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Predicting Market-Volatility from Federal Reserve Board Meeting Minutes NLP for Finance

Predicting markets has always had a certain appeal to researchers, and news can have huge impacts on markets. Announcements about corporate profits (or lack thereof), a change in management, a new promising product, talk of a takeover, even the weather. All these events can cause a company’s share price to move wildly up or down. To automate trading strategies that can leverage this news information, the mining and sifting of news takes place in computers dedicated to algorithmic trading, removing human emotions from the decision making process and speeding up reactions. The meetings of the federal reserve should also have a huge impact on the market since fiscal policy has a direct impact on the economy. The use of tools from natural language processing for finance has been studied in numerous publications. A survey is available in (Mittermayer and Knolmayer, 2006a). In this work, we perform experiments in predicting volatilities of several financial indicators based on the publicly available minutes of the Federal Reserve Board meetings.