Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı

17 16

Proje Grubu: EEEAG Sayfa Sayısı: 240 Proje No: 215E248 Proje Bitiş Tarihi: 01.11.2019 Metin Dili: Türkçe İndeks Tarihi: 05-11-2020

Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı

Öz:
Bu çalışmada ilk aşamada hisse senetleri ve gün içi işlem yapılabilen yatırım fonları (ETF) kullanılarak derin öğrenme tabanlı alım satım modelleri geliştirilmiştir. Daha sonra bu geliştirilen modellerin belirlediği alım satım noktaları üzerinden piyasa şartlarını da göz önünde bulunduracak şekilde opsiyon alım satım stratejileri geliştirilmiştir. Burada temel amaç ilk aşamada uzun vadeli al-ve-tut stratejisinden daha iyi başarım sağlayacak hisse senedi ve ETF tabanlı al-sat modelleri oluşturmak, daha sonra da opsiyonların kaldıraç özelliğini de devreye sokarak riski arttırmadan başarımı daha da üst seviyelere çıkartacak modellerin geliştirilmesi olmuştur. Çalışma kapsamında Dow30 hisse senetleri ve en yaygın kullanılan ETF verileri kullanılarak birbirlerinden farklı çok sayıda model geliştirilmiştir. Geliştirilen modeller 2007-2018 yılları arasında test edilmiş ve %10-%15 yıllık getiri sağlanmıştır. Bu çalışmalara ilaveten geliştirilen modellerin ölçeklenmesinin fizibilitesine yönelik model paralelleştirme ve büyük veri analizi çalışmaları gerçekleşmiştir ve çekirdek veya işlemci sayısı ile orantılı hız kazanımı (speedup) elde edilmiştir. Ayrıca farklı alım-satım modellerini öğrenerek kararlar verebilen etmen tabanlı alım-satım sistemleri üzerinde de çalışılmıştır. Projenin ilerleyen dönemlerinde ise alım-satım modellerinde ve buna bağlı al-sat noktalarının belirlenmesinde bir olgunluğa eriştikten sonra yatırım getirisini arttırmaya yönelik opsiyon alım-satım stratejileri geliştirilmiştir. Bu çabalar sonucunda sistem başarımı %60-70 getiri sağlayabilecek seviyelere ulaşmıştır. Ayrıca proje kapsamında opsiyon fiyatlama konusunda da çalışmalar yapılmıştır. Proje kapsamında son olarak geliştirilen bu modeller BIST ve VIOP verileri kullanılarak Türk finansal piyasalarına uyarlanmıştır. Burada da borsa endeksinden daha yüksek getiri sağlayan sonuçlar elde edilmiştir. Ayrıca projeden elde edilen kazanımların bilgi birikimine dönüşmesi ve etkisinin artması amacıyla genel kullanıcıların faydalanabileceği bir yazılım platformu oluşturulmuş ve bu sistem Github üzerinden paylaşıma açılmıştır. Proje kapsamında şu ana kadar 2 adet SCI endeksli dergi yayını basılmıştır. Ayrıca 2 farklı tarama makalesi SCI endeksli, yüksek etki değerli bir dergide şu anda basım aşamasındadır. Bunların haricinde bir makale hakem incelemesindedir. 4 adet de uluslararası hakemli konferans bildirisi yayınlanmıştır.
Anahtar Kelime: vıop bıst dow30 algoritmik alım-satım finansal opsiyonlar finansal tahmin derin öğrenme

Erişim Türü: Erişime Açık
  • Abdual-Salam, M. E., Abdul-Kader, H. M., & Abdel-Wahed, W. F. (2010). Comparative study between differential evolution and particle swarm optimization algorithms in training of feedforward neural network for stock price prediction. INFOS2010 - 2010 7th International Conference on Informatics and Systems, 1–8.
  • Amilon, H. (2003). A neural network versus Black-Scholes: a comparison of pricing and hedging performances. Journal of Forecasting, 22(4), 317–335. https://doi.org/10.1002/for.867
  • Andreou, P. C., Charalambous, C., & Martzoukos, S. H. (2008). Pricing and trading European options by combining artificial neural networks and parametric models with implied parameters. European Journal of Operational Research, 185(3), 1415–1433. https://doi.org/10.1016/j.ejor.2005.03.081
  • Armano, G., Marchesi, M., & Murru, A. (2005). A hybrid genetic-neural architecture for stock indexes forecasting. Information Sciences, 170(1), 3–33. https://doi.org/10.1016/j.ins.2003.03.023
  • Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques {textendash} Part {II}: Soft computing methods. Expert Systems with Applications, 36(3), 5932–5941. https://doi.org/10.1016/j.eswa.2008.07.006
  • Audrino, F., & Colangelo, D. (2009). Semi-parametric forecasts of the implied volatility surface using regression trees. Statistics and Computing, 20(4), 421–434. https://doi.org/10.1007/s11222-009-9134-y
  • Backhouse, R. E. (2009). An Engine, not a Camera: How Financial Models Shape Markets, Donald MacKenzie. MIT Press, 2006, x + 377 pages. Economics and Philosophy, 25(1), 99– 106. https://doi.org/10.1017/S0266267108002307
  • Ballings, M., den Poel, D. Van, Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42(20), 7046–7056. https://doi.org/10.1016/j.eswa.2015.05.013
  • Bao, Y., Yang, Y., Xiong, T., & Zhang, J. (2011). A Comparative Study of Multi-step-ahead Prediction for Crude Oil Price with Support Vector Regression. In 2011 Fourth International Joint Conference on Computational Sciences and Optimization. IEEE. https://doi.org/10.1109/cso.2011.70
  • Bauer, R., Cosemans, M., & Eichholtz, P. (2009). Option trading and individual investor performance. Journal of Banking & Finance, 33(4), 731–746. https://doi.org/10.1016/j.jbankfin.2008.11.005
  • Bisoi, R., & Dash, P. K. (2014). A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter. Applied Soft Computing, 19, 41– 56. https://doi.org/10.1016/j.asoc.2014.01.039
  • Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637–654. https://doi.org/10.1086/260062
  • Brasileiro, R. C., Souza, V. L. F., Fernandes, B. J. T., & Oliveira, A. L. I. (2013). Automatic method for stock trading combining technical analysis and the Artificial Bee Colony Algorithm. In 2013 {IEEE} Congress on Evolutionary Computation. IEEE. https://doi.org/10.1109/cec.2013.6557780
  • Briza, A. C., & Naval, P. C. (2011). Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data. Applied Soft Computing, 11(1), 1191–1201. https://doi.org/10.1016/j.asoc.2010.02.017
  • Canelas, A., Neves, R., & Horta, N. (2013). A {SAX}-{GA} approach to evolve investment strategies on financial markets based on pattern discovery techniques. Expert Systems with Applications, 40(5), 1579–1590. https://doi.org/10.1016/j.eswa.2012.09.002
  • Cao, L. J., & Tay, F. E. H. (2003). Support vector machine with adaptive parameters in financial time series forecasting. {IEEE} Transactions on Neural Networks, 14(6), 1506–1518. https://doi.org/10.1109/tnn.2003.820556
  • Cao, L., & Tay, F. E. H. (2001). Financial Forecasting Using Support Vector Machines. Neural Computing & Applications, 10(2), 184–192. https://doi.org/10.1007/s005210170010
  • Cao, X. (2017). User behavior analysis and data trading in multi-agent systems.
  • Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications, 55, 194–211. https://doi.org/10.1016/j.eswa.2016.02.006
  • Cavalcante, R. C., & Oliveira, A. L. I. (2014). An autonomous trader agent for the stock market based on online sequential extreme learning machine ensemble. In 2014 International Joint Conference on Neural Networks ({IJCNN}). IEEE. https://doi.org/10.1109/ijcnn.2014.6889870
  • Chalasani, P., Jha, S., & Saias, I. (1999). Approximate Option Pricing. Algorithmica, 25(1), 2–21. https://doi.org/10.1007/pl00009280
  • Chang, P.-C., & Liu, C.-H. (2008). A {TSK} type fuzzy rule based system for stock price prediction. Expert Systems with Applications, 34(1), 135–144. https://doi.org/10.1016/j.eswa.2006.08.020
  • Chen, A.-S., Leung, M. T., & Daouk, H. (2003). Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Computers & Operations Research, 30(6), 901–923. https://doi.org/10.1016/s0305-0548(02)00037-0
  • Chen, J.-F., Chen, W., Huang, C., Huang, S.-H., & Chen, A.-P. (2016). Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks. In 2016 7th International Conference on Cloud Computing and Big Data (CCBD) (pp. 87–92). IEEE. https://doi.org/10.1109/CCBD.2016.027
  • Chen, J. (2010). {SVM} application of financial time series forecasting using empirical technical indicators. In 2010 International Conference on Information, Networking and Automation ({ICINA}). IEEE. https://doi.org/10.1109/icina.2010.5636430
  • Cho, V. (2010). {MISMIS} {textendash} A comprehensive decision support system for stock market investment. Knowledge-Based Systems, 23(6), 626–633. https://doi.org/10.1016/j.knosys.2010.04.009
  • Choudhury, S., Ghosh, S., Bhattacharya, A., Fernandes, K. J., & Tiwari, M. K. (2014). A real time clustering and {SVM} based price-volatility prediction for optimal trading strategy. Neurocomputing, 131, 419–426. https://doi.org/10.1016/j.neucom.2013.10.002
  • Choy, S. K., & Wei, J. (2012). Option trading: Information or differences of opinion? Journal of Banking & Finance, 36(8), 2299–2322. https://doi.org/10.1016/j.jbankfin.2012.04.010
  • de A. Araújo, R., & Ferreira, T. A. E. (2013). A Morphological-Rank-Linear evolutionary method for stock market prediction. Information Sciences, 237, 3–17. https://doi.org/10.1016/j.ins.2009.07.007
  • de Fortuny, E. J., Smedt, T. De, Martens, D., & Daelemans, W. (2014). Evaluating and understanding text-based stock price prediction models. Information Processing & Management, 50(2), 426–441. https://doi.org/10.1016/j.ipm.2013.12.002
  • de Oliveira, F. A., Zarate, L. E., de Azevedo Reis, M., & Nobre, C. N. (2011). The use of artificial neural networks in the analysis and prediction of stock prices. In 2011 {IEEE} International Conference on Systems, Man, and Cybernetics. IEEE. https://doi.org/10.1109/icsmc.2011.6083990
  • Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017). Deep Direct Reinforcement Learning for Financial Signal Representation and Trading. IEEE Transactions on Neural Networks and Learning Systems, 28(3), 653–664. https://doi.org/10.1109/TNNLS.2016.2522401
  • Dhar, S., Mukherjee, T., & Ghoshal, A. K. (2010). Performance evaluation of Neural Network approach in financial prediction: Evidence from Indian Market. Proceedings of 2010 International Conference on Communication and Computational Intelligence, INCOCCI2010, 597–602.
  • Dixon, M., Klabjan, D., & Bang, J. H. (2015). Implementing deep neural networks for financial market prediction on the Intel Xeon Phi. In Proceedings of the 8th Workshop on High Performance Computational Finance - {WHPCF} {textquotesingle}15. {ACM} Press. https://doi.org/10.1145/2830556.2830562
  • Eberhart, R., & Kennedy, J. (n.d.). A new optimizer using particle swarm theory. In MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39–43). IEEE. https://doi.org/10.1109/MHS.1995.494215
  • El-Masry, A. M., Ghaly, M. F., Khalafallah, M. A., & El-Fayed, Y. A. (2002). Chemical and biochemical degradation of waste cellulosic materials. Journal of Scientific and Industrial Research, 61(9), 719–725.
  • Evans, C., Pappas, K., & Xhafa, F. (2013). Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation. Mathematical and Computer Modelling, 58(5–6), 1249–1266. https://doi.org/10.1016/j.mcm.2013.02.002
  • Figlewski, S. (1997). Forecasting Volatility. Financial Markets, Institutions and Instruments, 6(1), 1–88. https://doi.org/10.1111/1468-0416.00009
  • Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054
  • Friesen, G. C., Weller, P. A., & Dunham, L. M. (2009). Price trends and patterns in technical analysis: A theoretical and empirical examination. Journal of Banking & Finance, 33(6), 1089–1100. https://doi.org/10.1016/j.jbankfin.2008.12.010
  • Gencay, R., & Gibson, R. (2007). Model Risk for European-Style Stock Index Options. IEEE Transactions on Neural Networks, 18(1), 193–202. https://doi.org/10.1109/TNN.2006.883005
  • Ghazali, R., Hussain, A. J., Nawi, N. M., & Mohamad, B. (2009). Non-stationary and stationary prediction of financial time series using dynamic ridge polynomial neural network. Neurocomputing, 72(10–12), 2359–2367. https://doi.org/10.1016/j.neucom.2008.12.005
  • Gradojevic, N., Gencay, R., & Kukolj, D. (2009). Option Pricing With Modular Neural Networks. IEEE Transactions on Neural Networks, 20(4), 626–637. https://doi.org/10.1109/TNN.2008.2011130
  • Gultekin, N. B., Rogalski, R. J., & Tinic, S. M. (1982). Option Pricing Model Estimates: Some Empirical Results. Financial Management, 11(1), 58. https://doi.org/10.2307/3665506
  • Gunduz, H., Yaslan, Y., & Cataltepe, Z. (2017). Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations. Knowledge-Based Systems, 137, 138–148. https://doi.org/10.1016/j.knosys.2017.09.023
  • Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389–10397. https://doi.org/10.1016/j.eswa.2011.02.068
  • Hadavandi, E., Shavandi, H., & Ghanbari, A. (2010). Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowledge-Based Systems, 23(8), 800– 808. https://doi.org/10.1016/j.knosys.2010.05.004
  • Hagenau, M., Liebmann, M., & Neumann, D. (2013). Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision Support Systems, 55(3), 685–697. https://doi.org/10.1016/j.dss.2013.02.006
  • Hassan, M. R., Nath, B., & Kirley, M. (2007). A fusion model of {HMM}, {ANN} and {GA} for stock market forecasting. Expert Systems with Applications, 33(1), 171–180. https://doi.org/10.1016/j.eswa.2006.04.007
  • Hoffmann, A. O. I., & Shefrin, H. (2014). Technical analysis and individual investors. Journal of Economic Behavior & Organization, 107, 487–511. https://doi.org/10.1016/j.jebo.2014.04.002
  • Hsieh, T.-J., Hsiao, H.-F., & Yeh, W.-C. (2012). Mining financial distress trend data using penalty guided support vector machines based on hybrid of particle swarm optimization and artificial bee colony algorithm. Neurocomputing, 82, 196–206. https://doi.org/10.1016/j.neucom.2011.11.020
  • Hu, J. (2014). Does option trading convey stock price information? Journal of Financial Economics, 111(3), 625–645. https://doi.org/10.1016/j.jfineco.2013.12.004 Huang, C.-F. (2012). A hybrid stock selection model using genetic algorithms and support vector regression. Applied Soft Computing, 12(2), 807–818. https://doi.org/10.1016/j.asoc.2011.10.009
  • Huang, C.-L., & Tsai, C.-Y. (2009). A hybrid {SOFM}-{SVR} with a filter-based feature selection for stock market forecasting. Expert Systems with Applications, 36(2), 1529–1539. https://doi.org/10.1016/j.eswa.2007.11.062
  • Huang, C., Huang, L., & Han, T. (2012). Financial time series forecasting based on wavelet kernel support vector machine. In 2012 8th International Conference on Natural Computation. IEEE. https://doi.org/10.1109/icnc.2012.6234569
  • Huang, H., Pasquier, M., & Quek, C. (2009). Financial Market Trading System With a Hierarchical Coevolutionary Fuzzy Predictive Model. {IEEE} Transactions on Evolutionary Computation, 13(1), 56–70. https://doi.org/10.1109/tevc.2008.911682
  • Huang, S.-C., Li, C.-C., Lee, C.-W., & Chang, M. J. (2010). Combining ICA with kernel based regressions for trading support systems on financial options. In Advances in Intelligent Decision Technologies (pp. 163–169). Springer.
  • Huang, W., Nakamori, Y., & Wang, S.-Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513–2522. https://doi.org/10.1016/j.cor.2004.03.016
  • HUTCHINSON, J. M., LO, A. W., & POGGIO, T. (1994). A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks. The Journal of Finance, 49(3), 851–889. https://doi.org/10.1111/j.1540-6261.1994.tb00081.x
  • Jasemi, M., Kimiagari, A. M., & Memariani, A. (2011). A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick. Expert Systems with Applications, 38(4), 3884–3890. https://doi.org/10.1016/j.eswa.2010.09.049
  • Kamo, T., & Dagli, C. (2009). Hybrid approach to the Japanese candlestick method for financial forecasting. Expert Systems with Applications, 36(3), 5023–5030. https://doi.org/10.1016/j.eswa.2008.06.050
  • Kayal, A. (2010). A Neural Networks filtering mechanism for foreign exchange trading signals. In 2010 {IEEE} International Conference on Intelligent Computing and Intelligent Systems. IEEE. https://doi.org/10.1109/icicisys.2010.5658495
  • Kearney, C., & Liu, S. (2014). Textual sentiment in finance: A survey of methods and models. International Review of Financial Analysis, 33, 171–185. https://doi.org/10.1016/j.irfa.2014.02.006
  • Khashei, M., Bijari, M., & Ardali, G. A. R. (2009). Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks ({ANNs}). Neurocomputing, 72(4–6), 956–967. https://doi.org/10.1016/j.neucom.2008.04.017
  • Kim, K. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1–2), 307–319. https://doi.org/10.1016/s0925-2312(03)00372-2
  • Kim, K., & Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications, 19(2), 125–132. https://doi.org/10.1016/s0957-4174(00)00027-0
  • Kim, Y., & Enke, D. (2016). Using Neural Networks to Forecast Volatility for an Asset Allocation Strategy Based on the Target Volatility. Procedia Computer Science, 95, 281–286. https://doi.org/10.1016/J.PROCS.2016.09.335
  • Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689–702. https://doi.org/10.1016/j.ejor.2016.10.031
  • Kuremoto, T., Kimura, S., Kobayashi, K., & Obayashi, M. (2014). Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing, 137, 47–56. https://doi.org/10.1016/j.neucom.2013.03.047
  • Kwon, Y.-K., & Moon, B.-R. (2007). A Hybrid Neurogenetic Approach for Stock Forecasting. {IEEE} Transactions on Neural Networks, 18(3), 851–864. https://doi.org/10.1109/tnn.2007.891629
  • Lai, R. K., Fan, C.-Y., Huang, W.-H., & Chang, P.-C. (2009). Evolving and clustering fuzzy decision tree for financial time series data forecasting. Expert Systems with Applications, 36(2), 3761– 3773. https://doi.org/10.1016/j.eswa.2008.02.025
  • Lasfer, A., El-Baz, H., & Zualkernan, I. (2013). Neural Network design parameters for forecasting financial time series. In 2013 5th International Conference on Modeling, Simulation and Applied Optimization ({ICMSAO}). IEEE. https://doi.org/10.1109/icmsao.2013.6552553
  • Lee, M.-C. (2009). Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, 36(8), 10896–10904. https://doi.org/10.1016/j.eswa.2009.02.038
  • Leigh, W., Purvis, R., & Ragusa, J. M. (2002). Forecasting the {NYSE} composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support. Decision Support Systems, 32(4), 361–377. https://doi.org/10.1016/s0167-9236(01)00121-x
  • Li, Q., Wang, T., Gong, Q., Chen, Y., Lin, Z., & Song, S. (2014). Media-aware quantitative trading based on public Web information. Decision Support Systems, 61, 93–105. https://doi.org/10.1016/j.dss.2014.01.013
  • Li, X., Deng, Z., & Luo, J. (2009). Trading strategy design in financial investment through a turning points prediction scheme. Expert Systems with Applications, 36(4), 7818–7826. https://doi.org/10.1016/j.eswa.2008.11.014
  • Liang, X., Zhang, H., Xiao, J., & Chen, Y. (2009). Improving option price forecasts with neural networks and support vector regressions. Neurocomputing, 72(13–15), 3055–3065. https://doi.org/10.1016/J.NEUCOM.2009.03.015
  • Liao, Z., & Wang, J. (2010). Forecasting model of global stock index by stochastic time effective neural network. Expert Systems with Applications, 37(1), 834–841. https://doi.org/10.1016/j.eswa.2009.05.086
  • Lin, F., Liang, D., Yeh, C.-C., & Huang, J.-C. (2014). Novel feature selection methods to financial distress prediction. Expert Systems with Applications, 41(5), 2472–2483. https://doi.org/10.1016/j.eswa.2013.09.047
  • Liu, F., & Wang, J. (2012). Fluctuation prediction of stock market index by Legendre neural network with random time strength function. Neurocomputing, 83, 12–21. https://doi.org/10.1016/j.neucom.2011.09.033
  • Lu, C.-J., & Wu, J.-Y. (2011). An efficient {CMAC} neural network for stock index forecasting. Expert Systems with Applications, 38(12), 15194–15201. https://doi.org/10.1016/j.eswa.2011.05.082
  • Luo, Y., Liu, K., & Davis, D. N. (2002). A multi-agent decision support system for stock trading. IEEE Network, 16(1), 20–27.
  • MACBETH, J. D., & MERVILLE, L. J. (1979). An Empirical Examination of the Black-Scholes Call Option Pricing Model. The Journal of Finance, 34(5), 1173–1186. https://doi.org/10.1111/j.1540-6261.1979.tb00063.x
  • Majhi, R., Panda, G., Majhi, B., & Sahoo, G. (2009). Efficient prediction of stock market indices using adaptive bacterial foraging optimization ({ABFO}) and {BFO} based techniques. Expert Systems with Applications, 36(6), 10097–10104. https://doi.org/10.1016/j.eswa.2009.01.012
  • Malliaris, M., & Salchenberger, L. (1996). Using neural networks to forecast the S&P 100 implied volatility. Neurocomputing, 10(2), 183–195. https://doi.org/10.1016/0925- 2312(95)00019-4
  • Martinez, L. C., da Hora, D. N., de M. Palotti, J. R., Meira, W., & Pappa, G. L. (2009). From an artificial neural network to a stock market day-trading system: A case study on the {BM}&#x00026$mathsemicolon$F {BOVESPA}. In 2009 International Joint Conference on Neural Networks. IEEE. https://doi.org/10.1109/ijcnn.2009.5179050
  • Montesdeoca, L., & Niranjan, M. (2016). Extending the feature set of a data-driven artificial neural network model of pricing financial options. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1–6). IEEE. https://doi.org/10.1109/SSCI.2016.7850014
  • Morelli, M. J., Montagna, G., Nicrosini, O., Treccani, M., Farina, M., & Amato, P. (2004). Pricing financial derivatives with neural networks. Physica A: Statistical Mechanics and Its Applications, 338(1–2), 160–165. https://doi.org/10.1016/J.PHYSA.2004.02.038
  • Mugwagwa, T., Ramiah, V., Naughton, T., & Moosa, I. (2012). The efficiency of the buy-write strategy: Evidence from Australia. Journal of International Financial Markets, Institutions and Money, 22(2), 305–328. https://doi.org/10.1016/j.intfin.2011.10.001
  • Naik, P. K., & Padhi, P. (2015). Stock Market Volatility and Equity Trading Volume: Empirical Examination from Brazil, Russia, India and China (BRIC). Global Business Review, 16(5_suppl), 28S-45S. https://doi.org/10.1177/0972150915601235
  • Osler, C. L. (2000). Support for resistance: technical analysis and intraday exchange rates. Economic Policy Review, 6(2).
  • Ozbayoglu, A., & Erkut, U. (2010). Stock Market Technical Indicator Optimization by Genetic Algorithms. In Intelligent Engineering Systems through Artificial Neural Networks (Vol. 20, pp. 589–596). ASME.
  • Park, K., & Shin, H. (2013). Stock price prediction based on a complex interrelation network of economic factors. Engineering Applications of Artificial Intelligence, 26(5–6), 1550–1561. https://doi.org/10.1016/j.engappai.2013.01.009
  • Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4), 2162–2172. https://doi.org/10.1016/j.eswa.2014.10.031
  • Pinto, J. M., Neves, R. F., & Horta, N. (2015). Boosting Trading Strategies performance using {VIX} indicator together with a dual-objective Evolutionary Computation optimizer. Expert Systems with Applications, 42(19), 6699–6716. https://doi.org/10.1016/j.eswa.2015.04.056
  • Poon, S.-H., & Granger, C. W. J. (2003). Forecasting Volatility in Financial Markets: A Review. Journal of Economic Literature, 41(2), 478–539. https://doi.org/10.1257/002205103765762743
  • Prasain, H., Jha, G. K., Thulasiraman, P., & Thulasiram, R. (2010). A parallel Particle swarm optimization algorithm for option pricing. In 2010 {IEEE} International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum ({IPDPSW}). IEEE. https://doi.org/10.1109/ipdpsw.2010.5470706
  • Ribeiro, B., & Lopes, N. (2011). Deep belief networks for financial prediction. In International Conference on Neural Information Processing (pp. 766–773).
  • Rodr’iguez-González, A., Garc’ia-Crespo, Á., Colomo-Palacios, R., Iglesias, F. G., & GómezBerb’is, J. M. (2011). {CAST}: Using neural networks to improve trading systems based on technical analysis by means of the {RSI} financial indicator. Expert Systems with Applications, 38(9), 11489–11500. https://doi.org/10.1016/j.eswa.2011.03.023
  • Roll, R., Schwartz, E., & Subrahmanyam, A. (2010). O/S: The relative trading activity in options and stock. Journal of Financial Economics, 96(1), 1–17. https://doi.org/10.1016/j.jfineco.2009.11.004
  • Samur, Z. ltüzer. (2009). The Use of Artificial Neural Network in Option Pricing : The Case of S & P 100 Index Options.
  • Sezer, O. B., Ozbayoglu, M., & Dogdu, E. (2017). A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters. Procedia Computer Science, 114, 473–480. https://doi.org/10.1016/j.procs.2017.09.031
  • Shahpazov, V. L., Velev, V. B., & Doukovska, L. A. (2013). Design and application of Artificial Neural Networks for predicting the values of indexes on the Bulgarian Stock market. In 2013
  • Signal Processing Symposium ({SPS}). IEEE. https://doi.org/10.1109/sps.2013.6623604 Sheu, H.-J., & Wei, Y.-C. (2011). Effective options trading strategies based on volatility forecasting recruiting investor sentiment. Expert Systems with Applications, 38(1), 585–596. https://doi.org/10.1016/j.eswa.2010.07.007
  • Si, Y.-W., & Yin, J. (2013). {OBST}-based segmentation approach to financial time series. Engineering Applications of Artificial Intelligence, 26(10), 2581–2596. https://doi.org/10.1016/j.engappai.2013.08.015
  • Suzuki, K., Shimokawa, T., & Misawa, T. (2009). Agent-Based Approach to Option Pricing Anomalies. IEEE Transactions on Evolutionary Computation, 13(5), 959–972. https://doi.org/10.1109/TEVC.2008.2011745
  • Tan, Y., & Zhu, Y. (2010). Fireworks algorithm for optimization. In International conference in swarm intelligence (pp. 355–364).
  • Tan, Z., Quek, C., & Cheng, P. Y. K. (2011). Stock trading with cycles: A financial application of {ANFIS} and reinforcement learning. Expert Systems with Applications, 38(5), 4741–4755. https://doi.org/10.1016/j.eswa.2010.09.001
  • Tang, L., & Diao, X. (2017). Option pricing based on HMM and GARCH model. In 2017 29th Chinese Control And Decision Conference (CCDC) (pp. 3363–3368). IEEE. https://doi.org/10.1109/CCDC.2017.7979087
  • Ticknor, J. L. (2013). A Bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications, 40(14), 5501–5506. https://doi.org/10.1016/j.eswa.2013.04.013
  • Tino, P., Schittenkopf, C., & Dorffner, G. (2001). Financial volatility trading using recurrent neural networks. {IEEE} Transactions on Neural Networks, 12(4), 865–874. https://doi.org/10.1109/72.935096
  • Tsai, C.-F., & Hsiao, Y.-C. (2010). Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches. Decision Support Systems, 50(1), 258–269. https://doi.org/10.1016/j.dss.2010.08.028
  • Tsai, C. F., & Wang, S. P. (2009). Stock price forecasting by hybrid machine learning techniques. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 1, p. 60).
  • Tsinaslanidis, P. E., & Kugiumtzis, D. (2014). A prediction scheme using perceptually important points and dynamic time warping. Expert Systems with Applications, 41(15), 6848–6860. https://doi.org/10.1016/j.eswa.2014.04.028
  • TSUKUI, M., & FURUTA, K. (1997). An option pricing by eliminating observation noise when the model parameters are unknown. International Journal of Systems Science, 28(12), 1259– 1283. https://doi.org/10.1080/00207729708929483
  • Tung, W. L., & Quek, C. (2011). Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach. Expert Systems with Applications, 38(5), 4668–4688. https://doi.org/10.1016/j.eswa.2010.07.116
  • Ucar, I., Ozbayoglu, A. M., & Ucar, M. (2015). Developing a two level options trading strategy based on option pair optimization of spread strategies with evolutionary algorithms. In 2015 {IEEE} Congress on Evolutionary Computation ({CEC}). IEEE. https://doi.org/10.1109/cec.2015.7257199
  • Ucar, M., Bayram, I., & Ozbayoglu, A. M. (2013). A Two-level Cascade Evolutionary Computation based Covered Call Trading Model. Procedia Computer Science, 20, 472–477. https://doi.org/10.1016/j.procs.2013.09.305
  • Ülkü, N., & Prodan, E. (2013). Drivers of technical trend-following rules{textquotesingle} profitability in world stock markets. International Review of Financial Analysis, 30, 214–229. https://doi.org/10.1016/j.irfa.2013.08.005
  • Vanstone, B., Finnie, G., & Hahn, T. (2012). Creating trading systems with fundamental variables and neural networks: The Aby case study. Mathematics and Computers in Simulation, 86, 78–91. https://doi.org/10.1016/j.matcom.2011.01.002
  • Wang, H.-W. (2007). Exchange Options Pricing with Evolutionary Neural-based Fuzzy Inference Systems. International Journal of Computational Intelligence Research, 3(1). https://doi.org/10.5019/j.ijcir.2007.85
  • Wang, J., & Wang, J. (2015). Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing, 156, 68–78. https://doi.org/10.1016/j.neucom.2014.12.084
  • Wang, Y.-H. (2009). Nonlinear neural network forecasting model for stock index option price: Hybrid {GJR}{textendash}{GARCH} approach. Expert Systems with Applications, 36(1), 564–570. https://doi.org/10.1016/j.eswa.2007.09.056
  • Wang, Y., Wang, D., Zhang, S., Feng, Y., Li, S., & Zhou, Q. (2017). Deep Q-trading. Cslt.Riit.Tsinghua.Edu.Cn, 1–9.
  • Wu, H.-C. (2004). Pricing European options based on the fuzzy pattern of Black–Scholes formula. Computers & Operations Research, 31(7), 1069–1081. https://doi.org/10.1016/S0305- 0548(03)00065-0
  • Wu, H.-C. (2005). European option pricing under fuzzy environments. International Journal of Intelligent Systems, 20(1), 89–102. https://doi.org/10.1002/int.20055 Wu, L., & Shahidehpour, M. (2010). A Hybrid Model for Day-Ahead Price Forecasting. {IEEE} Transactions on Power Systems, 25(3), 1519–1530. https://doi.org/10.1109/tpwrs.2009.2039948
  • Xie, G. (2011). The Optimization of Share Price Prediction Model Based on Support Vector Machine. In 2011 International Conference on Control, Automation and Systems Engineering ({CASE}). IEEE. https://doi.org/10.1109/iccase.2011.5997714
  • Yang, X.-S., & Deb, S. (2013). Multiobjective cuckoo search for design optimization. Computers & Operations Research, 40(6), 1616–1624. https://doi.org/10.1016/j.cor.2011.09.026 Yao, J., Li, Y., & Tan, C. L. (2000). Option price forecasting using neural networks. Omega, 28(4), 455–466. https://doi.org/10.1016/S0305-0483(99)00066-3
  • Yin, J., Si, Y.-W., & Gong, Z. (2011). Financial time series segmentation based on Turning Points. In Proceedings 2011 International Conference on System Science and Engineering. IEEE. https://doi.org/10.1109/icsse.2011.5961935
  • Yu, L., Chen, H., Wang, S., & Lai, K. K. (2009). Evolving Least Squares Support Vector Machines for Stock Market Trend Mining. {IEEE} Transactions on Evolutionary Computation, 13(1), 87– 102. https://doi.org/10.1109/tevc.2008.928176
  • Zarandi, M. H. F., Rezaee, B., Turksen, I. B., & Neshat, E. (2009). A type-2 fuzzy rule-based expert system model for stock price analysis. Expert Systems with Applications, 36(1), 139–154. https://doi.org/10.1016/j.eswa.2007.09.034
  • Zhou, D., Li, J., & Ma, W. (2009). Clustering Based on {LLE} For Financial Multivariate Time 215 Series. In 2009 International Conference on Management and Service Science. IEEE. https://doi.org/10.1109/icmss.2009.5305089
  • Zhu, M., & Wang, L. (2010). Intelligent trading using support vector regression and multilayer perceptrons optimized with genetic algorithms. In The 2010 International Joint Conference on Neural Networks ({IJCNN}). IEEE. https://doi.org/10.1109/ijcnn.2010.5596301
APA ÖZBAYOĞLU M, TOKAT E (2019). Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı. , 1 - 240.
Chicago ÖZBAYOĞLU Murat Ahmet,TOKAT Ekin Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı. (2019): 1 - 240.
MLA ÖZBAYOĞLU Murat Ahmet,TOKAT Ekin Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı. , 2019, ss.1 - 240.
AMA ÖZBAYOĞLU M,TOKAT E Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı. . 2019; 1 - 240.
Vancouver ÖZBAYOĞLU M,TOKAT E Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı. . 2019; 1 - 240.
IEEE ÖZBAYOĞLU M,TOKAT E "Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı." , ss.1 - 240, 2019.
ISNAD ÖZBAYOĞLU, Murat Ahmet - TOKAT, Ekin. "Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı". (2019), 1-240.
APA ÖZBAYOĞLU M, TOKAT E (2019). Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı. , 1 - 240.
Chicago ÖZBAYOĞLU Murat Ahmet,TOKAT Ekin Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı. (2019): 1 - 240.
MLA ÖZBAYOĞLU Murat Ahmet,TOKAT Ekin Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı. , 2019, ss.1 - 240.
AMA ÖZBAYOĞLU M,TOKAT E Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı. . 2019; 1 - 240.
Vancouver ÖZBAYOĞLU M,TOKAT E Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı. . 2019; 1 - 240.
IEEE ÖZBAYOĞLU M,TOKAT E "Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı." , ss.1 - 240, 2019.
ISNAD ÖZBAYOĞLU, Murat Ahmet - TOKAT, Ekin. "Derin Öğrenme ve Evrimsel Algoritma Tabanlı Opsiyon Alım-Satım Stratejileri Eniyilemesi için Çok-Etmenli bir Benzetim ve Başarım Testi Platformu Yazılımı". (2019), 1-240.