Research articles for the 2020-04-06

A Knightian Irreversible Investment Problem
Giorgio Ferrari,Hanwu Li,Frank Riedel

In this paper, we study an irreversible investment problem under Knightian uncertainty. In a general framework, in which Knightian uncertainty is modeled through a set of multiple priors, we prove existence and uniqueness of the optimal investment plan, and derive necessary and sufficient conditions for optimality. This allows us to construct the optimal policy in terms of the solution to a stochastic backward equation under the worst-case scenario. In a time-homogeneous setting - where risk is driven by a geometric Brownian motion and Knightian uncertainty is realized through a so-called "k-ignorance" - we are able to provide the explicit form of the optimal irreversible investment plan.

A Stochastic Approach to Model Housing Markets: The US Housing Market Case
Yilmaz, Bilgi,Kestel, Sevtap
This study aims to estimate the price changes in housing markets using a stochastic process, which is defined in the form of stochastic differential equations (SDEs). It proposes a general SDEs system on the price structure in terms of house price index and mortgage rate to establish an effective process. As an empirical analysis, it applies a calibration procedure to an SDE on monthly S&P/Case-Shiller US National Home Price Index (HPI) and 30-year fixed mortgage rate to estimate parameters of differentiable functions defined in SDEs. The prediction power of the proposed stochastic model is justified through a Monte Carlo algorithm for one-year ahead monthly forecasts of the HPI returns. The results of the study show that the stochastic processes are flexible in terms of the choice of structure, compact with respect to the number of exogenous variables involved, and it is a literal method. Furthermore, this approach has a relatively high estimation power in forecasting the national house prices.

A taxonomy of learning dynamics in 2 x 2 games
Marco Pangallo,James Sanders,Tobias Galla,Doyne Farmer

Do boundedly rational players learn to choose equilibrium strategies as they play a game repeatedly? A large literature in behavioral game theory has proposed and experimentally tested various learning algorithms, but a comparative analysis of their equilibrium convergence properties is lacking. In this paper we analyze Experience-Weighted Attraction (EWA), which generalizes fictitious play, best reply dynamics, reinforcement learning and also replicator dynamics. We provide a comprehensive analytical characterization of the asymptotic behavior of EWA learning in $2\times 2$ games. We recover some well-known results in the limiting cases in which EWA reduces to the learning rules that it generalizes, but also obtain new results for other parameterizations. For example, we show that in coordination games EWA may only converge to the Pareto-efficient equilibrium, never reaching the Pareto-inefficient one; that in Prisoner Dilemma games it may converge to fixed points of mutual cooperation; and that in Matching Pennies games it may fail to converge to any fixed point, following instead limit cycles or chaos.

A weighted finite difference method for subdiffusive Black Scholes Model
Grzegorz Krzyżanowski,Marcin Magdziarz,Łukasz Płociniczak

In this paper we focus on the subdiffusive Black Scholes model. The main part of our work consists of the finite difference method as a numerical approach to the option pricing in the considered model. We derive the governing fractional differential equation and the related weighted numerical scheme being a generalization of the classical Crank-Nicolson scheme. The proposed method has $2-\alpha$ order of accuracy with respect to time where $\alpha\in(0,1)$ is the subdiffusion parameter, and $2$ with respect to space. Further, we provide the stability and convergence analysis. Finally, we present some numerical results.

AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment
Tianping Zhang,Yuanqi Li,Yifei Jin,Jian Li

The multi-factor model is a widely used model in quantitative investment. The success of a multi-factor model is largely determined by the effectiveness of the alpha factors used in the model. This paper proposes a new evolutionary algorithm called AutoAlpha to automatically generate effective formulaic alphas from massive stock datasets. Specifically, first we discover an inherent pattern of the formulaic alphas and propose a hierarchical structure to quickly locate the promising part of space for search. Then we propose a new Quality Diversity search based on the Principal Component Analysis (PCA-QD) to guide the search away from the well-explored space for more desirable results. Next, we utilize the warm start method and the replacement method to prevent the premature convergence problem. Based on the formulaic alphas we discover, we propose an ensemble learning-to-rank model for generating the portfolio. The backtests in the Chinese stock market and the comparisons with several baselines further demonstrate the effectiveness of AutoAlpha in mining formulaic alphas for quantitative trading.

Bolsas en España y USA en 1940-2020. ITBM y S&P (Stock Markets in Spain and USA in 1940-2020. ITBM and S&P)
Fernandez, Pablo,de Apellániz, Eduardo
Spanish Abstract: Se compara la evolución de la Bolsa en España (ITBM) y de la bolsa de USA (S&P composite) desde diciembre de 1940 hasta marzo de 2020. El ITBM fue más rentable que el S&P composite (incluyendo dividendos) sólo en las décadas 1961-1970 y 1991-2000. También se muestra la evolución de la inflación en España y USA y del tipo de cambio.Observando la evolución bursátil de los últimos 80 años, se comprueba que las bolsas españolas y americanas han tenido descensos muy superiores al del mes de marzo de 2020.English Abstract: The evolution of the Spanish Stock Market (ITBM) and the US Stock Market (S&P composite) from December 1940 to March 2020 is compared. The ITBM was more profitable than the S&P composite (including dividends) only in the decades 1961-1970 and 1991-2000.The evolution of inflation in Spain and the USA and of the exchange rate are also shown.Observing the stock market evolution of the last 80 years, it is verified that the Spanish and American stock markets have had declines much higher than in March 2020.

Challenges, Prospects and Role of Insurance on Economic Growth in Bangladesh
Ali, Mohammad
This paper aims to review the role of insurance on economic growth and to analyse challenges and prospects of insurance sector in Bangladesh. Based on the secondary data, this research critically reviews the previous studies to find the contribution of insurance on economic growth and prospects as well as challenges of insurance sector in the context of Bangladesh. Insurance has significant contribution on economic growth of a country that facilitates to create a strong capital base and gain economic independency. The study found that the key problems of this sector are deficiency of publicity, lack of qualified human resource, dearth of marketing policies, absence of business ethics, legal complexities, unskilled agents, poor IT support, insufficient return on investment, lack of transparency, lack of public awareness and traditional management. Therefore, initiating innovative marketing strategies, attracting and retaining talent, developing talent, increasing awareness, adapting information technology (IT), avoiding harmful competition, increasing return on investment, offering diversifies and attracting packages, adapting dynamic management style and implementing effective insurance policy are suggested to overcome the challenges of this sector.

Climate Transition Risk, Climate Sentiments, and Financial Stability in a Stock-Flow Consistent Approach
Dunz, Nepomuk,Naqvi, Asjad,Monasterolo, Irene
Aligning investments to the climate and sustainability targets requires the introduction of stable climate-aligned policies. In this regard, a global Carbon Tax (CT) and a revision of the microprudential banking framework via a Green Supporting Factor (GSF) have been advocated. However, our understanding of the conditions under which a GSF or a CT could contribute to scale up new green investments, or introduce new sources of risk for financial stability, is poor. In addition, the banking sector’s reaction to the policy announcements i.e. the climate sentiments via a revision of the lending conditions, have not been considered yet. Nevertheless, they could significantly affect the policies’ outcomes, credit supply and conditions, and financial stability at the bank and systemic level. We contribute to fill this knowledge gap by developing a Stock-Flow Consistent behavioral model of a high income country that embeds a non-linear adaptive function of banking sector’s climate sentiments. With the model, we assess the impact of the introduction of a GSF and a CT on the greening of the real economy and on the credit market conditions. We analyze the risk transmission channels from the credit market to the economy via loans contracts, and of the reinforcing feedback effects that could drive cascading macro-financial shocks. Our results suggest that the GSF could contribute to scale up green investments only in short term, while introducing potential trade-offs on financial stability. To foster the low-carbon transition while preventing unintended effects on firms non-performing loans and households’ budget, the introduction of a CT should be complemented with welfare measures. Finally, climate sentiments could be a game changer for the low-carbon transition by increasing investments’ alignment and preventing the risk of stranded assets.

Customer Concentration of Targets in Mergers and Acquisitions
Cheng, Mei,Jaggi, Jacob,Young, Spencer
We study how customer concentration of targets impacts the occurrence, structure and performance of M&A deals. We hypothesize that acquirers respond to customer concentration-related risk by (1) placing fewer bids for targets with greater customer concentration and (2) by using more stock payment in their offer. Using data on customer concentration and M&A deals from 1985 to 2016, we find consistent evidence supporting these predictions. We also find that these relations vary predictably with the uncertainty arising from major customer relationships. Finally, we examine whether acquirers are effective in screening targets and structuring deals based on customer concentration. We find that despite measures taken by acquirers, target customer concentration is negatively associated with acquirer post-acquisition abnormal returns. Our findings extend the literature by systematically documenting an important risk factor in M&A decisions and by quantifying the economic consequences of customer concentration.

Director Networks and Firm Value
Bakke, Tor-Erik,Black, Jeff,Mahmudi, Hamed,Linn, Scott C.
Are the professional networks of corporate directors valuable? More connected directors may have better information and more influence, which can increase firm value. However, these directors may also be busy or spread value-decreasing practices. To separate the effect of director networks on firm value from the effect of other value-relevant director attributes, we use the unexpected deaths of directors as a shock to the director networks of interlocked directors. By studying the announcement returns and using a difference-in-differences methodology, we find that this negative shock to director networks reduces firm value. This evidence suggests that director networks are valuable.

ESG investments: Filtering versus machine learning approaches
Carmine de Franco,Christophe Geissler,Vincent Margot,Bruno Monnier

We designed a machine learning algorithm that identifies patterns between ESG profiles and financial performances for companies in a large investment universe. The algorithm consists of regularly updated sets of rules that map regions into the high-dimensional space of ESG features to excess return predictions. The final aggregated predictions are transformed into scores which allow us to design simple strategies that screen the investment universe for stocks with positive scores. By linking the ESG features with financial performances in a non-linear way, our strategy based upon our machine learning algorithm turns out to be an efficient stock picking tool, which outperforms classic strategies that screen stocks according to their ESG ratings, as the popular best-in-class approach. Our paper brings new ideas in the growing field of financial literature that investigates the links between ESG behavior and the economy. We show indeed that there is clearly some form of alpha in the ESG profile of a company, but that this alpha can be accessed only with powerful, non-linear techniques such as machine learning.

Effects of the Affordable Care Act Dependent Coverage Mandate on Health Insurance Coverage for Individuals in Same-Sex Couples
Christopher S. Carpenter,Gilbert Gonzales,Tara McKay,Dario Sansone

A large body of research documents that the 2010 dependent coverage mandate of the Affordable Care Act was responsible for significantly increasing health insurance coverage among young adults. No prior research has examined whether sexual minority young adults also benefitted from the dependent coverage mandate, despite previous studies showing lower health insurance coverage among sexual minorities and the fact that their higher likelihood of strained relationships with their parents might predict a lower ability to use parental coverage. Our estimates from the American Community Surveys using difference-in-differences and event study models show that men in same-sex couples age 21-25 were significantly more likely to have any health insurance after 2010 compared to the associated change for slightly older 27 to 31-year-old men in same-sex couples. This increase is concentrated among employer-sponsored insurance, and it is robust to permutations of time periods and age groups. Effects for women in same-sex couples and men in different-sex couples are smaller than the associated effects for men in same-sex couples. These findings confirm the broad effects of expanded dependent coverage and suggest that eliminating the federal dependent mandate could reduce health insurance coverage among young adult sexual minorities in same-sex couples.

Equity Financing, Equity Lending, and Price Pressure: The Case of DRIP Arbitrage
Ang, Tze Chuan 'Chewie',Chang, Xin (Simba),Hu, Xiaoxiong,Verwijmeren, Patrick
Dividend reinvestment plans (DRIPs) with discount offer shareholders the choice between receiving cash dividends or additional shares at a discount. We provide evidence on DRIP arbitrage where DRIP arbitrageurs extract the DRIP discount through short-term equity borrowing. We show the relation between equity lending, equity financing, and stock returns through DRIP arbitrage. DRIP arbitrage increases search costs in the equity lending market and creates negative price pressure in the stock market around dividend dates. Restrictions on equity lending impede DRIP arbitrage and negatively affect equity financing. Our results are more pronounced when the demand for DRIP arbitrage is higher.

Financial Regulation of Consumer-Facing Fintech in India: Status Quo and Emerging Concerns
Chugh, Beni
This paper attempts to answer the question, how is fintech regulated in India? The paper first analyses the types of consumer-facing fintech activities that are currently prevalent in India. It identifies fourteen types of consumer-facing fintech activities in India. Together these fourteen types of activities constitute a typology of consumer-facing fintech activities in India. The paper further examines and compares the extent of financial regulation applicable to each fintech activity in the typology. A simple index of regulatory oversight is used to rank each fintech activity according to the financial regulation they attract. These rankings are summarised in a schematic to create the regulatory landscape of consumer-facing fintech activities in India. This regulatory landscape presents the status quo of financial regulation applicable to fintech in India. Clarity of the financial regulation applicable to fintech may help policymakers and regulators assess the appropriateness of their regulatory stance. The paper concludes with a discussion on some ways in which the financial regulator’s toolkit may be recalibrated to address the risks and preserve the opportunities attendant to fintech. Finally, by outlining how fintech is regulated in India, the paper hopes to start a discussion on the more pressing policy imperative of how fintech should be regulated in India.

Financing the Sustainable Development Goals
Lagoarde-segot, Thomas
This paper contends that carving out pathways to finance the SDG agenda entails to reconsider tacit assumptions regarding the functioning of financial systems. We first use a history of economic thought perspective to demonstrate the flaws of the loanable fund theory, which has come to underlie SDG finance strategies. We then introduce the alternative endogenous money theory using a consistent theoretical and accounting framework. This allows us to identify and discuss a set of financing mechanisms, which would permit to bridge the SDG budget gap. These mechanisms include the issuing of sovereign green bonds, the modification of the European Central Bank’s collateral framework, changes in capital adequacy ratios, a market of SDG lending certificates and the introduction of rediscounting policies. We back up the discussion with examples from economic history.

Fixed income portfolio optimisation: Interest rates, credit, and the efficient frontier
Richard J. Martin

Fixed income has received far less attention than equity portfolio optimisation since Markowitz' original work of 1952, partly as a result of the need to model rates and credit risk. We argue that the shape of the efficient frontier is mainly controlled by linear constraints, with the standard deviation relatively unimportant, and propose a two-factor model for its time evolution.

Heterogeneity in Corporate Debt Structures and the Transmission of Monetary Policy
Holm-Hadulla, Federic,Thürwächter, Claire
We study how differences in the aggregate structure of corporate debt financing affect the transmission of monetary policy. Using high-frequency financial market data to identify monetary policy shocks in a panel of euro area countries, we find that: bond finance dampens the overall response of firm credit to monetary policy shocks in economies with a high initial share of bond- relative to bank-based finance; this effect weakens, and may even reverse, in economies with a low share of bond financing; and the dampening effect of a larger bond financing share also attenuates the ultimate impact of monetary policy on economic activity. These findings point to corporate bond markets acting as a “spare tire” in situations when bank lending contracts.

How large are Pre-Default Costs of Financial Distress? Estimates from a Dynamic Model
Elkamhi, Redouane,Salerno, Marco
We estimate the costs of financial distress prior to default (pre-default costs) separately from the loss incurred at default (the loss given default) using a dynamic trade-off model of capital structure. We document that pre-default costs are on average equal to 6.5% of firm value per year, which translates into approximately 5.5% of the ex-ante firm value. Our study shows that accounting for pre-default costs significantly improves the ability of a trade-off model to match the empirically observed levels of leverage, default rates and loss incurred at default. Last, we show that the expected pre-default costs of financial distress vary significantly across industries, and are higher for firms that produce durable products.

Inside the Mind of a Stock Market Crash
Stefano Giglio,Matteo Maggiori,Johannes Stroebel,Stephen Utkus

We provide a data-driven analysis of how investor expectations about economic growth and stock market returns changed during the February-March 2020 stock market crash induced by the COVID-19 pandemic. We surveyed wealthy retail investors who are clients of Vanguard in mid-February 2020, around the all-time stock market high, and then again on March 11 and 12, after the stock market had collapsed by over 20%. The average investor turned more pessimistic about the short-run performance of both stock markets and the economy. Investors also perceived higher probability of both further extreme stock market declines and large declines in short-run real economic activity. In contrast, investors' expectations about the long run remained largely unchanged, and if anything improved. Disagreement among investors about economic and stock market outcomes also increased substantially. Our analysis is an input in both the design of the ongoing economic policy response and in further advancing economic theories.

Joint Modelling and Calibration of SPX and VIX by Optimal Transport
Ivan Guo,Gregoire Loeper,Jan Obloj,Shiyi Wang

This paper addresses the joint calibration problem of SPX options and VIX options or futures. We show that the problem can be formulated as a semimartingale optimal transport problem under a finite number of discrete constraints, in the spirit of [arXiv:1906.06478]. We introduce a PDE formulation along with its dual counterpart. The optimal processes can then be represented via the solutions of Hamilton-Jacobi-Bellman equations arising from the dual formulation. A numerical example shows that the model can be accurately calibrated to the SPX European options and the VIX futures simultaneously.

Kernel Estimation of Spot Volatility with Microstructure Noise Using Pre-Averaging
José E. Figueroa-López,Bei Wu

We first revisit the problem of kernel estimation of spot volatility in a general continuous It\^o semimartingale model in the absence of microstructure noise, and prove a Central Limit Theorem with optimal convergence rate, which is an extension of Figueroa and Li (2020) as we allow for a general two-sided kernel function. Next, to handle the microstructure noise of ultra high-frequency observations, we present a new type of pre-averaging/kernel estimator for spot volatility under the presence of additive microstructure noise. We prove Central Limit Theorems for the estimation error with an optimal rate and study the problems of optimal bandwidth and kernel selection. As in the case of a simple kernel estimator of spot volatility in the absence of microstructure noise, we show that the asymptotic variance of the pre-averaging/kernel estimator is minimal for exponential or Laplace kernels, hence, justifying the need of working with unbounded kernels as proposed in this work. Feasible implementation of the proposed estimators with optimal bandwidth is also developed. Monte Carlo experiments confirm the superior performance of the devised method.

Market Reactions to Changes in the Dow Jones Industrial Average Index
Biktimirov, Ph.D., CFA, Ernest N.,Xu, Yuanbin
Purpose âˆ' The purpose of this study is to examine changes in stock returns, liquidity, institutional ownership, analyst following, and investor awareness for companies added to and deleted from the Dow Jones Industrial Average (DJIA) index. Previous studies report conflicting evidence regarding the market reactions to changes in the DJIA index membership.Design/methodology/approach âˆ' This study uses the event study methodology to calculate abnormal returns and trading volume around the announcement and effective days of DJIA index changes from 1929 to 2015. It also tests for significant changes in liquidity, institutional ownership, analyst following, and investor awareness in the 1990â€"2015 period. Multivariate regressions are used to perform a simultaneous analysis of competing hypotheses.Findings â€" This study resolves the mixed results of previous DJIA index papers by documenting different stock price and trading volume reactions over the 1929â€"2015 period. Focusing on the most recent period, 1990â€"2015, the study finds that stocks added to (deleted from) the index experience a significant permanent stock price gain (loss). The observed stock price reaction seems to be associated with changes in liquidity proxies thus lending support for the liquidity hypothesis.Research limitations â€" Limited data availability for the periods prior to 1990 prevents this study from identifying the exact reasons for different stock price and trading volume reactions across sub-periods of the 1929â€"2015 period.Originality/value â€" This study provides the most comprehensive examination of market reactions to changes in the DJIA index and resolves the mixed results of previous studies. A better understanding of market reactions around the DJIA index changes can help both individual and institutional investors with developing effective trading strategies and index managing companies with designing optimal announcement policies.

Modern Information Technologies, Information Costs, and the Cost of Capital
Lai, Sandy,Lin, Chen,Ma, Xiaorong
This paper exploits a quasi-natural experiment - the staggered implementation of the SEC’s EDGAR system in 1993 - 1996 - as an exogenous shock to information dissemination technologies, which plausibly leads to a reduction in information acquisition costs for investors. We find evidence that firms’ costs of equity capital decline significantly after their switch from paper filings to mandatory electronic filings in EDGAR. The effect is stronger for small firms and firms with lower institutional ownership. We identify three transmission channels: liquidity, risk-taking, and corporate governance channels. The switch to EDGAR filing leads to higher stock liquidity, lower firm risk, and less earnings management for these firms.

On the Evolution of Cryptocurrency Market Efficiency
Akihiko Noda

This study examines whether the efficiency of cryptocurrency markets (Bitcoin and Ethereum) evolve over time based on Lo's (2004) adaptive market hypothesis (AMH). In particular, we measure the degree of market efficiency using a generalized least squares-based time-varying model that does not depend on sample size, unlike previous studies that used conventional methods. The empirical results show that (1) the degree of market efficiency varies with time in the markets, (2) the degree of market efficiency varies with time, (2) Bitcoin's market efficiency level is higher than that of Ethereum over most periods, and (3) a market with high market liquidity has been evolving. We conclude that the results support the AMH for the most established cryptocurrency market.

Optimal Behaviour in Solar Renewable Energy Certificate (SREC) Markets
Arvind Shrivats,Sebastian Jaimungal

SREC markets are a relatively novel market-based system to incentivize the production of energy from solar means. A regulator imposes a floor on the amount of energy each regulated firm must generate from solar power in a given period and provides them with certificates for each generated MWh. Firms offset these certificates against the floor and pay a penalty for any lacking certificates. Certificates are tradable assets, allowing firms to purchase/sell them freely. In this work, we formulate a stochastic control problem for generating and trading in SREC markets from a regulated firm's perspective. We account for generation and trading costs, the impact both have on SREC prices, provide a characterization of the optimal strategy, and develop a numerical algorithm to solve this control problem. Through numerical experiments, we explore how a firm who acts optimally behaves under various conditions. We find that an optimal firm's generation and trading behaviour can be separated into various regimes, based on the marginal benefit of obtaining an additional SREC, and validate our theoretical characterization of the optimal strategy. We also conduct parameter sensitivity experiments and conduct comparisons of the optimal strategy to other candidate strategies.

Optimal Iterative Threshold-Kernel Estimation of Jump Diffusion Processes
José E. Figueroa-López,Cheng Li,Jeffrey Nisen

In this paper, we propose a new threshold-kernel jump-detection method for jump-diffusion processes, which iteratively applies thresholding and kernel methods in an approximately optimal way to achieve improved finite-sample performance. We use the expected number of jump misclassifications as the objective function to optimally select the threshold parameter of the jump detection scheme. We prove that the objective function is quasi-convex and obtain a new second-order infill approximation of the optimal threshold in closed form. The approximate optimal threshold depends not only on the spot volatility, but also the jump intensity and the value of the jump density at the origin. Estimation methods for these quantities are then developed, where the spot volatility is estimated by a kernel estimator with thresholding and the value of the jump density at the origin is estimated by a density kernel estimator applied to those increments deemed to contain jumps by the chosen thresholding criterion. Due to the interdependency between the model parameters and the approximate optimal estimators built to estimate them, a type of iterative fixed-point algorithm is developed to implement them. Simulation studies for a prototypical stochastic volatility model show that it is not only feasible to implement the higher-order local optimal threshold scheme but also that this is superior to those based only on the first order approximation and/or on average values of the parameters over the estimation time period.

Optimal periodic dividend strategies for spectrally negative L\'evy processes with fixed transaction costs
Benjamin Avanzi,Hayden Lau,Bernard Wong

Maximising dividends is one classical stability criterion in actuarial risk theory. Motivated by the fact that dividends are paid periodically in real life, $\textit{periodic}$ dividend strategies were recently introduced (Albrecher, Gerber and Shiu, 2011). In this paper, we incorporate fixed transaction costs into the model and study the optimal periodic dividend strategy with fixed transaction costs for spectrally negative L\'evy processes.

The value function of a periodic $(b_u,b_l)$ strategy is calculated by means of exiting identities and It\^o's excusion when the surplus process is of unbounded variation. We show that a sufficient condition for optimality is that the L\'evy measure admits a density which is completely monotonic. Under such assumptions, a periodic $(b_u,b_l)$ strategy is confirmed to be optimal.

Results are illustrated.

PDGM: a Neural Network Approach to Solve Path-Dependent Partial Differential Equations
Yuri F. Saporito,Zhaoyu Zhang

In this paper, we propose a novel numerical method for Path-Dependent Partial Differential Equations (PPDEs). These equations firstly appeared in the seminal work of Dupire [2009], where the functional It\^o calculus was developed to deal with path-dependent financial derivatives contracts. More specificaly, we generalize the Deep Galerking Method (DGM) of Sirignano and Spiliopoulos [2018] to deal with these equations. The method, which we call Path-Dependent DGM (PDGM), consists of using a combination of feed-forward and Long Short-Term Memory architectures to model the solution of the PPDE. We then analyze several numerical examples, many from the Financial Mathematics literature, that show the capabilities of the method under very different situations.

Performance Peer Groups in CEO Compensation Contracts
Bakke, Tor-Erik,Mahmudi, Hamed,Newton, Ashley N.
We take advantage of comprehensive panel data available as a result of the 2006 SEC disclosure rules on relative performance evaluation (RPE) to (i) better understand how firms choose performance peer groups used in CEO RPE contracts and (ii) to investigate the causal impact of mandatory disclosure on the peer selection process. We find that while firms for the most part choose performance peers to better identify their CEOs’ impact on firms’ performance, they also tend to select underperforming peers. Dynamically, we find that peers that are added and retained every year are weaker than ones that were not chosen. These findings suggest managers may have some influence on the choice of performance peers. Lastly, using a quasi-natural experiment we find that the enhanced disclosure did not affect the tendency of firms to select underperforming peers.

Predicting Labor Shortages from Labor Demand and Labor Supply Data: A Machine Learning Approach
Nikolas Dawson,Marian-Andrei Rizoiu,Benjamin Johnston,Mary-Anne Williams

This research develops a Machine Learning approach able to predict labor shortages for occupations. We compile a unique dataset that incorporates both Labor Demand and Labor Supply occupational data in Australia from 2012 to 2018. This includes data from 1.3 million job advertisements (ads) and 20 official labor force measures. We use these data as explanatory variables and leverage the XGBoost classifier to predict yearly labor shortage classifications for 132 standardized occupations. The models we construct achieve macro-F1 average performance scores of up to 86 per cent. However, the more significant findings concern the class of features which are most predictive of labor shortage changes. Our results show that job ads data were the most predictive features for predicting year-to-year labor shortage changes for occupations. These findings are significant because they highlight the predictive value of job ads data when they are used as proxies for Labor Demand, and incorporated into labor market prediction models. This research provides a robust framework for predicting labor shortages, and their changes, and has the potential to assist policy-makers and businesses responsible for preparing labor markets for the future of work.

Productivity propagation with networks transformation
Satoshi Nakano,Kazuhiko Nishimura

We model sectoral production by cascading binary compounding processes. The sequence of processes is discovered in a self-similar hierarchical structure stylized in the economy-wide networks of production. Nested substitution elasticities and Hicks-neutral productivity growth are measured such that the general equilibrium feedbacks between all sectoral unit cost functions replicate the transformation of networks observed as a set of two temporally distant input-output coefficient matrices. We examine this system of unit cost functions to determine how idiosyncratic sectoral productivity shocks propagate into aggregate macroeconomic fluctuations in light of potential network transformation. Additionally, we study how sectoral productivity increments propagate into the dynamic general equilibrium, thereby allowing network transformation and ultimately producing social benefits.

Reconciling Behavioral Finance and Rational Finance
Shirvani, Abootaleb,Fabozzi, Frank J.,Racheva-Iotova, Borjana
In this paper, we explain main concepts of Prospect Theory and Cumulative Prospect Theory within the rational dynamic asset pricing framework. We derive option pricing formulas when asset returns are altered by a generalized Prospect Theory value function or a modified Prelec’s weighting probability function. We introduce new parametric classes for Prospect Theory value functions and probability weighting functions consistent with rational dynamic pricing theory. After the behavioral finance notion of “greed and fear” is studied from the perspective of rational dynamic asset pricing theory, we derive the corresponding option pricing formulas when asset returns follow continuous diffusions or discrete binomial trees. We define mixed subordinated variance gamma process to model asset return and derive the corresponding option pricing formula. Finally, we apply the proposed probability weighting functions to study the greedy or fearful disposition of option traders when asset returns follow a mixed subordinated variance gamma process. The results indicate availability bias and diminishing sensitivity of option traders.

SREP Exercise and Resolution Planning Outcomes as Inside Information under MAR
Sciarrone Alibrandi, Antonella,Malvagna, Ugo
When it comes to banks, disclosure â€" as a means towards market discipline â€" can be considered from the standpoint of both Basel’s Pillar 3 and (in case of listed banks) the Market Abuse Regulation. Especially in the latter context, it is controversial whether the specific layer of information consisting of a) the ECB’s measures taken when performing the yearly Supervisory Review and Evaluation Process (SREP), i.e. the so called SREP decision, and b) the resolution planning (including the setting of MREL levels), which the Single Resolution Board is responsible for, have to be considered as inside information under MAR. As it is apparent, this issue is highly critical when such disclosure reveals that a bank is in financial distress, as far as disclosure could potentially prompt an idiosyncratic crisis. According to the ESMA’s position, in both of the above-mentioned cases each credit institution has to assess, under its own responsibility, whether decisions taken by Supervision and Resolution Authorities, under SREP and resolution planning, actually have to be disclosed. This paper focuses on SREP exercise and resolution planning (including MREL calibration) decisions, assessing their relevance as inside information under the requirements of precision and price-sensitivity set out in Article 7 MAR, and the subsequent existence of an obligation to disclose under Article 17 MAR. The relevance of the SREP’s draft letter is also considered in this respect. Moreover, the paper investigates whether, and to what extent, a single bank’s stability concern can be considered as a «legitimate interest» for delayingdisclosure, either under Article 17(5) MAR (that is, when there is a public interest in delaying disclosure in order to preserve the financial system’s stability) or under Article 17(4) MAR (when delay meets the need of preserving the issuer’s legitimate interests).

Spanning analysis of stock market anomalies under Prospect Stochastic Dominance
Stelios Arvanitis,Olivier Scaillet,Nikolas Topaloglou

We develop and implement methods for determining whether introducing new securities or relaxing investment constraints improves the investment opportunity set for prospect investors. We formulate a new testing procedure for prospect spanning for two nested portfolio sets based on subsampling and Linear Programming. In an application, we use the prospect spanning framework to evaluate whether well-known anomalies are spanned by standard factors. We find that of the strategies considered, many expand the opportunity set of the prospect type investors, thus have real economic value for them. In-sample and out-of-sample results prove remarkably consistent in identifying genuine anomalies for prospect investors.

Statistical estimation of superhedging prices
Jan Obloj,Johannes Wiesel

We consider statistical estimation of superhedging prices using historical stock returns in a frictionless market with d traded assets. We introduce a plugin estimator based on empirical measures and show it is consistent but lacks suitable robustness. To address this we propose novel estimators which use a larger set of martingale measures defined through a tradeoff between the radius of Wasserstein balls around the empirical measure and the allowed norm of martingale densities. We establish consistency and robustness of these estimators and argue that they offer a superior performance relative to the plugin estimator. We generalise the results by replacing the superhedging criterion with acceptance relative to a risk measure. We further extend our study, in part, to the case of markets with traded options, to a multiperiod setting and to settings with model uncertainty. We also study convergence rates of estimators and convergence of superhedging strategies.

The Cost of Bank Regulatory Capital
Plosser, Matthew C.,Santos, João A. C.
The Basel I Accord introduced a discontinuity in required capital for undrawn credit commitments. While banks had to set aside capital when they extended commitments with maturities in excess of one year, short-term commitments were not subject to a capital requirement. We use this difference to infer the price banks are willing to pay to reduce regulatory capital, and to ascertain the role capital regulation plays on the composition of credit in the economy. Our results show that following Basel I, short-term commitments increased as undrawn fees declined (relative to those of long-term commitments). These results are robust and appear to be driven by the Basel I Accord: they are more prevalent among low-capital banks and do not hold in several placebo tests. We estimate that banks are willing to pay at least $0.05 to reduce regulatory capital by one dollar. While this inferred cost might appear to be low, our results show that the relative decline in short-term commitment prices induced by Basel I triggered a large shift in quantities, underscoring the sensitivity of credit to regulatory capital regimes.

The Easy Life of Superstar CEOS: Evidence from Shareholder Proposals and Mutual Fund Voting
David, Thomas,Di Giuli, Alberta,Petit-Romec, Arthur
This paper examines the effect of “superstar” CEOs (i.e. CEOs that win prestigious business awards) on the voting behavior in shareholder proposals. We show that the superstar status strongly affects the outcome of shareholder proposals in favor of the management, both compared to all non-superstar CEOs and to a matched sample of nonâ€"winning CEOs. The superstar status affects the vote of all shareholders in general and of mutual funds in particular and is more pronounced in governance proposals and contested ones, showing that CEOs benefit from their superstar status by becoming immune to changes in governance policies promoted by shareholders.

The Effect of Announcement as the Host of Asian Games Xviii on the Indonesian Stock Market
Gumanti, Tatang Ary
In 2014, Indonesia was announced to host the 2018 XVIII Asian Games, the biggest sports events in Asia. This announcement is expected to have a positive impact on the country’s economy and investors as there would be thousands of spectators from both domestic and overseas. As a direct impact of the event is that Indonesia will prepare all the venue and it has to be build. This study examines whether the announcement is reacted by the capital market participants. To do so, it tests a total of 25 companies in the infrastructure, utility and transportation sector listed on the Indonesia Stock Exchange. A standard event study methodology is employed through examination of the existence of abnormal return surrounding the event. Results show that there are abnormal returns on two days before and two days after the announcement. However, overall, there are no significant abnormal returns in the period before and after the announcement. The study does not find significant difference of abnormal returns between before and after the announcement. In addition, there was no difference in trading volume activity before and after the announcement as the host of the XVIII Asian Games. In summary, the capital market participants do not regard the event to be significant issue that determines their investment decision in the capital market.

The Fellowship of LIBOR: A Study of Spurious Interbank Correlations by the Method of Wigner-Ville Function
Peter B. Lerner

The manipulation of LIBOR by a group of banks became one of the major blows to the remaining confidence in financial industry. Yet, despite an enormous amount of popular literature on the subject, rigorous time-series studies are few. In my paper, I discuss the following hypothesis. Namely, if we should assume for a statistical null, the quotes, which were submitted by the member banks were true, the deviations from the LIBOR should have been entirely random because they were determined by idiosyncratic conditions by the member banks. This hypothesis can be statistically verified. Serial correlations of the rates, which cannot be explained by the differences in credit qualities of the member banks or the domicile Governments, were subjected to correlation tests. A new econometric method--the analysis of the Wigner-Ville function borrowed from quantum mechanics and signal processing--is used and explained for the statistical interpretation of regression residuals.

The Framework for the Prediction of the Critical Turning Period for Outbreak of COVID-19 Spread in China based on the iSEIR Model
Yuan, George Xianzhi,Di, Lan,Gu, Yudi,Qian, Guoqi,Qian, Xiaosong
The goal of this study is to show how we establish a general framework for the prediction of the critical so-called “Turning Period” which would play a very important role in assisting better plans for the time frame of emergence plans, in particular for associated looking forward planning such as the battle with the current spread from pandemics of COVID-19 worldwide. By assessing the performance of prediction by using the iSEIR model for the timeline of the spread’s mechanics of COVID-19 in Wuhan on dates of Feb.6 and Feb.10, 2020 by using the concept of "Turning Time Period (Time Period)" to forecast the time frame for the control of the epidemic outbreak measured by a reduction in the number of people infected, it shows that our iSEIR model (an extension of the SEIR model) works very well to accurately predicted that “the COVID-19 situation in China would peak around mid- to late February as early as February 7, 2020”. This review also shows that the identification of the Turning Time Period is the key to have a successful implementation for emergency plan as it provides a timeline for effective actions and solutions to combat a pandemic by reducing as much unexpected risk as soon as possible. Our study also indicates that the implementation of the emergency program in the practice associated with the “Isolation Control Program (or, say, Wuhan Quarantine Program )” since January23, 2020 by China in national level may be a good experiences by other countries and regions to take a lesson.

The Market for Socially Responsible Investing: A Review of the Developments
Camilleri, Mark Anthony
Purpose: This contribution explains how socially responsible investing (SRI) has evolved in the last few decades and sheds light on its latest developments. It describes different forms of SRI in the financial markets; and deliberates on the rationale for the utilization of positive and negative screenings of listed businesses and public organizations. Design/methodology/approach: A comprehensive literature review suggests that the providers of financial capital are increasingly allocating funds toward positive impact and sustainable investments. Therefore, this descriptive paper provides a factual summary of the proliferation of SRI products in financial markets. Afterwards it presents the opportunities and challenges facing the stakeholders of SRI. Findings: This research presents a historic overview on the growth of SRI products in the financial services industry. It clarifies that the market for responsible investing has recently led to an increase in a number of stakeholders, including contractors, non-governmental organizations (NGOs) and research firms who are involved in the scrutinization of the businesses’ environmental, social and governance (ESG) behaviors.Originality/value: This discursive contribution raises awareness on the screenings of positive impact and sustainable investments. The researcher contends that today’s socially responsible investors are increasingly analyzing the businesses’ non-financial performance, including their ESG credentials. In conclusion this paper puts forward future research avenues in this promising field of study.

The Rise of Corporate Net Lending among G7 Countries: A Firm-Level Analysis
Villani, Davide
In recent decades, corporate net lending has been increasing in several developed countries. This paper discusses the impact of financialisation and income distribution on the level of corporate net lending among G7 countries. We argue that financialisation affects the level of corporate net lending through firms` re-organisation towards a model of accumulation based on the maximisation of “shareholder value” and through the negative impact on investment. Moreover, the reduction in the wage share can increase the capacity of accumulation of liquidity of corporations, increasing the gap between corporate savings and investment, leading to the rise in net lending. We test our hypotheses using panel data of publicly listed non-financial corporations for the period 1990-2015. According to our findings the process of financialisation has a positive impact on the level of net lending after 2001, while the wage share at the firm-level has a strong negative impact on the level of net lending throughout the whole period.

The Rise of Fintech Lending to Small Businesses: Businesses’ Perspectives on Borrowing
Barkley, Brett,Schweitzer, Mark E.
Online lending through fintech firms is a rapidly expanding segment of the financial market that is receiving much attention from investors and increasing scrutiny from regulators. Research is only beginning to assess how fintech firms’ entry is altering the choices and outcomes of small businesses that borrow from them. The Federal Reserve Small Business Credit Survey is a unique data source on the experiences of business owners with new and more traditional sources of credit. We find that the businesses using online lenders are not representative of small and medium-size enterprise in the US. Businesses borrowing online are younger, smaller, and less profitable. Through reaching borrowers less likely to be served by traditional lenders fintech lenders have substantially expanded the small business finance market. We apply treatment effects estimators to flexibly control for composition differences in the borrowers. After controlling for compositional differences between online and bank borrower, we find that loan application amounts are generally smaller with fintech lenders; businesses that receive fintech loans expect more revenue and employment growth than those receiving a bank loan; and businesses that borrow from banks are more satisfied than businesses that borrow online, which are still more satisfied than businesses who were denied credit. These results highlight issues that the financial industry and regulators should examine as fintech lending to small businesses continues to expand.

The Spillover Effect of Customer CEO Myopia on Supplier Firms
Jia, Yifan,Wang, Zheng,Wu, Jing,Zhang, Zilong
This paper shows that customer CEOs’ short-term equity incentives impose a negative spillover effect on the real investment decisions of their supplier firms. Specifically, we find that CEOs’ short-term incentives, measured by CEOs’ vesting equity in a given quarter, are negatively associated with suppliers’ investments in R&D expenditures in the same quarter. The reduction of suppliers’ R&D is not fully explained by the decline in customers’ own R&D or capital expenditures resulting from customer CEOs’ short-term incentives. Furthermore, we find that customer CEOs’ short-term incentives have a less pronounced effect on reducing suppliers’ R&D investments when the customers have less bargaining power, are younger, are considered more trustworthy, or are monitored by blockholders. The effect is also less pronounced when the suppliers have higher capital redeployability. At last, we show that supplier firms also reduce their trade credit offering and inventory cost. Taken together, we argue that customer CEOs’ myopic behavior, induced by their short-term incentives, reduces suppliers’ incentive to commit to relationship-specific investments.

The Total Return and Risk to Residential Real Estate
Eichholtz, Piet,Korevaar, Matthijs,Lindenthal, Thies,Tallec, Ronan
This paper estimates the total rate of return to residential real estate investments based on 120,658 hand-collected archival observations of prices, rents, taxes and costs for individual houses in Paris (1809-1942) and Amsterdam (1900-1979). The annualized real total return, net of costs and taxes, is 4.2% for Paris and 5.0% for Amsterdam, and entirely comes from rental yields. At the property-level, the yield at purchase is an important determinant of the total holding period return, even for longer holding periods. In the short-term, idiosyncratic risk is the dominant component of total risk, but its importance reduces over time.

The Value of ETF Liquidity
Khomyn, Marta,Putniņš, Tālis J.,Zoican, Marius
We model how ETFs compete and set fees. We show that ETF secondary market liquidity plays a key role in determining fees and leads to liquidity clienteles. More liquid ETFs charge higher fees in equilibrium and attract shorter horizon investors that are more sensitive to liquidity than to fees. The higher turnover of these investors sustains the ETF's high liquidity, allowing the ETF to maintain a higher fee and extract a rent. These liquidity rents create a first-mover advantage among ETFs and impact investor welfare. Our empirical tests confirm the presence of liquidity clienteles and show that ETF fee differentials provide a novel measure of the value of liquidity. Our findings resolve the apparent paradox that ETFs with higher fees than their competitors can not only survive, but flourish in equilibrium due to the value of liquidity.

The illiquidity network of stocks in China's market crash
Xiaoling Tan,Jichang Zhao

The stock market of China experienced an abrupt crash in 2015 and evaporated over one third of the market value. Given its associations with fear and fine-resolutions in frequency, the illiquidity of stocks may offer a promising perspective of understanding and even signaling the market crash. In this study, by connecting stocks that mutually explain illiquidity fluctuations, a illiquidity network is established to model the market. It is found that as compared to non-crash days, the market is more densely connected on crash days due to heavier but more homogeneous illiquidity dependencies that facilitate abrupt collapses. Critical socks in the illiquidity network, in particular the ones in sector of finance are targeted for inspection because of their crucial roles in taking over and passing on the losing of illiquidity. The cascading failures of stocks in market crash is profiled as disseminating from small degrees to high degrees that usually locate in the core of the illiquidity network and then back to the periphery. And by counting the days with random failures in previous five days, an early single is implemented to successfully warn more than half crash days, especially those consecutive ones at early phase. Our results would help market practitioners like regulators detect and prevent risk of crash in advance.

What do online listings tell us about the housing market?
Michele Loberto,Andrea Luciani,Marco Pangallo

Traditional data sources for the analysis of housing markets show several limitations, that recently started to be overcome using data coming from housing sales advertisements (ads) websites. In this paper, using a large dataset of ads in Italy, we provide the first comprehensive analysis of the problems and potential of these data. The main problem is that multiple ads ("duplicates") can correspond to the same housing unit. We show that this issue is mainly caused by sellers' attempt to increase visibility of their listings. Duplicates lead to misrepresentation of the volume and composition of housing supply, but this bias can be corrected by identifying duplicates with machine learning tools. We then focus on the potential of these data. We show that the timeliness, granularity, and online nature of these data allow monitoring of housing demand, supply and liquidity, and that the (asking) prices posted on the website can be more informative than transaction prices.

Where Do We Stand in Cryptocurrencies Economic Research? A Survey Based on Hybrid Analysis
Bariviera, Aurelio F.,Merediz-Solà, Ignasi
This survey develops a dual analysis, consisting, first, in a bibliometric examination and, second, in a close literature review of all the scientific production around cryptocurrencies conducted in economics so far. The aim of this paper is twofold. On the one hand, proposes a methodological hybrid approach to perform comprehensive literature reviews. On the other hand, we provide an updated state of the art in cryptocurrency economic literature. Our methodology emerges as relevant when the topic comprises a large number of papers, that make unrealistic to perform a detailed reading of all the papers. This dual perspective offers a full landscape of cryptocurrency economic research. Firstly, by means of the distant reading provided by machine learning bibliometric techniques, we are able to identify main topics, journals, key authors, and other macro aggregates. Secondly, based on the information provided by the previous stage, the traditional literature review provides a closer look at methodologies, data sources and other details of the papers. In this way, we offer a classification and analysis of the mounting research produced in a relative short time span.

Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros
Francisco Blasques,Vladimír Holý,Petra Tomanová

In finance, durations between successive transactions are usually modeled by the autoregressive conditional duration model based on a continuous distribution omitting zero values. Zero or close-to-zero durations can be caused by either split transactions or independent transactions. We propose a discrete model allowing for excessive zero values based on the zero-inflated negative binomial distribution with score dynamics. This model allows to distinguish between the processes generating split and standard transactions. We use the existing theory on score models to establish the invertibility of the score filter and verify that sufficient conditions hold for the consistency and asymptotic normality of the maximum likelihood of the model parameters. In an empirical study of DJIA stocks, we find that split transactions cause on average 63% of close-to-zero values. Furthermore, the loss of decimal places in the proposed approach is less severe than incorrect treatment of close-to-zero values in continuous models.