Clustering stock price time series data to generate stock trading recommendations: An empirical study. (15th March 2017)

• Record Type: Journal Article
• Authors: Nair, Binoy B.; Kumar, P.K. Saravana; Sakthivel, N.R.; Vipin, U.
• Is Part Of: Expert systems with applications. Volume 70(2017)
• Page Start: 20
• Page End: 36
• Publication Date: 2017-03-15
• Subjects: Stock -- Trading -- Recommender -- Clustering -- Time-series; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.11.002
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Trading recommender system based on mining historical stock price data is proposed. Regression tree and Self Organizing Maps are used to generate temporal clusters. Temporal clusters are then used to generate trading recommendations. 16 recommender system variants are evaluated on US, UK, India and Brazil stocks. Proposed recommenders are capable of generating profitable trade recommendations. Abstract: Predicting the stock market is considered to be a very difficult task due to its non-linear and dynamic nature. Our proposed system is designed in such a way that even a layman can use it. It reduces the burden on the user. The user's job is to give only the recent closing prices of a stock as input and the proposed Recommender system will instruct him when to buy and when to sell if it is profitable or not to buy share in case if it is not profitable to do trading. Using soft computing based techniques is considered to be more suitable for predicting trends in stock market where the data is chaotic and large in number. The soft computing based systems are capable of extracting relevant information from large sets of data by discovering hidden patterns in the data. Here regression trees are used for dimensionality reduction and clustering is done with the help of Self Organizing Maps (SOM). The proposed system is designed to assist stock market investors identify possible profit-making opportunities and also help in developing a better understanding on how to extract the relevant information from stock price data.