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Financial Stock Market Forecast Using Data Mining in Palestine * Dr. Eng

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Financial Stock Market Forecast Using Data Mining in Palestine * Dr. Eng Financial Stock Market Forecast Using Data Mining in Palestine * Dr. Eng. Yousef Abuzir ** Mr. AbdulRahman M.Baraka *** * Received:11/2/2018, Accepted: 15/4/2018 DOI: https://doi.org/10.5281/zenodo.2576331 ** Professor/ Al-Quds Open University/Palestine *** Instructor/ Al-Quds Open University/Palestine 38 Palestinian Journal of Technology & Applied Sciences - No. 2 - January 2019 اخلطاأ ملتو�سط اجلذر الرتبيعي اإىل )0.010+/- 0.00( وهذه الن�سبة تفوق ن�سبة الدقة التي تو�سلت لها الأبحاث :Abstract ذات العﻻقة. This research involves building a model for الكلمات املفتاحية: ال�سبكة الع�سبية الذكية، التنقيب forecasting stock price movements using data يف البيانات، خوارزمية باك بروباغي�سن، بور�سة فل�سطني. mining techniques with Artificial Intelligence classifier. Concerning the forecasting problem, we tackle the challenge of choosing Palestinian Stock Exchange (PSX) as an input data in the INTRODUCTION period between 2005-2017 and processing it accordingly to improve the predicting accuracy for the Palestinian stock market prices. We also With the emergence of information technology address the issue of evaluating the prediction such as the World Wide Web and social networks, performance. Our results show that the best a large amount of data becomes available to all configuration for ANN is 6-5-1, which means of us. Recently, data mining has received growing six inputs, five neurons in hidden layer and one attention as a means to analyze and process a large output. ANN classifier is feed-forward multi- amount of financial data (Berson et al, 2011) and layer perceptron neural network that is trained (Leventhal, 2010). One of the challenging tasks with back propagation algorithm. In addition, the for researchers, traders and investors is predicting results show we have a high degree of accuracy stock price movements. Prediction of accurate for prediction (0.010 +/- 0.000 for Root Mean prices of the stock presents a challenging task Squared Error), this accuracy is higher than other for all practitioners in stock market like traders related researches. and investors (Navale et al., 2016), (Huang et al., 2016). In general, the local and international stock Keywords: ANN, Data Mining, Back market and company stock prices can be affected Propagation Algorithm, Palestinian Stock by many factors such as economic, political, war Exchange. and civil unrest, natural disasters and terrorism, social and psychological. These factors shape, affect and interact with stock movement patterns. التنبوؤ باأ�صعار اﻻأ�صهم يف فل�صطني In business and management, there is a need با�صتخدام تقنيات التنقيب يف البيانات to understand the situations that occur in the stock markets domain to assist us in the analysis ملخص: and investment decisions. In this study, we will discuss a research targeting stock markets يف هذا البحث �سوف نبني منوذجا للتنبوؤ بحركات (domain to observe and record changes (events اأ�سعار الأ�سهم با�ستخدام تقنيات التنقيب يف البيانات when they happen, collect them and understand مع م�سنف الذكاء ال�سطناعي. وفيما يتعلق مب�سكلة ,the meaning of each one of them. As well as التنبوؤ، فاإننا نت�سدى للتحدي املتمثل يف اختيار البور�سة study most important factors and attributes that may affect stock prices and their integration in the الفل�سطينية كبيانات مدخﻻت يف الفرتة الزمنية )-2005 .classification 2017( ومعاجلتها وفقا لذلك لتح�سني دقة التنبوؤ لأ�سعار �سوق الأ�سهم الفل�سطينية. كما نعالج م�ساألة تقييم اأداء We will build a model for forecasting stock price movements using data mining techniques التنبوؤ. وتبني نتائجنا اأن اأف�سل تكوين لل�سبكة الع�سبية with Artificial Intelligence classifier. Concerning الذكية هو 6-5-1 مما يعني �ستة مدخﻻت، وخم�سة the forecasting problem, we tackle the challenge خﻻيا ع�سبية يف الطبقة اخلفية وخمرج واحد. واأن م�سنف (of choosing Palestinian Stock Exchange (PEX ال�سبكة الع�سبية الذكية هو عبارة عن �سبكة ع�سبية as an input data and process it accordingly to improve the predicting accuracy for the Palestinian متعددة الطبقات موجهة اإىل الأمام يتم تدريبها عن stock market prices. We also address the issue of طريق خوارزمية باك بروباغي�سن. كذلك بينت النتائج اأننا .evaluating the prediction performance ا�ستطعنا احل�سول على ن�سبة تنبوؤ عالية حيث و�سلت ن�سبة 39 Dr. Eng. Yousef Abuzir Financial Stock Market Forecast Using Data Mining in Palest ine Mr. AbdulRahman M.Baraka Prediction of stock market plays an important be used in an attempt to create an improved role in supporting financial decisions and stock system. business (Chen, 2009), (Dase & Pawar, 2010) and ♦ Design, implement and evaluate the proposed (Vaisla & Bhatt, 2010). The main purpose of this model that uses data mining techniques to research is to investigate if and how data mining generate automatically trading signals. techniques can be used to predict the future trends ♦ Build a prediction classifier by using of stock prices by analyzing financial data that is Artificial Intelligence technique (Fuzzy logic posted on the financial web pages. or Artificial Neural Network). There are many articles published on Marketing practitioners need forecasts to computational stock market prediction that uses determine which new products or services to numbers like stock price, volumes, company introduce or not; which markets to enter or exit; income, company cost, and so on, instead of textual and which products to promote, sales people on information from news articles (Schumaker & the other hand, use forecasts to make sales plans Chen, 2010), (Schumaker & Zhang, 2012), (Pan that are generally based on estimates of future & Poteshman, 2006) and (Robertson et al., 2007). sales. It is important to investigate how stock markets react to breaking news because if we know this Hence having a good knowledge about we can create fast-computerized systems that share price movement in the future serves the automatically analyze new news articles before interest of financial professionals and investors. the market has had time to adjust itself to the new This knowledge about the future boosts their information. Doing this opens up the possibility confidence by way of consulting and investing. for making much more profit on stock trades and It goes without saying that forecasting methods exchanges. which will predict the future movement of share prices with the least error margin will be of much The results of this research will help managers interest to financial professional and investors. of financial portfolios to understand the underlying factors behind the forecasting accuracy of stocks There are many forecasting methods in in the Palestinian Stock Exchange and other stock projecting price movement of stocks. In this exchanges. This will further boost the confidence study, data mining methods will be employed in of stakeholders in the financial industry to do more forecasting the price movement of stocks. business with less risk. It will provide companies The main aim of this research is to build and users with the ability to predict Palestinian a model that forecasts stock price movements stock market prices. using data mining techniques. A case study and In this research, we aim to identify, experiments will be presented and discussed investigate, analyze, evaluate and apply data based on applying data mining for the analysis of mining techniques in stock movement in the Palestinian Stock Exchange web. The performance Palestinian stock market (PEX, 2016) in order to of the experiments will be evaluated based on predict expected stock prices. different data mining methods. The objectives of this research are as follows: The rest of the paper is outlined as follows; a ♦ Study existing Palestinian Exchange data brief description about related researches. Section on the official web site (PEX, 2016) for 3 presents the methodology used for experimenting automatically analyzing financial data with the Back propagation (BP) algorithm. Then we the main focus on systems that use data analyzed and discussed our approach experiment mining methods for their prediction of future setup, results and discussions. The final section price trends. represents the conclusion. ♦ Study other external factors that may affect Palestinian market in particular. Literature Survey ♦ Investigate data mining methods that might The empirical study uses the variables of 40 Palestinian Journal of Technology & Applied Sciences - No. 2 - January 2019 technical analysis of stock market indicators for large set of data. They have presented a review predicting stock market prices. A proposed model of literature about application of artificial neural is tested and evaluated using PEX (PEX, 2016) network (ANN) for stock market predictions and data and the produced results showed a high level from this literature; they have found that ANN is of accuracy in prediction. very useful for predicting world stock markets. Assaleh, K., El-Baz, H. and Al-Salkhadi, Pratyoosh Rai and Kajal Rai (Rai & Rai, S. (Assaleh et al., 2011) present two prediction 2016) have found from the comparison that models for predicting stock prices. The first the problem of stock index prediction is one of model was developed using back propagation the most popular targets for various prediction feed forward neural networks. The second model methods in the area of finance and economics. In was developed using polynomial classifiers (PC), their article, the researchers have described the as a first time application for PC to be used in comparison of different neural network types for stock prices prediction. The inputs to both models stock prediction. The prediction was carried out were identical, and both models were trained and by modular neural network, ARIMA-based neural tested on the same data. The study was conducted network, Genetic algorithm, Amnestic neural on Dubai Financial Market as an emerging market network, Multi-Branch neural network, etc. The and applied on two of the market’s leading stocks. authors have also performed comparative analysis In general, both models achieved very good results of all these types of neural network (NN).
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