Analysis and control of variability by using fuzzy individual control charts

Abstract The detection of changes in a process within shortest time provides significant benefits in terms of cost and quality. When considering the cost which would show up because of delays in identifying variability, detecting the deviation in the process accurately and quickly has a great importance for investors. In this paper, return volatility in the Borsa Istanbul-30 index (BIST-30) has been analyzed and a fuzzy control chart for individual measurements (FCCIM) has been proposed for use in determining and controlling in the variables of the BIST-30 index. For this purpose, firstly exponential smoothing method is used to forecast the variability of stock price of BIST-30 index by using MINITAB statistical software, and then a fuzzy control chart for individual measurements (FCCIM) which are fuzzy individual control chart (FICC) and fuzzy moving range control chart (FMRCC) with fuzzy control rules have been developed to be used in determining the variability of the process. For this aim, some fuzzy rules have been defined by using Ms EXCEL in fuzzy control chart for individual measurements. A real case application from Istanbul Stock Exchange for BIST-30 has been managed to check the effectiveness of suggested fuzzy control charts.

[1]  E. S. Gardner EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II , 2006 .

[2]  Marion O. Adebiyi,et al.  Stock Price Prediction using Neural Network with Hybridized Market Indicators , 2012 .

[3]  Chih-Fong Tsai,et al.  Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches , 2010, Decis. Support Syst..

[4]  J. Jarrett,et al.  Daily variation and predicting stock market returns for the frankfurter börse (stock market) , 2008 .

[5]  Nihal Erginel,et al.  Fuzzy individual and moving range control charts with α-cuts , 2008 .

[6]  J. Lewellen,et al.  Predicting Returns with Financial Ratios , 2002 .

[7]  Cengiz Kahraman,et al.  An alternative approach to fuzzy control charts: Direct fuzzy approach , 2007, Inf. Sci..

[8]  Ömer Kaan Baykan,et al.  Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange , 2011, Expert Syst. Appl..

[9]  Tuncay Can,et al.  Saklı Markov modelleri kullanılarak Türkiye’de dolar kurundaki değişimin tahmin edilmesi , 2009 .

[10]  Tzvi Raz,et al.  On the construction of control charts using linguistic variables , 1990 .

[11]  Cengiz Kahraman,et al.  Evaluating the Packing Process in Food Industry Using Fuzzy $\tilde{\bar{X}}$ and $\tilde{S}$ Control Charts , 2011, Int. J. Comput. Intell. Syst..

[12]  J. Parwada,et al.  Predicting stock price movements: an ordered probit analysis on the Australian Securities Exchange , 2012 .

[13]  Mohammad Hossein Fazel Zarandi,et al.  A Neuro-fuzzy Multi-objective Design of Shewhart Control Charts , 2007, Analysis and Design of Intelligent Systems using Soft Computing Techniques.

[14]  Murat Kulahci,et al.  Introduction to Time Series Analysis and Forecasting , 2008 .

[15]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[16]  Cengiz Kahraman,et al.  Process capability analyses with fuzzy parameters , 2011, Expert Syst. Appl..

[17]  Hassen Taleb,et al.  On fuzzy and probabilistic control charts , 2002 .

[18]  L. Yao,et al.  Predictive ability and profitability of simple technical trading rules: Recent evidence from Southeast Asian stock markets , 2013 .

[19]  Ebru Turanoglu,et al.  Fuzzy Acceptance Sampling and Characteristic Curves , 2012, Int. J. Comput. Intell. Syst..

[20]  Kevin J. Doubleday,et al.  Application of Markov Chains to Stock Trends , 2011 .

[21]  Guray Kucukkocaoglu,et al.  Closing price manipulation in Borsa Istanbul and the impact of call auction sessions , 2015 .

[22]  Nikolaos Eriotis,et al.  How rewarding is technical analysis? Evidence from Athens Stock Exchange , 2006, Oper. Res..

[23]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[24]  W. A. Shewhart,et al.  Quality control charts , 1926 .

[25]  ErginelNihal,et al.  Development of fuzzy control charts using α-cuts , 2009 .

[26]  K. S. Adewole,et al.  Stock Trend Prediction Using Regression Analysis - A Data Mining Approach , 2011 .

[27]  Waldemar Karwowski,et al.  Fuzzy concepts in production management research: a review , 1986 .

[28]  Technical analysis and the stochastic properties of the Jordanian stock market index return , 2006 .

[29]  Rob J Hyndman,et al.  Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation , 2016 .

[30]  Mieko Tanaka-Yamawaki,et al.  Trend predictions of tick-wise stock prices by means of technical indicators selected by genetic algorithm , 2007, Artificial Life and Robotics.

[31]  Cengiz Kahraman,et al.  Development of fuzzy process control charts and fuzzy unnatural pattern analyses , 2006, Comput. Stat. Data Anal..

[32]  Chi-Bin Cheng,et al.  Fuzzy process control: construction of control charts with fuzzy numbers , 2005, Fuzzy Sets Syst..

[33]  Ahmet Çelik,et al.  A fuzzy approach to define sample size for attributes control chart in multistage processes: An application in engine valve manufacturing process , 2008, Appl. Soft Comput..

[34]  Avijan Dutta,et al.  Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression , 2012 .

[35]  Peter Miu,et al.  Canadian stock market multiples and their predictive content , 2008 .

[36]  Chin Lee,et al.  Can financial ratios predict the Malaysian stock return , 2008 .

[37]  Estefania Argente,et al.  Application of neural networks to stock prediction in “pool” companies , 2003, Appl. Artif. Intell..

[38]  Liquidity and Equity Returns in Borsa Istanbul , 2015 .

[39]  Nihal Erginel,et al.  Development of fuzzy X-R and X-S control charts using alpha-cuts , 2009, Inf. Sci..

[40]  T. Raz,et al.  Probabilistic and membership approaches in the construction of control charts for linguistic data , 1990 .

[41]  Ihsan Kaya,et al.  A genetic algorithm approach to determine the sample size for attribute control charts , 2009, Inf. Sci..

[42]  Miguel Rocha,et al.  Time Series Forecasting by Evolutionary Neural Networks , 2005 .

[43]  Mohammad Saleh Owlia,et al.  Multi-objective design of X control charts with fuzzy process parameters using the hybrid epsilon constraint PSO , 2015, Appl. Soft Comput..

[44]  Cengiz Kahraman,et al.  Fuzzy exponentially weighted moving average control chart for univariate data with a real case application , 2014, Appl. Soft Comput..

[45]  Cengiz Kahraman,et al.  Bulanık kontrol diyagramı modellerinin geliştirilmesi: Direkt bulanık yaklaşım , 2011 .

[46]  Arzu Tektas,et al.  YAPAY SİNİR AĞLARI VE FİNANS ALANINA UYGULANMASI: HİSSE SENEDİ FİYAT TAHMİNLEMESİ , 2010 .

[47]  Cengiz Kahraman,et al.  Process capability analyses based on fuzzy measurements and fuzzy control charts , 2011, Expert Syst. Appl..

[48]  N. Erginel Evaluating the Packing Process in Food Industry Using Fuzzy X ~ and S ~ Control Charts , 2011 .

[49]  Wee Ching Pok,et al.  The Return Predictability and Market Efficiency of the KLSE CI Stock Index Futures Market , 2012 .

[50]  Hiroshi Ohta,et al.  Control charts for process average and variability based on linguistic data , 1993 .