Affiliations: Department of Electronics and Communication Engineering, Coimbatore Campus Amrita Vishwa Vidyapeetham (University), Tamilnadu, India
Correspondence:
[*]
Corresponding author: Binoy B. Nair, Department of Electronics and Communication Engineering, Coimbatore Campus Amrita Vishwa Vidyapeetham (University), Tamilnadu, PIN-641112, India. Tel.: +91 9994375559; E-mail:[email protected]
Abstract: Generating consistent profits from stock markets is considered to be a
challenging task, especially due to the nonlinear nature of the stock price
movements. Traders need to have a deep understanding of the market behavior
patterns in order to trade successfully. In this study, a GA optimized
technical indicator decision tree-SVM based intelligent recommender system
is proposed, which can learn patterns from the stock price movements and
then recommend appropriate one-day-ahead trading strategy. The recommender
system takes the task of identifying stock price patterns on itself,
allowing even a lay-user, who is not well versed in stock market behavior,
to trade profitably on a consistent basis. The efficacy of the proposed
system is validated on four different stocks belonging to two different
stock markets (India and UK) over three different time frames for each
stock. Performance of the proposed system is validated using fifteen
different measures. Performance is compared with traditional technical
indicator based trading and the traditional buy and hold strategy. Results
indicate that the proposed system is capable of generating profits for all
the stocks in both the stock markets considered.
