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

Impact of environmental inputs on reverse-engineering approach to network structures

Background: Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. Results: With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. Conclusion: We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations

Cointegration, causality and domestic portfolio diversification in the Cyprus Stock Exchange

In this paper we provide an investigation on the potential benefits that may exist for portfolio managers, private and institutional investors from domestic portfolio diversification. We employ daily data for the period 1996-2002 from the Cyprus Stock Exchange, recently established emerging market. Cointegration as well as linear and nonlinear causality analysis is used in order to reveal whether there are benefits from domestic portfolio diversification. The cointegration analysis leads to the conclusion that we are unable to reject the null hypothesis of no cointegration in most bivariate cases of the 56 pairs of sectoral indices and this finding is taken to imply that the are benefits from portfolio diversification, when domestic investors construct portfolios which include stocks from the sectors which are not cointegrated. Furthermore, the application of linear and nonlinear Granger causality leads to a pattern of causality between these pairs of sectoral indices which is almost identical and therefore the linearity hypothesis is rejected. Furthermore, based on our causality analysis we provide evidence that traders and investors in the CSE set up short-run investment strategies. Moreover, this implies that the Cypriot investors do not adopt contrarian and momentum investment strategies. Therefore, we argue that the investors in the Cyprus stock market exhibit myopic investment behaviour.cointegration, Granger causality, nonlinear causality, domestic portfolio