Hybrid Intelligent Systems for Stock Market Analysis

By Ajith Abraham, Baikunth Nath, Prabhat Kumar Mahanti. 2001

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Citations in the corpus

Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches (2010, background): “, (2001) [1] NASDAQ BPNN, neuro-fuzzy system Fundamental indexes PCA Hulme and Xu (2001) [17] Australian Stock Exchange (ASX) GA-based NN Fundamental indexes –”
Elliott Wave Theory and neuro-fuzzy systems, in stock market prediction: The WASP system (2011, background): “Abraham, Nath, and Mahanti (2001) combined a neural network with a neuro- ll rights reserved.”
Surveying stock market forecasting techniques - Part II: Soft computing methods (2009, methodology): “…with closing prices of established markets like the Dow Jones, the S&P, or with exchange rates of strong currencies like USD, EURO, YEN include, Ajith et al. (2003a) who uses besides the main Stock Index that it forecasts another 7 indexes closing prices; Huang et al. (2005) who uses the S&P…”
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting (2010, background): “Considering this idea an obvious complexity of the problem paves the way for the importance of intelligent prediction paradigms [1].”
A TSK type fuzzy rule based system for stock price prediction (2008, background): “Abraham, Baikunth, and Mahanti (2001) investigated hybridized SC techniques for automated stock market forecasting and trend analysis.”
Multi-Agent Forex Trading System (2010, methodology): “Financial time series prediction mechanisms have been implemented using data mining models such as RBF recurrent networks [1] and other types of neural networks [2][3][4], association rules [5], nearest neighbor models [6], support vector machines [7] and more complex hybrid approaches [8][ 9 ].”
Financial time series forecasting with machine learning techniques: a survey (2010, unknown): “Day 31 [1], [2], [3], [4], [6], [7], [8], [9], [10], [13], [14], [17], [19], [20], [21], [22], [24], [27], [28], [31], [32], [33], [34], [35], [36], [37], [40], [41], [42], [44], [45] Week 3 [18], [23], [43] Month 3 [26], [38], [39] Multiple / Other 9 [5], [11], [12], [15], [16], [25], [29], [30],…”
Stock trend forecasting in turbulent market periods using neuro-fuzzy systems (2015, methodology): “Abraham et al. (2001) used ANN training with a conjugate algorithm and a neuro fuzzy system to analyze the stock market, whereas Armano et al. (2005) created a hybrid genetic-neural architecture system for forecasting next day’s trend of the S&P500 index.”
Stock market forecasting using hidden Markov model: a new approach (2005, background): “Key Words: HMM, stock market forecasting, financial time series, feature selection”
A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting (2008, background): “It has been a new tendency that combining the SC technologies of NNs, fuzzy logic (FL), and genetic algorithms (GAs) may significantly improve an analysis [1], [2], [9], [11]–[13], [19], [24], [25], [27], [31], [36], [39], [40], [51], [53], [55]– [57], [61].”
Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques (2015, methodology): “Hybridized soft computing techniques for automated stock market forecasting and trend analysis was introduced by Abraham, Nath, and Mahanti (2001).”
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, methodology): “More methods such as PCA and Autoencoder (AE) are actually tested in their works but, for simplicity, we only report their best results and our model achieves better performance.”