Stock Return Predictability: A Bayesian Model Selection Perspective

By K. Cremers. 2002

359 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/bf905171d1a4872d09af65ee759210b06ae9cfde

How researchers see this paper

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

In Finance

1. Guofu Zhou
2. Allan G. Timmermann
3. Serena Ng
4. Jun Tu
5. Barbara Rossi
6. A. Inoue
7. Mark W. Watson
8. Lutz Kilian

In Management Science and Operations Research

1. Allan G. Timmermann
2. Serena Ng
3. Lutz Kilian
4. Mark W. Watson
5. Guofu Zhou
6. James H. Stock
7. A. Inoue
8. Jun Tu

Citations in the corpus

• Predictability of stock returns and asset allocation under structural breaks (2011, background): “frequent, small breaks can be difficult to make in practice (Elliott and Mueller (2006)).”
• Forecast Combination and Bayesian Model Averaging - A Prior Sensitivity Analysis (2012, background): “Results from a simulation study point to considerable improvements of averaged forecasts in terms of direction of changes over individual models which is in line with findings of Crespo Cuaresma (2007).”
• How Important Is the Financial Media in Global Markets (2011, methodology): “21 This methodology is similar to the one used by Cremers (2002) in selecting important market-wide predictor variables.”
• An Economic Evaluation of Empirical Exchange Rate Models (2009, neutral): “2For studies of asset return predictability following this approach see also Kandell and Stambaugh (1996), Barberis (2000), Baks, Metrick and Wachter (2001), Bauer (2001), Shanken and Tamayo (2001), Avramov (2002), and Cremers (2002).”
• Stock Return Predictability and Model Uncertainty (2002, neutral): “After the writing of previous versions, I become aware of the work by Cremers (2000) who implements a Bayesian approach in return predictability.”
• The Levered Equity Risk Premium and Credit Spreads: A Unified Framework (2010, neutral): “Cremers et al. (2008) study credit spreads in a pure structural model, where firm value is subject to exogenous jumps.”
• Complete subset regressions (2013, methodology): “Predictability of US stock returns by means of combinations of models based on different sets of predictors has been considered by studies such as Avramov (2002), Cremers (2002), and Rapach et al. (2010).”
• Forecasting the realized range-based volatility using dynamic model averaging approach (2017, methodology): “This problem can be overcome by Bayesian model averaging (BMA) and Bayesian model selection (BMS) procedures (Cremers, 2002; Koop and Potter, 2004; Wright, 2008).”
• How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation (2005, methodology): “More sophisticated methods of forecast model averaging weight individual forecasts by the posterior probabilities of each forecast model (see, e.g., Min and Zellner 1993, Avramov 2002, Cremers 2002, Wright 2003a and Koop and Potter 2003 for applications in econometrics).”
• Time-varying short-horizon predictability ☆ (2008, methodology): “mension of this interaction, the smoothing of useful information, as shown by Mankiw and Miron (1986), Gray (1996), Davig and Leeper (2007), Lintner (1956), Kumar (1988), and Fudenberg and Tirole (1995).”
• Data-generating process uncertainty: What difference does it make in portfolio decisions? (2004, methodology): “Although the alternative Bayesian paradigm, as applied by Pástor and Stambaugh (1999, 2000), Barberis (2000), Wang (2001), Lewellen and Shanken (2002), Cremers (2002), Avramov (2002), Avramov and Chao (2002) and many others, provides profound insights into a variety of financial issues, none of thes”
• Predictable Returns and Asset Allocation: Should a Skeptical Investor Time the Market? (2009, background): “Other studies on predictability include those of Avramov (2002) and Cremers (2002), who show that Bayesian inference can lead to superior model selection. Our paper is also related to recent work by Shanken and Tamayo (2005), who jointly model time variation in risk and expected return in a Bayesian”
• A Comprehensive Look at the Empirical Performance ofEquity Premium Prediction II (2008, neutral): “Finally, we also entertain a “kitchen sink” regression, with all of the above variables (Cremers (2002)), that we name “all.”
• Exchange Rates and Fundamentals: Evidence on the Economic Value of Predictability (2005, methodology): “We do this by 2This decision-theoretic approach has also been used recently by Avramov (2002), Bauer (2001), Cremers (2002), Shanken and Tamayo (2001) and Tamayo (2002).”
• Sequential Learning, Predictability, and Optimal Portfolio Returns: Sequential Learning, Predictability, and Optimal Portfolio Returns (2014, neutral): “3For recent results in this area and extensive citations, see Shiller (1981), Hodrick (1992), Stambaugh (1999), Avramov (2002), Cremers (2002), Ferson et al. (2003), Lewellen (2004), Torous, Valkanov, and Yan (2004), Campbell and Yogo (2006), Cochrane (2008), Ang and Bekaert (2007), Xia (2007),…”
• Asset Allocation With a High Dimensional Latent Factor Stochastic Volatility Model (2006, neutral): “Some recent examples are Lettau and Ludvigson (2001), Cremers (2002), Avramov (2002), and Goyal and Welch (2003).”
• Forecasting with Many Predictors (2006, result): “Cremers (2002) undertook a similar study with n = 14 predictors [there is partial overlap between Avramov’s (2002) and Cremers’ (2002) predictors] and found improvements in in-sample fit and pseudo-out-of-sample forecasting performance comparable to those found by Avramov (2002).”