Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index

By Kyoung-jae Kim, Ingoo Han. 2000

747 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Financial market prediction system with Evolino neural network and Delphi method (2013, background): “Kim and Han (2000) proposed a new hybrid of a genetic algorithm with artificial neural networks.”
Application of type-2 neuro-fuzzy modeling in stock price prediction (2012, methodology): “The given training data set is partitioned into clusters through input-similarity and output-similarity tests, and a type-2 TSK rule is derived from each cluster to form a fuzzy rule base.”
On-Demand Forecasting of Stock Prices Using a Real-Time Predictor (2003, background): “These models, however, have their limitations owing to the tremendous noise and complex dimensionality of stock price data [7] and, besides, the quantity of data itself may also interfere with the learning of patterns.”
AN INVESTIGATION OF THE HYBRID FORECASTING MODELS FOR STOCK PRICE VARIATION IN TAIWAN (2004, neutral): “文獻探討國內外有許多針對股價預測之文獻，主要研究方向可分為大盤走勢分析、類股與產業分析、各股股價預測、以及分析方法與影響股價因子關係之研究四類，本研究主要是根據大盤指數作未來走勢之預測。而本節將針對國內外之股價預測相關文獻作一整理與回顧。 2.1 國外股價預測之文獻 Lapedes [18]使用倒傳遞類神經網路預測每週收盤指數，所得預測之正確率為 61%高於移動平均法的 53%。Kimoto與 Asakawa [16]運用倒傳遞類神經網路以技術性與經濟性指標作為輸入變數，結果顯示正確率達 98%。Kamijo與 Tanigawa [15]利用類神經網路以過去三年 K 線圖”
Pattern graph tracking-based stock price prediction using big data (2018, methodology): “After selecting the historical dataset, to generate the predicted stock data, we use an artificial neural network algorithm because it is the most widely used in stock price forecasts ([9, 28, 33]) and has a high predictive power as an advantage over learning by iterative adjustment.”
A Hybrid Machine Learning System for Stock Market Forecasting (2008, methodology): “Other methods that have been used to forecast the stock market include Bayesian belief networks [5], evolutionary algorithms [6] [7], classifier systems [8], and fuzzy sets [9].”
A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick (2011, result): “There are also many other researches like Chenoweth, Obradovic, and Stephenlee (1996), Enke and Thawornwong (2005), Kim and Han (2000), Kuo, Li, Cheng, and Ho (2004), Leigh, Hightower, and Modani (2005), Saad, Prokhorov, and Wunsch (1998) that have utilized various types of NNs for TA and stock…”
Oppositional biogeography-based optimization (2009, methodology): “In finance, EC has been employed for bankruptcy [84, 85, 86] and stock predictions [87, 88, 89].”
Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches (2010, methodology): “Kim and Han (2000) [24] Daily Korea stock price index (KOSPI) A hybrid model of BPNN and GA 12 Technical indexes GA”
Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis (2010, methodology): “Kim and Han (2000) used a NN modified by a GA to predict the stock price index.”