Citations


Contact Us

For Marketing, Sales and Subscriptions Inquiries
2637 E Atlantic Blvd #43110
Pompano Beach, FL 33062
USA

Conference List

Financial Risk and Management Reviews

June 2015, Volume 1, 2, pp 53-67

Stock Market Index Prediction with Neural Network during Financial Crises: A Review on Bist-100

Åžakir SAKARYA

,

Mehmet YAVUZ

,

Aslan Deniz KARAOÄžLAN

,

Necati ÖZDEMİR

Åžakir SAKARYA 1
Mehmet YAVUZ 2

Aslan Deniz KARAOÄžLAN 3
Necati ÖZDEMİR 4

  1. Balikesir University, Department of Business Administration, Cağiş Campus, Balıkesir, Turkey 1

  2. Necmettin Erbakan University, Fakulty of Science, Department of Mathematics-Computer Sciences, Meram, Konya, Turkey 2

  3. Balikesir University, Department of Industrial Engineering, Cağiş Campus, Balıkesir, Turkey 3

  4. Balikesir University, Department of Mathematics, Cağiş Campus, Balıkesir, Turkey 4

Pages: 53-67

DOI: 10.18488/journal.89/2015.1.2/89.2.53.67

Share :


Abstract:

Predetermining the future value of a variable is both quite important and rather difficult process in financial markets. In this context, especially in the last 15 years, Artificial Neural Networks (ANNs) are widely used in order to resolve various kinds of financial problems such as performing portfolio construction, stock index, and bankruptcy prediction. This study examines the predictability of daily and weekly returns of Borsa İstanbul (BIST)-100 Index during global crisis period (July 2007-December 2009) by using ANN. It differs from other similar studies in the literature as it: i) covers global crisis period, ii) predicts index value of the next day and next week and finally iii) uses seven different economic parameters (variables) as input. The results obtained suggest that ANN can be used quite successfully in this area and foresee correctly the value for next day and next week with an accuracy margin error of less than 5% even for unknown samples. The ANN model in this study is developed using MATLAB R2008b.


Contribution/ Originality

Keywords:


Video:

Reference:

  1. Aghababaeyan, R., K. Najeeb Ahmad and S. Tamanna, 2011. Forecasting the Tehran stock market by artificial neural network. International Journal of Advanced Computer Science and Applications, Special Issue on Artificial Intelligence: 13-17. Available from http://dx.doi.org/10.14569/SpecialIssue.2011.010303#sthash.YWRIBxW0.dpuf.
  2. Akel, V. and M.F. Bayramo?lu, 2008. Kriz dönemlerinde yapay sinir a?lar? ile finansal ongörüde bulunma: ?MKB-100 endeksi Örne?i. In International Symposium on International Capital Flows and Emerging Markets, Turkey: 24-27.
  3. Demirtas, M. and K. Aslan Deniz, 2012. Optimization of PI parameters for DSP-based permanent magnet brushless motor drive using response surface methodology. Energy Conversion and Management, 56: 104-111.
  4. Desai, J., A. Trivedi and N.A. Joshi, 2012. Forecasting of stock market indices using artificial neural network. Shri Chimanbhai Patel Institutes, Ahmedabad Working Paper No. CPI/MBA/2013/0003.
  5. Diler, A.?., 2003. ?MKB Ulusal 100 endeksinin yönünün Yapay Sinir A?lar?: Hata Geriye Yayma Yöntemi ile Tahmin Edilmesi. Türkiye'de Banklar, Sermaye Piyasas? ve Ekonomik Büyüme: Koentegrasyon ve Nedensellik Analizi (1989-2000), 81.
  6. Gençtürk, M., 2009. Finansal Kriz dönemlerinde makroekonomik faktörlerin hisse senedi fiyatlar?na Etkisi. Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 14(1): 127-136.
  7. Guresen, E., G. Kayakutlu and T.U. Daim, 2011. Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8): 10389-10397.
  8. Kara, Y., M.A. Boyacioglu and Ö.K. Baykan, 2011. Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul stock exchange. Expert Systems with Applications, 38(5): 5311-5319.
  9. Karaoglan, A.D., 2011. An integrated neural network structure for recognizing autocorrelated and trending processes. Mathematical & Computational Applications, 16(2): 514-523.
  10. Kimoto, T., K. Asakawa, M. Yoda and M. Takeoka, 1990. In: Stock market prediction system with modular neural network. Proceedings of the International Joint Conference on Neural Networks. pp: 1-6.
  11. Kutlu, B. and B. Bertan, 2009. Yapay Sinir A?lar? ile Borsa Endeksi Tahmini. Yönetim, ?stanbul Üniversitesi ??letme Fakültesi ??letme ?ktisadi Enstitüsü Dergisi, 20(63): 25-40.
  12. Li, F. and C. Liu, 2009. Application study of BP neural network on stock market prediction. Ninth International Conference on Hybrid Intelligent Systems. IEEE, pp: 174-178.
  13. Mallaris, M. and S. Linda, 1996. Using neural networks to forecast the S&P 100 implied volatility. Neurocomputing, 10(2): 83-195.
  14. Mizuno, H., M. Kosaka, H. Yajima and N. Komoda, 1998. Application of neural network to technical analysis of stock market prediction. Studies in Informatic and Control, 7(3): 111-120.
  15. O’connor, N. and G.M. Michael, 2005. A neural network approach to predicting stock exchange movements using external factors. Knowledge-Based Systems, 19(5): 371-378.
  16. Oh, K.J., T.Y. Kim and C. Kim, 2006. An early warning system for detection of financial crisis using financial market volatility. Expert Systems, 23(2): 83-98.
  17. Öztemel, E., 2003. Yapay sinir a?lar?. ?stanbul: Papatya Yay?nc?l?k.
  18. Phua, P.K.H., D. Ming and W. Lin, 2000. Neural network with genetic algorithms for stocks prediction. Fifth Conference of the Association of Asian-Pacific Operations Research Societies Proceedings, 5th - 7th July, Singapore.
  19. Vural Bar??, B., 2007. Yapay sinir a?lar? ?le finansal tahmin. Yüksek Lisans Tezi, Ankara Üniversitesi Sosyal Bilimler Enstitüsü, ??letme Anabilim Dal?, Ankara.
  20. Wang, J.Z., J.J. Wang, Z.G. Zhang and S.P. Guo, 2011. Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38(11): 14346-14355.
  21. Y?ld?z, B., 2001. Finansal Ba?ar?s?zl???n Öngörülmesinde Yapay Sinir A?? Kullan?m? ve Halka Aç?k ?irketlerde Ampirik Bir Uygulama. ?MKB Dergisi, 5(17): 51-67.
  22. Yildiz, B., A. Yalama and M. Coskun, 2008. Forecasting the Istanbul stock exchange national 100 index using an artificial neural network. World Academy of Science, Engineering and Technology, 46: 36-39.
  23. Yoon, Y. and S. George, 1991. Predicting stock price performance: A neural network approach. Proceedings of the 24th Annual Hawaii International Conference on System Sciences, 4: 156-162.
  24. Yoon, Y., G. Swales and T.M. Margvio, 1993. A comparison of discriminant analysis versus artificial neural networks. Journal of the Operational Research Society, 44(1): 51-60.

Statistics:

Google Scholor ideas Microsoft Academic Search bing Google Scholor

Funding:

Competing Interests:

Acknowledgement:


Related Article

( 1 ) Testing the Random Walks in Korea Stock Exchange
( 2 ) Kashmir Peasant Economy under Dogra’s: A Case Study of Agrarian Produce and Livestock 1885-1925 A.D
( 3 ) Driving Role of Institutional Investors in the Indian Stock Market in Short and Long-Run – An Empirical Study
( 4 ) Reminiscing Stock Splits Announcement: A Malaysian Case
( 5 ) Testing the Random Walk: The Case of Hong Kong Stock Exchange
( 6 ) Earnings Management and Stock Market Returns
( 7 ) Stock Market Performance and Economic Growth: Evidence from Nigeria Employing Vector Error Correction Model Framework
( 8 ) Stock Market Index Prediction with Neural Network during Financial Crises: A Review on Bist-100
( 9 ) The Impact of the Global Financial Crisis on the Debt, Liquidity, Growth, and Volume of Companies in Palestine Stock Exchange
( 10 ) Studying the Integration of Damascus Securities Exchange with Selected Stock Markets
( 11 ) Does Gold Price Lead or Lags Islamic Stock Market and Strategy Commodity Price? A Study from Malaysia
( 12 ) The Effect of Internal and External Factors of Companies on Profitability and its Implications on Stock Price Index of State-Owned Banks
( 13 ) Regime Changes in the Volatility of Stock Markets
( 14 ) Optimal Stock Portfolio Issuers of Building Construction Registered in LQ45 Based on the Markowitz Approach
( 17 ) The Dynamics of Land Market and Food Security in Malawi
( 19 ) The Biodiesel Market in Brazil and the Prospects for the Sector
( 21 ) Estimating Value at Risk for Sukuk Market Using Generalized Auto Regressive Conditional Heteroskedasticity Models
( 22 ) The Mediating Effect of Market Orientation on the Relationship between Entrepreneurial Orientation Dimensions and Organizational Performance: A Study on Banks in Libya
( 24 ) Market Share and Profitability Relationship: A Study of the Banking Sector in Nigeria
( 25 ) Is Alumni Salary an Appropriate Metric for University Marketers?
( 27 ) Financial Market Predictions with Factorization Machines: Trading the Opening Hour Based on Overnight Social Media Data
( 28 ) Incorporating Islamic Ethic Elements into Marketing Mix Paradigm
( 29 ) Impact of Telecommunications Market Liberalization on Labor Productivity in Economic Community of West African States
( 30 ) Foreign Direct Investment in Zimbabwe: The Role of Uncertainty, Exports, Cost of Capital, Corruption and Market Size
( 31 ) Impact of Network Finance Development on Inflation: Evidence from Chinese Market
( 32 ) Correlation between Financial Difficulties and Financing Strategies among Market Stallholders in Batangas City
( 34 ) Assessing the Perceived Value of Customers for being Satisfied towards the Sustainability of Hypermarket in Malaysia
( 35 ) Determinants of Exchange Rate in Nigeria: A Comparison of the Official and Parallel Market Rates
( 36 ) Can Small-Cap Active Funds Substantially Outperform the Market Over Time?
( 37 ) Exchange Rate Volatility, Foreign Exchange Market Intervention and Asymmetric Preferences
( 38 ) New Evidence on the Link Between Income Inequality and Misery Index: A Nonlinear Time Series Analysis
( 39 ) How Telecommunication Development Aids Economic Growth: Evidence from Itu Ict Development Index (IDI) Top Five Countries for African Region
( 40 ) Impact of Food Beverage Price Index and Exchange Rate Volatility on Economic Growth
( 41 ) The Sustainable Livelihoods Index: A Tool To Assess the Ability and Preparedness of the Rural Poor in Receiving Entrepreneurial Project
( 44 ) Forecasting Equity Index Volatility: Empirical Evidence from Japan, UK and USA Data
( 45 ) Multivariate Analysis in Formulation of the Benefit-Cost Index Related to the Sugarcane Production System in the Quirinopolis Municipality Productive Center, Goias, Brazil
( 47 ) On the Prediction of the Inflation Crises of South Africa Using Markov-Switching Bayesian Vector Autoregressive and Logistic Regression Models
( 49 ) Can the Interdependence Effect and the Contagion Phenomena be Related with One Another?
( 50 ) An Inventory Model with Lost Sale is Time Dependent
( 51 ) Ensuring Quality Provision of Education for All: Discovering Challenges Faced by Teachers of Students with Learning Disabilities in Regular Primary Schools in Masvingo District
( 53 ) A Review upon the Changes Which Come With the Model upon Turkish Metropolitan Municipality within the Provincial Administrative Boundaries
( 54 ) Milk or Wine: Are Critical Infrastructure Protection Architectures Improving with Age?
( 55 ) The Correlation Between the Palestinian Civil Society Institutions and the Universities (Status: The Palestine Technical University Kadourie with Tulkarm Institutions)
( 56 ) Rural - Urban Balance as a Measure of Socio-Economic Development with Special Reference to Iran
( 57 ) A Review on EU Transportation Projects within the Case of Turkey for European Union Regional Development Policy
( 59 ) Testing the PPP Using Unit Root Tests with Structural Breaks: Evidence from Politically Unstable Arab Countries
( 60 ) Specifying Quality of Governance in Transition Economies with Cluster Analysis
( 62 ) Triple Hurdle Model with Zero Expenditures
( 63 ) Satisfaction with Applications Fuels the Growth of Mobile Wallet Use in Thailand
( 64 ) Analysis of Effect of Profitability, Capital, Risk Financing, the Sharia Supervisory Board and Capabilities Zakat in Islamic Perspective with Circular Approach Causastion on Islamic Banks in Indonesia
( 65 ) The Effectiveness of the Production of Healthy Rice in Comparison with other Rice Varieties in the Upper Northern Region of Thailand
( 68 ) Does Managerial Emotional Biases Affect Debt Maturity Preference? Bayesian Network Method: Evidence from Tunisia
( 69 ) Analyzing Factors Affecting the Success of Social Media Posts for B2b Networks: A Fractional-Factorial Design Approach
( 70 ) Entrepreneurial Networking and Women Entrepreneurs’ Contribution to Employment Creation in Rivers State, Nigeria
( 73 ) Gaining Trust After the Financial Crisis in the Nigerian Economy: A Conceptual Framework
( 74 ) Financial Integration and International Risk Diversification
( 75 ) Teachers Characteristics and Students’ Performance Level in Senior Secondary School Financial Accounting
( 76 ) The Nexus between Financial Crisis and Household Consumption: Evidence from Emerging Countries
( 77 ) Relationships between Financial Development and Economic Growth: A New Approach by Inputs