{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T04:08:10Z","timestamp":1741752490752,"version":"3.38.0"},"reference-count":39,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2020,9,30]]},"abstract":"<jats:p>Among FinTech research and applications, forecasting financial time series data has been a challenging task because this kind of data is typically quite noisy and non-stationary. A recent line of financial research centers around trading through financial data on the microscopic level, which is the holy grail of high-frequency trading (HFT), as the higher the data frequency, the more profitable opportunities may appear. The advancement in HFT modeling has also facilitated more understanding towards price formation because the supply and demand of a stock can be comprehended more easily from the microstructure of the order book. Instead of traditional statistical methods, there has been increasing demand for the development of more reliable prediction models due to the recent progress in Computational Intelligence (CI) technologies. In this study, we aim to develop novel CI-based methodologies for the forecasting task of price movement in HFT. Our goal is to conduct a study for autonomous genetic-based models that allow the forecasting systems to self-evolve. The results show that our proposed method can improve upon the previous ones and advance the current state of Fintech research.<\/jats:p>","DOI":"10.3233\/ida-194592","type":"journal-article","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T17:34:28Z","timestamp":1601660068000},"page":"1175-1206","source":"Crossref","is-referenced-by-count":2,"title":["Autonomous self-evolving forecasting models for price movement in high frequency trading: Evidence from Taiwan"],"prefix":"10.1177","volume":"24","author":[{"given":"Chien-Feng","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National University of Kaohsiung, Nanzih District, Kaohsiung, Taiwan"}]},{"given":"Hsiao-Chi","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Information Management, Ming Chuan University, Taipei 111, Taiwan"}]},{"given":"Po-Chun","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National University of Kaohsiung, Nanzih District, Kaohsiung, Taiwan"}]},{"given":"Bao Rong","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National University of Kaohsiung, Nanzih District, Kaohsiung, Taiwan"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-194592_ref1","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/BF00126626","article-title":"Introduction to financial forecasting","volume":"6","author":"Abu-Mostafa","year":"1996","journal-title":"Applied Intelligence"},{"key":"10.3233\/IDA-194592_ref2","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.asoc.2006.03.004","article-title":"A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets","volume":"7","author":"Kim","year":"2007","journal-title":"Applied Soft Computing"},{"key":"10.3233\/IDA-194592_ref4","doi-asserted-by":"crossref","unstructured":"A. Tsantekidis et al., Forecasting stock prices from the limit order book using convolutional neural networks, in: Proceedings of the 2017 IEEE 19th Conference on Business Informatics (CBI), Thessaloniki, 2017, pp. 7\u201312.","DOI":"10.1109\/CBI.2017.23"},{"issue":"1","key":"10.3233\/IDA-194592_ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40854-015-0003-8","article-title":"Will high-frequency trading practices transform the financial markets in the Asia Pacific Region","volume":"1","author":"Kauffman","year":"2015","journal-title":"Financial Innovation"},{"issue":"6","key":"10.3233\/IDA-194592_ref7","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1088\/1469-7688\/3\/6\/307","article-title":"Statistical theory of the continuous double auction","volume":"3","author":"Smith","year":"2003","journal-title":"Quantitative Finance"},{"issue":"1","key":"10.3233\/IDA-194592_ref9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1137\/110856605","article-title":"Price dynamics in a Markovian limit order market","volume":"4","author":"Cont","year":"2013","journal-title":"SIAM Journal on Financial Mathematics"},{"key":"10.3233\/IDA-194592_ref10","first-page":"57","article-title":"Direct estimation of equity market impact","volume":"18","author":"Almgren","year":"2005","journal-title":"Risk"},{"key":"10.3233\/IDA-194592_ref12","doi-asserted-by":"crossref","unstructured":"S. Delattre, C.Y. Robert and M. Rosenbaum, Estimating the efficient price from the order flow: A Brownian Cox process approach, Stochastic Processes and Their Applications 123(7) (2013), 2603\u20132619.","DOI":"10.1016\/j.spa.2013.04.012"},{"issue":"2","key":"10.3233\/IDA-194592_ref13","doi-asserted-by":"crossref","first-page":"426","DOI":"10.3150\/11-BEJ407","article-title":"Estimation of the lead-lag parameter from non-synchronous data","volume":"19","author":"Hoffmann","year":"2013","journal-title":"Bernoulli"},{"issue":"1","key":"10.3233\/IDA-194592_ref14","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1111\/j.1467-9965.2010.00454.x","article-title":"Volatility and covariation estimation when microstructure noise and trading times are endogenous","volume":"22","author":"Robert","year":"2012","journal-title":"Mathematical Finance"},{"issue":"2","key":"10.3233\/IDA-194592_ref15","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.asoc.2011.10.009","article-title":"A hybrid stock selection model using genetic algorithms and support vector regression","volume":"12","author":"Huang","year":"2012","journal-title":"Applied Soft Computing"},{"key":"10.3233\/IDA-194592_ref16","unstructured":"T. Jun and L. He, Genetic optimization of BP neural network in the application of suspicious financial transactions pattern recognition, in: Proceedings of the International Conference on Management of E-commerce and E-government (ICMeCG), 2012, pp. 280\u2013284."},{"key":"10.3233\/IDA-194592_ref17","unstructured":"L. Jing, Data modeling for searching abnormal noise in stock market based on genetic algorithm, in: Proceedings of the 2010 International Symposium on Computational Intelligence and Design, Vol. 2, 2010, pp. 129\u2013131."},{"key":"10.3233\/IDA-194592_ref18","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/S0167-9236(03)00087-3","article-title":"EDDIE-Automation, a decision support tool for financial forecasting","volume":"37","author":"Tsang","year":"2004","journal-title":"Decision Support Systems"},{"key":"10.3233\/IDA-194592_ref20","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1016\/S0378-4266(98)00059-4","article-title":"Genetic algorithms applications in the analysis of insolvency risk","volume":"22","author":"Varetto","year":"1998","journal-title":"Journal of Banking & Finance"},{"key":"10.3233\/IDA-194592_ref22","doi-asserted-by":"crossref","unstructured":"P. Parracho, R. Neves and N. Horta, Trading with optimized uptrend and downtrend pattern templates using a genetic algorithm kernel, in: Proceedings of the IEEE Congress on Evolutionary Computation, 2011, pp. 1895\u20131901.","DOI":"10.1109\/CEC.2011.5949846"},{"key":"10.3233\/IDA-194592_ref23","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.ins.2009.07.007","article-title":"A morphological-rank-linear evolutionary method for stock market prediction","volume":"237","author":"Ara\u00fajo","year":"2013","journal-title":"Information Sciences"},{"key":"10.3233\/IDA-194592_ref24","doi-asserted-by":"crossref","unstructured":"D. Bernardo, H. Hagras and E. Tsang, A genetic type-2 fuzzy logic based system for financial applications modelling and prediction, in: Proceedings of the 2013 IEEE International Conference on Fuzzy Systems, 2013, pp. 1\u20138.","DOI":"10.1109\/FUZZ-IEEE.2013.6622310"},{"key":"10.3233\/IDA-194592_ref25","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s10898-011-9692-3","article-title":"Asset portfolio optimization using support vector machines and real-coded genetic algorithm","volume":"53","author":"Gupta","year":"2012","journal-title":"Journal of Global Optimization"},{"key":"10.3233\/IDA-194592_ref26","doi-asserted-by":"crossref","first-page":"2069","DOI":"10.12988\/ams.2015.5188","article-title":"Portfolio selection problem using generalized differential evolution","volume":"9","author":"Adebiyi","year":"2015","journal-title":"Applied Mathematical Sciences"},{"key":"10.3233\/IDA-194592_ref27","doi-asserted-by":"crossref","first-page":"89","DOI":"10.2298\/CSIS121024017R","article-title":"The mean-value at risk static portfolio optimization using genetic algorithm","volume":"11","author":"Rankovic\u2019","year":"2014","journal-title":"Computer Science and Information Systems"},{"key":"10.3233\/IDA-194592_ref28","doi-asserted-by":"crossref","unstructured":"K. Chen, Y. Zhou and F. Dai, A LSTM-based method for stock returns prediction: A case study of China stock market, in: Proceedings of the 2015 IEEE International Conference on Big Data, 2015, pp. 2823\u20132824.","DOI":"10.1109\/BigData.2015.7364089"},{"key":"10.3233\/IDA-194592_ref29","doi-asserted-by":"crossref","unstructured":"M. Hiransha et al., NSE stock market prediction using deep-learning models, Procedia Computer Science 132 (2018), 1351\u20131362.","DOI":"10.1016\/j.procs.2018.05.050"},{"key":"10.3233\/IDA-194592_ref30","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.jocs.2017.08.018","article-title":"Sequence classification of the limit order book using recurrent neural networks","volume":"24","author":"Dixon","year":"2017","journal-title":"Journal of Computational Science"},{"key":"10.3233\/IDA-194592_ref31","doi-asserted-by":"crossref","unstructured":"D.T. Tran et al., Tensor representation in high frequency financial data for price change prediction, in: 2017 IEEE Symposium Series on Computational Intelligence, 2017, pp. 1\u20137.","DOI":"10.1109\/SSCI.2017.8280812"},{"key":"10.3233\/IDA-194592_ref32","doi-asserted-by":"crossref","unstructured":"A. Tsantekidis et al, Forecasting stock prices from the limit order book using convolutional neural networks, in: Proceedings of the 2017 IEEE Conference on Business Informatics, Vol. 1, 2017, pp. 7\u201312.","DOI":"10.1109\/CBI.2017.23"},{"key":"10.3233\/IDA-194592_ref33","doi-asserted-by":"crossref","unstructured":"N. Passalis et al., Time-series classification using neural bag-of-features, in: Proceedings of the European Signal Processing Conference, 2017, pp. 301\u2013305.","DOI":"10.23919\/EUSIPCO.2017.8081217"},{"key":"10.3233\/IDA-194592_ref34","doi-asserted-by":"crossref","first-page":"3765","DOI":"10.3390\/su10103765","article-title":"Genetic algorithm-optimized long short-term memory network for stock market prediction","volume":"10","author":"Chung","year":"2018","journal-title":"Sustainability"},{"key":"10.3233\/IDA-194592_ref35","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/S0957-4174(00)00027-0","article-title":"Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index","volume":"19","author":"Kim","year":"2000","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/IDA-194592_ref36","doi-asserted-by":"crossref","first-page":"2342","DOI":"10.1016\/j.neucom.2005.12.138","article-title":"Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm","volume":"70","author":"Cai","year":"2007","journal-title":"Neurocomputing"},{"key":"10.3233\/IDA-194592_ref37","doi-asserted-by":"crossref","first-page":"3234","DOI":"10.1016\/j.eswa.2014.12.003","article-title":"Recurrent neural network and a hybrid model for prediction of stock returns","volume":"42","author":"Rather","year":"2015","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/IDA-194592_ref38","doi-asserted-by":"crossref","first-page":"7684","DOI":"10.1016\/j.eswa.2015.06.001","article-title":"Genetic algorithms and Darwinian approaches in financial applications: A survey","volume":"42","author":"Aguilar-Rivera","year":"2015","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/IDA-194592_ref39","doi-asserted-by":"crossref","unstructured":"M. Shao et al., Guided fast local search for speeding up a financial forecasting algorithm, in: Proceedings of the IEEE Conference on Computational Intelligence for Financial Engineering and Economics, 2014, pp. 325\u2013332.","DOI":"10.1109\/CIFEr.2014.6924091"},{"key":"10.3233\/IDA-194592_ref40","unstructured":"E.P. Tsang et al., EDDIE in financial decision making, Journal of Management and Economics 4(4) (2000)."},{"key":"10.3233\/IDA-194592_ref41","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.asoc.2014.06.041","article-title":"Evolutionary optimization of sparsely connected and time-lagged neural networks for time series forecasting","volume":"23","author":"Peralta Donate","year":"2014","journal-title":"Applied Soft Computing Journal"},{"issue":"3","key":"10.3233\/IDA-194592_ref42","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1086\/209650","article-title":"Drift-independent volatility estimation based on high, low, open, and close prices","volume":"73","author":"Yang","year":"2000","journal-title":"The Journal of Business"},{"issue":"3","key":"10.3233\/IDA-194592_ref45","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1287\/opre.1090.0780","article-title":"A stochastic model for order book dynamics","volume":"58","author":"Cont","year":"2010","journal-title":"Operations Research"},{"key":"10.3233\/IDA-194592_ref46","first-page":"19","article-title":"Infer the order imbalance in a call auction market \u2013 evidence from taiwan stock market","volume":"16","author":"Hu","year":"2008","journal-title":"Journal of Financial Studies"},{"issue":"3","key":"10.3233\/IDA-194592_ref48","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/14697680701381228","article-title":"High frequency trading in a limit order book","volume":"8","author":"Avellaneda","year":"2008","journal-title":"Quantitative Finance"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-194592","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T00:50:53Z","timestamp":1741654253000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-194592"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,30]]},"references-count":39,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/ida-194592","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"type":"print","value":"1088-467X"},{"type":"electronic","value":"1571-4128"}],"subject":[],"published":{"date-parts":[[2020,9,30]]}}}