Presentation Abstracts
Kay Giesecke, Stanford University
Learning Illiquid Asset Prices
Many asset classes—including private equity, real estate, corporate, municipal, and mortgage bonds, as well as structured products—are illiquid, with prices that are observed only sporadically. Yet frequent, objective, and accurate price estimates are essential for portfolio valuation, investment decision-making, and regulatory compliance. This paper develops point and interval estimators for illiquid asset prices, offering both consistency guarantees and non-asymptotic coverage for the resulting intervals. The estimators are based on a scalable, assumption-light semiparametric conditional factor model for the quantiles of asset prices. Empirical results for mortgage-backed securities demonstrate the method’s effectiveness and represent the first treatment of illiquid MBS pricing in the literature, highlighting the bond and market characteristics that drive prices. This is joint work with Junting Duan and Yang Fan (Stanford).
Lisa, Goldberg, BlackRock
Terry Hendershott, University of California, Berkeley
All-to-All Liquidity in Corporate Bonds
Open Trading (OT) on the largest electronic corporate-bond request-for-quote (RFQ) platform allows investor-to-investor trading. Despite only 2% of trades occurring directly between investors, OT succeeds in attracting new liquidity providers (7%) and creating new client-dealer links (3%). These result in OT improving prices by 3bps on average, 1bp from OT winning, and 2bps from dealers improving bids, which reduces trading costs by 15-30%. However, OT worsens prices in RFQs with few dealers, which have the largest winner’s curse. A model and counterfactual experiments suggest how market redesign could mitigate adverse selection and improve liquidity in corporate bond markets.
Martin Lettau, University of California, Berkeley
High Dimensional Factor Models and the Factor Zoo
This paper proposes a new approach to the “factor zoo” conundrum. Instead of applying dimension-reduction methods to a large set of portfolio returns obtained from sorts on characteristics, I construct factors that summarize the information in characteristics across assets and then sort assets into portfolios according to these “characteristic factors”. I estimate the model on a data set of mutual fund characteristics. Since the data set is 3-dimensional (characteristics of funds over time), characteristic factors are based on a tensor factor model (TFM) that is a generalization of 2-dimensional PCA. I find that parsimonious TFM captures over 90% of the variation in the data set. Pricing factors derived from the TFM have high Sharpe ratios and capture the cross-section of fund returns better than standard benchmark models.
Eben Lazarus, University of California, Berkeley
Interest Rates and Equity Valuations
The literature often seeks to determine the effect of interest rates on equity valuations, but both are endogenous and their comovement depends on the structural drivers underlying interest-rate changes. We show that changes in real rates can come from changes in expected growth, risk, or “pure discounting.” We characterize the effect on equity valuations for each of the three shocks, and show that only pure discount rate shocks are transmitted one for one to equity valuations, with little or negative transmission of growth and risk shocks. Implementing our decomposition with a global panel of growth expectations and asset prices, we find:(i) a weak unconditional relation between stock valuations and real rates, but (ii) a strong relation between valuations and the pure discounting component of rates, with pure discount rate changes explaining over 80% of the cross-country changes in stock valuations since 1990. In the US data, we find that 35% of the decline in interest rates is attributable to the pure discounting term, implying that only a fraction of the change in rates has passed through directly to equities. We also use our decomposition to speak to higher-frequency returns; explain interest-rate exposures in the cross-section of stocks; estimate a sizable duration-matched equity premium; and unpack the effects of policy-induced interest-rate shocks.
Andrew Lo, Massachusetts Institute of Technology
Ananth Madhavan, University of California, Berkeley
Can Bonds Still Diversify Multi-Asset Portfolios? Income versus Duration in Distinct Correlation Regimes
We analyze the diversification benefits of fixed-income instruments under time-varying
correlations. We estimate monthly, non-overlapping correlations from daily 10-year Treasury
bond returns over the period January 1962 to May 2025.With these data, we use a hidden Markov model to identify three distinct correlation regimes – negative, zero, and positive. These regimes are highly persistent and differ in their diversification value depending on the importance of income versus duration as drivers of bond returns. Dynamically altering the mix of multi-asset portfolio allocations based on the most likely correlation regime materially improves returns while controlling risk.
John Mulvey, Princeton University
Reinforcement Learning for Enhanced Investment Performance, Opportunities and Challenges