Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets

By Guido J. Deboeck. 1994

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yaodong Yang
2. Ying Wen
3. Chih-Feng Liu
4. Tian-Shyug Lee
5. Jun Wang
6. Peng Peng
7. Shie-Jue Lee
8. Chi-Yuan Yeh

Citations in the corpus

• Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games (2017, background): “Correspondence to Jun Wang, jun.wang@cs.ucl.ac.uk. gaming on the stock markets (Deboeck 1994), ad bidding agents competing with each other over online advertising exchanges (Wang, Zhang, and Yuan 2017), and e-commerce collaborative filtering recommenders predicting user interests through the wisdom…”
• Support vector machine with adaptive parameters in financial time series forecasting (2003, background): “INANCIAL time series are inherently noisy and nonstationary [1], [2].”
• Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games (2017, background): “Typical examples include the trading robots gaming on the stock markets [13], ad bidding agents competing with each other over online advertising exchanges [14], and e-commerce collaborative filtering recommenders [15] predicting user interests through the wisdom of the crowd [16].”
• Financial Forecasting Using Support Vector Machines (2001, background): “As explained by Deboeck and Yaser [1,2], financial time series are inherently noisy, nonstationary and deterministically chaotic.”
• A case study on using neural networks to perform technical forecasting of forex (2000, background): “Other examples using neural networks in currency applications include [3] [7] [9] [18] [25] [23].”
• Application of type-2 neuro-fuzzy modeling in stock price prediction (2012, background): “Yu and Huarng [6] proposed a weighted model for forecasting the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) [33], and claimed its superiority over conventional models.”
• Financial time series forecasting using independent component analysis and support vector regression (2009, background): “Moreover, the noise in the data could lead to over-fitting or under-fitting problems [7, 19].”
• Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum (2003, unknown): “tracking [102, 103, 437, 551, 611, 727, 910, 947, 974, 999, 1237, 1376, 1390, 1431, 2092, 1793, 2041, 196] trading [344, 329, 1251, 1475, 1473, 1474, 1682] traffic [207, 226, 498, 526, 611, 657, 703, 705, 704, 1444, 1627] transputer [994] traveling salesman problem (TSP) [31, 47, 48, 154, 268, 487, ”
• Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model (2016, background): “Forecasting of the financial index is characterized by data intensity, noise, non-stationarity, unstructured nature, high degree of uncertainty, and hidden relationships [6 – 8].”