On the predictability of stock market behavior using StockTwits sentiment and posting volume

Series title : Lecture notes in computer science, vol. 8154Inthisstudy,weexploreddatafromStockTwits,amicroblog- ging platform exclusively dedicated to the stock market. We produced several indicators and analyzed their value when predicting three market variables: returns, volatility and trading volume. For six major stocks, we measured posting volume and sentiment indicators. We advance on the previous studies on this subject by considering a large time period, using a robust forecasting exercise and performing a statistical test of forecasting ability. In contrast with previous studies, we find no evidence of return predictability using sentiment indicators, and of information content of posting volume for forecasting volatility. However, there is ev- idence that posting volume can improve the forecasts of trading volume, which is useful for measuring stock liquidity (e.g. assets easily sold).This work is funded by FEDER, through the program COMPETE and the Portuguese Foundation for Science and Technology (FCT), within the project FCOMP-01-0124-FEDER-022674. The also authors wish to thank StockTwits for kindly providing their data

Social Media Sentiment and Stock Return: A Signalling Theory Explanation for Application of the Natural Langrage Processing Approaches

Social media, especially microblogs, have potentials to develop significant unavoidable factors in investment decision-making, because of its use for capturing human sentiment. In this paper, by applying Signalling theory and Natural Language Processing (NLP) technique, we concern social media sentiment as a signal to stock return which is based on human the sentiment, which may lead to price fluctuation in the market. We take the strength of signal into consideration, introducing the sentiment of traditional media to compare with social media sentiment in different industry. The empirical result of this paper will prove the relationship between social media sentiment and stock return. It will also reflect on analyzing the changes of stock price given different strength of signals in both positive and negative way. The entire study will be viewed as a guideline for investors to filter and smartly use the huge numbers of information when making investment decision

You are What You Say: The Influence of Company Tweets on Its Stock Performance

This paper investigates the relationship between Twitter metrics and stock price performance of a company. The objective of this research is to contribute to the area of research that seeks to uncover the business value of social media platforms. Building on prior research, this paper identifies two categories of metrics that have been used to examine the relationship between Twitter metrics and stock performance of a company, namely traffic and motivation. While traffic is measured as volume of tweets, motivation is measured from two perspectives; polarity (positive, neutral, and negative) and emotion (positive emotion and negative emotion). Unstructured data from Twitter and Yahoo finance Website about Amazon was gathered to test the study hypothesis. A combination of machine learning techniques for text analytics and hierarchical regression analysis was used to analyze the data. Results indicate that emotional motivation expressed in tweets sent out by a company positively influences the company’s stock performance

Social Media and Forecasting Stock Price Change

The Stock Market is a big influence on both national and international economies. Stock prices are driven by a number of factors: industry performance, company news and performance, investor confidence, micro and macro economic factors like employment rates, wage rates, etc. Stock pricing trends can be gauged from the factors that drive it as well as from the stock\u27s historical performance. As fluctuations in stock prices become more volatile and unpredictable, forecasting models help reduce some of the randomness involved in investing and financial decision making. Users on social media platforms like twitter, StockTwits, and eToro discuss issues related to the stock market. Can the analysis of posts on StockTwits add value to the existing features of stock price predicting models? An existing model that uses twits as features was extended to include sentiment analysis of the text referenced by the URL in the twits to see if the model accuracy did improve. Initial results indicate that the addition of sentiment analysis of the text referenced by the URL does not improve the performance of the model when all twits for a given day are taken into account since the model only identifies the direction of change and not the degree of change. The stock prediction model achieves 65% accuracy compared to the base case accuracy of 44% and augmenting the model with sentiment analysis did not change the accuracy. The study highlights some interesting observations regarding users on the StockTwits social media platform and proposes the need for a domain specific sentiment analyzer in future work

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

Political-obsessed environment and investor sentiments: pricing liquidity through the microblogging behavioral perspective

Pakistan's political instability has pushed its economic system to the brink of collapse. Considering this political turmoil, this study addresses the behavior of liquidity providers against microblogging-opinionated information. The behavioral perspective was quantified through multiple linear regressions, the Bayesian theorem, and the vector error correction technique. Before this political crisis, sentiment indicators were linked to the liquidity-conditional cost for the same trading session. In the political uncertainty environment, pessimistic opinions were the sole concern of the liquidity providers during the same trading session. The liquidity facilitator was observed to price the liquidity in light of pessimistic sentiments. The Bayesian theorem suggested a higher posterior probability for the occurrence of the liquidity-facilitating cost in response to the pessimistic sentiments. Nevertheless, the past time series changes for the sentiment indicators were irrelevant in determining changes in cost-based liquidity for the next trading session

Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals

The increasing availability of "big" (large volume) social media data has motivated a great deal of research in applying sentiment analysis to predict the movement of prices within financial markets. Previous work in this field investigates how the true sentiment of text (i.e. positive or negative opinions) can be used for financial predictions, based on the assumption that sentiments expressed online are representative of the true market sentiment. Here we consider the converse idea, that using the stock price as the ground-truth in the system may be a better indication of sentiment. Tweets are labelled as Buy or Sell dependent on whether the stock price discussed rose or fell over the following hour, and from this, stock-specific dictionaries are built for individual companies. A Bayesian classifier is used to generate stock predictions, which are input to an automated trading algorithm. Placing 468 trades over a 1 month period yields a return rate of 5.18%, which annualises to approximately 83% per annum. This approach performs significantly better than random chance and outperforms two baseline sentiment analysis methods tested.Comment: 8 pages, 6 figures. To be presented at IEEE Symposium on Computational Intelligence in Financial Engineering (CIFEr), Bengaluru, November 18-21, 201