Transformations  in
Business & Economics

USING RULE TEXT MINING BASED ALGORITHM TO SUPPORT THE STOCK MARKET INVESTMENT DECISION
Salam Al-augby, Kesra Nermend

ABSTRACT. This work aims to design and implement a rule text-mining based algorithm to analyse the headlines automatically and implement the analysing program called News Analysis Program (NAP), which is based on this algorithm and may help to support the short-term investment decision makers as a part of the Decision Support System. A manual analysis of 1133 headlines was done, which was used for selecting the keywords for bag of words and further training and verification. The linguistic dictionary Harvard IV Psycho Social and the software Wordsmith 4 were used as a part of the bag of words used in this algorithm. Alarabia.net and Reuters.com news are treated as a source of media noise that has an influence on the value of stock quoted on the stock market. The correspondence ratio between the manual and automatic analysis is 88.79% for the pattern that reflected the effect of news on the bank sector's stocks throughout October, November and December 2012. News Analysis Program is implemented in the Python programming environment .

KEYWORDS: news headlines, text-mining, media, NAP, text categorization.

JEL classification: C6, Y8.