Stock price pattern recognition-a recurrent neural network approach

By K. Kamijo, T. Tanigawa. 1990

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Citations in the corpus

Deep Learning Stock Volatility with Google Domestic Trends (2015, background): “For instance, this early (1990) work [2] is among the first of several which use recurrent neural nets to predict stock prices, Ref.”
A hybrid ARIMA and support vector machines model in stock price forecasting (2005, methodology): “Kamijo and Tanigawa [2] developed a pattern recognition technique to predict the stock prices on the Tokyo Stock Exchange.”
Evolving Least Squares Support Vector Machines for Stock Market Trend Mining (2009, methodology): “Besides the MLFNN, other neural network types, such as probabilistic neural network (PNN) [13-15] recurrent neural network (RNN) [16-17] are also applied to stock market price prediction.”
AN INVESTIGATION OF THE HYBRID FORECASTING MODELS FOR STOCK PRICE VARIATION IN TAIWAN (2004, unknown): “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”
Application Of Neural Network To Technical Analysis Of Stock Market Prediction (2001, background): “Some are applied to predicting future price or rate of changes [4], and some are applied to recognizing certain price patterns that are characteristic of future price changes [5].”
Personal recommendation using deep recurrent neural networks in NetEase (2016, methodology): “It becomes an effective tool for processing nonlinear time series applications [10][17], such as handwriting recognition [6] and speech recognition [7].”
Application of Neural Networks to Stock Market Prediction (2000, methodology): “In this paper [10], the author uses a feedforward neural network with the last n stock index values as inputs and the next N-n values as the outputs.”
Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index (2000, methodology): “In particular, the existence of continuous data and large amount of data may pose a challenging task to explicit concepts extraction from the raw data due to the huge amount of data space determined by continuous features (Liu & Setiono, 1996).”
Sentiment-aware stock market prediction: A deep learning method (2017, background): “This early work [23] is among the first a few which use recurrent neural nets to predict stock prices, [24] instead reported a volatility forecasting model, [25] incorporated public mood data in the up and down direction prediction of market fund Dow Jones Industrial Average.”
Deep Direct Reinforcement Learning for Financial Signal Representation and Trading (2017, background): “In the context of the stock data learning, various feature representation strategies have been proposed from multiple views [20]–[22].”
Automated trading with performance weighted random forests and seasonality (2014, background): “Since then, a plethora of research has attempted to find predictive rules for US (Refenes, Zapranis, & Francis, 1995; Enke & Thawornwong, 2005), Japanese (Kamijo & Tanigawa, 1990) and many other stock prices as well as a multitude of other financial assets (Chen, Leung, & Daouk, 2003; Cheng, Wanger,”
Financial time series forecasting using support vector machines (2003, background): “The early days of these studies focused on application of ANNs to stock market prediction (for instance [2] [6] [11] [13] [19] [23]).”
A survey of the application of soft computing to investment and financial trading (2003, unknown): “Technical Analysis 23 [20], [19], [28], [27], [26], [25], [24], [23], [22], [21], [31], [30], [5], [4], [2], [18], [16], [15], [14], [12], [10], [9], [8]”