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Predict financial and stock markets with textual analysis
Textual analysis in financial markets consists of analyzing text data (news, tweets...) in order to determine the general sentiment of the market and predict its direction. The easiest technique to detect the sentiment of a news release or a tweet is by counting the number of positive words and subtracting them by the number of negative words. For this, we will need a dictionary for sentiment analysis or a list of positive and negative words in a financial context.
Loughran and McDonald Financial Sentiment Dictionaries are used by this textual analysis function. Positive and negative words are hard coded. You can find the list by opening the function using "Tools -> Create Functions". There you will find approximately 353 positive words (example: improve, profitable, efficient...) and 2337 negative words (example: felony, deterioration, termination...).
This sentiment analysis function gets text data from the database and field name you provide. It then reads each item and gives it a score based on how many positive and negative words it contains. If there are several trading news or tweets in the same bar then the result is summed up.
Example: (Plot the score of each tweet on a chart)
b = SentimentPN("stocktwits", "title");
plot(b, "", colorBlue);
Trading financial instruments, including foreign exchange on margin, carries a high level of risk and is not suitable for all investors. The high degree of leverage can work against you as well as for you. Before deciding to invest in financial instruments or foreign exchange you should carefully consider your investment objectives, level of experience, and risk appetite. The possibility exists that you could sustain a loss of some or all of your initial investment and therefore you should not invest money that you cannot afford to lose. You should be aware of all the risks associated with trading and seek advice from an independent financial advisor if you have any doubts.
