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MACHINE LEARNING IN THE FINANCE DOMAIN

STOCK MARKET DATA

The stock data contains the following fields: Open Price:The open price is the price at which a stock starts trading at the beginning of a trading session

High Price: indicates the maximum price reached by the stock during the day and reflects the level of buying pressure and bullish sentiment.

Low Price:It indicates the minimum price reached by the stock during the day and reflects the level of selling pressure and bearish sentiment.

Close Price:The close price is final price at which a stock trades at the end of a trading session

Volume:The volume is the number of shares traded during a trading session

HEADLINES

The headlines contains the news headlines for each day, a particular day coulf have as many different headlines as possible The fields include headlines city from which the head lines are made

how the analysis is done

The headlines is first processed so that we can know the degree of sentiment that is being expressed in the in the news, whether positive , negative or neutral These Sentiment analysis techniques are employed to evaluate the mood of investors and assess its potential impact on stock prices, more negative sentiment could potentially lower the stock prices driving more the sale of stock prices

The stock market data is then combined with the news headlines for that particular stock market day.

The end goal

The end goal is to provide a guide whether an investor should go ahead and purchase a stock depending on the historical trend and what the predictions are saying about the next days' market . Should the predcategory into the future be good, then the investor can take the leap of faith and buy a stock.

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Using machine learning and natural language processing to predict the stock market prices

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