Expected Asset Returns Research Articles

Estimating Expected Asset Returns with the Present Value Model of Consumption and Fed Forecasts

Abstract This paper utilizes Greenbook forecasts of consumption and income to estimate expected asset returns through a present value model of consumption. The study finds that, despite the valuable information contained in Greenbook forecasts, the expected asset returns obtained from this approach do not provide meaningful insights into future asset returns. This contrasts with previous literature suggesting predictability using the present-value model.

Multitrend Conditional Value at Risk for Portfolio Optimization.

Trend representation has been attracting more and more attention recently in portfolio optimization (PO) via machine learning methods. It adopts concepts and phenomena from the field of empirical and behavioral finance when little prior knowledge is obtained or strict statistical assumptions cannot be guaranteed. It is used mostly in estimating the expected asset returns, but hardly in measuring risk. To fill this gap, we propose a novel multitrend conditional value at risk (MT-CVaR), which embeds multiple trends and their influences in CVaR. Besides, we propose a novel PO model with this MT-CVaR as the risk metric and then design a solving algorithm based on the interior point method to compute the portfolio. Extensive experiments on six benchmark datasets from diverse financial markets with different frequencies show that MT-CVaR achieves the state-of-the-art investing performance and risk management.

Robust and Sparse Portfolio: Optimization Models and Algorithms

The robust and sparse portfolio selection problem is one of the most-popular and -frequently studied problems in the optimization and financial literature. By considering the uncertainty of the parameters, the goal is to construct a sparse portfolio with low volatility and decent returns, subject to other investment constraints. In this paper, we propose a new portfolio selection model, which considers the perturbation in the asset return matrix and the parameter uncertainty in the expected asset return. We define three types of stationary points of the penalty problem: the Karush–Kuhn–Tucker point, the strong Karush–Kuhn–Tucker point, and the partial minimizer. We analyze the relationship between these stationary points and the local/global minimizer of the penalty model under mild conditions. We design a penalty alternating-direction method to obtain the solutions. Compared with several existing portfolio models on seven real-world datasets, extensive numerical experiments demonstrate the robustness and effectiveness of our model in generating lower volatility.

EXAMINING THE IMPACT OF DEBT MATURITY TIME, EXPECTED RETURN AND VOLATILITY ON PROBABILITY OF DEFAULT IN CREDIT RISK MODELLING: THE CASE OF MERTON AND MKMV MODELS

In order to model default risk, this article examines the impact of debt maturity time, volatility, and expected asset return on probability of default (PD). The study compares the probability of default produced by the Merton and Moody's KMV (MKMV) methodologies and add modifying time, volatility, and expected return on assets to see how they affect the probabilities of default produced. It utilizes the balance sheet from Apple Inc. (AAPL) recorded from 2019 September 29 to 2022 September 29 for the current and total liabilities and asset values in order to calculate the Probability of Default. The process begins by determining the distances to default (DD) for Merton and MKMV using the balance sheet, and then use the DDs to determine the likelihood of default (PD). Results indicates that, the MKMV approach compares favorably to the Merton approach.

Gone with the Vol: A Decline in Asset Return Predictability During the Great Moderation

We document and examine a significant shift in the comovement of asset returns and macroeconomic volatility during the Great Moderation. Strong U.S. stock and bond return predictability from several macroeconomic volatility series before 1982 was followed by a significant predictability decline during the Great Moderation (1982–2008). These findings are robust to alternative empirical specifications and out-of-sample tests. In a calibrated equilibrium model with time-varying volatility, the predictability decline requires changes in several model elements. Lower return predictability is consistent with stronger policy responses to inflation and output, a larger slope in the New Keynesian Phillips curve, and reduced sensitivity of both macroeconomic and financial variables to a volatility factor. Our results contribute to the examination of macroeconomic volatility as a driver of expected asset returns and the instability in predictive regressions. We further identify sources of the Great Moderation using asset price dynamics. This paper was accepted by Tomasz Piskorski, finance. Supplemental Material: Data files and the online appendix are available at https://doi.org/10.1287/mnsc.2022.4429 .

How the Closure of a U.S. Tax Loophole May Affect Investor Portfolios.

In the United States, exchange-traded funds can defer capital gains taxes of their investors by taking advantage of a legal loophole. To quantify the impact of this tax loophole on investor portfolios, we study a rank-dependent expected utility model. We develop an approximation formula for the sensitivity of the optimal investment strategy with respect to changes in the expected asset returns. By applying this approximation formula, we are able to quantitatively estimate how much investor portfolios may change depending on the investment horizon if the tax loophole is closed.

“Beta” with “Size Premium” an Augmented Approach in the Frontier Equity Market: Evidence from Dhaka Stock Exchange

Abstract Traditionally, firm size has adopted in numerous heuristic asset pricing models as a determining factor of expected stock returns. So far as like systematic risk “beta”, there is diminutive consensus over the magnitude and firmness of the “size” premium. Converging on the controversy this article attempts to examine the traditional Capital Asset Pricing Model (CAPM) and “size” augmented CAPM in the Dhaka Stock Exchange (DSE). The goal of this article is to examine the impact of an overall market factor and factor related to the firm size risk on expected stock returns at the portfolio level. Our sample encompasses non-financial stocks listed in DSE, with daily observations starting from January 2014 to December 2018. Depending on Market Capitalization and Book-to-Market Ratios we construct nine different portfolios, Ordinary Least Square (OLS) regression methodology is used to examine the models. Unlike common reckoning, we observe the strong existence of the “size” effect in frontier equity market DSE and has a tangible impact on explaining expected stock returns at the portfolio level. Additionally, the “size” augmented Capital Asset Pricing Model explains DSE better than the standard CAPM, may indeed be a good tool for a realistic assessment of the expected asset returns, and can improve the description of equilibrium in the Frontier equity market DSE. JEL classification numbers: E44, G11, G12. Keywords: Capital Asset Pricing Model (CAPM), Size Premium, Frontier Equity Market.

Moneyness, Underlying Asset Volatility, and the Cross-Section of Option Returns

Abstract We study the effect of an asset’s volatility on the expected returns of European options on the asset. Deriving predictions from a stochastic discount factor model, we show that the effect depends on whether variations in the asset’s volatility are driven by systematic or idiosyncratic volatility. While idiosyncratic-volatility-induced variations only affect the option elasticity, systematic-volatility-induced variations also oppositely affect the expected return of the asset. Since the expected asset return (elasticity) effect dominates for options with more linear (non-linear) payoffs, systematic volatility prices sufficiently in-the-money (out-of-the-money) options with the opposite (same) sign as idiosyncratic volatility. Using single-stock calls as test assets, double-sorted portfolios and Fama–MacBeth (1973) regressions broadly support the model’s predictions.

A Robust Test for Monotonicity in Asset Returns

Abstract In this paper, we propose a robust test of monotonicity in asset returns that is valid under a general setting. We develop a test that allows for dependent data and is robust to conditional heteroskedasticity or heavy-tailed distributions of return differentials. Many postulated theories in economics and finance assume monotonic relationships between expected asset returns and certain underlying characteristics of an asset. Existing tests in literature fail to control the probability of a type 1 error or have low power under heavy-tailed distributions of return differentials. Monte Carlo simulations illustrate that our test statistic has a correct empirical size under all data-generating processes together with a similar power to other tests. Conversely, alternative tests are nonconservative under conditional heteroskedasticity or heavy-tailed distributions of return differentials. We also present an empirical application on the monotonicity of returns on various portfolios sorts that highlights the usefulness of our approach.

How Wall Street's "Dirty Little Secret" Affects Investor Portfolios

In the United States, exchange-traded funds can defer capital gains taxes of their investors by taking advantage of a legal loophole, sometimes referred to as Wall Street's dirty little secret. To quantify the impact of this tax loophole on investor portfolios, we study a rank-dependent expected utility model. We develop an approximation formula for the sensitivity of the optimal investment strategy with respect to changes in the expected asset returns. By applying this approximation formula, we are able to quantitatively estimate how much investor portfolios may change depending on the investment horizon if the tax loophole is closed.

Mean-variance portfolio optimization based on ordinal information

We propose a new approach to integrate qualitative views, in particular ordering relations among expected asset returns, in the well-known Black-Litterman (BL) framework. We assume investor views to be stochastic and adapt the BL-formula for the posterior expectation of asset returns, conditioned on ordering information. The new estimator is computed by applying an importance sampling technique. Using data from the EUROSTOXX 50 and the S&P 100, respectively, we empirically evaluate the forecast quality of our new approach in comparison to existing, but methodologically different, approaches from the literature and assess the performance of our model in a mean-variance portfolio context. We find that our approach mostly achieves the highest predictive power, irrespective of the dataset, the assumed level of accuracy of the ordering information, and mostly irrespective of the investor’s confidence in the qualitative view, even though the improvement resulting from our approach is moderate. We observe a similar behaviour in the context of portfolio performance analysis.

Demographics, Wealth, and Global Imbalances in the Twenty-First Century

We use a sufficient statistic approach to quantify the general equilibrium effects of population aging on wealth accumulation, expected asset returns, and global imbalances. Combining population forecasts with household survey data from 25 countries, we measure the compositional effect of aging: how a changing age distribution affects wealth-to-GDP, holding the age profiles of assets and labor income fixed. In a baseline overlapping generations model this statistic, in conjunction with cross-sectional information and two standard macro parameters, pins down general equilibrium outcomes. Since the compositional effect is positive, large, and heterogeneous across countries, our model predicts that population aging will increase wealth-to-GDP ratios, lower asset returns, and widen global imbalances through the twenty-first century. These conclusions extend to a richer model in which bequests, individual savings, and the tax-and-transfer system all respond to demographic change.

Demographics, Wealth, and Global Imbalances in the Twenty-First Century

We use a sufficient statistic approach to quantify the general equilibrium effects of population aging on wealth accumulation, expected asset returns, and global imbalances. Combining population forecasts with household survey data from 25 countries, we measure the compositional effect of aging: how a changing age distribution affects wealth-to-GDP, holding the age profiles of assets and labor income fixed. In a baseline overlapping generations model this statistic, in conjunction with cross-sectional information and two standard macro parameters, pins down general equilibrium outcomes. Since the compositional effect is positive, large, and heterogeneous across countries, our model predicts that population aging will increase wealth-to- GDP ratios, lower asset returns, and widen global imbalances through the twenty-first century. These conclusions extend to a richer model in which bequests, individual savings, and the tax-and-transfer system all respond to demographic change.

A Theoretical Framework for Law and Macro-Finance

What role (if any) should the design of the legal protections of creditors in bankruptcy law play in the regulatory frameworks for financial markets? In this paper, I consider the effects of such protections within the macro-financial theory of leverage cycles of John Geanakoplos. The central tenet of the theory is that leverage is procyclical. That effect exists because creditors tend to lend more in a credit boom, when their expectations for asset returns are optimistic than in a credit bust, when their expectations are pessimistic. I find that strong creditor rights have strong procyclical effects. In a credit boom, when expected asset returns are higher, leverage decreases, creating incentives for creditors to underwrite additional leverage without a substantial impact on its price. The stronger the rights, the more certain recoveries and, as a result, the incentives to underwrite additional debt. By creating such incentives, strong legal protections of creditors exacerbate the credit boom. In contrast, in a credit bust, when expectations for asset returns are pessimistic, strong creditor rights have procyclical effects because they incentivize creditors to force deleveraging or foreclose to maximize recoveries. On the positive side, the framework explains why countries with stronger creditor rights are more likely to experience a violent credit boom. On the normative side, the policy implication is that, in limited circumstances, the strength of creditor rights should be designed in a countercyclical fashion. Through a countercyclical design of creditor rights, policymakers should encourage creditors to lend into a credit bust but discourage them from lending into a credit boom. Recognizing the practical challenges of countercyclical adaptations of the strength of creditor rights, this paper suggests an alternative time-invariant regulatory framework implementing strict limits on leverage on all actors of the economy, from sovereigns, through banks, firms to households. The goal of the framework is to limit credit booms and smoothen the economic cycle.

Which is the Correct Discount Rate? Arithmetic Versus Geometric Mean

The paper revisits the two major concepts for average historical returns, i. e., the arithmetic mean and the geometric mean, in order to clarify which approach must be used for which application. Conducting a rigorous derivation with a geometric Brownian motion, we can explain that the appropriate discount rate refers to the mean discrete return and, therefore, to the arithmetic mean rather than the often wrongly applied geometric mean. Likewise, the prominent CAPM relationship between the expected asset return and the expected market return is only valid for the arithmetic mean rather than the geometric mean. Using historical data for the German stock index, we illustrate that an inconsistent application can cause severe deviations from the meaningful ex-ante expected performance of an asset, the true discount rate, the true CAPM risk-adjusted return, and the intended performance scenarios of packaged retail and insurance-based investment products (PRIIPs) within the key information documents (KIDs).

Research trends: Forest investments as a financial asset class

This paper serves as an introduction to a Special Issue (SI) of the Journal of Forest Policy and Economics dedicated to forest investment and finance. The Special Issue stemmed from an International Forest Business Conference (IFBC2018) held in Poland on 6–8 June 2018. It includes papers based on speeches at that conference, as well as others on similar topics not presented at the conference. Topics addressed in this Special Issue include timberland investments, forest finance, intensively managed plantations, risk, uncertainty and decision-making, business structures and investment strategy, timber prices and forecasts, forestry contractors, and taxes. Details papers on these subjects follow in this Special Issue.Our finance research overview here provides a historical background and summary of forest investments and finance, focusing on forest equity investments made by institutional investors and in forestry real estate investment trusts, not including public lands or individual small private forest landowners. Forest assets as an investment class have expanded due to analytical advances, ownership changes, globalized markets, and sustainable forestry factors. Key components affecting forestry investments are expected asset returns, portfolio diversification, inflation hedge, and liquidity and natural risks.Topics detailed in this Special Issue include timberland investments, forest finance, intensively managed plantations, risk, uncertainty and decision-making, business structures and investment strategy, timber prices and forecasts, forestry contractors, and taxes. Finally, we identify knowledge gaps regarding forest investments for future research.

Statistical properties of estimators for the log-optimal portfolio

The best constant re-balanced portfolio represents the standard estimator for the log-optimal portfolio. It is shown that a quadratic approximation of log-returns works very well on a daily basis and a mean-variance estimator is proposed as an alternative to the best constant re-balanced portfolio. It can easily be computed and the numerical algorithm is very fast even if the number of dimensions is high. Some small-sample and the basic large-sample properties of the estimators are derived. The asymptotic results can be used for constructing hypothesis tests and for computing confidence regions. For this purpose, one should apply a finite-sample correction, which substantially improves the large-sample approximation. However, it is shown that the impact of estimation errors concerning the expected asset returns is serious. The given results confirm a general rule, which has become folklore during the last decades, namely that portfolio optimization typically fails on estimating expected asset returns.

Ambiguity Aversion and Asset Price Dynamics

We derive the equilibrium asset expected returns when there is ambiguity in asset expected returns, as well as ambiguity in asset return variances. In our model, ambiguity risk is systematic in nature and is non-diversifiable. Under regularity conditions, expected asset returns are linearly increasing in variance risk and ambiguity risk. We show that a beta pricing model can be derived from the equilibrium expected return function, which contains a systematic return factor and an ambiguity portfolio return factor, where the ambiguity portfolio weights are determined within the model. We test our model empirically and we obtain the model-implied results.

Gone With the Vol: A Decline in Asset Return Predictability During the Great Moderation

We document a significant shift in the comovement of asset returns and macroeconomic volatility during the Great Moderation. Strong U.S. stock and bond return predictability from several macroeconomic volatility series before 1982 was followed by a significant predictability decline during the Great Moderation (1982-2008). These findings are robust to alternative empirical specifications and out-of-sample tests. In a calibrated time-varying-volatility equilibrium model, the predictability decline is consistent with changes in monetary policy and shock dynamics. A stronger policy response to inflation and lower cost-push shock variance reduce the sensitivity of macroeconomic variables and asset returns to a persistent volatility factor, explaining the lower predictability. The results contribute to examine macroeconomic volatility as a driver of expected asset returns, and identify sources of the Great Moderation using asset price dynamics.

Asset Pricing with Downside Liquidity Risks

We develop a parsimonious liquidity-adjusted downside capital asset pricing model to investigate whether phenomena such as downward liquidity spirals and flights to liquidity impact expected asset returns. We find strong empirical support for the model. Downside liquidity risk (sensitivity of stock liquidity to negative market returns) has an economically meaningful return premium that is 10 times larger than its symmetric analogue. The expected liquidity level and downside market risk are also associated with meaningful return premiums. Downside liquidity risk and its associated premium are higher during periods of low marketwide liquidity and for stocks that are relatively small, illiquid, volatile, and have high book-to-market ratios. These results are consistent with investors requiring compensation for holding assets susceptible to adverse liquidity phenomena. Our findings suggest that mitigation of downside liquidity risk can lower firms’ cost of capital. This paper was accepted by Lauren Cohen, finance.