Working Papers

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Subjective Beliefs and the Portfolio Allocations of Institutional Investors (with Aleksandar Andonov, Spencer Couts, Johnathan Loudis, and Andrea Rossi)

      * Conferences: 2026 IPC Spring Research Symposium; 2025 Annual Valuation Workshop at University of Washington

Abstract: We study how subjective beliefs shape the portfolio allocations of institutional investors. Linking the multi-asset allocations of U.S. public pension funds to the long-term capital market assumptions of their consultants, we examine the extent to which differences in subjective expected returns, volatilities, and correlations map into differences in portfolio weights. We embed these belief inputs in a mean-variance framework that incorporates fund-consultant belief wedges, heterogeneous risk aversion, non-negative weight constraints, and a benchmarking incentive due to frictions. We find that pension fund allocations are significantly linked to belief-implied mean-variance efficient allocations across pension funds, across asset classes, and over time. Accounting for frictions is essential: it dramatically increases the pass through and explanatory power of beliefs to portfolio allocations. Overall, our results show that beliefs play a central role in institutional portfolio decisions and that frictions critically shape their transmission into observed allocations.

The Pricing of Geopolitical Tensions over a Century (with Alessandro Melone and Andrea Ricciardi)

      * Conferences: 2026 WFA; 2026 Asian Bureau of Finance and Economic Research (ABFER); 2026 FSU Truist Beach Conference; 2026 World Symposium on Investment Research; 2025 Annual Finance Conference at WashU; 2025 Stanford SITE Conference; 2025 SAFE Asset Pricing Workshop; 2025 Volatility Institute Annual Conference at NYU Shanghai; 2025 International Behavioural Finance Conference at Chicago Booth; 2025 CREDIT     

      * Media Coverage: Barron’s

Abstract: We study the asset pricing implications of geopolitical tensions using nearly 100 years of data. Leveraging widely adopted news-based geopolitical risk indices, we find that geopolitical threats (GPT) and acts (GPA) have markedly different effects. GPT aligns closely with geopolitical risk perceptions and decisions of investors and firms. Thus, GPT is priced across individual US stocks, equity anomalies, international equity and bond indices, and it forecasts country-level equity premia. In contrast, GPA exhibits weaker and less stable links to the beliefs and decisions of investors and firms as well as to variation in risk premia across assets and over time. Importantly, our results are incremental to existing news-based indices of macro-financial uncertainty, including those capturing war-related discourse and economic or trade policy risk. Overall, our findings underscore the importance of forward-looking measures like GPT for understanding how news-based uncertainty affects investment decisions and asset prices.

Out-of-Sample Alphas Post-Publication (with Johnathan Loudis and Richard Ogden)

      * Conferences: 2025 ASU Sonoran Winter Finance Conference; 2025 International Behavioural Finance Conference at Chicago Booth

      * Media Coverage: Quant Ratio

Abstract: Anomaly strategies generate positive and significant CAPM alphas post-publication. Existing explanations include non-market risks, trading costs, and investment frictions. This paper introduces a complementary channel: when a new anomaly strategy is published, investors face uncertainty in identifying the optimal weight to allocate to the anomaly in order to achieve a positive alpha post-publication, making the strategy less appealing. Empirically, we find that the average post-publication alpha of anomaly strategies is close to zero when optimal weights are estimated out-of-sample using pre-publication data. This finding is robust across specifications, including those using empirical Bayesian shrinkage and machine learning to estimate weights. Conceptually, this suggests investors have little incentive to add a new anomaly strategy to their portfolios. While investors can generate positive out-of-sample alphas by combining multiple anomaly strategies via shrinkage methods, we show the demand from such investors is insufficient to eliminate alphas in equilibrium.

The Private Capital Alpha (with Gregory Brown and Wendy Hu)

      * Conferences: 2025 MFA; 2025 IPC Current Issues in Alternatives Research Symposium; 2024 Inquire Europe; 2023 IPC & PERC Private Equity Research Symposium

Abstract: Alpha is the standard risk-adjusted performance metric for most asset classes. Private capital is a notable exception because the usual alpha estimation method ignores the economic realities of investing in private markets. In this paper, we propose a framework to estimate the alphas of private capital asset classes for typical limited partners through simulations that account for the illiquidity and underdiversification inherent to private markets. We then combine a large sample of 5,028 U.S. buyout, venture capital, and real estate funds from 1987 to 2022 to estimate the alphas of these private capital asset classes. We find that buyout as an asset class generated a significant annual alpha of 2.5% during our sample period. In contrast, over our sample period, the venture capital alpha was larger but statistically unreliable whereas the real estate alpha was smaller and also statistically insignificant.

Institutional Investors’ Subjective Risk Premia: Time Variation and Disagreement (with Spencer Couts, Yicheng Liu, and Johnathan Loudis)

      * Conferences: 2026 SFS Cavalcade; 2026 Finance Down Under; 2026 Young Scholars Finance Consortium; 2025 Carey Finance Conference at Johns Hopkins; 2025 Helsinki Finance Summit on Investor Behavior; 2025 Ohio State Finance Alumni Conference; 2025 FSU Truist Beach Conference; 2025 MFA; 2025 Hedge Fund Research Conference; 2024 Annual Valuation Workshop at Wharton; 2024 Wabash River Conference at Purdue

      * Best Paper Award, Finance Down Under (J Spencer Martin Best Paper Award)

Abstract: We study the role of subjective risk premia in explaining variation in institutional investors’ subjective expected returns, including both time variation and disagreement. Our analysis uses long-term Capital Market Assumptions from asset managers and investment consultants from 1987 to 2022. Market risk premia account for most of the countercyclicality and overall time variation in expected returns, with the remainder driven by alphas (perceived mispricing). The risk premia effect stems almost entirely from time variation in perceived risk quantities rather than risk price (risk aversion). Market risk premia also explain most of the expected return disagreement, but here alphas play a significant role, and risk price and risk quantities contribute roughly equally to the risk premia effect. These results provide benchmark moments that asset pricing models should match to be consistent with institutional investors’ beliefs.

Payout-Based Asset Pricing (with Andreas Stathopoulos)

      * Conferences: 2025 WFA; 2025 EFA; 2025 UConn Finance Conference; 2024 Workshop on Investment and Production-Based Asset Pricing; 2024 UW Foster Summer Conference; 2024 U of Michigan New Frontiers in Asset Pricing Mitsui Symposium; 2024 World Symposium on Investment Research, 2024 MFA Meeting

Abstract: Firms’ payout decisions respond to expected returns: everything else equal, firms invest less and pay out more when their cost of capital increases. Given investors’ demand for firm payout, market clearing implies that productivity and payout demand dynamics fully determine equilibrium asset prices and returns. Using this logic, we propose a payout-based asset pricing framework. To operationalize it, we introduce a quantitative model, calibrating the productivity and payout demand processes to match aggregate U.S. corporate output and payout moments. Model-implied payout yields and firm returns match key empirical moments, and model-implied expected returns predict future firm returns in the data.

The Subjective Risk and Return Expectations of Institutional Investors (with Spencer Couts and Johnathan Loudis)

      * Journal of Finance, Revise & Resubmit

      * Conferences: 2024 AFA Meeting; 2024 EFA Meeting; 2024 FIRS Meeting; 2024 NFA Meeting; 2024 Adam Smith Workshop; 2024 Annual Finance Conference at WashU; 2024 UW Foster Summer Conference; 2024 Helsinki Finance Summit on Investor Behavior; 2024 Alpine Finance Summit; 2023 Paris December Finance Meeting; 2023 INSEAD Finance Symposium; 2023 Annual Valuation Workshop at USC; 2023 Brazilian Finance Society Meeting

      * Median Coverage: Invesco Risk & Reward

Abstract: 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, there is a strong and positive subjective risk-return tradeoff, with most of the variability in subjective expected returns due to variability in subjective risk premia (compensation for market beta) as opposed to subjective alphas. Second, belief variation and the positive risk-return tradeoff are both stronger across asset classes than across institutions. And third, the subjective expected returns of these institutions predict subsequent realized returns across asset classes and over time. Taken together, our findings imply that models with subjective beliefs should reflect a risk-return tradeoff. Additionally, accounting for this subjective risk-return tradeoff when modeling multiple asset classes is even more important than incorporating average belief distortions or belief heterogeneity in our setting.

From Bonds to Dividend Strips: Decomposing the Equity Premia Term Structure (with Spencer Andrews)

      * Conferences: 2025 Paris December Finance Meeting, 2022 Finance Down Under; 2021 CFEA; 2021 MFA Meeting; 2021 SBFin Workshop

Abstract: We construct a Stochastic Discount Factor that jointly prices nominal and real bonds as well as various equity portfolios from 1972 to 2022. Combining it with yield dynamics across these markets, we estimate term structures of risk premia for real bonds, nominal bonds, and equities. We then decompose equity risk premia into term, inflation, and cash flow risk premia components—where cash flow risk premia denote expected returns of dividend strips in excess of maturity-matched nominal bond strips. Term and inflation risk premia rise with maturity, while cash flow risk premia are hump-shaped and relatively low at long maturities. Moreover, short-maturity equity risk premia variation over time is mainly driven by cash flow risk premia, whereas long-maturity equity risk premia variation is dominated by term and inflation risk premia. These findings imply that credible explanations for the equity excess volatility puzzle must operate through bond risk premia dynamics.

What Moves Equity Markets? A Term Structure Decomposition for Stock Returns

      * Conferences: 2023 AFA Meeting; 2023 University of Iowa Big Data and Machine Learning Conference; 2022 European Winter Finance Summit; 2022 MFA Meeting; 2022 Craig Holden Memorial Finance Conference at Indiana University; 2022 AFBC; 2020 NFA

      * Best Paper Award, European Winter Finance Summit (Sudipto Bhattacharya Memorial Prize)

Abstract: Several papers decompose stock returns into cash flow and discount rate news to study equity volatility. This paper develops an alternative decomposition based on variation in dividend present values with different maturities. From 1960 to 2021, roughly 60% of US equity volatility comes from the value of dividends with maturities beyond 20 years, implying an “equity volatility duration” above 30 years. Extending the data over time and across countries, I also show a secular increase in the importance of long-term dividends in driving equity volatility. Finally, standard asset pricing models understate the role of long-term dividends in explaining equity volatility.