News which Moves the Market: Assessing the Impact of Published Financial News on the Stock Market

Recent years have seen a large increase in the volume of financial news available to investors daily. What has traditionally been restricted to print media has now evolved to include the internet and satellite television as important media sources for financial news. With this overwhelming flow of information available to investors, the impact of financial news on market prices is at best uncertain. In this paper, a computational text-scoring methodology will be employed to uncover behavioral responses by investors to negative news. The empirical methodology employed in this paper will consist of three parts. Firstly, through the General Inquirer (GI) content analysis software, a sentiment score is derived from daily news articles published in the Wall Street Journal. The second part will be an analysis of the sentiment time series which was obtained, where comparison will be made to existing barometers of market sentiment and market volatility. The final part of the modeling methodology which will be presented is a predictive model of market implied volatility using daily news scores as the main input. In conclusion, it is found that high negative news scores do not necessarily predict negative abnormal returns in the S&P 500 across a 1-day to 5-day window. However, high negative news scores are highly correlated with higher market volatility. Given that the negative news is published prior to the market's trading start in the morning; we are able to utilize this information to construct a predictive model of the CBOE VIX index

Social Network Financial Sentiment: Constructing Proxies and Testing Returns Predictability on S&P500 Futures Returns

This article examines the ability of StockTwits social network sentiment proxies to predict S&P500 Futures. Positive and negative levels and first-difference sentiment proxies were constructed from 59,907,378 tweets. Using the lexicon approach and Loughran-McDonald positive and negative word lists, this study considers the tweets’ informal language and 140-character constraint. It was found that one standard deviation of change in negative word sentiment compared to the previous day predicts lower S&P500 Futures by 3.4 basis points after controlling for past returns and macroeconomic variables. The results are robust to macro announcements, futures turnover, major Asian and European market returns, the day-of-the-week effect, the January effect, and the holiday effect. Investors can easily replicate the methodology to construct the social network sentiment proxies introduced in this study and employ these proxies in their investment strategies. This study hopes to spur more research to construct and improve social network sentiment proxies for various financial markets

Two Essays on Investor Emotions and Their Effects in Financial Markets

This dissertation provides empirical evidences on media-based investor emotions in predicting stock return, conditional volatility, and stock and bond return comovements. We first studied the interaction between US media content and the US stock market returns and volatility. We utilize propriety investor sentiment measures developed by Thompson Reuters MarketPsych. We select four measures of investor sentiment that reflect both pessimism and optimism of small investors. Our objective is two-fold. First, we examine the ability of these sentiment measures to predict market returns. For this purpose, we use dynamic Vector Auto-Regressive models. Second, we are interested in exploring the effects of these sentiment measures on the market returns and volatility. For this purpose, we utilize a Threshold-GARCH model. Next, we investigated the effect of investor emotions (fear, gloom, joy and optimism) in financial futures markets by using Thompson Reuters MarketPsych Indices. The purpose of this study is three fold. First, we investigate the extent of usefulness of informational content of our sentiment measures in predicting stock futures and treasures futures returns using daily data for different measures of emotional sentiments. Second, we investigate whether emotion sentiments affect financial futures returns and volatilities. Third, we explore the role of emotion sentiment factors in volatility transmission in financial futures markets. To the best of our knowledge, this is the first study that extensively explores the role of investors’ sentiment in the most liquid contracts (S&P 500 futures and 10-year Treasury notes) in futures markets

Essays on Financial Applications of Nonlinear Models

In this thesis, we examine the relationship between news and the stock market. Further, we explore methods and build new nonlinear models for forecasting stock price movement and portfolio optimization based on past stock prices and on one type of big data, news items, which are obtained through the RavenPack News Analytics Global Equities editions. The thesis consists of three essays. In Essay 1, we investigate the relationship between news items and stock prices using the artificial neural network (ANN) model. First, we use Granger causality to ascertain how news items affect stock prices. The results show that news volume is not the Granger cause of stock price change; rather, news sentiment is. Second, we test the semi–strong form efficient market hypothesis, whereas most existing research testing efficient market hypothesis focuses on the weak–form version. Our ANN strategies consistently outperform the passive buy–and–hold strategy and this finding is apparently at odds with the notion of the efficient market hypothesis. Finally, using news sentiment analytics from RavenPack Dow Jones News Analytics, we show positive profitability with out–of–sample prediction using the proposed ANN strategies for Google Inc. (NASDAQ: GOOG). In Essay 2, we expand the utility of the information from news volume and news sentiments to encompass portfolio diversification. For the Dow Jones Industrial Average (DJIA) components, we assign different weights to build portfolios according to their weekly news volumes or news sentiments. Our results show that news volume contributes to portfolio variance both in–sample and out–of–sample: positive news sentiment contributes to the portfolio return in–sample, while negative contributes to the portfolio return out–of–sample, which is a consequence of investors overreacting to the news sentiment. Further, we propose a novel approach to portfolio diversification using the k–Nearest Neighbors (kNN) algorithm based on the idea that news sentiment correlates with stock returns. Out–of–sample results indicate that such strategy dominates the benchmark DJIA index portfolio. In Essay 3, we propose a new model called the Combined Markov and Hidden Markov Model (CMHMM), in which observation is affected by a Markov model and an HMM (Hidden Markov Model) model. The three fundamental questions of the CMHMM are discussed. Further, the application of the CMHMM, in which the news sentiment is one observation and the stock return is the other, is discussed. The empirical results of the trading strategy based on the CMHMM show the potential applications of the proposed model in finance. This thesis contributes to the literature in a number of ways. First, it extends the literature on financial applications of nonlinear models. We explore the applications of the ANNs and kNN in the financial market. Besides, the proposed new CMHMM model adheres to the nature of the stock market and has better potential prediction ability. Second, the empirical results from this dissertation contribute to the understanding of the relationship between news and the stock market. For instance, our research found that news volume contributes to the portfolio return and that investors overreact to news sentiment—a phenomenon that has been discussed by other scholars from different angles

A framework for smart traffic management using heterogeneous data sources

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Traffic congestion constitutes a social, economic and environmental issue to modern cities as it can negatively impact travel times, fuel consumption and carbon emissions. Traffic forecasting and incident detection systems are fundamental areas of Intelligent Transportation Systems (ITS) that have been widely researched in the last decade. These systems provide real time information about traffic congestion and other unexpected incidents that can support traffic management agencies to activate strategies and notify users accordingly. However, existing techniques suffer from high false alarm rate and incorrect traffic measurements. In recent years, there has been an increasing interest in integrating different types of data sources to achieve higher precision in traffic forecasting and incident detection techniques. In fact, a considerable amount of literature has grown around the influence of integrating data from heterogeneous data sources into existing traffic management systems. This thesis presents a Smart Traffic Management framework for future cities. The proposed framework fusions different data sources and technologies to improve traffic prediction and incident detection systems. It is composed of two components: social media and simulator component. The social media component consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated using Natural Language Processing (NLP) techniques. Finally, with the purpose of further analysing user emotions within the tweet, stress and relaxation strength detection is performed. The proposed text classification algorithm outperformed similar studies in the literature and demonstrated to be more accurate than other machine learning algorithms in the same dataset. Results from the stress and relaxation analysis detected a significant amount of stress in 40% of the tweets, while the other portion did not show any emotions associated with them. This information can potentially be used for policy making in transportation, to understand the users��� perception of the transportation network. The simulator component proposes an optimisation procedure for determining missing roundabouts and urban roads flow distribution using constrained optimisation. Existing imputation methodologies have been developed on straight section of highways and their applicability for more complex networks have not been validated. This task presented a solution for the unavailability of roadway sensors in specific parts of the network and was able to successfully predict the missing values with very low percentage error. The proposed imputation methodology can serve as an aid for existing traffic forecasting and incident detection methodologies, as well as for the development of more realistic simulation networks

A system to predict the S&P 500 using a bio-inspired algorithm

The goal of this research was to develop an algorithmic system capable of predicting the directional trend of the S&P 500 financial index. The approach I have taken was inspired by the biology of the human retina. Extensive research has been published attempting to predict different financial markets using historical data, testing on an in-sample and trend basis with many employing sophisticated mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, I moved to an out-of-sample strategy and am able to predict tomorrow’s (t+1) directional trend of the S&P 500 at 55.1%. The key elements that underpin my bio-inspired out-of-sample system are: Identification of 51 financial market data (FMD) inputs, including other indices, currency pairs, swap rates, that affect the 500 component companies of the S&P 500. The use of an extensive historical data set, comprising the actual daily closing prices of the chosen 51 FMD inputs and S&P 500. The ability to compute this large data set in a time frame of less than 24 hours. The data set was fed into a linear regression algorithm to determine the predicted value of tomorrow’s (t+1) S&P 500 closing price. This process was initially carried out in MatLab which proved the concept of my approach, but (3) above was not met. In order to successfully meet the requirement of handling such a large data set to complete the prediction target on time, I decided to adopt a novel graphics processing unit (GPU) based computational architecture. Through extensive optimisation of my GPU engine, I was able to achieve a sufficient speed up of 150x to meet (3). In achieving my optimum directional trend of 55.1%, an extensive range of tests exploring a number of trade offs were carried out using an 8 year data set. The results I have obtained will form the basis of a commercial investment fund. It should be noted that my algorithm uses financial data of the past 60-days, and as such would not be able to predict rapid market changes such as a stock market crash

Empirical Analysis of Natural Gas Markets

Recent developments in the natural gas industry warrant new analysis of related issues. Environmental, social, and governance (ESG) investments have accelerated the shift away from coal as the dominant source of electricity. Its low environmental impact, reduced volume, and broad availability make liquefied natural gas (LNG) a popular alternative, during this time of transition between traditional fuels and newer options. In the United States, the shale gas revolution has made natural gas a game changer. In this book, we focus on empirical analyses of the natural gas market and its growing relevance worldwide