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

Information diffusion in the U.S. real estate investment trust market

This study examines the information diffusion process in the U.S. Real Estate Investment Trust (REIT) market with a focus on the impacts of changing market environments, information supply, and information demand on the lead-lag effect. The results suggest that a significant lead-lag relationship exists between the lagged returns of big REITs and the current returns of small REITs. This relationship has slightly decreased along with policy and environment changes that occurred in the U.S. REIT market during the study period from 1986 to 2012, while still remaining significant in the most recent REIT market. The process of information diffusion is becoming unstable in recent years and the reverse lead-lag effect from small REITs to big REITs is observed especially when REIT market liquidity and return volatility are high. The lead-lag effect among REITs is driven largely by slow adjustment to negative information, which is magnified by a lack of information supply, especially as demand for such information increases. Finally, information flow from REITs with more media coverage to those with less media coverage becomes even more sluggish than the information flow from big REITs to small REITs

Being on the field when the game is still under way. The financial press and stock markets in times of crisis

This paper looks at the relationship between negative news and stock markets in times of global crisis, such as the 2008/2009 period. We analysed one year of front page banner headlines of three financial newspapers, the Wall Street Journal, Financial Times, and Il Sole24ore to examine the influence of bad news both on stock market volatility and dynamic correlation. Our results show that the press and markets influenced each other in generating market volatility and in particular, that the Wall Street Journal had a crucial effect both on the volatility and correlation between the US and foreign markets. We also found significant differences between newspapers in their interpretation of the crisis, with the Financial Times being significantly pessimistic even in phases of low market volatility. Our results confirm the reflexive nature of stock markets. When the situation is uncertain and unpredictable, market behaviour may even reflect qualitative, big picture, and subjective information such as streamers in a newspaper, whose economic and informative value is questionable

Mood and the Market: Can Press Reports of Investors’ Mood Predict Stock Prices?

We examined whether press reports on the collective mood of investors can predict changes in stock prices. We collected data on the use of emotion words in newspaper reports on traders’ affect, coded these emotion words according to their location on an affective circumplex in terms of pleasantness and activation level, and created indices of collective mood for each trading day. Then, by using time series analyses, we examined whether these mood indices, depicting investors’ emotion on a given trading day, could predict the next day’s opening price of the stock market. The strongest findings showed that activated pleasant mood predicted increases in NASDAQ prices, while activated unpleasant mood predicted decreases in NASDAQ prices. We conclude that both valence and activation levels of collective mood are important in predicting trend continuation in stock prices

Big Data Financial Sentiment Analysis in the European Bond Markets

We exploit the novel Global Database of Events, Language and Tone (GDELT) to construct news-based financial sentiment measures capturing investor\u2019s opinions for three European countries, Italy, Spain and France. We study whether deterioration in investor\u2019s sentiment implies a rise in interest rates with respect to their German counterparts. Finally, we look at the link between agents\u2019 sentiment and their portfolio exposure on the Italian, French and Spanish markets

Predikce abnormálních výnosů bank pomocí textové analýzy výročních zpráv - Přístup založený na neuronových sítích

This paper aims to extract both sentiment and bag-of-words information from the annual reports of U.S. banks. The sentiment analysis is based on two commonly used finance-specific dictionaries, while the bag-of-words are selected according to their tf-idf. We combine these features with financial indicators to predict abnormal bank stock returns using a neural network with dropout regularization and rectified linear units. We show that this method outperforms other machine learning algorithms (Naïve Bayes, Support Vector Machine, C4.5 decision tree, and k-nearest neighbour classifier) in predicting positive/negative abnormal stock returns. Thus, this neural network seems to be well suited for text classification tasks working with sparse high-dimensional data. We also show that the quality of the prediction significantly increased when using the combination of financial indicators and bigrams and trigrams, respectively.Tento článek si klade za cíl získávat z výročních zpráv amerických bank jak sentiment, tak informaci ve formě bag-of-words. Analýza sentimentu je založena na dvou běžně používaných finančních slovnících, zatímco bag-of-words jsou vybírány v závislosti na tf-idf. Kombinujeme tyto atributy společně s finančními ukazateli s cílem predikce abnormálních výnosů bank pomocí neuronové sítě s regularizací a rektifikovanými lineárními jednotkami. Ukázali jsme, že tato metoda překonává ostatní algoritmy strojového učení (Naivní Bayes, podpůrné vektorové stroje, rozhodovací strom C4.5 a klasifikátor k-nejbližšího souseda) v predikci pozitivních/negativních abnormálních výnosů. Proto se tato neuronová síť zdá být vyhovující pro úlohy klasifikace textu, kde se pracuje s řídkými vysoce dimenzionálními daty. Také ukazujeme, že se kvalita predikce významně zvýšila při použití kombinace finančních ukazatelů a bigramů (trigramů)