If you are starting out in the field of finance and you ask any experienced portfolio manager, investment banker or research analyst for advice on how to improve your skills, it’s very likely that one of their suggestions would be to read financial news. Most people working in financial services read financial and business news regularly in order to stay in touch with the markets. Watch for trends and look out for unexpected events that may affect their portfolios. The rise of social media has created another source that we can use to measure the pulse of the markets. For example, negative tweets about accompany whether thereby investors, or customers, may provide a lead for investors signaling that it may be a good idea to follow up and analyze that stock in more detail. Companies that receive lots of positive attention from both news, media and social media may have a higher chance of being over bought. As much of the general public hear as positive mentions about a particular company, they may also jump in to buy the stock hoping to ride the wave of a stock that appears to just keep going up. This may push the stock to a price that’s above what’s supported by fundamentals. At which point the stock may be a potential candidate to short sell. Notice how we don’t have to assume that every news article or a social media post is the definitive truth about the health of a company. This data is really measuring the sentiment of people whether they’re investors, customers, or public figures with lots of followers. Financial analysts have been applying natural language processing or NLP on news and social media to analyze public sentiment towards companies. In other words, they’re trying to turn raw text into a signal about whether people have a positive or negative view of a stock’s future. This is similar to tracking the sentiment of financial research analysts except with news and social media there isn’t always a clearly labeled buy, hold or sell rating. NLP can be used to estimate how likely a news article blog post or social media posts can be categorized as buy, hold or sell. In term two of this program, you will learn how to use NLP and deep learning to analyze social media data and apply these skills in order to generate Alpha factors.