Predictive Regressions: A Present-value Approach

Binsbergen, Jules van; Koijen, Ralph S. J.

doi:10.3386/w16263

Cited by 58 publications

(104 citation statements)

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“…Similarly, define expected returns on REITS by

x_{t}

x_{t} \equiv E_{t} false[r_{t + 1} false]

and assume an AR(1) for

x_{t}

following Lettau and Van Nieuwerburgh (), van Binsbergen and Koijen () and Koijen and van Nieuwerburgh ():

x_{t} = false(1 - ρ_{x} false) \bar{x} + ρ_{x} x_{t - 1} + ε_{t}^{x} .

Under this assumption, the return term in Equation can be written as a function of the current period's expected return in excess of the long‐run mean:

E_{t} [0true \sum_{j = 1}^{+ \infty} ρ^{j - 1} false(r_{t + j} - \bar{x} false)] = \frac{1}{1 - ρ ρ_{x}} false(x_{t} - \bar{x} false) .

…”

Section: Valuation Frameworkmentioning

confidence: 99%

“…We assume that

ρ_{g} = 0.60

. It reflects the fact that expected dividend growth on stocks, and especially on REITS, moves at business‐cycle frequencies, rather than at generational frequencies (van Binsbergen and Koijen , Koijen and van Nieuwerburgh ). We note that is still substantially above the 12‐month autocorrelation of realized dividend growth which we estimate to be 0.15.…”

Section: Valuation Frameworkmentioning

confidence: 99%

“…The 12‐month autocorrelations of the log price‐dividend ratio on REITS is 0.76. For the stock market as a whole we know that expected returns are about as persistent as the price‐dividend ratio (van Binsbergen and Koijen , Koijen and van Nieuwerburgh ). Our results confirm that this is also the case for REITS, except that the price‐dividend ratio on REITS is less persistent than that for the stock market as a whole.…”

Section: Valuation Frameworkmentioning

confidence: 99%

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Why are REITS Currently So Expensive?

Nieuwerburgh

2018

Real Estate Economics

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Over the last several years, the price of listed real estate stocks has been unusually high relative to dividends. I find that neither low interest rates nor low risk premia can account for the high valuation ratios. Lower interest rates have been offset by rising risk premia to keep expected returns close to average. Instead, the market has priced in future income growth on commercial properties far above the growth rates seen in the data. High implied growth rates are less extreme for nontraditional REIT sectors. Income growth expectations are also less extreme for international listed real estate.

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“…Similarly, define expected returns on REITS by

x_{t}

x_{t} \equiv E_{t} false[r_{t + 1} false]

and assume an AR(1) for

x_{t}

following Lettau and Van Nieuwerburgh (), van Binsbergen and Koijen () and Koijen and van Nieuwerburgh ():

x_{t} = false(1 - ρ_{x} false) \bar{x} + ρ_{x} x_{t - 1} + ε_{t}^{x} .

Under this assumption, the return term in Equation can be written as a function of the current period's expected return in excess of the long‐run mean:

E_{t} [0true \sum_{j = 1}^{+ \infty} ρ^{j - 1} false(r_{t + j} - \bar{x} false)] = \frac{1}{1 - ρ ρ_{x}} false(x_{t} - \bar{x} false) .

…”

Section: Valuation Frameworkmentioning

confidence: 99%

“…We assume that

ρ_{g} = 0.60

Section: Valuation Frameworkmentioning

confidence: 99%

Section: Valuation Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Why are REITS Currently So Expensive?

Nieuwerburgh

2018

Real Estate Economics

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“…The reciprocal problem arises in forecasting

Δ c f_{t + 1}

. To overcome this difficulty, researchers have taken present value approaches that account for the joint relationship among

v_{t}

μ_{t}

, and

g_{t}

(see Cochrane (), Lettau and Van Nieuwerburgh (), van Binsbergen and Koijen ()). While this begins to disentangle the link between prices and expectations, these joint systems continue to rely solely on aggregate variables.…”

mentioning

confidence: 99%

“…Many return and dividend growth forecasting studies exclude the Great Depression from their forecast samples. Recent examples include van Binsbergen and Koijen (), Lacerda and Santa‐Clara (), and Cochrane ().…”

mentioning

confidence: 99%

Market Expectations in the Cross‐Section of Present Values

Kelly¹,

Pruitt

2013

The Journal of Finance

459

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Returns and cash flow growth for the aggregate U.S. stock market are highly and robustly predictable. Using a single factor extracted from the cross-section of book-tomarket ratios, we find an out-of-sample return forecasting R 2 of 13% at the annual frequency (0.9% monthly). We document similar out-of-sample predictability for returns on value, size, momentum, and industry portfolios. We present a model linking aggregate market expectations to disaggregated valuation ratios in a latent factor system. Spreads in value portfolios' exposures to economic shocks are key to identifying predictability and are consistent with duration-based theories of the value premium.THE MOST COMMON APPROACH to measuring aggregate return and cash flow expectations is predictive regression. As suggested by the present value relationship between prices, discount rates, and future cash flows, research shows that the aggregate price-dividend ratio is among the most informative predictive variables. Typical in-sample estimates find that about 10% of annual return variation can be accounted for by forecasts based on the aggregate book-tomarket ratio, but find little or no out-of-sample predictive power.1 In this paper we show that reliance on aggregate quantities drastically understates the degree of value ratios' predictive content for both returns and cash flow growth, and hence understates the volatility of investor expectations. Our estimates suggest that as much as 13% of the out-of-sample variation in annual market returns (as much as 12% for dividend growth), and somewhat more of the insample variation, can be explained by the cross-section of past disaggregated value ratios.To harness disaggregated information we represent the cross-section of assetspecific book-to-market ratios as a dynamic latent factor model. We relate disaggregated value ratios to aggregate expected market returns and cash flow growth. Our model is based on the idea that the same dynamic state variables driving aggregate expectations also govern the dynamics of the entire panel * Kelly is with Booth School of Business, University of Chicago, and Pruitt is with the Board of Governors of the Federal Reserve System. The view expressed here are those of the authors and do not necessarily reflect the views of the Federal Reserve System or its staff.1 See Cochrane (2005) and Koijen and Van Nieuwerburgh (2011) for surveys of return and cash flow predictability evidence using the aggregate price-dividend ratio. Similar results obtain from forecasts based on the aggregate book-to-market ratio.

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