Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules

By Ramazan Gencay. 1999

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Citations in the corpus

Technical Trading-Rule Profitability, Data Snooping, and Reality Check: Evidence from the Foreign Exchange Market (2005, unknown): “Other studies in this area include Gencay (1999), Lee and Mathur (1996), Neely et al. (1997), Taylor (1994), and Sweeney (1988).”
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].”
Predicting the direction of stock market prices using tree-based classifiers (2019, methodology): “…stated briefly, the use of prediction algorithms to determine future trends in stock market prices (Widom 1995; Hellstrom and Holmstromm 1998; Gencay 1999; Li et al. 2014; Dai and Zhang 2013; Timmermann and Granger 2004; Bao and Yang 2008) contradicts a basic rule in finance known as the…”
Stock Market Embedding and Prediction: A Deep Learning Method (2018, background): “Gencay [9,10] found technical trading rules such as using a moving average are more successful at predicting exchange rates than the models based on the assumption that exchange rates follow a random walk.”
The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis (2007, background): “…based on head and shoulders patterns (Chang and Osler, 1999; Lucke, 2003), candlestick formations (Fiess and MacDonald, 2002), neural networks (Gencay, 1999), genetic programming (Neely, Weller and Dittmar, 1997; Neely and Weller, 1999, 2001), Markov switching models (Marsh, 2000; Dueker and…”
Inference on Predictability of Foreign Exchange Rates via Generalized Spectrum and Nonlinear Time Series Models (2003, background): “Gencay (1999) finds that buy-sell signals of simple technical trading rules generated from using nearest neighbors and neural networks yield a lower MSFE than the martingale model.”
Forecasting Exchange Rates: A Robust Regression Approach (2007, background): “Brooks (1997) and Gencay (1999) also document some predictability of daily exchange rates using these models.”
Neural networks performance in exchange rate prediction (2016, background): “Among those who investigated neural networks and their implication for forecasting of financial markets were Kuan and White [12], as well as Swanson and White [13] who prove usefulness of neural networks in economic time series., Qi and Madala [14] show its usefulness for the stock market, and Genca”
A Combined Signal Approach to Technical Analysis on the S&P 500 (2011, unknown): “Furthermore, several studies have recently emerged that signify the informational content of technical trading rules (Brock, LeBaron and Lakonishok (1992), Lisi and Medio (1997), Gençay (1999), Lo, Mamaysky and Wang (2000), and Lento, Gradojevic, and Wright (2007)).”