Neural Networks in the Capital Markets

By A. Refenes. 1994

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Citations in the corpus

Classification-based financial markets prediction using deep neural networks (2016, neutral): “Traditionally, researchers will iteratively experiment with a handful of signals to train a level based method, such as vector autoregres-sion, for each instrument (see for example Kaastra and Boyd (1995); Refenes (1994); Trippi and DeSieno (1992)).”
Environmental Variables and Real Estate Prices (2001, unknown): “1990; Kendall and Ord, 1993; Azoff, 1994; Taylor, 1994; Refenes, 1995).”
Pattern recognition and neural networks (1996, background): “Predicting suitable habitat for Tsetse flies (Ripley, 1993) . Financial trading ( Refenes, 1995 ).”
Sequence classification of the limit order book using recurrent neural networks (2017, methodology): “The application of artiﬁcial neural networks (ANNs) to uni-variate ﬁnancial time series methods are well documented (Faraway and Chatﬁeld, 1998; Refenes, 1994; Trippi and DeSieno, 1992; Kaastra and Boyd, 1995).”
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach (2006, background): “…of artificial intelligence (AI) techniques, mainly artificial neural networks, have been applied to technical financial forecasting [13]–[15] as they have the ability to learn complex nonlinear mapping and self-adaptation for different statistical distributions (please refer to [16]…”
Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference (2001, methodology): “For examples of these models and the use of neural networks and other machine learning methods applied to finance see [2, 1, 46].”
Architecture Selection Strategies for Neural Networks: Application to Corporate Bond Rating Predicti (1995, unknown): “In a later study, Singleton and Surkan (1991; 1993) extended their ex-periments using more data and hold-out-one cross-validation to correctly classify 73% of the bondsinto the S&P ratings and 97% into Moody's ratings.”
Handbook of Neural Network Signal Processing (2000, methodology): “1 Perceptron Model An MLP is a variant of the original perceptron model proposed by Rosenblatt in the 1950s [2].”
Monotonic Networks (1997, methodology): “Finally, the work of Abu-Mostafa on learning from hints (Abu-Mostafa, 1990) has shown both theoretically (Abu-Mostafa, 1993) and experimentally (Abu-Mostafa, 1995) that the use of prior knowledge can be highly beneficial to learning systems.”
Meta learning evolutionary artificial neural networks (2004, background): “…networks in the scientific community is fueled by the many successful and promising applications especially to tasks of optimization [26], speech recognition [16], pattern recognition [12], signal processing [57], function approximation [79], control problems [3] [5], financial modeling [67] etc..”