Investing for the Long Run When Returns are Predictable

By Nicholas Barberis. 2000

1,278 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

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Citations in the corpus

Deep Learning for Predicting Asset Returns (2018, methodology): “Cross-sectional time series predictability is studied using predictive regressions, see Kandel and Stam-baugh (1996), Barberis (2000) and Welch and Goyal (2008).”
An Econometric Model of Nonlinear Dynamics in the Joint Distribution of Stock and Bond Returns (2005, methodology): “Our model thus nests as a special case the standard linear (single-state) model used in much of the asset allocation literature; see e.g. Barberis (2000).”
Canonical vine copulas in the context of modern portfolio management: Are they worth it? (2013, methodology): “…portfolio dimensions by using the CVC model and mathematically expanding the SC.1 A multi-period long-term investment horizon study is necessary as Barberis (2000) finds that multi-period decisions are substantially different from single-period decisions due to hedging demands if investment…”
Model Uncertainty, Thick Modelling and the Predictability of Stock Returns (2005, positive): “This choice seems to be particularly strong in the light of the results obtained by Bayesian line of research, which stresses the importance of the estimation risk for portfolio allocation (see for example, Barberis,2000, Kandel and Stanbaugh,1996).”
On the Long-Run Volatility of Stocks (2018, methodology): “First, Barberis (2000) worked with the predictive regression framework of Stambaugh (1999) and his results are in line with the conventional view that stocks are less risky over long horizons. Pettenuzzo and Timmermann (2011) worked in the same predictive regression framework and incorporated model ”
Chapter 21 - Household Finance: An Emerging Field∗ (2013, positive): “44 Two prominent behavioral explanations of the disposition effect are that investors have an irrational belief in mean-reversion, or that they derive utility directly from realizing gains and losses, a notion labeled “realization utility” by Barberis and Xiong (2009).”
Asset Allocation With a High Dimensional Latent Factor Stochastic Volatility Model (2006, neutral): “3 See, for example, Barberis (2000), Handa and Tiwari (2004), Kandel and Stambaugh (1996), Lynch and Balduzzi (2000), Pesaran and Timmermann (1995), and Tamayo (2002).”
Biases and Variance in Value Function Estimates (2004, background): “For example, Xia (2001) and Barberis (2000) investigate how dynamic learning about stock return predictability affects optimal portfolio allocations.”
Predictability of stock returns and asset allocation under structural breaks (2011, background): “Studies of asset allocation under return predictability (e.g., Barberis (2000), Campbell and Viceira (2001), Campbell, Chan, and Viceira (2003) and Kandel and Stambaugh (1996)) have mostly used vector autoregressions (VARs) to capture the relation between asset returns and predictor variables.”
The Risk Perceptions of Individual Investors (2008, positive): “Barberis and Huang [2001] find that loss aversion over the individual stock returns can help explain high and volatile stock returns observed”
Choosing Outcomes versus Choosing Products: Consumer‐Focused Retirement Investment Advice (2008, background): “Predictions of CRRA: A common classical assumption is that investors’ risk preferences possess a property called constant relative risk aversion, or CRRA (Arrow 1970; Barberis 2000; Pratt 1964; Safra and Segal 1998).”
Learning in Financial Markets (2009, background): “Recent contributions include Kandel and Stambaugh (1996), Barberis (2000), Pástor (2000), Pástor and Stambaugh (2000, 2002b), Tu and Zhou (2004, 2008), Avramov (2004), Brandt et al (2005), Avramov and Chordia (2006), Avramov and Wermers (2006), Kan and Zhou (2007), and Wachter and Warusawitharana…”
Technical analysis: An asset allocation perspective on the use of moving averages (2009, background): “, Bhattacharya and Galpin, 2006), and because popular portfolio optimization strategies (see, e.g., Litterman, 2003; Meucci, 2005) are also fixed strategies, any improvement over fixed strategies is of practical importance, which might be one of the reasons that technical analysis is widely used.(3)”
Labor Income Dynamics at Business-Cycle Frequencies:Implications for Portfolio Choice (2011, background): “This is one of the key results from the recent literature exploring portfolio choices by a multiperiod agent in the presence of return predictability: see, for example, Campbell and Viceira (1999), Barberis (2000) and Balduzzi and Lynch (1999).”
Out-of-Sample Predictions of Bond Excess Returns and Forward Rates: An Asset Allocation Perspective (2010, positive): “The performance measures are calculated for the out-of-sample period January 1970 through December 2010 assuming = 5; in line with the value used in previous studies (Barberis, 2000; Della Corte et al., 2008 and the references therein).”