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Novel approach for predicting the rise and fall of stock index for a specific company 2
Novel approach for predicting the rise and fall of stock index for a specific company 2
Novel approach for predicting the rise and fall of stock index for a specific company 2
Novel approach for predicting the rise and fall of stock index for a specific company 2
Novel approach for predicting the rise and fall of stock index for a specific company 2
Novel approach for predicting the rise and fall of stock index for a specific company 2
Novel approach for predicting the rise and fall of stock index for a specific company 2
Novel approach for predicting the rise and fall of stock index for a specific company 2
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Novel approach for predicting the rise and fall of stock index for a specific company 2

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  • 1. INTERNATIONALComputer Engineering and Technology ENGINEERING International Journal of JOURNAL OF COMPUTER (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME & TECHNOLOGY (IJCET)ISSN 0976 – 6367(Print)ISSN 0976 – 6375(Online) IJCETVolume 4, Issue 2, March – April (2013), pp. 23-30© IAEME: www.iaeme.com/ijcet.aspJournal Impact Factor (2013): 6.1302 (Calculated by GISI) ©IAEMEwww.jifactor.com NOVEL APPROACH FOR PREDICTING THE RISE AND FALL OF STOCK INDEX FOR A SPECIFIC COMPANY Anirudh Prabhu1, Aldrin Rodrigues1, Chirag Chauhan1, G.Anuradha2 1 Student, St. Francis Institute of Technology, Borivali (West), Mumbai – 4001031 2 Associate Professor, St. Francis Institute of Technology, Borivali (West), Mumbai – 4001032 ABSTRACT The financial market is highly fluctuating in nature. If investment strategies are not adequately planned and designed, it may lead to high monetary losses. There’s a high risk involved in stock market investment as the investment strategies are purely based on expert knowledge about the market conditions and some amounts of instincts. In our proposed method using the insight of technical analysis and the concept of fuzzy inference system, we try to analyse the previous buy and sell scenarios and try to predict whether to go for buying or selling of stocks the next day. Since the investor obviously wants to make profits and minimize the losses, the motivation behind this project is to minimize the risk involved in an investment by suggesting a profitable investment plan and keeping the investors away from a non-profitable deal based on extensive analysis of the current market trends. Keywords: Subtractive Clustering, Prediction, ANFIS, Stocks, I. INTRODUCTION Stock data analysis has remained one of the challenging time series analysis problems over the years. Because of the complexity and instructions, the problem has received attention from many researchers. In this paper we attempt to predict the percentage rise and fall of the price of a specific stock index using a linear implementation of subtractive clustering, neuro-fuzzy inference systems and a novel proposed algorithm to calculate the contribution factor of the different clusters formed. The main strength of neuro-fuzzy 23
  • 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEMEinference systems is that they can combine the human-like reasoning style of fuzzy systemswith the connectionist structure and learning ability of neural networks. We use subtractiveclustering because it is an easy and efficient method for automatically generating fuzzyinference systems by detecting clusters in input-output training data. The ANFIS helps topredict the rise and fall using previous set patterns. Using the proposed method, one canpredict the percentage rise and fall of a specific stock index. This method will be very usefulfor intra-day traders as they are more interested at making small profits on a day to day basis.II. PREVIOUS WORK Vaidehi .V , et. al[1] build a prediction system to predict the future occurrence of anevent. It is a combination of a clustering algorithm and fuzzy system identification whichproves effective in improving the efficiency of the prediction. To train the prediction system,historical data is obtained from the web. Data specific to any company stock is obtained andis recorded. This recorded information is studied and parameters containing only thenecessary inputs to the prediction system. The subtractive clustering algorithm is used for itscomputational advantages and fuzzy rules are formed using system identification technique. [5] Analyses stock market price prediction based on a Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) fuzzy neural network. Stock price prediction is a problem thatrequires online adaptive systems with high accuracy performance. The proposed GSETSKframework uses a novel Multidimensional-Scaling Growing Clustering (MSGC) algorithmwhich mimics the human cognitive process to flexibly generate fuzzy rules without any aprior knowledge. AdemolaOlayemi (2007) mentioned that forecast performance improves when pre-processed data is used. He has used a general pre-processing method, based on multi scalewavelet decomposition to provide a local representation of time series data prior to theapplication of fuzzy models [2]. [7] presents an innovative approach for indicating stock market decisions that theinvestor should take for minimizing the risk involved in making investments. The systemuses Adaptive Neuro-Fuzzy Inference System (ANFIS) for taking decisions based on thevalues of technical indicators. Among the various technical indicators available, the systemuses weighted moving averages, divergence and RSI (Relative Strength Index). [3] reviews four of the most representative off-line clustering techniques: K-meansclustering, Fuzzy Cmeans clustering, Mountain clustering, and Subtractive clustering.III. SUBTRACTIVE CLUSTERING Finding similarities in data and putting similar data into groups can be done using dataclustering. Clustering partitions a data set into several groups such that the similarity within agroup is larger than that among groups [8]. Subtractive clustering is a technique for automatically generating fuzzy inferencesystems by detecting clusters in input-output training data. The measure of potential for adata point is estimated based on the distance of this data point from all other data points.Therefore, a data point lying in a heap of other data points will have a high chance of being acluster centre, while a data point which is located in an area of diffused and not concentrateddata points will have a low chance of being a cluster centre. 24
  • 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME After measuring the potential of every data point, the data point with the greatestpotential value is selected as the first cluster centre. To find the next cluster centre, potentialsof data points must be revised. For each data point, an amount proportional to its distance tothe first cluster centre will be subtracted. This reduces the chance of a data point near the firstcluster being selected as the next cluster centre. After revising the potential of all data points,the data point with the maximum potential will be selected as the next cluster centre. Thepotential of data points in the first step is measured as [9]: 2 n − α || x i − x j ||pi = ∑ e − − > Equation 1 j =1Where, 4α = r2And xi is the ithdata point and ra is a vector which consists of positive constants and rrepresents the hyper sphere cluster radius in data space. The constant a is effectively theradius defining a neighbourhood; data points outside this radius have little influence on thepotential. The potential which has been calculated through Equation 1 for a given point, is afunction of that points distance to all other points, and the data point which corresponds to *maximum potential value is the first cluster centre. Let p1 denotes the maximum potential, ifx1* denotes the first cluster centre corresponding to p1 . * np1 = U p i − − > Equation 2 * i =1Where U denotes the maximum of al1 pi sTo revise the potential values and select the next cluster, the following formula is suggested. * 2 * − β || xi − x j || pi = pi − p e 1 - - > Equation 3Where, 4β= 2 rb 25
  • 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEMEAnd rb is a vector which consists of positive constants and is called the hyper sphere rpenalty radius. The constant b is effectively the radius defining the neighborhood whichwill have measurable reductions in potential. To avoid cluster centres being close to each r rother, b must be greater than a . A desirable relation is as follows [9]:rb = 1 . 5 ra − − > Equation 4IV. OUR CONTRIBUTION One of the problems with subtractive clustering is that it can perform clusteringwith only two factors at a time. So, in this paper we propose a simple function/algorithmto compute the effects and contribution more than two factors. Let us call this function as“CF Algorithm” (Contribution Factor Algorithm). The algorithm is as follows: Combine the outcomes from both graphs. • Construct graph 1 and graph 2 as Closing price Vs Volume and High price Vs Low price respectively. Initialize cluster radius CL1 and CL2 from graphs 1 and 2 respectively and the corresponding distance of current data point as d 1 and d 2 respectively. 26
  • 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME Calculate initial contribution factor R0 and new contribution factor Rn as follows: If CL1 > CL2 then, CL1 R0 = CL2  d d  R n = R0 +  1 − 2   CL CL   1 2  Else CL2 R0 = CL1  d d  Rn = R0 +  2 − 1   CL CL   2 1  V. PROPOSED APPROACH The paper proposes that if the following algorithm is used in the exact sequence then an accurate prediction of the fall and rise of the stock index can be made. The algorithm is as follows:Step 1: Fix initial parameters as Closing price, Volume, High Price and Low Price.Step 2: Obtain the historical data of the above parameters for a specific period (e.g. 5 years)of a particular stock index.Step 3: Map data points as Closing Price vs. Volume and High Price vs. Low Price ondifferent graphs.Step 4: Perform subtractive clustering on individual graphs to get clusters and cluster centers.Step 5: Convert the cluster into initial rules.Step 6: Predict outcomes from individual graphs using ANFIS Modelling.Step 7: Implement CF Algorithm.Step 8: Stop 27
  • 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME 28
  • 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEMEVI. EXAMPLE Closing Price Vs Volume 10000000 90000000 80000000 70000000 60000000 50000000 Closing Price Vs Volume 40000000 30000000 20000000 10000000 0 0 20 40 60 80 High Vs Low 80 70 60 50 40 High Vs Low 30 20 10 0 0 10 20 30 40 50 60 70 80Above are the graph clusters formed by 4 years of Accenture’s stock data. 29
  • 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEMEVII. CONCLUSION/FUTURE SCOPE The proposed paper aims to help intraday traders efficiently deal with the rise and fallof specific stock index. Using the given algorithm, additional number of factors or parameterscan be added to further accurately predict progress of stocks. The algorithm used can beimprovised and improved for advance research.VIII. REFERENCES[1] Vaidehi .V, Monica .S, Mohamed Sheik Safeer .S, Deepika .M4, Sangeetha .S, “APrediction System Based on Fuzzy Logic”.[2] NassimHomayouni and Ali Amiri, “Stock price prediction using a fusion model ofwavelet, fuzzy logic and ANN”.[3] KhaledHammouda, “A Comparative Study of Data Clustering Techniques”.[4] WeiYang, “Stock Price Prediction based on Fuzzy Logic”.[5] Ngoc Nam Nguyen and Chai Quek, Member, IEEE, “Stock Price Prediction usingGeneric Self-Evolving Takagi-Sugeno-Kang (GSETSK) Fuzzy Neural Network”.[6] Akbar Esfahanipour, ParvinMardani, “An ANFIS Model for Stock Price Prediction: TheCase of Tehran Stock Exchange”.[7] Samarth Agrawal, Manoj Jindal, G. N. Pillai, “Momentum Analysis based Stock MarketPrediction using Adaptive Neuro-Fuzzy Inference System (ANFIS)”[8] Jang, J.-S. R., Sun, C.-T., Mizutani, E., “Neuro-Fuzzy and Soft Computing – AComputational Approach to Learning and Machine Intelligence,” Prentice Hall.[9] Chiu, S. L.; 1994, "Fuzzy model identification based on cluster estimation", Journal ofIntelligent and Fuzzy Systems, 2, John Wiley & Sons, pp. 267-278.[10] K. V. Sujatha and S. Meenakshi Sundaram, “Regression, Theil’s and MLPForecasting Models of Stock Index” International journal of Computer Engineering &Technology (IJCET), Volume 1, Issue 1, 2010, pp. 82 - 91, ISSN Print: 0976 – 6367,ISSN Online: 0976 – 6375, Published by IAEME. 30

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