For people who are not exposed to the financial markets but who would be interested to invest in stocks for them it is important to know which ticker symbols to follow, based on which investment decisions can be made. In this work, we propose how we can classify stock ticker symbols from tweets vertically using P-Trees. The solution described in this paper analyzes 3000 financial and news symbols vertically from the Twitter platform and finds the ticker symbols which are most frequently been discussed. It also provides an ability to scan through the tweet texts associated with the common ticker occurrences so that the context can be identified to help users make better informed business decisions. The paper also discusses on the bias of investors and the affect it has on the volatility of the stocks in the market.
['Arijit Chatterjee', 'William Perrizo']
Classifying Stocks using P-Trees and Investor Sentiment