Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter

Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never systematically been investigated before. Here, we study 9M tweets related to stocks of the 5 main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice - referred to as cashtag piggybacking - perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Among the findings of our study is that as much as 71% of the authors of suspicious financial tweets are classified as bots by a state-of-the-art spambot detection algorithm. Furthermore, 37% of them were suspended by Twitter a few months after our investigation. Our results call for the adoption of spam and bot detection techniques in all studies and applications that exploit user-generated content for predicting the stock market

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

Identifying Expert Investors on Financial Microblog via Artificial Neural Networks

In the recent years, thanks to social media platform, a plethora of information has been available to financial investors, that were traditionally dependent from financial institutions advisors. Strategies are now shared among web users, performances of stocks are commented in web communities and hints and suggestions are travelling on the internet with a fast pace, in a way that was unthinkable few years before. Several attempts have been made in the recent past, to predict Market movements and trends from activity of Financial Social Networks participants, and to evaluate if contributions from individuals with high level of expertise distinguish themselves from the rest of crowd. The Present Work is leveraging 6 years of tweets extracted from the financial platform StockTwits.com, deep diving in its content, and proposing a predictive Neural Network algorithm of Multi-Layer Perceptron type, based on features derived from text, social network and sentiment analysis. Users have been classified based on the performance achieved during the training, consistence of their prediction has been verified throughout the time and, finally, a trading strategy has been proposed based on following the top actors. The outcomes highlighted that expert investors are outperforming the wisdom of the crowd, and the trading schema put together generated a return of 38.6%, in 2015, when S&P500 had a slightly negative balance

Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks

Financial news contains useful information on public companies and the market. In this paper we apply the popular word embedding methods and deep neural networks to leverage financial news to predict stock price movements in the market. Experimental results have shown that our proposed methods are simple but very effective, which can significantly improve the stock prediction accuracy on a standard financial database over the baseline system using only the historical price information.Comment: 5 pages, 2 figures, technical repor

An experiment in integrating sentiment features for tech stock prediction in Twitter

Economic analysis indicates a relationship between consumer sentiment and stock price movements. In this study we harness features from Twitter messages to capture public mood related to four Tech companies for predicting the daily up and down price movements of these companies’ NASDAQ stocks. We propose a novel model combining features namely positive and negative sentiment, consumer confidence in the product with respect to ‘bullish’ or ‘bearish’ lexicon and three previous stock market movement days. The features are employed in a Decision Tree classifier using cross-fold validation to yield accuracies of 82.93%,80.49%, 75.61% and 75.00% in predicting the daily up and down changes of Apple (AAPL), Google (GOOG), Microsoft (MSFT) and Amazon (AMZN) stocks respectively in a 41 market day sample

Exploring Financial Microblogs: Analysis of Users' Trading Profiles with Multivariate Statistical Methods

StockTwits is a Social Media focused on finance that is receiving increasing attention from finance experts and enthusiasts. In this work, StockTwits’ users are studied considering some of their self-declared characteristics, such as trading experience, holding period of the stocks, and trading approach. A Correspondence Analysis is carried out to investigate the relationships among these characteristics, the Simple Correspondence Analysis is applied to study the relationships between the approach and the holding period. The association between these variables and the experience is studied with the Multiple Correspondence Analysis. In the end, a cluster analysis carried out with hierarchical clustering is used to study the structure of the StockTwits community on the basis of the same characteristics. The analyses highlighted that the way users label their own approach and primary holding period reflect the objective relation linking technical strategy with short-term investments and fundamental approach with long-term ones. Moreover, it showed a weak relation of the experience in trading with the other features, configuring self-reported experience as a more cross-sectional characteristic

A Sentiment Analysis of Twitter Content as a Predictor of Exchange Rate Movements

Recently, social media, particularly microblogs, have become highly valuableinformation resources for many investors. Previous studies examined general stockmarket movements, whereas in this paper, USD/TRY currency movements based on thechange in the number of positive, negative and neutral tweets are analyzed. Weinvestigate the relationship between Twitter content categorized as sentiments, such asBuy, Sell and Neutral, with USD/TRY currency movements. The results suggest thatthere exists a relationship between the number of tweets and the change in USD/TRYexchange rate