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Working Papers

* Presentations: 2024 AFA, 2023 Tel Aviv University Finance Conference (cancelled), 2023 EUROFIDAI – ESSEC Paris December Finance Meeting, 2023 INSEAD Finance Symposium, 2023 USC Finance Workshop on Valuations


We use the long-term Capital Market Assumptions of major asset managers and institutional investor consultants from 1987 to 2022 to provide three stylized facts about their subjective risk and return expectations on 19 asset classes. First, the subjective distribution of asset class returns is well described by a 1-factor structure, with this single risk factor typically explaining more than 65% of the subjective variability in asset class returns. Second, at least 80% of the variability in subjective expected returns is due to variability in subjective risk premia (compensation for beta) as opposed to subjective mispricing (alpha). And third, subjective risk and return expectations vary much more across asset classes than across institutions. Our findings imply that models with subjective beliefs should reflect a risk-return tradeoff. Additionally, accounting for this risk-return trade-off is even more important than incorporating belief heterogeneity across institutional investors when modeling multiple asset classes.

Revise & Resubmit

* Presentations: Midwest Finance Association, FSU-UF-UCF Critical Issues in Real Estate Research Symposium, FMA Annual Meeting, Conference, Pitt/OSU/Penn State/CMU Finance Conference (Carnegie Mellon), 10th Annual Hedge Fund and Private Equity Conference (Dauphine), University of Cincinnati, Cornell University, the University of Melbourne, the University of Notre Dame, The Ohio State University, Santa Clara University, the Office of Financial Research, the University of Southern California, the University of Virginia.

Figure 4aaa - Q-adjusted Return Quintile
Figure 3 - Queue Quintiles.png

Open-end funds provide a liquidity transformation service by issuing and redeeming shares that are more liquid than their assets. However, because these assets are illiquid, managers need time to transfer capital to the underlying market. Liquidity buffers and liquidity restrictions enable this. Additionally, because of this illiquidity, their returns are predictable and susceptible to NAV-timing strategies which transfer wealth. I show NAV-timing strategies appear profitable on paper and investors appear to follow these strategies. I also show liquidity restrictions protect against these NAV-timing risks while liquidity buffers do not. In fact, liquidity buffers amplify them when added to liquidity restrictions.

Smooth Return Drivers: Evidence from Private Equity Real Estate Funds

* Presentations: 2024 AREUEA-ASSSA (San Antonio)2023 IPC Spring Research Symposium (University of North Carolina, Chapel Hill); 2023 FSU-UF Critical Issues in Real Estate Symposium (Florida State University)

Published & Forthcoming Papers

Review of Financial Studies (accepted)

* Presentations: 2020 Northern Finance Association Conference (upcoming), 2020 SFS Cavalcade North America Conference (University of Indiana), 2020 Ohio State Finance Alumni Conference, 2020 Institute for Private Capital Research Symposium (University of North Carolina), University of Arizona, University of North Carolina - Chapel Hill

Fund-level Autocorrelation.png
Aggregate-level Autocorrelation-2.png


Funds that invest in illiquid assets report returns with spurious autocorrelation. Consequently, investors need to unsmooth returns when evaluating the risk exposures of these funds. We show that funds investing in similar assets have a common source of spurious autocorrelation, which is not addressed by commonly-used unsmoothing methods, leading to underestimation of systematic risk. To address this issue, we propose a generalization of these unsmoothing techniques and apply it to hedge funds and commercial real estate funds. Our empirical results indicate our method significantly improves the measurement of risk exposures and risk-adjusted performance, with stronger results for more illiquid funds.

The Journal of Real Estate Finance and Economics (2022)

* Presentations:
3rd Annual REALPAC/Ryerson Canadian Commercial Real Estate Research Symposium (Ryerson), 2020 Commercial Real Estate Data Association Conference (University of North Carolina), 2020 Real Estate Finance and Investment Symposium (University of Cambridge, University of Florida, University of Geneva, & National University of Singapore), 2021 NCREIF Winter Conference (upcoming), 2019 3rd Annual Private Markets Research Conference (Switzerland)

Figure 1B - Noncore over time.png
Figure 3B - Core Allocations and Queues.

This paper documents that funds with greater non-core allocations have higher market risk exposure, β, but lower returns. Additionally, it documents that one reason their returns are lower is because they poorly time their investment into these properties. Open-end private real estate funds have higher non-core allocations at the top of the market and lower allocations at the bottom. As such, these funds are disproportionately exposed to the downside of the market. Lastly, I find that reaching for yield and fund flow pressure are important determinants of this phenomenon. Funds buy relatively more non-core properties when either the market return expectations or their net queues are smaller. Buying more core properties when queues are larger enables managers to place capital quicker, but it also hurts existing investors by decreasing their market risk exposure at the time when it is the most desirable and beneficial.

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