Market Expectations in the Cross Section of Present Values

Kelly, Bryan T.; Pruitt, Seth

doi:10.2139/ssrn.1752543

Cited by 140 publications

(193 citation statements)

References 76 publications

(38 reference statements)

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“…Prominent examples include asset return predictability, where we could use the cross section of book-to-market ratios to forecast aggregate market returns (Campbell and Shiller, 1988; Polk et al, 2006; Kelly and Pruitt, 2013). To this end, we specify our data generating process as…”

Section: Numerical Studiesmentioning

confidence: 99%

Sufficient forecasting using factor models

Fan

Xue

Yao

2017

Journal of Econometrics

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We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect. The dimensionality was first reduced via a high-dimensional (approximate) factor model implemented by the principal component analysis. Using the extracted factors, we develop a novel forecasting method called the sufficient forecasting, which provides a set of sufficient predictive indices, inferred from high-dimensional predictors, to deliver additional predictive power. The projected principal component analysis will be employed to enhance the accuracy of inferred factors when a semi-parametric (approximate) factor model is assumed. Our method is also applicable to cross-sectional sufficient regression using extracted factors. The connection between the sufficient forecasting and the deep learning architecture is explicitly stated. The sufficient forecasting correctly estimates projection indices of the underlying factors even in the presence of a nonparametric forecasting function. The proposed method extends the sufficient dimension reduction to high-dimensional regimes by condensing the cross-sectional information through factor models. We derive asymptotic properties for the estimate of the central subspace spanned by these projection directions as well as the estimates of the sufficient predictive indices. We further show that the natural method of running multiple regression of target on estimated factors yields a linear estimate that actually falls into this central subspace. Our method and theory allow the number of predictors to be larger than the number of observations. We finally demonstrate that the sufficient forecasting improves upon the linear forecasting in both simulation studies and an empirical study of forecasting macroeconomic variables.

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Section: Numerical Studiesmentioning

confidence: 99%

Sufficient forecasting using factor models

Fan

Xue

Yao

2017

Journal of Econometrics

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“…Its advantage compared to the latter is that the factor loadings (which are constant in our case) are determined so as to maximize the covariance of the proxies with the endogenous variable. 10 A discussion on the advantages of PLS can also be found in Kelly and Pruitt (2013) and Huang et al (2015). The PLS algorithm is based on Hastie et al (2009).…”

Section: The Construction Of the Sentiment Indexmentioning

confidence: 99%

Market sentiment in commodity futures returns

Gao

Süss

2015

Journal of Empirical Finance

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We identify a strong presence of sentiment exposure in commodity futures returns. Sentiment is able to provide additional explanatory power for comovement among commodity futures beyond the macro-and equity-related sources. Commodity futures with low open interest growth, high volatilities, low momentum, or low futures basis are more sensitive to change in sentiment. Similar to Baker and Wurgler (2006), we construct a market sentiment index by Partial Least Squares regressions (PLS) with non-return based stock market proxies, in particular higher moments of the option implied return distribution. Moreover, our sentiment index can be built on a daily basis.

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“…Our starting point is the three‐pass regression filter 3PRF, which was developed by Kelly and Pruitt (). 3PRF, which is an extension of partial least squares (PLS), enables us to obtain targeted factors for forecasting a specific variable of interest (GDP growth in our case) in a simple and intuitive manner.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Gross Domestic Product Growth with large Unbalanced Data Sets: The Mixed Frequency Three-pass Regression Filter

Hepenstrick

Marcellino

2018

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Gross domestic product (GDP) is a key summary of macroeconomic conditions and it is closely monitored both by policy makers and by decision makers in the private sector. However, it is only available on a quarterly frequency, and in many countries it is released with a substantial delay. There are, however, many higher frequency and more timely economic and financial indicators that could be used for nowcasting and short‐term forecasting GDP. Against this backdrop, we propose a modification of the three‐pass regression filter to make it applicable to large mixed frequency data sets with ragged edges in a forecasting context. The resulting method, labelled MF‐3PRF, is very simple but compares well with alternative mixed frequency factor estimation procedures in terms of theoretical properties and actual GDP nowcasting and forecasting for the USA and a variety of other countries.

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Market Expectations in the Cross Section of Present Values

Cited by 140 publications

References 76 publications

Sufficient forecasting using factor models

Sufficient forecasting using factor models

Market sentiment in commodity futures returns

Forecasting Gross Domestic Product Growth with large Unbalanced Data Sets: The Mixed Frequency Three-pass Regression Filter

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