Indicator circuits with incremental clustering and its applications on classification of firm’s performance and detection of high-yield stocks in the medium-term

• Main Authors: Natchanan Kiatrungwilaikun, Komsan Suriya, Narissara Eiamkanitchat
• Format: Book Series
• Published: 2018
• Subjects: Computer Science
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84952690581&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/55552
• Institution: Chiang Mai University

© Springer International Publishing Switzerland 2016. This paper introduces the indicator circuit with incremental clustering (ICIC) and shows that the ICIC works better than the indicator circuit with reference points (ICRP) for the evaluation of the telecommunications companies’ performance presented in Suriya Int. J. Intell. Technol. Appl. Stat. vol 8, pp 103–112 (2015) [4]. Moreover, it also extends the ICIC to detect high-yield stocks in the Stock Exchange of Thailand. It classifies 134 stocks by 6 indicators; E/P ratio (the inverse of P/E ratio), BV/P ratio (the inverse of P/BV ratio), return on equity (ROE), growth of the E/P ratio, dividend growth, and ROE growth with the data at the end of 2013. It justifies the performance of the model by the yield of the stock measured at the peak price of each stock during April 1st, 2014 to March 31st, 2015. The buying date is the first trading day on the second quarter of 2014, when most of the 2013 financial statements have already been announced. Surprisingly, the method detects the low-yield stocks instead of the high-yield ones. Therefore, it acts like a warning signal to investors to avoid the low-yields.