# Research

## Serhiy Kozak

Assistant Professor of Finance

**Research Interests:** empirical and theoretical asset pricing, financial economics, machine learning.

## Published and forthcoming

**Journal of Finance**, June 2018, 73(3), 1183-1223 (with Stefan Nagel and Shri Santosh).

JF version | Data | BibTeX

*Abstract:*** **We argue that tests of reduced-form factor models and horse races between "characteristics" and "covariances" cannot discriminate between alternative models of investor beliefs. Since asset returns have substantial commonality, absence of near-arbitrage opportunities implies that the SDF can be represented as a function of a few dominant sources of return variation. As long as some arbitrageurs are present, this conclusion applies even in an economy in which all cross-sectional variation in expected returns is caused by sentiment. Sentiment investor demand results in substantial mispricing only if arbitrageurs are exposed to factor risk when taking the other side of these trades.

**Journal of Financial Economics***,* February 2020, 135(2), 271-292, **Lead Article** (with Stefan Nagel and Shrihari Santosh).

JFE version | Data | Code | Internet Appendix | BibTeX | Slides (TeX)

*Abstract:*** **We construct a robust stochastic discount factor (SDF) that summarizes the joint explanatory power of a large number of cross-sectional stock return predictors. Our method achieves robust out-of-sample performance in this high-dimensional setting by imposing an economically motivated prior on SDF coefficients that shrinks the contributions of low-variance principal components of the candidate factors. While empirical asset pricing research has focused on SDFs with a small number of characteristics-based factors—e.g., the four- or five-factor models discussed in the recent literature—we find that such a characteristics-sparse SDF cannot adequately summarize the cross-section of expected stock returns. However, a relatively small number of principal components of the universe of potential characteristics-based factors can approximate the SDF quite well.

**Journal of Financial Economics**, September 2020, 137(3), 740-751(with Shri Santosh).

JFE version | Data | Internet Appendix | BibTeX

*Abstract:*** **Controlling for changes in wealth, the price of "discount-rate" risk reveals whether increases in equity risk premia represent "good" or "bad" news to rational investors. We employ a new empirical methodology and find that the price is negative. This finding suggests that discount rates are high during times of high marginal utility of wealth. Our approach relies on using future realized market returns to consistently estimate covariances of asset returns with the market risk premium. Covariances drive observed patterns in a broad cross-section of stock and bond expected returns.

**Review of Financial Studies**, May 2020, 33(5), 1980-2018 (with Valentin Haddad and Shri Santosh).

2018 Q-Group Jack Treynor Prize

RFS version | Data | Internet Appendix | BibTeX

*Abstract:*** **The optimal factor timing portfolio is equivalent to the stochastic discount factor. We propose and implement a method to characterize both empirically. Our approach imposes restrictions on the dynamics of expected returns which lead to an economically plausible SDF. Market-neutral equity factors are strongly and robustly predictable. Exploiting this predictability leads to substantial improvement in portfolio performance relative to static factor investing. The variance of the corresponding SDF is larger, more variable over time, and exhibits different cyclical behavior than estimates ignoring this fact. These results pose new challenges for theories that aim to match the cross-section of stock returns.

## Working Papers

**Journal of Monetary Economics**, Revise and Resubmit.

BibTeX

*Abstract:*** **I present a production-based general equilibrium model that jointly prices bond and stock returns. The model produces time-varying correlation between stock and long-term default-free real bond returns that changes in both magnitude and sign. The real term premium is also time-varying and changes sign. To generate these results, the model incorporates time-varying risk aversion within Epstein-Zin preferences and two physical technologies with different exposure to cash-flow risk. Bonds hedge risk-aversion (discount-rate) shocks and command negative term premium through this channel. Capital (cash-flow) shocks produce comovement of bond and stock returns and positive term premium. The relative strength of these two mechanisms varies over time.

(with Stefano Giglio and Bryan Kelly)

Slides | Data | BibTeX

*Abstract:*** **We use a large cross-section of equity returns to estimate a rich affine model of equity prices, dividends, returns and their dynamics. Using the model, we can price dividend strips of the aggregate market index, as well as any other well-diversified equity portfolio. We do not use any dividend strips data in the estimation of the model; however, model-implied equity yields generated by the model match closely the equity yields from the traded dividend forwards reported in the literature. Our model can therefore be used to extend the data on the term structure of discount rates in three dimensions: (i) over time, back to the 1970s; (ii) across maturities, since we are not limited by the maturities of actually traded dividend claims; and most importantly, (iii) across portfolios, since we generate a term structure for any portfolio of stocks (e.g., small or value stocks). The new term structure data generated by our model (e.g., separate term structures for value, growth, investment and other portfolios, observed over a span of 45 years that covers several recessions) represent new empirical moments that can be used to guide and evaluate asset pricing models.

Slides | Data | BibTeX

*Abstract:*** **Characteristics-based asset pricing implicitly assumes that factor betas or risk prices are linear functions of pre-specified characteristics. Present value identities, such as Campbell-Shiller or clean-surplus accounting, however, clearly predict that expected returns are highly non-linear functions of all characteristics. While basic non-linearities can be easily accommodated by adding non-linear functions to the set of characteristics, the problem quickly becomes infeasible once interactions of characteristics are considered. I propose a method to construct a stochastic discount factor (SDF) when the set of characteristics is extended to an arbitrary---potentially infinitely-dimensional---set of non-linear functions of original characteristics. The method borrows ideas from a machine learning technique known as the "kernel trick" to circumvent the curse of dimensionality. I find that allowing for interactions and non-linearities of characteristics leads to substantially more efficient SDFs; out-of-sample Sharpe ratios for the implied MVE portfolio double.

(with Denis Sosyura)

BibTeX

*Abstract:*** **We exploit staggered removals of interstate banking restrictions to identify the causal effect of access to credit on households’ stock market participation and asset allocation. Using micro data on retail brokerage accounts and proprietary data on personal credit histories from TransUnion, we document two effects of the loosening of credit constraints on households’ financial decisions. First, households enter the stock market by opening new brokerage accounts. Second, households increase their asset allocation to risky assets and reduce their allocation to cash, consistent with a lower need for precautionary savings. The effects are stronger for younger and more credit constrained investors. Overall, we establish one of the first direct links between access to credit and households’ investment decisions.