Affiliation : Rotman School of Management, University of Toronto

Title : In-sample and Out-of-sample Sharpe Ratios of Multi-factor Asset Pricing Models

Abstract : For many multi-factor asset pricing models proposed in the recent literature, their implied tangency portfolios have substantially higher sample Sharpe ratios than that of the value-weighted market portfolio. In contrast, such high sample Sharpe ratio is rarely delivered by professional fund managers. This makes it difficult for us to justify using these asset pricing models for performance evaluation. In this paper, we explore if estimation risk can explain why the high sample Sharpe ratios of asset pricing models are difficult to realize in reality. In particular, we provide finite sample and asymptotic analyses of the joint distribution of in-sample and out-of-sample Sharpe ratios of a multi-factor asset pricing model. For an investor who does not know the mean and covariance matrix of the factors in a model, the out-of-sample Sharpe ratio of an asset pricing model is substantially worse than its in-sample Sharpe ratio. After taking into account of estimation risk, our analysis suggests that many of the newly proposed asset pricing models do not provide superior out-of-sample performance than the value-weighted market portfolio.

Réunion Microsoft Teams

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Research papers from all fields of finance are welcome.

Keynote Speakers
Prof. dr. Stijn Van Nieuwerburgh
Stijn Van Nieuwerburgh is the Earle W.Kazis and Benjamin Schore Professor of Real Estate and Professor of Finance at Columbia University's Graduate School of Business, which he joined in July 2018. His research
lies in the intersection of housing, asset pricing, and macroeconomics. He has published in top journals in Finance and Economics, including Journal of Political Economy, American Economic Review, Econometrica, Review of
Economic Studies, Journal of Finance, Review of Financial Studies, Journal of Financial Economics. He is Editor at the Review of Financial Studies. He is a Faculty Research Associate at the National Bureau of Economic
Research and at the Center for European Policy Research.

Prof. dr. Marike Knoef
Marike Knoef is full Professor of Empirical Microeconomics at Leiden University and Director at Netspar (Network for Studies on Pensions, Aging and Retirement). Her research lies in the fields of pensions, social
security, labor economics and health. Marike is crown member of the Social and Economic Council and likes to bridge the gap between research and practice. She has published in leading journals in Economics,
including Journal of Public Economics, European Economic Review, Journal of Health Economics, Journal of Human Resources, and Journal of Economic Behavior & Organization.

Submission
Full working papers should be sent (in pdf) no later than December 23, 2022 to bfrf2023@gmail.com.
Please include the following information in your email when submitting the paper:
• An abstract of the paper, not exceeding 300 words
• The subfield of finance for which you would like to submit among the following list: Asset Pricing/Management; Corporate Finance/Governance; Banking; Microstructure; International
Finance; Financial Econometrics; Macro-Finance; Systemic Risk; Financial Networks, Quantitative Finance, Insurance, Pensions, Stochastic modelling, Sustainable Finance, Climate Finance, Risk
Management
• Contact information (name, affiliation, email) for all authors
Submissions will be carefully examined by a scientific committee of experts. We intend to ask for every presented paper a discussant. A notice of acceptance for presentation will be sent at the latest
by January 30, 2023.

Organizers
Steven Vanduffel (VUB), Kris Boudt (UGent), Koen Inghelbrecht (UGent), Georges Hübner (ULg), Julien Hambuckers (ULg), Hans Degryse (KU Leuven), Leonardo Iania (UCL), Marc Deloof (UA),
Anneleen Michiels (UHasselt), Kim Oosterlinck (ULB), Laetitia Pozniak (UMons), Jean-Yves Gnabo (UNamur), Anouk Claes (USaint-Louis), David Veredas (Vlerick), Raf Wouters (NBB).

Jean-Bernard Chatelain (Paris school of Economics)

will give a presentation on

Persistence-Dependent Optimal Policy Rules

Abstract:

A policy target (for example inflation) may depend on exogenous cost-push shocks with different degrees of persistence, including non-stationary shocks. For example, these shocks may affect energy or imported prices. The larger the persistence of these exogenous shocks, the larger the welfare losses and the larger the optimal response of policy instrument to these exogenous shocks. This policy maker's response to exogenous shocks is optimal because it decreases the sensitivity of the policy target to the cost-push shock, down to be near zero for very high persistence up to non stationary shocks.

"Sustainable Finance: Effective Communication in Digital Settings"

LFIN contact person: James Thewissen

"Decomposing asymmetric information in equity options"

LFIN contact person: James Thewissen

"Do Managers Walk the Talk on Environmental and Social Issues?"

LFIN contact person: James Thewissen

SPEAKERS:

Glenn Rudebusch

Glenn Rudebusch is a nonresident senior fellow at the Brookings Institution with the Hutchins Center on Fiscal & Monetary Policy and a senior fellow at New York University in the Volatility and Risk Institute of the Stern School of Business. Previously, Dr. Rudebusch spent several decades in Economic Research at the Federal Reserve Board and the Federal Reserve Bank of San Francisco, where he was most recently an Executive Vice President and Senior Policy Advisor. His research has been published American Economic Review, Journal of Political Economy, Journal of Monetary Economics, and Review of Finance

Michael D. Bauer

Michael D. Bauer is a Professor of Economics at Universität Hamburg, and a research fellow at CEPR, CESifo, and the IMFS. He is currently on leave from the Federal Reserve Bank of San Francisco.

Michael's research on macro-finance and climate finance has been published in leading economics and finance journals including the American Economic Review, the Economic Journal, and the Journal of Finance

More information about htis event via this link

will give a presentation on

SAFE Artificial Intelligence in Finance

Abstract:

Financial technologies, boosted by the availability of machine learning models, are expanding in all areas of finance: from payments (peer to peer lending) to asset management (robot advisors) to payments (blockchain coins). Machine learning models typically achieve a high accuracy at the expense of an insufficient explainability. Moreover, according to the proposed regulations, high-risk AI applications based on machine learning must be ``trustworthy'', and comply with a set of mandatory requirements, such as Sustainability and Fairness. To date there are no standardised metrics that can ensure an overall assessment of the trustworthiness of AI applications in finance.
To fill the gap, we propose a set of integrated statistical methods, based on the Lorenz Zonoid tool, that can be used to assess and monitor over time whether an AI application is trustworthy. Specifically, the methods will measure Sustainability (in terms of robustness with respect to anomalous data), Accuracy (in terms of predictive accuracy), Fairness (in terms of prediction bias across different population groups) and Explainability (in terms of human understanding and oversight).
We apply our proposal to an easily downloadable dataset, that concerns financial prices, to make our proposal easily reproducible.

will give a presentation on

Sequential Cauchy Combination Test for Multiple Testing Problems with Financial Applications

Abstract:

We introduce a simple tool to control for false discoveries and identify individual signals when there are many tests, the test statistics are correlated, and the signals are potentially sparse. The tool applies the Cauchy combination test recursively on a sequence of expanding subsets of p-values and is referred to as the sequential Cauchy combination test. While the original Cauchy combination test aims for a global statement over a set of null hypotheses by summing transformed p-values, the sequential version determines which p-values trigger the rejection of the global null. The test achieves strong familywise error rate control and is less conservative than existing controlling procedures when the test statistics are dependent, leading to higher global powers and successful detection rates. As illustrations, we consider two popular financial econometric applications for which the test statistics have either serial dependence or cross-sectional dependence: monitoring drift bursts in asset prices and searching for assets with a nonzero alpha. The sequential Cauchy combination test is a preferable alternative in both cases in simulation settings and leads to higher detection rates than benchmark procedures in empirics.

will give a presentation on

Two is better than one: Regularized shrinkage of large minimum variance portfolios

Abstract:

In this paper we construct a shrinkage estimator of the global minimum variance (GMV) portfolio by a combination of two techniques: Tikhonov regularization and direct shrinkage of portfolio weights. More specifically, we employ a double shrinkage approach, where the covariance matrix and portfolio weights are shrunk simultaneously. The ridge parameter controls the stability of the covariance matrix, while the portfolio shrinkage intensity shrinks the regularized portfolio weights to a predefined target. Both parameters simultaneously minimize with probability one the out-of-sample variance as the number of assets p and the sample size n tend to infinity, while their ratio p/n tends to a constant c>0. This method can also be seen as the optimal combination of the well-established linear shrinkage approach of Ledoit and Wolf (2004, JMVA) and the shrinkage of the portfolio weights by Bodnar, Parolya and Schmid (2018, EJOR). No specific distribution is assumed for the asset returns except of the assumption of finite 4+epsilon moments for any small epsilon>0. The performance of the double shrinkage estimator is investigated via extensive simulation and empirical studies. The suggested method significantly outperforms its predecessor (without regularization) and the nonlinear shrinkage approach in terms of the out-of-sample variance, Sharpe ratio and other empirical measures in the majority of scenarios. Moreover, it obeys the most stable portfolio weights with uniformly smallest turnover.

PDF: https://arxiv.org/abs/2202.06666

will give a presentation on

Asset-Pricing Factors with Economic Targets

Abstract:

We propose a method to estimate latent asset-pricing factors that incorporates economically motivated targets for both cross-sectional and time-series properties of the factors. Cross-sectional targets may capture the shape of loadings (monotonicity of expected returns across characteristic-sorted portfolios) or the pricing span of exogenous state variables (macroeconomic innovations or intermediary-based risk factors). Time-series targets may capture overall expected returns or mispricing relative to a benchmark reduced-form model. Using a large- scale set of assets, we show that these targets nudge risk factors to better span the pricing kernel, leading to substantially higher Sharpe ratios and lower pricing errors than conventional approaches.

PDF: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4344837

will give a presentation on

Detecting and dating possibly distinct structural breaks in the covariance structure of financial assets

Abstract:

This paper aims to identify and date contagion by accounting for possibly distinct structural breaks among the covariance structure of financial assets. We propose an efficient three-steps procedure that applies the Lagrange Multiplier test, in particular the SupLM statistic, among the DCC-GARCH model parameters. Monte Carlo experiments show that our procedure possess good power and accurately detects the location of the true breaking points. We explore contagion between the government bond and stock markets of advanced and emerging economies. Evidence of common shifts in the covariance structure coincides with the European Sovereign Debt Crisis, the Taper Tantrum originated in United States in mid-2013 and the Covid-19 pandemic.

PDF: https://dial.uclouvain.be/pr/boreal/object/boreal:273269

will give a presentation on

Natural rate chimera and bond pricing reality

Abstract : 

We build a novel macro-finance model that combines a semi-structural macroeconomic module with arbitrage-free yield-curve dynamics. We estimate it for the United States and the euro area using a Bayesian approach and jointly infer the real equilibrium interest rate (r∗), trend inflation (π∗), and term premia. Similar to Bauer and Rudebusch (2020, AER), π∗ and r∗ constitute a time-varying trend for the nominal short-term rate in our model, rendering estimated term premia more stable than standard yield curve models operating with time- invariant means. In line with the literature, our r∗ estimates display a distinct decline over the last four decades.

PDF: https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2612~672f094742.en.pdf

Model Risk Quantification in Commercial Banking: A Statistical Framework

Abstract :

A framework for quantifying model risks in commercial banking is proposed. Model Uncertainty is investigated from different angles with the aim to capture risks stemming from the model itself as well as its interaction with wider frameworks. As a first step we investigate model misspecification by assessing erroneous functional forms, ineffective variable selection, and calibration issues. Then the focus moves to model sensitivity to estimate the impact of either portfolio changes and external shocks. Finally, interactions among various models are scrutinized. Our final goal is to derive a distribution of indicators for summarizing the impact of model uncertainty on synthetic measures like bank’s economic, capital, liquidity ratios, and so on. Governance impacts are summarized in terms of the definition of a comprehensive model appetite framework with corresponding tolerance bands. Ex-ante assessment and continuous monitoring allow for a thorough improvement of the whole model risk management process.

Louvain Finance Day – May 17th, 2023

The 2023 edition of the Louvain Finance Day will be dedicated to the role finance in view of the forthcoming climate and energy challenges.
 

• Please find here the detailed programme or download the programme (PDF).

	Jean Boissinot	Banque de France
	Marie Cavitte	FNRS & UCLouvain
	Frederic Degembe	ING Belgium
	Hervé Jeanmart	École polytechnique de Louvain, UCLouvain
	Philippe Wallez	ING Belgium

will give a presentation on

Predictive identification robust confidence sets with application to tail risk measures

Abstract:

This work proposes a novel method for constructing confidence sets for predictions in parametric or semi-parametric models, that are valid for out-of-sample inference and are identification-robust. This procedure involves two steps: first, a simultaneous robust confidence set for the model’s parameters that are hard to identify is constructed through the inversion of an out-of-sample goodness of fit test; second, this set is projected to construct a simultaneous confidence set for predictions. We focus on financial risk measures, specifically on Value at Risk and Expected Shortfall, for which an illustrative example on GARCH returns is provided, along with Monte Carlo simulations for coverage levels. Finally, an application of the proposed method is applied to the Technology Select Sector SPDR Fund’s returns.

will give a presentation on

Intermittent power generation and risk premia on electricity futures markets

Abstract:

We investigate the hidden financial costs of the increase in the share of intermittent renewable power generation in the electricity production mix. Because electricity can not be stored, the prices of the electricity futures contracts that enable industry participants to hedge against potentially large variations in spot prices can not be determined by arbitrage. We develop a model in which electricity retailers buy electricity from conventional and intermittent power generators to match final demand. At equilibrium, futures prices encompass a risk premium which sign and magnitude depend on the market participants' risk exposures. We structurally estimate the parameters of the model using intraday limit order book data on the German/Austrian electricity day-ahead and futures markets (2013-2018). This enables us to propose a counterfactual analysis in which we vary the characteristics of the distribution of intermittent power generation. Our simulations suggest that an increase in the share of intermittent renewable power generation or its volatility raise non-diversifiable risks, resulting in an increase in the risk premium.

will give a presentation on

Value-relevance of crowdfunding risk factors topology: A topic modeling approach

will give a presentation on

Does it pay to be gender-friendly? Evidence from Portfolio Strategies

Abstract : In this study, we examine whether gender-based investment strategies have an impact on financial performance. We consider all existing gender-equality funds and stock indices, matched with their conventional benchmarks, over the 2016-2022 period. First, we assess their risk-adjusted returns, accounting for possible heterogeneity across gender-based strategies. Second, we examine the impact of two exogenous shocks: the Covid-19 crisis and the #MeToo movement. Overall, we find that gender-equality strategies do not provide significantly different risk-adjusted returns, compared to their conventional benchmarks. In addition, we find that gender-equality funds have not been more resilient during the Covid-19 pandemic, and that there is no significant impact of the #MeToo movement. These results do not support the standard “business case” argument for gender-equality as an investment providing better performance or lower risk. However, they do not call into question the promotion of gender diversity at the investment, company or workplace levels, which is desirable in itself.

will give a presentation on

Is it Alpha or Beta? Decomposing Hedge Fund Returns When Models are Misspecified

Abstract:

The decomposition of hedge fund returns is hampered by model misspecification. To address this issue, we develop a novel approach to compare models in a large population of funds. This comparison, which accounts for misspecification-driven estimation errors, sharpens the separation between alpha and beta. Our analysis reveals that: (i) prominent models are as misspecified as the CAPM, (ii) several factors—primarily time-series momentum, variance, carry—capture hedge fund strategies and lower performance, (iii) alpha and beta components correlate negatively and vary substantially across funds, consistent with equilibrium models featuring search costs, and (iv) fund valuation is sensitive to investor sophistication.

PDF: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3661751

will give a presentation on

Deep Learning Name Concentration Risk for Portfolios of Multilateral Development Banks

Abstract:

As institutions that predominantly lend to sovereign borrowers to achieve their development goals, Multilateral Development Banks’ (MDBs) loan portfolios typically consist of a small number of borrowers. The approaches currently applied by rating agencies to account for this exposure to single name concentration risk in the assessment of MDBs’ capital adequacy have been criticized for being overly conservative and thereby restricting MDBs’ lending headroom. In this paper, we suggest a new deep learning approach for the quantification of single name concentrations in MDB portfolios. Based on simulated as well as stylized portfolios that adequately represent MDB portfolios, we demonstrate the accuracy of our new approach and its superior performance compared to existing analytical methods for assessing name concentration risk in small and concentrated portfolios.

will give a presentation on

US monetary policy spillovers to developing countries: the commodity-financial channel

Authors: Erica Perego (CEPII), Vincent Bodart (IRES, UCL), François Courtoy (European Commission)

Abstract:

As commodities are exchanged as international nancial securities, commodity prices are affected by the global financial cycle through international investors' portfolios rebalancing decisions. We label this mechanism as the commodity-nancial channel. With this evidence in mind, this paper reconsiders the macroeconomic adjustments of developing commodity-exporting countries to a US monetary policy shock, highlighting the role of this new channel. We provide a suggestive evidence that commodity-dependent economies are hit more strongly by a US monetary policy the more they are commodity-dependent, while controlling for the standard transmission channels of US monetary policy shocks. We then build a model for a low-income small open economy that is a commodity and manufacturing producer and which has a limited access to international financial markets. By increasing the degree of commodity-dependence, we show that the new transmission mechanism i) amplies more and more the reaction of the economy; while ii) changing qualitatively the sectoral responses. This suggests that commodity-dependent countries are more vulnerable to unfavourable international monetary disturbances than other small open economies. In particular, through the commodity-financial channel, even those small open economies that are disconnected from international financial markets can be affected by the global financial cycle.

will give a presentation on

Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models

Abstract:

In multivariate time series systems, key insights can be obtained by discovering lead-lag relationships inherent in the data, which refer to the dependence between two time series shifted in time relative to one another, and which can be leveraged for the purposes of control, forecasting or clustering. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models. Within our framework, the envisioned pipeline takes as input a set of time series, and creates an enlarged universe of extracted subsequence time series from each input time series, via a sliding window approach. This is then followed by an application of various clustering techniques, (such as k-means++ and spectral clustering), employing a variety of pairwise similarity measures, including nonlinear ones. Once the clusters have been extracted, lead-lag estimates across clusters are robustly aggregated to enhance the identification of the consistent relationships in the original universe. We establish connections to the multireference alignment problem for both the homogeneous and heterogeneous settings. Since multivariate time series are ubiquitous in a wide range of domains, we demonstrate that our method is not only able to robustly detect lead-lag relationships in financial markets, but can also yield insightful results when applied to an environmental data set.

will give a presentation on

A Simple Powerful Test for Global Minimum Variance Portfolio Weights

Abstract:

Realized global minimum variance portfolio (GMVP) weights are computed from inverted realized covariance matrices. Conventional statistical tests for GMVP weights are based on the vector difference between realized GMVP weights and those under the null hypothesis. However, inversion of realized covariance matrices for computing realized GMVP weights amplifies estimation errors which deteriorates both size and power of such tests, especially when the number of intraday returns is not much larger than the number of assets in the GMVP. To resolve this problem, we propose a novel approximation vector to replace the vector difference. Under the null hypothesis this approximation vector has the same limit distribution as the vector difference, but its computation is simple as it does not require matrix inversion. We analytically derive stochastic properties of the approximation vector and suggest the corresponding sta- tistical test. In Monte Carlo simulations we analyze the size and power of our novel test and illustrate its advantages. In the empirical application we show the usefulness of the new test on GMVP weights for practical purposes.

will give a presentation on

Electricity Forward Premium: Renewable Integration and Skewness Preference

Abstract:

This paper presents new components that explain the risk premium priced in electricity forward and futures contracts. These components relate to the inclusion of renewable power sources in electricity markets. We build upon the equilibrium pricing model presented by Bessembinder and Lemmon (2002), which comes from a time wherein intermittent renewable power supply was negligible. We extend their framework by including intermittent supply from zero marginal costs renewable power sources such as wind and solar and by assuming that agents consider mean-variance-skewness preferences instead of mean-variance only. Beyond variance and skewness of wholesale spot prices as components found before, we show that components that relate to the covariance and coskewness between renewable supply and spot prices explain the power forward risk premium as well. We find empirical evidence that these new components are statistically significant and improve the explanatory power of empirical regressions. Our results suggest the importance of considering the asymmetry of renewable supply shocks in explaining electricity forward premiums.

will give a presentation on

Climate stress-testing for mortgage default probability

Abstract:

Extreme natural disasters like tropical cyclones have a low probability of materialising but a high social and economic impact, including spillover to financial institutions. We focus on the mortgage portfolio in Louisiana (one of the most disaster-prone countries in the U.S.) and propose a framework to perform a climate stress testing exercise of the default probability. First, we estimate a dynamic credit scoring model based on a survival analysis incorporating the damage index from the wind speed of tropical cyclones among climate-related variables. Second, we estimate scenarios of wind speed from tropical cyclones with different return periods (up to a scenario of 1-in-1,000 years) and the relative damage index using a power function as a damage function. Our findings suggest that coastline areas are the most exposed to severe damage from tropical cyclones. If the country is exposed to an event with a very large return period of 1-in-1,000 years, the probability of default increases by about nine percentage points compared to a baseline scenario in the absence of tropical cyclones. This finding is, however, mitigated by insurance coverage present in the country.

(Ruhr Universitat Bochum)

will give a presentation on:

A Simple Powerful Test for Global Minimum Variance Portfolio Weights

Realized global minimum variance portfolio (GMVP) weights are computed from inverted realized covariance matrices. Conventional statistical tests for GMVP weights are based on the vector difference between realized GMVP weights and those under the null hypothesis. However, inversion of realized covariance matrices for computing realized GMVP weights amplifies estimation errors which deteriorates both size and power of such tests, especially when the number of intraday returns is not much larger than the number of assets in the GMVP. To resolve this problem, we propose a novel approximation vector to replace the vector difference. Under the null hypothesis this approximation vector has the same limit distribution as the vector difference, but its computation is simple as it does not require matrix inversion. We analytically derive stochastic properties of the approximation vector and suggest the corresponding sta- tistical test. In Monte Carlo simulations we analyze the size and power of our novel test and illustrate its advantages. In the empirical application we show the usefulness of the new test on GMVP weights for practical purposes.

(The University of Warwick)

will give a presentation on :

Noisy Prices and Return-Based Anomalies in Corporate Bonds

We argue that the documented large abnormal returns to investors from corporate bond anomalies such as return reversals and momentum mainly stem from ignoring market microstructure noise in transaction-based bond prices and relying on ad hoc return winsorization. To address these issues, we construct bond data that is largely free of microstructure noise and closely mimics industry-grade quote data. We revisit prior findings in the literature and provide conclusive evidence that return-based anomalies, once properly constructed, generate negligible average returns and alphas. Finally, we show that the considered return-based factors (and their underlying signals) are not related to average bond returns.

• Cesare ROBOTTI : https://www.cesarerobotti.com

(University of Orleans)

will give a presentation on :

Algorithmic vs. Human Portfolio Choice

Robo-advisors that provide investment advice online on the basis of risk profiling questionnaires have recently made a breakthrough in the investment management industry. The validity and reliability of these questionnaires is crucial as profiling inaccuracies can lead to a mismatch between investment proposals and retail investors’ preferences. This paper uses data from a robo-advisor that makes portfolio recommendations to its potential users and lets them choose their risk exposure after having received this recommendation. Comprehensive information about savers’ characteristics allow us to investigate how the robo-advisor’s algorithm maps questionnaire’s answers into recommendations and to what extent users follow or deviate from the recommendation. The results provide evidence that risk profiles recommended by the robo-advisor are qualitatively aligned with financial portfolio theory. Although a variety ofinformation is used by the algorithm, the recommendation is heavily based on answers about financial risk taking. A large majority of users follow the recommendation and as a result are also strongly influenced by their declared propensity to take financial risk. Factors influencing the recommendation like age, financial wealth and short-term liquidity needs are downplayed by savers. Factors not exploited by the algorithm like saving’s goals or professional occupation impact investors’ portfolio choice. Gender, although not taken into account by the algorithm, still influences savers’ choice after controlling for a wealth of potential confoundings.

• Béatrice Boulu-Reshef : http://www.beatrice-boulu-reshef.info

(Ghent University)

will give a presentation on

Choice lists and ‘standard patterns’ of risk-taking

Abstract :
The fourfold pattern of risk attitudes has been called ‘the most distinctive implication of prospect theory’. Here, we compare risk-taking measured with certainty equivalents (CEs) to risk-taking when presenting the choices in the CE-lists one- by-one in a binary choice setup. CE data indicate a clear fourfold pattern. Binary choices, on the other hand, indicate uniform risk aversion for gains, and uniform risk seeking for losses. This surprising result appears pervasive based on 1) an online experiment with rich data; and 2) a meta-analysis of CEs calculated from existing PT estimates. Our results thus challenge the predictive ability of PT beyond the restrictive realm of CEs, which are arguably a poor proxy for most real-world decisions.

• Ranoua BOUCHOUICHA : https://www.ranouabouchouicha.com

Sébastien Pouget

(Toulouse University)

invited by Catherine D'Hondt

will give a presentation on :

Investor Valuation for Socially Responsible Assets: A Willingness to Pay Experiment

Abstract : 

We present an experimental study of investors’ willingness to pay for socially responsible assets. We design an initial public offering experiment in which various assets may be issued with an identical financial risk-return profile but with different intensity and timing of societal benefits. The societal benefits are represented in the experiment by a donation to a charity that materializes only if the asset is issued. In the experiment, subjects attribute a positive value to societal benefits for large but not for low levels of expected donation. Moreover, when the societal benefit occurs along with bad financial performance, assets suffer from a price discount compared to cases in which it occurs with good performance. This implies that utility functions appear to be non-separable in wealth and societal benefit. We offer implications for the design of corporate social responsibility policies and for the pricing of responsible assets.

 Sébastien Pouget

Julien Hambuckers

(Université de Liège)

invited by Nathan Lassance

will give a presentation on :

LASSO-type penalization methods in distributional regression models, with application to hedge funds systemic risk analysis

Abstract : 

For many applications in finance, it is of interest to provide full probabilistic forecasts, which are able to assign plausibilities to each predicted outcome. Therefore, attention is shifting from conditional mean models to probabilistic distributional models capturing location, scale, shape and other aspects of the response distribution. One of the most established models for distributional regression is the generalized additive model for location, scale and shape (GAMLSS). However, in high-dimensional data set-ups, classical fitting procedures for GAMLSS often become rather unstable and methods for variable selection are desirable. 

In this talk, I will present first a regularization approach for high-dimensional data set-ups in the framework of GAMLSS, introduced in Groll et al. (2019). It is designed for linear covariate effects and is based on L1-type penalties. Beyond the conventional least absolute shrinkage and selection operator (LASSO) for metric covariates, we propose also group and fused LASSO-type penalization to deal with categorical predictors. Then, I illustrate the usefulness of this approach in the problem of measuring the systemic risk of hedge funds, where a regression structure in an extreme value regression model allows to escape small-sample issues and provide insights on its dynamics over time.

 Julien Hambuckers

Daniele Massacci (King's College London)
Invited by Nathan Lassance

will give a presentation on :

State-dependent comovement between factor models

Abstract : 

We study regime-specific comovement between two large panels of variables, each exhibiting an approximate factor structure. Within each panel, we identify threshold-type regimes through shifts in the factor loadings. For the resulting regimes, we measure the between-panel comovements generated by the common components. Our measures can then be interpreted as model-implied covariance and correlation under the assumption of an underlying factor structure in each panel. We propose estimators for our measures, derive their asymptotic properties, and develop inferential procedures to test for changes in comovement between different regimes. We then study comovement across financial asset classes and obtain three results for U.S. portfolio stock returns in downside relatively to upside periods: (i) their comovement with U.S. Treasuries becomes stronger and more negative; (ii) they exhibit significant heterogeneous changes in comovement with equity index call and put options; (iii) their comovement with U.S. corporate bond portfolios becomes positive and significant. Risk management implications are discussed.

  Daniele Massacci

Vali Asimit (Bayes Business School) 

will give a presentation on : 

Risk Budgeting under General Risk Measures. 

Abstract : 

We provide an ample characterization for Risk Budgeting/Parity portfolios with general convex and homogeneous risk preferences for long-only portfolios, as well as for long-short portfolios. We propose a more general novel definition of Risk Budgeting/ Parity portfolios that is less restrictive than the classical definition, and it guarantees their existence and uniqueness, at least for the long-only case. This case is shown to always be less risky than the Equal Weighted portfolio and a thorough mathematical characterization of Risk Budgeting/Parity portfolios is also provided. Equivalent properties are concluded for long-short risk budgeting portfolios under some additional conditions. We provide new insights about the Risk Budgeting/Parity portfolios, including that those portfolios are a rich subset of the newly coined set of Generalized Weighted Mean Constrained portfolios that, according to our knowledge, is defined for the first time in this paper. This new class of portfolios contains other portfolios with good performance, e.g., norm constrained and shortsale-constrained portfolios.

9:00-9:30     Registration / Welcome

9:30-10:30   Talk 1: “Gamification and copy trading in finance: an experiment”
[Speaker: Marie-Hélène Broihanne, University of Strasbourg – France]

10:30-11:30  Talk 2: “Nudging investors towards sustainability - Evidence from a
field experiment”
[Speaker: Stefan Zeisberger, Radboud University – The Netherlands & Zurich University – Switzerland]

11:30-12:30  Talk 3: “Perception of Sustainable Investing”
[Speaker: Julia Meyer, Zurich University – Switzerland]

12:30-13:30  Lunch break

13:30-14:30  Talk 4: “A Choice-Based Approach to the Measurement of Inflation
Expectations”
[Speaker: Stefan Trautmann, Heidelberg University - Germany]

14:30-15:30 Talk 5: “Green illusions? Identifying and correcting misperceptions
about the impact of sustainable equity funds”
[Speaker: Gunnar Gutsche, Paderborn University - Germany]

15:30-16:30 Drink

Registration (by May 15) is free but mandatory
(by email to catherine.dhondt@uclouvain.be)

Syrine Ayachi (UNamur) 

will give a presentation on : 

Green Bonds and Competitive Markets: Exploring Incentives for Sustainable Financing. 

Abstract : 

This paper examines the impact of market competition on firms' decisions to issue green bonds, with emphasis on whether competition from firms with previous green bond issuances influences such a decision. Using a Cragg model on a sample of green bond issuers from the MSCI World Index between 2012 and 2023, we find that greater competition significantly increases the likelihood of green bond issuance. Firms are more likely to issue green bonds when a greater proportion of their competitors have already done so, supporting the signaling theory, where firms use green bond issuance to communicate positive information about their financial stability and environmental commitments. Furthermore, competition from firms that have previously issued green bonds leads to an increase in the amount of green bonds issued, while competition from non-issuers has the opposite effect, aligning with the differentiation strategy. Our findings also highlight the influence of corporate governance in green bond issuance, showing that firms with a higher proportion of independent directors and female executives are more likely to adopt green financing. Additionally, firm size plays a significant role in green bond issuance. Large companies dominate the market, with superior access to capital markets, more financial resources, and better investors' confidence. In contrast, smaller firms, despite showing early interest in green bonds, face greater constraints that limit their ability to issue green bonds on a large scale. Macroeconomic factors such as trade openness and inflation also positively influence green bond issuance, and it indicates how the general economic environment affects firms' sustainable financing. These results connect green bond issuance to competitive pressures and provide valuable insights into how competitive forces, firm characteristics, and macroeconomic conditions collectively determine the adoption of sustainable financial practices worldwide. Understanding these dynamics is crucial for policymakers, investors, and business leaders seeking to foster green financing and accelerate the transition to a more sustainable economy.

Klaas Mulier (UGent) 

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The poor, the rich, and the credit channel of monetary policy. 

Abstract : 

Monetary policy can have contrasting effects on economic inequality via distinct channels. We examine an effect working via the credit channel, whereby monetary policy induces heterogeneous access to credit for business owners based on their wealth. Using unique data on business loan applications from small firms, we find that monetary expansions increase the likelihood to approve loan applications, particularly so for low-wealth entrepreneurs, translating to higher future income and wealth. Survey data from 19 euro area countries on loan applications by SMEs confirms these findings, and shows that the effects transmit especially via weakly capitalized and less liquid banks.

Topic : Navigating through Economic and Financial Uncertainty 

Room : Louvain-la-Neuve, Pl. Cardinal Mercier, Socrate 11 

Agenda : 

4:30 Introduction by Prof. Frédéric Vrins (Chairman of LFIN, UCLouvain, LIDAM) & Philippe Wallez (Head of ESG & Public Affairs, ING)

4:45 Title to be announced by Pierre Wunsch (Governor of the National Bank of Belgium)

Abstract to be announced

5:15 Title to be announced by Charlotte de Montpellier (Senior Economist, ING)

Abstract to be announced

5:45 Disruption by Design: America and the Emerging Global Order by Matthew Lehrfeld (Senior Director at American Chamber of Commerce in Belgium).

President Trump’s second administration isn’t just rewriting U.S. foreign policy; it is rewriting the rules of global business. Europe now faces a world where its partnership with America is uncertain, China is ascendant, and the old playbook no longer applies. In this environment, it is not the scrappy start-ups but the global giants with capital, reach, and political leverage that are best positioned to turn disruption into dominance.

6:15 Questions & Answers
6:45 Cocktail
7:45 End
 

Jean-Baptiste Hasse

(Université Aix-Marseille)

will give a presentation on 

ESG Mutual Fund Attributes and Investor Behavior

Abstract: 

funds. Specifically, we examine whether financial and extrafinancial attributes enter the investor’s decision problem additively or interact multiplicatively. We derive the return elasticity of investor demand under both hypotheses, which yields distinct predictions for the flow-performance relationship. Using a dataset of 6,965 European active equity mutual funds from August 2018 to June 2025, we empirically test these competing hypotheses. Our results indicate that the sensitivity of fund flows to lagged returns is greater for ESG funds than for conventional funds. Accordingly, we reject specifications in which financial and extrafinancial
attributes enter the investor’s decision problem additively. Our findings provide new insights into investor demand for fund attributes and ESG-washing practices.
Keywords: Sustainable investing; Mutual funds; Investor behavior; Cash flows.

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Gaëlle Le Fol 

(Université Paris Dauphine) 

will give a presentation on 

Who Measures and Who Reacts? ESG Scores, Institutional Demand, and Asset Pricing

Abstract : 

How does ESG information reach asset prices, and why does the answer depend on who produces the rating? Using a causal mediation framework applied to U.S.\ equities over 2016--2025 with Refinitiv and MSCI ratings, we show that approximately 19\% of the negative ESG premium is transmitted through institutional portfolio reallocation --- a lower bound, given the exclusion of neutral investors and the growth of passive management. The decomposition reveals that the market absorbs demand symmetrically regardless of investor type, yet ESG-averse investors respond far more aggressively than ESG-oriented ones, generating an implicit short that dominates the indirect channel. This separation between symmetric market structure and asymmetric behaviour answers the paper's titular questions: \emph{who reacts} is revealed by investor heterogeneity, \emph{who measures} by the provider-specific activation of demand. Quasi-experimental evidence from index reconstitutions confirms causality.

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