On the Predictability of Stock Returns: An Asset-Allocation Perspective

By Shmuel Kandel, Rob Stambaugh. 1996

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Citations in the corpus

Deep Learning for Predicting Asset Returns (2018, background): “Kandel and Stambaugh (1996) discuss the difficulty of the model estimation and prediction in the presence of parameter uncertainty.”
Good Times or Bad Times? Investors' Uncertainty and Stock Returns (2009, unknown): “Keim and Stambaugh (1986); Fama and French (1988, 1989); and Kandel and Stambaugh (1996) find that the default premium is positively correlated with future stock returns.”
Market timing and return prediction under model instability (2002, background): “Studies that allow the regression model (but not the window size) to change over time include Pesaran and Timmermann (1995,2000) and Perez-Quiros and Timmermann (2000). (5)For example, Brandt (1999, page 1611) comments that “Returns and forecasting variables must have a time-invariant Markov structu”
Investor Attention and Stock Returns (2020, unknown): “Clearly, the R2OS statistics of aggregate attention indices are much larger than 0.5%, suggesting substantial economic significance (Kandel and Stambaugh (1996)).”
Technical analysis: An asset allocation perspective on the use of moving averages (2009, methodology): “Kandel and Stambaugh (1996) show that the economic loss can be significant when one ignores predictability 12 The results when L ¼ 100 are similar and omitted for brevity. completely when a small degree of predictability exists in the data.”
Portfolio selection with higher moments (2010, unknown): “Kandel and Stambaugh (1996) examine predictability of stock returns when allocating between stocks and cash by a risk-averse Bayesian investor.”
Stock price volatility and equity premium (2001, background): “2 For example, Benninga and Protopapadakis (1990) and Kandel and Stambaugh (1990) were able to match the first and second moments of consumption growth and riskless and equity returns by treating equity as a levered claim on aggregate consumption and choosing the degree of leverage to match the vola”
Multivariate Shrinkage for Optimal Portfolio Weights (2007, background): “Klein and Bawa (1976), Jorion (1986), Kandel and Stambaugh (1996), Kan and Zhou (2007) advocate different types of Bayesian methods for portfolio selection.”
Nonparametric Applications of Bayesian Inference (2003, methodology): “Such a posterior distribution is called for to include parameter uncertainty in the decision making formulation; see, for example, the work of Rossi, McCulloch, and Allenby (1995), Kandel and Stambaugh (1996), and Barberis (2000).”
Financial flexibility and the choice between dividends and stock repurchases (2000, unknown): “For example, much research has examined the extent to which expected returns can be predicted using dividend yields (see Campbell and Shiller, 1988; Fama and French, 1988; Kandel and Stambaugh, 1996; Kothari and Shanken, 1997; or Ponti! and Schall, 1998).”
On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation (2004, unknown): “See Table 4 for the complete set of results.”
Predictability of stock returns and asset allocation under structural breaks (2011, methodology): “Following Kandel and Stambaugh (1996) and Barberis (2000), we assume that the investor has access to a risk-free asset with constant return, rf , and a risky stock market portfolio with returns in excess of the risk-free rate, rT+1; :::; rT+h; where h is the investment horizon.”
Equilibrium Asset Pricing and Portfolio Choice Under Asymmetric Information (2010, unknown): “In the past (e.g., Kandel and Stambaugh 1996), estimation risk has 8 Proposition 6 in DeMarzo and Skiadas (1998) establishes that a CAPM holds in equilibrium.”