for its users to publish text, pictures, links, messages or videos without the need to be able to program. Users post reviews on products and services they bought, write about their interests and intentions or give their opinions and views on political subjects. SM has also been a key factor in mass movements such as the Arab Spring and the Occupy Wall Street protests and is used for human aid and disaster relief (HADR). There is a growing interest in SM analysis from organisations for detecting new trends, getting user opinions on their products and services or finding out about their online reputation. Companies such as Amazon or eBay use SM data for their recommendation engines and to generate more business. TV stations buy data about opinions on their TV programs from Facebook to find out what the popularity of a certain TV show is. Companies such as Topsy, Gnip, DataSift and Zoomph have built their entire business models around SM analysis. The purpose of this thesis is to explore the economic value of Twitter tweets. The economic value is determined by trying to predict the share price of a company. If the share price of a company can be predicted using SM data, it should be possible to deduce a monetary value. There is limited research on determining the economic value of SM data for “nowcasting”, predicting the present, and for forecasting. This study aims to determine the monetary value of Twitter by correlating the daily frequencies of positive and negative Tweets about the Apple company and some of its most popular products with the development of the Apple Inc. share price. If the number of positive tweets about Apple increases and the share price follows this development, the tweets have predictive information about the share price. A literature review has found that there is a growing interest in analysing SM data from different industries. A lot of research is conducted studying SM from various perspectives. Many studies try to determine the impact of online marketing campaigns or try to quantify the value of social capital. Others, in the area of behavioural economics, focus on the influence of SM on decision-making. There are studies trying to predict financial indicators such as the Dow Jones Industrial Average (DJIA). However, the literature review has indicated that there is no study correlating sentiment polarity on products and companies in tweets with the share price of the company. The theoretical framework used in this study is based on Computational Social Science (CSS) and Big Data. Supporting theories of CSS are Social Media Mining (SMM) and sentiment analysis. Supporting theories of Big Data are Data Mining (DM) and Predictive Analysis (PA). Machine learning (ML) techniques have been adopted to analyse and classify the tweets. In the first stage of the study, a body of tweets was collected and pre-processed, and then analysed for their sentiment polarity towards Apple Inc., the iPad and the iPhone. Several datasets were created using different pre-processing and analysis methods. The tweet frequencies were then represented as time series. The time series were analysed against the share price time series using the Granger causality test to determine if one time series has predictive information about the share price time series over the same period of time. For this study, several Predictive Analytics (PA) techniques on tweets were evaluated to predict the Apple share price. To collect and analyse the data, a framework has been developed based on the LingPipe (LingPipe 2015) Natural Language Processing (NLP) tool kit for sentiment analysis, and using R, the functional language and environment for statistical computing, for correlation analysis. Twitter provides an API (Application Programming Interface) to access and collect its data programmatically. Whereas no clear correlation could be determined, at least one dataset was showed to have some predictive information on the development of the Apple share price. The other datasets did not show to have any predictive capabilities. There are many data analysis and PA techniques. The techniques applied in this study did not indicate a direct correlation. However, some results suggest that this is due to noise or asymmetric distributions in the datasets. The study contributes to the literature by providing a quantitative analysis of SM data, for example tweets about Apple and its most popular products, the iPad and iPhone. It shows how SM data can be used for PA. It contributes to the literature on Big Data and SMM by showing how SM data can be collected, analysed and classified and explore if the share price of a company can be determined based on sentiment time series. It may ultimately lead to better decision making, for instance for investments or share buyback

Doctor of Philosophy

dissertationDue to the popularity of Web 2.0 and Social Media in the last decade, the percolation of user generated content (UGC) has rapidly increased. In the financial realm, this results in the emergence of virtual investing communities (VIC) to the investing public. There is an on-going debate among scholars and practitioners on whether such UGC contain valuable investing information or mainly noise. I investigate two major studies in my dissertation. First I examine the relationship between peer influence and information quality in the context of individual characteristics in stock microblogging. Surprisingly, I discover that the set of individual characteristics that relate to peer influence is not synonymous with those that relate to high information quality. In relating to information quality, influentials who are frequently mentioned by peers due to their name value are likely to possess higher information quality while those who are better at diffusing information via retweets are likely to associate with lower information quality. Second I propose a study to explore predictability of stock microblog dimensions and features over stock price directional movements using data mining classification techniques. I find that author-ticker-day dimension produces the highest predictive accuracy inferring that this dimension is able to capture both relevant author and ticker information as compared to author-day and ticker-day. In addition to these two studies, I also explore two topics: network structure of co-tweeted tickers and sentiment annotation via crowdsourcing. I do this in order to understand and uncover new features as well as new outcome indicators with the objective of improving predictive accuracy of the classification or saliency of the explanatory models. My dissertation work extends the frontier in understanding the relationship between financial UGC, specifically stock microblogging with relevant phenomena as well as predictive outcomes

Stock Market Prediction via Deep Learning Techniques: A Survey

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction

Critical review of text mining and sentiment analysis for stock market prediction

The paper is aimed at a critical review of the literature dealing with text mining and sentiment analysis for stock market prediction. The aim of this work is to create a critical review of the literature, especially with regard to the latest findings of research articles in the selected topic strictly focused on stock markets represented by stock indices or stock titles. This requires examining and critically analyzing the methods used in the analysis of sentiment from textual data, with special regard to the possibility of generalization and transferability of research results. For this reason, an analytical approach is also used in working with the literature and a critical approach in its organization, especially for completeness, coherence, and consistency. Based on the selected criteria, 260 articles corresponding to the subject area are selected from the world databases of Web of Science and Scopus. These studies are graphically captured through bibliometric analysis. Subsequently, the selection of articles was narrowed to 49. The outputs are synthesized and the main findings and limits of the current state of research are highlighted with possible future directions of subsequent research