Nonlinear Predictability of Stock Returns Using Financial and Economic Variables

By Min Qi. 1999

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Citations in the corpus

Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market? (2018, background): “Among the many predictive efforts over the last few decades [1]–[3], some have had success using quantitative methods [4]–[9], such as autoregressive integrated moving average (ARIMA) models, artificial neural networks, support vector machine, and neuro-fuzzy based systems, but because of the nonlin”
Deep Learning for Predicting Asset Returns (2018, background): “…economics and ﬁnance, see Gal-lant and White (1988a,b); Hornik et al. (1989); Gallant and White (1992); Kuan and White (1994); Hutchinson et al. (1994); Lo (1994); Qi (1999); Jones (2006); Sirignano et al. (2016) and Heaton et al. (2017), deep learning is new to characteristic-based asset pricing.”
Surveying stock market forecasting techniques - Part II: Soft computing methods (2009, background): “index price, Dow Jones index value, NASDAQ index value, HIS index value, NIKKEI index value Qi (1999) Nine financial and economic variables Quah and Srinivasan (1999) Economical, political and firm/stock specific factors Raposo et al.”
Nonlinear predictability of stock market returns: Evidence from nonparametric and threshold models (2001, methodology): “Recent exceptions to this is the work of Qi (1999) who uses a neural network method, and thus provides flexibility in the choice between linear and nonlinear models, to examine the predictability of US stock returns, and Perez-Quiros and Timmermann (2000) who use a Markov switching model to examine…”
Avoiding Pitfalls in Neural Network Research (2007, unknown): “Finally, Section X provides concluding remarks.”
A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick (2011, methodology): “Among them NNs have become a popular tool for financial decision making (Cheng, McClain, & Kelly, 1997; Dutta & Shekhar, 1988; Kiang, Chi, & Tam, 1993; Leigh, Purvis, & Ragusa, 2002; Lin & Lin, 1993; Qi, 1999; Shah & Murtaza, 2000; Tam & Kiang, 1992; Tang, de Almeida, & Fishwick, 1990; Walczak, 2001”
Nonlinear neural network forecasting model for stock index option price: Hybrid GJR-GARCH approach (2009, methodology): “Such as, related studies have empirically estimated and forecasted stock prices (Black & McMillan, 2004; Donaldson & Kamstra, 1996; Jasic & Wood, 2004; Kanas, 2001; Kanas & Yannopoulos1, 2001; Maasoumi & Racine, 2002; Qi, 1999; Rapach & Wohar, 2005; Shively, 2003) and stock volatilities (Dunis & Hua”
A Dependence Metric for Possibly Nonlinear Processes (2004, unknown): “Recent examples of unexplained nonlinear dependence in the residuals of (GARCH and other) models for exchange rates and S&P returns are, Hsieh (1989), Hong and White (2000), Qi (1999) and references therein, and Maasoumi and Racine (2002).”
Entropy and predictability of stock market returns (2002, methodology): “Wefollow Pesaran & Timmermann (1995) and Qi (1999) who only onsidered the ase of holdingeither sto k or bonds, but not both simultaneously.”