An intelligent recommender system for stock trading

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 techn ical 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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 28 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.