Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions
Tài liệu tham khảo
Van Rooij, 2011, Financial literacy and stock market participation, J. Financ. Econ., 101, 449, 10.1016/j.jfineco.2011.03.006
Bonanno, 2001, Levels of complexity in financial markets, Physica A, 299, 16, 10.1016/S0378-4371(01)00279-5
Thakkar, 2020, CREST: Cross-reference to exchange-based stock trend prediction using long short-term memory, Procedia Comput. Sci., 167, 616, 10.1016/j.procs.2020.03.328
Keller, 2006, Investing in stocks: The influence of financial risk attitude and values-related money and stock market attitudes, J. Econ. Psychol., 27, 285, 10.1016/j.joep.2005.07.002
Cavalcante, 2016, Computational intelligence and financial markets: A survey and future directions, Expert Syst. Appl., 55, 194, 10.1016/j.eswa.2016.02.006
Chen, 2016, Technical, fundamental, and combined information for separating winners from losers, Pac.-Basin Finance J., 39, 224, 10.1016/j.pacfin.2016.06.008
Castanedo, 2013, A review of data fusion techniques, Sci. World J., 2013, 10.1155/2013/704504
Kazienko, 2015
Gabriela ğiĠan, 2015, The efficient market hypothesis: Review of specialized literature and empirical research, Procedia Econ. Finance, 32, 442, 10.1016/S2212-5671(15)01416-1
Shleifer, 2000
Copur, 2015
Khuntia, 2018, Adaptive market hypothesis and evolving predictability of bitcoin, Econom. Lett., 167, 26, 10.1016/j.econlet.2018.03.005
Lo, 2005, Reconciling efficient markets with behavioral finance: the adaptive markets hypothesis, J. Invest. Consult., 7, 21
Kim, 2011, Stock return predictability and the adaptive markets hypothesis: evidence from century-long US data, J. Empir. Financ., 18, 868, 10.1016/j.jempfin.2011.08.002
Urquhart, 2016, Are stock markets really efficient? Evidence of the adaptive market hypothesis, Int. Rev. Financ. Anal., 47, 39, 10.1016/j.irfa.2016.06.011
Charles, 2012, Exchange-rate return predictability and the adaptive markets hypothesis: Evidence from major foreign exchange rates, J. Int. Money Finance, 31, 1607, 10.1016/j.jimonfin.2012.03.003
Chu, 2019, The adaptive market hypothesis in the high frequency cryptocurrency market, Int. Rev. Financ. Anal., 64, 221, 10.1016/j.irfa.2019.05.008
Kute, 2013, A survey on stock market prediction techniques, Int. J. Sci. Res.
Woźniak, 2014, A survey of multiple classifier systems as hybrid systems, Inf. Fusion, 16, 3, 10.1016/j.inffus.2013.04.006
Balazs, 2016, Opinion mining and information fusion: a survey, Inf. Fusion, 27, 95, 10.1016/j.inffus.2015.06.002
Ibidapo, 2017, Soft computing techniques for stock market prediction: A literature survey, Covenant J. Inform. Commun. Technol., 5
Lau, 2019, A survey of data fusion in smart city applications, Inf. Fusion, 52, 357, 10.1016/j.inffus.2019.05.004
Zhao, 2019, Multi-source knowledge fusion: A survey, 119
Rundo, 2019, Machine learning for quantitative finance applications: A survey, Appl. Sci., 9, 5574, 10.3390/app9245574
Ozbayoglu, 2020
Durrant-Whyte, 1990, Sensor models and multisensor integration, 73
Haleh, 2011, A new approach to forecasting stock price with EKF data fusion, Int. J. Trade Econ. Finance, 2, 109, 10.7763/IJTEF.2011.V2.87
Rosenkrantz, 2003
Sharpe, 2004
B.A. Moghaddam, H. Haleh, S. Ebrahimijam, Forecasting trend and stock price with adaptive extended kalman filter data fusion, in: Proceedings of IEEE International Conference on Economics and Finance Research, 2011, pp. 119–123.
Weng, 2017, Stock market one-day ahead movement prediction using disparate data sources, Expert Syst. Appl., 79, 153, 10.1016/j.eswa.2017.02.041
Zhang, 2018, Improving stock market prediction via heterogeneous information fusion, Knowl.-Based Syst., 143, 236, 10.1016/j.knosys.2017.12.025
Huang, 2018, A tensor-based sub-mode coordinate algorithm for stock prediction, 716
Park, 2017, Information fusion of stock prices and sentiment in social media using granger causality, 614
Park, 2019, Prediction of stock prices with sentiment fusion and SVM granger causality, 207
Long, 2020, An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in Chinese stock exchange market, Appl. Soft Comput., 10.1016/j.asoc.2020.106205
Lundblad, 2007, The risk return tradeoff in the long run: 1836–2003, J. Financ. Econ., 85, 123, 10.1016/j.jfineco.2006.06.003
Rachev, 2000
Rachev, 2005
Zhang, 2013, Based on information fusion technique with data mining in the application of finance early-warning, Procedia Comput. Sci., 17, 695, 10.1016/j.procs.2013.05.090
Li, 2014, Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information, Neurocomputing, 142, 228, 10.1016/j.neucom.2014.04.043
He, 2015, Social media data assisted inference with application to stock prediction, 1801
Yi, 2015, Port customer credit risk prediction based on internal and external information fusion, Open Cybern. Syst. J., 9, 10.2174/1874110X01509011323
Oztekin, 2016, A data analytic approach to forecasting daily stock returns in an emerging market, European J. Oper. Res., 253, 697, 10.1016/j.ejor.2016.02.056
Feuerriegel, 2018, Long-term stock index forecasting based on text mining of regulatory disclosures, Decis. Support Syst., 112, 88, 10.1016/j.dss.2018.06.008
K. Teymourian, M. Rohde, A. Paschke, Fusion of background knowledge and streams of events, in: Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems, 2012, pp. 302–313.
K. Teymourian, M. Rohde, A. Paschke, Knowledge-based processing of complex stock market events, in: Proceedings of the 15th International Conference on Extending Database Technology, 2012, pp. 594–597.
Xu, 2014, Belief fusion of predictions of industries in China’s stock market, 348
Chang, 2015, Market analysis and trading strategies with Bayesian networks, 1922
Shao, 2016, The application of AUV navigation based on adaptive extended Kalman filter, 1
Rokach, 2016, Decision forest: Twenty years of research, Inf. Fusion, 27, 111, 10.1016/j.inffus.2015.06.005
Karamizadeh, 2014, Advantage and drawback of support vector machine functionality, 63
Qiu, 2016, Predicting the direction of stock market index movement using an optimized artificial neural network model, PLoS One, 11, 10.1371/journal.pone.0155133
Du, 2013, Information fusion techniques for change detection from multi-temporal remote sensing images, Inf. Fusion, 14, 19, 10.1016/j.inffus.2012.05.003
Guo, 2014, A feature fusion based forecasting model for financial time series, PLoS One, 9, 10.1371/journal.pone.0101113
Kaur, 2016, Minimal variability OWA operator combining ANFIS and fuzzy c-means for forecasting BSE index, Math. Comput. Simulation, 122, 69, 10.1016/j.matcom.2015.12.001
Kim, 2019, Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data, PLoS One, 14, 10.1371/journal.pone.0212320
Picasso, 2019, Technical analysis and sentiment embeddings for market trend prediction, Expert Syst. Appl., 135, 60, 10.1016/j.eswa.2019.06.014
Ang, 2014, Portfolio choice with illiquid assets, Manage. Sci., 60, 2737, 10.1287/mnsc.2014.1986
Irukulapati, 2018, Long-term portfolio management using attribute selection and combinatorial fusion, 593
Bedworth, 2000, The omnibus model: a new model of data fusion?, IEEE Aerosp. Electron. Syst. Mag., 15, 30, 10.1109/62.839632
Arandjelović, 2014, Discriminative extended canonical correlation analysis for pattern set matching, Mach. Learn., 94, 353, 10.1007/s10994-013-5380-5
He, 2019, Prediction of listed companies’ revenue based on model-fused, 257
Gunasekaran, 2011, A fusion model integrating ANFIS and artificial immune algorithm for forecasting Indian stock market, J. Appl. Sci., 11, 3028, 10.3923/jas.2011.3028.3033
Homayouni, 2011, Stock price prediction using a fusion model of wavelet, fuzzy logic and ANN, 277
Cheng, 2013, OWA-based ANFIS model for TAIEX forecasting, Econ. Model., 30, 442, 10.1016/j.econmod.2012.09.047
Ticknor, 2013, A Bayesian regularized artificial neural network for stock market forecasting, Expert Syst. Appl., 40, 5501, 10.1016/j.eswa.2013.04.013
Hassan, 2007, A fusion model of HMM, ANN and GA for stock market forecasting, Expert Syst. Appl., 33, 171, 10.1016/j.eswa.2006.04.007
Patel, 2015, Predicting stock market index using fusion of machine learning techniques, Expert Syst. Appl., 42, 2162, 10.1016/j.eswa.2014.10.031
Jhaveri, 2016, Financial market prediction using hybridized neural approach, 009
Zhang, 2017, A fusion financial prediction strategy based on RNN and representative pattern discovery, 92
Wang, 2018, Combining the wisdom of crowds and technical analysis for financial market prediction using deep random subspace ensembles, Neurocomputing, 299, 51, 10.1016/j.neucom.2018.02.095
Mohamad, 2006, Diversification across economic sectors and implication on portfolio investments in Malaysia, Int. J. Econ. Manage., 1, 155
Driessen, 2007, International portfolio diversification benefits: Cross-country evidence from a local perspective, J. Bank. Financ., 31, 1693, 10.1016/j.jbankfin.2006.11.006
Kim, 2019, Geopolitical risk and trading patterns of foreign and domestic investors: Evidence from Korea, Asia-Pac. J. Financ. Stud., 48, 269, 10.1111/ajfs.12253
Chaudhary, 2013, Impact of behavioral finance in investment decisions and strategies–a fresh approach, Int. J. Manage. Res. Bus. Strategy, 2, 85
Brandt, 2010, Portfolio choice problems, 269
Paiva, 2019, Decision-making for financial trading: A fusion approach of machine learning and portfolio selection, Expert Syst. Appl., 115, 635, 10.1016/j.eswa.2018.08.003
Wu, 2012, Credit risk assessment and decision making by a fusion approach, Knowl.-Based Syst., 35, 102, 10.1016/j.knosys.2012.04.025
Barak, 2017, Fusion of multiple diverse predictors in stock market, Inf. Fusion, 36, 90, 10.1016/j.inffus.2016.11.006
Harnovinsah, 2017, The mediation influence of value relevance of accounting information, investment decision and dividend policy on the relationship between profitability and the company’s value, J. Akunt., 21, 170, 10.24912/ja.v21i2.193
Wei, 2011, A fusion ANFIS model for forecasting EPS of leading industries in Taiwan, 1
Wei, 2011, Fusion anfis model based on ar for forecasting eps of leading industries, Int. J. Innovative Comput. Inf. Control, 7, 5445
Xiaofang, 2019, Research and implementation of SVM and bootstrapping fusion algorithm in emotion analysis of stock review texts, DEStech Trans. Comput. Sci. Eng.
Thakkar, 2020, A comprehensive survey on portfolio optimization, stock price and trend prediction using particle swarm optimization, Arch. Comput. Methods Eng., 10.1007/s11831-020-09448-8
Atsalakis, 2009, Surveying stock market forecasting techniques–Part II: Soft computing methods, Expert Syst. Appl., 36, 5932, 10.1016/j.eswa.2008.07.006
Azeem, 2015, An analysis of applications and possibilities of neural networks (fuzzy, logic and genetic algorithm) in finance and accounting, Donnish J. Bus. Finance Manage. Res., 1, 9
Giannone, 2014, Short-term inflation projections: A Bayesian vector autoregressive approach, Int. J. Forecast., 30, 635, 10.1016/j.ijforecast.2013.01.012
Salleh, 2017, Adaptive neuro-fuzzy inference system: Overview, strengths, limitations, and solutions, 527
Salles, 2018, Improving random forests by neighborhood projection for effective text classification, Inf. Syst., 77, 1, 10.1016/j.is.2018.05.006
Delen, 2010, A comparative analysis of machine learning techniques for student retention management, Decis. Support Syst., 49, 498, 10.1016/j.dss.2010.06.003
Fuller, 2011, An investigation of data and text mining methods for real world deception detection, Expert Syst. Appl., 38, 8392, 10.1016/j.eswa.2011.01.032
Bastı, 2015, Analyzing initial public offerings’ short-term performance using decision trees and SVMs, Decis. Support Syst., 73, 15, 10.1016/j.dss.2015.02.011
Teh, 2016, The asian correction can be quantitatively forecasted using a statistical model of fusion-fission processes, PLoS One, 11, 10.1371/journal.pone.0163842
Kahneman, 1979, Prospect theory: An analysis of decisions under risk, Econometrica, 47, 278, 10.2307/1914185
Iovane, 2016, Multi indicator approach via mathematical inference for price dynamics in information fusion context, Inform. Sci., 373, 183, 10.1016/j.ins.2016.08.063
Luo, 2017, Combining multiple algorithms for portfolio management using combinatorial fusion, 361
Das, 2016, Fusion with sentiment scores for market research, 1003
Liu, 2017, H-fuse: Efficient fusion of aggregated historical data, 786
Zhou, 2017, One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies, Inf. Fusion, 36, 80, 10.1016/j.inffus.2016.11.009
Sang, 2019, An uncertain possibility-probability information fusion method under interval type-2 fuzzy environment and its application in stock selection, Inform. Sci., 504, 546, 10.1016/j.ins.2019.07.032
Evans, 2019, A methodology for the resolution of cashtag collisions on twitter–a natural language processing & data fusion approach, Expert Syst. Appl., 127, 353, 10.1016/j.eswa.2019.03.019
Nachouki, 2008, Multi-data source fusion, Inf. Fusion, 9, 523, 10.1016/j.inffus.2007.12.001
Shroff, 2014, Prescriptive information fusion, 1
Dong, 2015
Sun, 2005, A new method of feature fusion and its application in image recognition, Pattern Recognit., 38, 2437, 10.1016/j.patcog.2004.12.013
Mangai, 2010, A survey of decision fusion and feature fusion strategies for pattern classification, IETE Tech. Rev., 27, 293, 10.4103/0256-4602.64604
Rajput, 2016, Stock market forecasting techniques: literature survey, Int. J. Comput. Sci. Mobile Comput., 5, 500
Sutkatti, 2019, Stock market forecasting techniques: A survey, Int. Res. J. Eng. Technol., 6
Kumar, 2020, Stock market forecasting using computational intelligence: A survey, Arch. Comput. Methods Eng., 1
