TAILIEUCHUNG - Twitter mood predicts the stock market.

In order to increase competitiveness of the company in this economic situation, the production volumes are being increased by more rational use of company resources, by organization of long-term cooperation with providers of resources and by searching for new markets and analyzing client’s solvency. The increase of production volumes is being based on the existing, already concluded realization agreements and already signed letters of intent. The interests of the commercial company are the care for social protection and welfare of its employees, because only thus the development of society can be sustainable. In order to reduce financial risks,. | 1 Twitter mood predicts the stock market. Johan Bollen1 Huina Mao1 Xiao-Jun Zeng2. authors made equal contributions. arXiv 14 Oct 2010 Abstract Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large . can societies experience mood states that affect their collective decision making By extension is the public mood correlated or even predictive of economic indicators Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average DJIA over time. We analyze the text content of daily Twitter feeds by two mood tracking tools namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States GPOMS that measures mood in terms of 6 dimensions Calm Alert Sure Vital Kind and Happy . We cross-validate the resulting mood time series by comparing their ability to detect the public s response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states as measured by the OpinionFinder and GPOMS mood time series are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6 . Index Terms stock market prediction twitter mood analysis. I. INTRODUCTION STOCK market prediction has attracted much attention from academia as well as business. But can the stock market really be predicted Early research on stock market prediction 1 2 3 was based on random walk theory and the Efficient Market .

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