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

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-known 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

Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods

A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is published on a regular basis and in most occasions is freely distributed. The present Ph.D. Thesis deals with the problem of inferring information - or patterns in general - about events emerging in real life based on the contents of this textual stream. We show that it is possible to extract valuable information about social phenomena, such as an epidemic or even rainfall rates, by automatic analysis of the content published in Social Media, and in particular Twitter, using Statistical Machine Learning methods. An important intermediate task regards the formation and identification of features which characterise a target event; we select and use those textual features in several linear, non-linear and hybrid inference approaches achieving a significantly good performance in terms of the applied loss function. By examining further this rich data set, we also propose methods for extracting various types of mood signals revealing how affective norms - at least within the social web's population - evolve during the day and how significant events emerging in the real world are influencing them. Lastly, we present some preliminary findings showing several spatiotemporal characteristics of this textual information as well as the potential of using it to tackle tasks such as the prediction of voting intentions.Comment: PhD thesis, 238 pages, 9 chapters, 2 appendices, 58 figures, 49 table

Analysing behavioural factors that impact financial stock returns. The case of COVID-19 pandemic in the financial markets.

This thesis represents a pivotal advancement in the realm of behavioural finance, seamlessly integrating both classical and state-of-the-art models. It navigates the performance and applicability of the Irrational Fractional Brownian Motion (IFBM) model, while also delving into the propagation of investor sentiment, emphasizing the indispensable role of hands-on experiences in understanding, applying, and refining complex financial models. Financial markets, characterized by ’fat tails’ in price change distributions, often challenge traditional models such as the Geometric Brownian Motion (GBM). Addressing this, the research pivots towards the Irrational Fractional Brownian Motion Model (IFBM), a groundbreaking model initially proposed by (Dhesi and Ausloos, 2016) and further enriched in (Dhesi et al., 2019). This model, tailored to encapsulate the ’fat tail’ behaviour in asset returns, serves as the linchpin for the first chapter of this thesis. Under the insightful guidance of Gurjeet Dhesi, a co-author of the IFBM model, we delved into its intricacies and practical applications. The first chapter aspires to evaluate the IFBM’s performance in real-world scenarios, enhancing its methodological robustness. To achieve this, a tailored algorithm was crafted for its rigorous testing, alongside the application of a modified Chi-square test for stability assessment. Furthermore, the deployment of Shannon’s entropy, from an information theory perspective, offers a nuanced understanding of the model. The S&P500 data is wielded as an empirical testing bed, reflecting real-world financial market dynamics. Upon confirming the model’s robustness, the IFBM is then applied to FTSE data during the tumultuous COVID-19 phase. This period, marked by extraordinary market oscillations, serves as an ideal backdrop to assess the IFBM’s capability in tracking extreme market shifts. Transitioning to the second chapter, the focus shifts to the potentially influential realm of investor sentiment, seen as one of the many factors contributing to fat tails’ presence in return distributions. Building on insights from (Baker and Wurgler, 2007), we examine the potential impact of political speeches and daily briefings from 10 Downing Street during the COVID-19 crisis on market sentiment. Recognizing the profound market impact of such communications, the chapter seeks correlations between these briefings and market fluctuations. Employing advanced Natural Language Processing (NLP) techniques, this chapter harnesses the power of the Bidirectional Encoder Representations from Transformers (BERT) algorithm (Devlin et al., 2018) to extract sentiment from governmental communications. By comparing the derived sentiment scores with stock market indices’ performance metrics, potential relationships between public communications and market trajectories are unveiled. This approach represents a melding of traditional finance theory with state-of-the-art machine learning techniques, offering a fresh lens through which the dynamics of market behaviour can be understood in the context of external communications. In conclusion, this thesis provides an intricate examination of the IFBM model’s performance and the influence of investor sentiment, especially under crisis conditions. This exploration not only advances the discourse in behavioural finance but also underscores the pivotal role of sophisticated models in understanding and predicting market trajectories

Developing Event Identification Methods for Structured and Unstructured Data Streams

Data, now more than ever before, are continuously being generated in huge volumes, andat rapid speed. Data may originate from various sources, for instance: sensor readings,financial transactions, social networks, etc.. A data stream is a continuous sequence ofdata arriving in almost real-time and often at a high speed. In this thesis, we are interested in benefiting from the availability of such data anddeveloping methods for detecting the occurrence of events from data streams, such as atext stream and a price time-series stream. Hence, we have explored event identificationfrom structured and unstructured data streams in the domain of finance. We employ the Directional Change (DC) approach to high frequency time-seriesstreams to identify significant price transitions (i.e. events). DC is an event-basedapproach for summarizing price movements based on a fixed, a-priori threshold. Wepropose a dynamic threshold definition method, which replaces the fixed threshold andis appropriate for markets that operate over specific opening and closing times. Adynamic threshold provides more flexibility and extends the DC approach allowing theidentification of price changes in continuously changing environments. With the proliferation of social media data reporting on all aspects of human activ-ity, being able to automatically identify events is becoming increasingly important. Wepresent a framework for detecting the occurring events on a daily basis, via social net-work streams. We develop and extend a Frequent Pattern Mining method by proposinga dynamic support definition method to replace the fixed support. As the number oftext posts streamed each day is not fixed, a dynamic support, can adapt to the natureof data streams and can improve the identification of events. Finally, we explore whether we can bring together the insights from the time-seriesstream and the social network stream to understand if events as identified from bothstreams can be correlated