Predictability of Stock Returns: Robustness and Economic Significance

By M. Pesaran, Allan G. Timmermann. 1995

How researchers see this paper

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

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”
Robust out-of-sample inference (2000, methodology): “Pesaran and Timmermann (1995) also use this technique.”
Model Uncertainty, Thick Modelling and the Predictability of Stock Returns (2005, unknown): “Pesaran and Timmermann (1995) (henceforth, P&T) consider a time-varying parameterization for the forecasting model to find that the predictive power of various economic factors over stock returns changes through time and tends to vary with the volatility of returns.”
Risk sensitive asset allocation (2000, background): “For example, Pesaran and Timmermann (1995) examined the robustness of the evidence on predictability of US stock returns with respect to seven factors: dividend yield, earnings-price ratio, 1 month T-bill rate, 12 month T-bill rate, in#ation rate, change in industrial production, and monetary…”
Complete subset regressions (2013, methodology): “…across complete subset regressions with the same 11 Utility-based measures of forecast performance have been widely used in studies of stock return predictability; see Pesaran and Timmermann (1995) for an early example. number of predictor variables and thus the same degree of model complexity.”
Instability of Return Prediction Models (2006, background): “Chordia (2002), Bekaert and Hodrick (1992), Bossaerts and Hillion (1999), Brandt (1999), Campbell (1987), Campbell and Shiller (1988), Cochrane (1991), Fama and Schwert (1977), Fama and French (1988), Ferson and Harvey (1991), French, Schwert and Stambaugh (1987), Keim and Stambaugh (1986), Lamont (”
Model Averaging and Value-at-Risk Based Evaluation of Large Multi Asset Volatility Models for Risk Management (2004, unknown): “…the evaluation sample includes the recent periods of large stock market falls and contrast the outcome of recursive modelling applied to S&P mean returns reported in Pesaran and Timmermann (1995) where the best model selected for the monthly excess returns tend to change quite frequently over time.”
Stock Return Predictability: A Bayesian Model Selection Perspective (2002, unknown): “Particularly, we choose a natural conjugate g-prior specification for / [see Zellner (1986)], a popular choice in Bayesian statistics that adopts a full correlation structure between the included predictors [see also Poirier (1985), Laud and Ibrahim (1995, 1996), and Fernandez, Ley and Steel (2001)]”
What Central Bankers Need to Know about Forecasting Oil Prices (2014, unknown): “11 the third quarter, the gains in directional accuracy are statistically significant based on the test of the null of no directional accuracy discussed in Pesaran and Timmermann (2009). 3.”
Exchange Rates and Fundamentals: Footloose or Evolving Relationship? (2009, methodology): “Speci cally, we employ a recursive procedure originally developed by Pesaran and Timmermann (1995) which allows us to select, quarter by quarter, the best model on the basis of a variety of statistical criteria within all possible combinations of fundamentals, allowing for the explanatory variables…”
The use of data mining and neural networks for forecasting stock market returns (2005, background): “However, there is some evidence in the literature suggesting that traditional measures of forecasting performance may not be strongly related to profits from trading (Pesaran and Timmermann, 1995).”