New Frontiers in Asset Pricing

Date
Mon, Jul 18 2022, 9:30am - Wed, Jul 20 2022, 3:15pm PDT
Location
Lucas Conference Center, Room A
Landau Economics Building
579 Jane Stanford Way, Stanford
[In-person session]
Organized by
  • Kenneth Judd, Hoover Institution, Stanford University
  • Walter Pohl, Norwegian School of Economics
  • Karl Schmedders, IMD Lausanne
  • Ole Wilms, University of Hamburg and Tilburg University.

This session is for asset pricing papers on the frontier of the discipline.  Particular areas of focus are in macrofinance, computation, machine learning, and climate finance.

Possible topics include but are not limited to the following: asset pricing, investor heterogeneity, learning and ambiguity, new preference structures for pricing models, or using machine learning to understand the cross-section of returns. As the analysis of such models often requires the use of computational methods, we encourage submissions that develop and make use of new numerical techniques.

A particular area of interest is climate finance, where both climate change and the policy responses to climate change present new risks in asset pricing markets.  Topics of interest include asset pricing with heterogeneous agents and disaster risks, credit risk modeling for possibly stranded assets, the implications of integrated assessment models for financial risks, and methodological advances in solution methods for complex analyses of climate finance models.

In This Session

Monday, July 18, 2022

Jul 18

9:00 am - 9:30 am PDT

Check-In and Breakfast

Jul 18

9:30 am - 10:15 am PDT

Asset Pricing with Panel Trees under Global Split Criteria

Presented by: Lin William Cong (Cornell University)
Co-author(s): Guanhao Feng (City University of Hong Kong), Jingyu He (City University of Hong Kong), and Xin He (City University of Hong Kong)

We introduce a class of interpretable tree-based models (P-Tree) for analyzing (unbalanced) panel data, with iterative and global (instead of recursive and local) split criteria. We apply P-Tree to split the cross section of asset returns under the no-arbitrage condition, generating a stochastic discount factor model and diversified test portfolios for asset pricing. P-Tree visualizes nonlinear feature interactions, accommodates time-series splits, and allows interactions between macroeconomic states and asset characteristics. In an empirical study of U.S. equities, data-driven P-Tree reveals that long-term reversal, volume volatility, and industry-adjusted market equity interact to drive cross-sectional return variation, and that inflation constitutes the most critical regime-switching when interacting with firm characteristics. P-Tree models consistently outperform known observable and latent factor models at pricing individual asset and portfolio returns, while delivering profitable and transparent trading strategies utilizing characteristic interactions. Notably, the efficient portfolio on P-Tree factors generates a monthly risk-adjusted alpha of 2.46% and an annualized Sharpe ratio of 1.71 out of sample. The methodology is broadly applicable in building trees with vectorized outcomes and economic restrictions as split criteria to guard against overfitting and improve model performance.

Jul 18

10:15 am - 10:30 am PDT

Break

Jul 18

10:30 am - 11:15 am PDT

Asset Pricing with Misallocation

Presented by: Winston Wei Dou (University of Pennsylvania)
Co-author(s): Yan Ji (Hong Kong University of Science and Technology), Di Tian ( University of Pennsylvania), and Pengfei Wang (Peking University)

We develop an endogenous growth model with heterogeneous firms, intermediate goods, and financial frictions, in which misallocation emerges explicitly as a crucial state variable. In equilibrium, misallocation endogenously generates longrun uncertainty about economic growth by distorting innovation decisions, leading to significant welfare losses and risk premia in capital markets. Macroeconomic shocks that affect misallocation are likely to have overly persistent effects on aggregate growth. Using an empirical misallocation measure motivated by the model, we find evidence showing that misallocation captures low-frequency variations in both aggregate growth and stock returns. Empirically, a two-factor model with market and misallocation factors prices size, book-to-market, momentum, and bond portfolios with an R-squared and a mean absolute pricing error close to the Fama-French three-factor model.

Jul 18

11:15 am - 11:30 am PDT

Break

Jul 18

11:30 am - 12:15 pm PDT

Risk for Price: Using Generalized Demand System for Asset Pricing

Presented by: Yu Li (University of Minnesota)

I construct a non-parametric pricing kernel with consumption prices and expenditure by decomposing consumer’s indirect utility function. This pricing kernel establishes the fundamental connection between the intertemporal financial asset-holding and the intra-temporal consumption portfolio. I examine the pricing kernel in explaining variation of expected returns across diverse equity portfolios. This pricing kernel is more successful than traded-factor models and simple consumption-based models. Allowing the generalized non-homothetic preference, I find the price of good requires relatively greater risk compensation in the good-service two-sector economy. Dissecting the risk premium of expenditure and the risk premium of relative good price, I show that shrinking expenditure share in the good sector helps explain the structural transformation of risk premium.

Jul 18

12:15 pm - 1:30 pm PDT

Lunch

Jul 18

1:30 pm - 2:15 pm PDT

Main Street's Pain, Wall Street's Gain

Presented by: Nancy R. Xu (Boston College)
Co-author(s): Yang You (University of Hong Kong)

We propose fiscal policy expectation as a new mechanism in stock return responses to macro surprises. When the Main Street suffers more than expected, investors may expect a more generous Federal Government support and drive up expected cash flow growth and hence the aggregate stock prices, leading to a novel “Main Street pain, Wall Street gain” phenomenon. We provide both time series and cross section evidence. First, we find that, in the past 10 years, this phenomenon emerges when fiscal policy mentioning in newspapers is high. Second, our main cross-sectional identification exploits the Covid period (April 2020 to March 2021), which features an unprecedented fiscal spending in focus. We find that firms/industries that suffer more (unemployment surges, revenue declines) or receive more fiscal support show higher individual stock returns when Initial Jobless Claims are higher than expected. Lastly, we solve a conceptual asset pricing framework featuring a simple fiscal rule to reconcile our empirical results (e.g., pricing channel, cross-sectional source).

Jul 18

2:15 pm - 2:30 pm PDT

Break

Jul 18

2:30 pm - 3:15 pm PDT

Prices Are Less Elastic at More Aggregate Levels

Presented by: Jiacui Li (University of Utah)
Co-author(s): Zihan Lin (Stanford University)

Using order flow imbalance to estimate price impact, we find that the price elasticity to demand is higher at more aggregate levels. That is, pure idiosyncratic demand (e.g. buying a stock and hedging out all common return components) commands a smaller price elasticity of approximately two, and the parameter increases monotonically to around five for less diversifiable size-book/market style level demand. The results are consistent with liquidity providers being more reluctant to trade against price dislocations at more aggregate levels, possibly due to risk aversion or portfolio constraints, and inconsistent with information-based mechanisms of price impact. Together with the fact that there are large order flow imbalances at more aggregate levels, our results suggest that investor demand could be more important for driving price fluctuations at more aggregate levels.

Jul 18

3:15 pm - 3:30 pm PDT

Break

Jul 18

3:30 pm - 4:15 pm PDT

Heterogenous Beliefs and Pricing Green and Brown Assets

Presented by: Walter Pohl (Norwegian School of Economics)
Jul 18

6:00 pm - 8:00 pm PDT

Dinner

Tuesday, July 19, 2022

Jul 19

9:00 am - 9:30 am PDT

Check-In and Breakfast

Jul 19

9:30 am - 10:15 am PDT

Monetary Policy, Debt Structure and Credit Reallocation

Presented by: Yuchen Chen (University of Minnesota)

Unexpected monetary tightening predicts a contraction in aggregate corporate bonds but an expansion in bank loans. Using micro-data, I demonstrate that large and high rated firms with high collateral value and low default risk rebalance towards bank loans and away from corporate bonds, as the relative spread of bond over loan increases. This demand-side “credit substitution channel” and firm heterogeneity together explain the aggregate evidence. I develop a heterogeneous-agent New Keynesian model where bank loans are senior and safer (collateralized) than defaultable bonds but issued at a greater intermediation cost. My model can quantitatively explain the response of corporate bonds and bank loans with respect to monetary policy surprises. An interest rate hike raises default risk and thus the relative cost of bond financing. In response, large and unconstrained firms substitute bank loans for corporate bond, while constrained firms tend to issue more equity. The redistribution effect of this credit substitution channel amplifies the negative effects of interest rate hike on consumption and investment by 8% and 14%, respectively.

Jul 19

10:15 am - 10:30 am PDT

Break

Jul 19

10:30 am - 11:15 am PDT

Incomplete-Market Equilibrium with Unhedgeable Fundamentals and Heterogenous Agents

Presented by: Marko Hans Weber (National University of Singapore)
Co-author(s): Paolo Guasoni (Dublin City University)

We solve a general equilibrium model of an incomplete market with heterogeneous preferences, identifying first-order and second-order effects. Several long-lived agents with different absolute risk-aversion and discount rates make consumption and investment decisions, borrowing from and lending to each other, and trading a stock that pays a dividend whose growth rate has random fluctuations over time. For small fluctuations, the first-order equilibrium implies no trading in stocks, the existence of a representative agent, predictability of returns, multi-factor asset pricing, and that agents use a few public signals for consumption, borrowing, and lending. At the second-order, agents dynamically trade stocks and no representative agent exist. Instead, both the interest rate and asset prices depend on the dispersion of agents’ preferences and their shares of wealth. Dynamic trading arises from agents’ intertemporal hedging motive, even in the absence of personal labor income.

Jul 19

11:15 am - 11:30 am PDT

Break

Jul 19

11:30 am - 12:15 pm PDT

Leveraging the Disagreement on Climate Change: Theory and Evidence

Presented by: Toan Phan (The Federal Reserve Bank of Richmond)
Co-author(s): Laura Bakkensen (University of Arizona) and Tsz-Nga Wong (The Federal Reserve Bank of Richmond)

How do climate risks and heterogeneous climate beliefs impact financial markets? We present novel theoretical predictions and empirical evidence from the mortgage market for properties at risk from sea level rise (SLR). We first develop a competitive search model of defaultable debt contracts with heterogeneous beliefs over future SLR, where property price, loan amount, repayment, and maturity are endogenous. Unlike existing two-period heterogeneous beliefs models, our infinite horizon model allows for heterogeneity in maturity choices. In equilibrium, climate pessimists are more likely to leverage and to use longer maturity debt relative to optimists, trading their climate risk exposure to banks via long-term defaultable debt contracts. An expansionary monetary policy can induce more leverage by pessimists and make the mortgage market more vulnerable to climate change. We test several of the model implications using a propriety comprehensive data set of single-family home sales and associated mortgage contracts across the U.S. Atlantic Coast from 2001 to 2016. In line with our theory, we find that purchases of houses more exposed to SLR risk are more likely to be leveraged and tend to use mortgage contracts with longer maturity, despite lower property prices. These results are driven by buyers from counties with more pessimistic climate beliefs, who are more likely aware of future climate risks. Our results highlight the importance of heterogeneous climate beliefs in understanding the effects of climate change on the financial system.

Jul 19

12:15 pm - 1:30 pm PDT

Lunch

Jul 19

1:30 pm - 2:15 pm PDT

The Allocation of Socially Responsible Capital

Presented by: Benjamin N. Roth (Harvard Business School)
Co-author(s): Daniel Green (Harvard Business School)

Portfolio allocation decisions increasingly incorporate social values. We develop a tractable framework to study how competition between investors mediates the creation of social impact. Relative to the most common social-investing strategies, we identify alternative strategies that result in higher welfare and higher financial returns. From the firm perspective, increasing profitability can have a greater social impact than directly increasing social value creation. Whether investors and firms exhibit positive or negative assortative matching depends on the nature of social preferences. We present empirical evidence that socially-guided mutual funds allocate their capital inefficiently from the perspective of generating impact and financial returns.

Jul 19

2:15 pm - 2:30 pm PDT

Break

Jul 19

2:30 pm - 3:15 pm PDT

Rethinking Weitzman's Gamma Discounting in a Dynamic Stochastic Framework

Presented by: Johnson Kakeu (University of Prince Edward Island)
Co-author(s): Eun-jin Kim (Coventry University) and Rainer Hollerbach (University of Leeds)

Social discounting is a critical and contentious issue in evaluating the costs and benefits of climate policy, infrastructure projects, and other long-term public policies. In this paper, we look at the asymptotic statistical properties of the Weitzman (2001) gamma discounting in a dynamic stochastic continuous-time framework where individual discount rates are heterogeneous. We use the Fokker-Planck equation approach to compute the time-dependent distribution density of individual discount rates and the associated certainty-equivalent social discount factor and rate. We show that the certainty-equivalent social discount rate includes the Weitzman’s gamma discounting rate as a special case, in addition to terms capturing changes in the shape of the heterogeneity distributions of discount rates over time. It is shown that for short time horizons, the path of the social discount rate computed from the dynamic stochastic framework is in near perfect agreement with the Weitzman approach. However, for larger time horizons, the asymptotic statistical behavior of the certainty-equivalent social discount rate differs from the Weitzman’s discounting path because the statistical property of discount rates for the dynamic case is no longer stationary over large time horizons. Numerical simulations illustrate how changes in the initial distribution of discount rates and uncertainty shocks would lead to deviations from the Weitzman’s gamma discounting. Using several examples, we show that greater spread of the initial heterogeneity distribution of discount rates causes the certainty-equivalent social discount rate to decline more rapidly. In addition, the effects of truncating the initial distribution as well as the effects of the stochastic fluctuations on the certainty-equivalent discounting path and its degree of representativity for heterogeneous discounting agents are discussed.

Jul 19

3:15 pm - 3:30 pm PDT

Break

Jul 19

3:30 pm - 4:15 pm PDT

Corporate Pension Plans in Climate Change

Presented by: Yuree Lim (University of Wisconsin-La Crosse)

Climate change affects financial health of corporate pension plans. Using a representative sample of US corporate defined benefit pension plans, we show that firms affected by climate disasters are more likely to freeze their plans. Relatedly, the affected firms reduce employer contributions per participant, experience lower pension profitability, increase plan investments toward riskier assets in a failed effort to boost plan returns, and transfer assets from the pension. Using corporate defined contribution pension plans, we find that being hit by climate disaster, plan participants allocate more assets in equity, and they experience greater returns on its pension assets. Our evidence suggests that affected firms respond to the climate disaster shock by extracting internal cash flows from pension assets.

Wednesday, July 20, 2022

Jul 20

9:00 am - 9:30 am PDT

Check-In and Breakfast

Jul 20

9:30 am - 10:15 am PDT

The Virtue of Complexity in Machine Learning Portfolios

Presented by: Kangying Zhou (Yale School of Management)
Co-author(s): Bryan Kelly (Yale School of Management) and Semyon Malamud (Swiss Finance Institute)

We theoretically characterize the behavior of machine learning portfolios in the high complexity regime, i.e. when the number of parameters exceeds the number of observations. We demonstrate a surprising “virtue of complexity:” Sharpe ratios of machine learning portfolios generally increase with model parameterization, even with minimal regularization. Empirically, we document the virtue of complexity in US equity market timing strategies. High complexity models deliver economically large and statistically significant out-of-sample portfolio gains relative to simpler models, due in large part to their remarkable ability to predict recessions.

Jul 20

10:15 am - 10:30 am PDT

Break

Jul 20

10:30 am - 11:15 am PDT

Measuring Firm-Level Inflation Exposure: A Deep Learning Approach

Presented by: Linghang Zeng (Babson College)
Co-author(s): Sudheer Chava (Georgia Institute of Technology), Wendi Du (Georgia Institute of Technology), and Agam Shah (Georgia Institute of Technology)

We develop a novel measure of firm-level inflation exposure by applying a deep learning approach to firms’ earnings conference call transcripts. Our methodology not only identifies sentences that discuss price changes, but also differentiates price increases from price decreases, input prices from output prices, and future changes from past changes. To validate our methodology, we show that the correlation between our aggregate measure and PPI is 0.75. In the cross section, firms that have higher inflation exposure experience a strong negative stock reaction to earnings calls. This negative reaction is mostly driven by discussions on input price increases. Firms’ ex ante market power attenuates the negative market reaction. Consistent with the market reaction, firms with higher inflation exposure have higher future costs of goods sold. We also observe a negative drift in the firm’s stock return after the earnings call, suggesting that it takes time for investors to fully incorporate firm-level inflation exposure into stock prices

Jul 20

11:15 am - 11:30 am PDT

Break

Jul 20

11:30 am - 12:15 pm PDT

Arbitrage from A Bayesian's Perspective

Presented by: Ayan Bhattacharya (University of Chicago)

This paper builds a model of interactive belief hierarchies to derive the conditions under which judging an arbitrage opportunity requires Bayesian market participants to exercise their higher order beliefs. Methodologically, the approach of the paper is to take the standard asset pricing setup that gives rise to arbitrage and transform it into a Bayesian decision problem faced by a representative market agent. As a Bayesian, such an agent must carry a complete recursion of priors over the uncertainty about future asset payouts, the strategies employed by other market participants that are aggregated in the price, other market participants’ beliefs about the agent’s strategy, other market participants beliefs about what the agent believes their strategies to be, and so on ad infinitum. Defining this infinite recursion of priors — the belief hierarchy so to speak — along with how they update gives the Bayesian decision problem equivalent to the standard asset pricing formulation of the question. In this setting, any update to the belief hierarchy of an agent is one of two kinds: a change in belief about the asset payouts, or a change in belief about the strategies and beliefs employed by other market participants. The main results of the paper show that an arbitrage trade corresponds to special updates of the second kind. When an agent
anticipates market participant responses will be generated using k levels of the belief hierarchy but finds that the actual asset prices are supported by k + 1 or higher levels, there is an arbitrage opportunity. It is shown that the presence of arbitrage depends on both the responsiveness of the market aggregation mapping and the degree of optimality of the belief hierarchies employed by market agents. The paper connects the foundations of finance to the foundations of game theory by identifying a bridge from market arbitrage to market participant belief hierarchies.

Jul 20

12:15 pm - 1:30 pm PDT

Lunch

Jul 20

1:30 pm - 2:15 pm PDT

Conditional Latent Factor Models Via Econometrics-Based Neural Networks

Presented by: Hao Ma (Swiss Finance Institute and University of Lugano)

I develop a hybrid methodology that incorporates an econometric identification strategy into artificial neural networks when studying conditional latent factor models. The time-varying betas are assumed to be unknown functions of numerous firm characteristics, and the statistical factors are population cross-sectional OLS estimators for given beta values. Hence, identifying betas and factors boils down to identifying only the function of betas, which is equivalent to solving a constrained optimization problem. For estimation, I construct neural networks customized to solve the constrained optimization problem, which gives a feasible non-parametric estimator for the function of betas. Empirically, I conduct my analysis on a large unbalanced panel of monthly data on US individual stocks with around 30, 000 firms, 516 months, and 94 characteristics. I find that 1) the hybrid method outperforms the benchmark econometric method and the neural networks method in terms of explaining out-of-sample return variation, 2) betas are highly non-linear in firm characteristics, 3) two conditional factors explain over 95% variation of the factor space, and 4) hybrid methods with literature-based characteristics (e.g., book-to-market ratio) outperform ones with COMPUSTAT raw features (e.g., book value and market value), emphasizing the value of academic knowledge from an angle of Man vs. Machine.

Jul 20

2:15 pm - 2:30 pm PDT

Break

Jul 20

2:30 pm - 3:15 pm PDT

TBA

Presented by: Kenneth Judd (Hoover Institution)