Research articles for the 2020-10-12

A Model-Based Approach to Determine the Number of Scenarios and Scenario Probabilities for Loan Loss Provision Calculations Under the Accounting Standards of IFRS 9 and US-GAAP CECL
Blümke, Oliver
The accounting standards of the International Financial Reporting Standards (IFRS) and the United States Generally Accepted Accounting Principles (US-GAAP) require from financial institutions to consider multiple macroeconomic scenarios when calculating loan loss provisions. At present, however, it is unclear how to determine the number of scenarios and scenario probabilities without resorting to - often subjective - expert judgement. The paper discusses a model-based approach and proposes to use hidden Markov models to determine the number of relevant scenarios and scenario probabilities. The tool of the hidden Markov model allows to use established model selection criteria, such as the Akaike information criterion, to decide on the number of scenarios. Hidden Markov models also provide estimates of the transition matrix of the hidden states, which constitute the required conditional scenario probabilities. The tool of the hidden Markov model is discussed by using a time series of defaults from Standard & Poor's.

Anticorruption, Political Connections, and Corporate Cash Policy: Evidence from Politician Downfalls in China
Ren, Haohan,Zhao, Xiaofeng
We examine how firms change their cash policies in response to the downfall of corrupt politicians in China. We find that firms connected to their local government increase cash holdings when high-profile politician downfalls occur in the government. Consistent with the precautionary saving argument, the effect is stronger for firms that have greater investment opportunities or face greater financial constraints. Compared to unaffected firms, affected firms save more cash out of cash flows and have a higher marginal value of cash holdings. Overall, we show that the collapse of firms' political connections has significant impacts on those firms' financial policies.

Are CLO Collateral and Tranche Ratings Disconnected?
Griffin, John M.,Nickerson, Jordan
Between March and August 2020, S&P and Moody's downgraded approximately 25% of the collateral feeding into CLOs. We calculate the value of CLO tranche downgrades to be 2%, modestly increasing to 5.5% when considering negative watches. This paper examines possible explanations for this disconnect in rating actions. We find no evidence that: rating agency model-implied risk disproportionately affect junior tranches, collateral downgrades were too severe as compared to market prices, that CLOs accumulated protective cushions prior to the COVID crisis, or that managers are creating value by purchasing undervalued assets. We find support for both: 1) non-model considerations by rating agencies, as reported values indicate 8% of AAA tranches do not currently meet S&P's modeling criteria, and 2) portfolio managers are trading in a manner to make CLOs appear safer by rating agency criteria. Overall, our findings have current relevance for policymakers as CLOs appear considerably riskier than current ratings suggest.

Bank Risk-Taking and Monetary Policy Transmission: Evidence from China
Li, Xiaoming,Liu, Zheng,Peng, Yuchao,Xu, Zhiwei
We present evidence that tightened bank capital requirements after China implemented the Basel III capital regulations in 2013 have reduced bank risk-taking following expansionary monetary policy shocks. Under the new regulations, a bank can boost its effective capital adequacy ratio (CAR) by raising capital or by increasing the share of lending to low-risk borrowers. Using confidential loan-level data from a large Chinese commercial bank, merged with firm-level data on a large set of manufacturing firms, we document robust evidence that the share of new bank loans extended to state-owned enterprises (SOEs) increased significantly following an expansionary monetary policy shock after 2013, but not before, because SOE loans receive high credit ratings under government guarantees. Since SOEs are on average less productive than private firms, shifts in bank lending toward SOEs reduce aggregate productivity, as our province-level evidence confirms. We construct a two-sector general equilibrium model with bank portfolio choices and show that, under calibrated parameters, banks increase the share of SOE lending in response to monetary policy easing, leading to persistent declines in total factor productivity that partially offset the expansionary effects of monetary policy.

COVID-19 Crisis: The Impact of Trade and Economic Contagion on Developing Countries
Pitigala, Nihal
The ongoing novel coronavirus (COVID-19) pandemic poses an unprecedented challenge to public health on a global scale. As governments are scrambling to safeguard peoples’ health, the contagion has fast extended to the economic sphere, creating an international economic crisis, unique in the scale and characteristics not seen since the Great Depression. COVID-19, unleashed simultaneous global demand and supply shocks, spanning across all nodes and centers of economic activity across the world, as businesses shut down and consumers were confined to contain the contagion. With the global economy interconnected more than ever, the rate of cross-economic contagion is likely to take an unparalleled economic toll. Despite massive stimulus packages being crafted as economic lifelines in some developed economies, global income this year is likely to shrink dramatically with significant adverse transmission effects to developing and emerging markets. The vulnerability of developing countries to the pandemic is particularly acute, with limited capacity to weather the immediate health crisis and expected economic fallout due to high pre-existing poverty rates, larger informal sectors, shallower financial markets, and less fiscal space to broaden the social safety net or to stimulate their domestic economies. A protracted global depression threatens deep disruptive and lasting structural impact to their economies. The resulting risks, uncertainties, and threats to economic and social stability pose a threat to security, particularly in countries already suffering pre-pandemic.

Corporate Social Responsibility in Supply Chain: Green or Greenwashing?
Shi, Yilin,Wu, Jing,Zhang, Yu
Perception regarding a focal firm's corporate social responsibility (CSR) depends not only on itself but also on its known suppliers. This paper provides the first empirical evidence linking CSR and supply chain information disclosure together. Specifically, it uncovers robust evidence that firms greenwash their CSR image via voluntarily disclosing environmentally responsible suppliers while concealing ``bad" ones. Exogenous variations in abnormal temperatures around the world drive variations in the selective disclosure of ``good" suppliers, supporting a causal interpretation of our finding. Supply chain greenwashing is more prevalent for firms who face higher competition, care more about their brand awareness, and for firms that are more profit-driven and held more by institutional investors. The greenwashing behavior mitigates after implementing mandatory CSR disclosure policies. Finally, firms who greenwash supply chains observe both sales and profitability increase, but only for the short-term.

Debt Relief, Liquidity Provision, and Sovereign Bond Spreads
Lang, Valentin,Mihalyi, David,Presbitero, Andrea
To mitigate the effects of the COVID-19 crisis, the international community has endorsed a program suspending debt service payments for poor countries. We study the effects of this NPV-neutral debt relief on sovereign borrowing costs. Using daily data on sovereign bond spreads and the synthetic control method, we show that countries eligible for debt relief experience a larger decline in borrowing costs compared to similar, ineligible countries. This decline is stronger for countries receiving a larger relief, suggesting that the effect works through liquidity provision. By contrast, our results do not support the concern that debt relief could generate stigma.

Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications
David Byrd,Antigoni Polychroniadou

Federated Learning enables a population of clients, working with a trusted server, to collaboratively learn a shared machine learning model while keeping each client's data within its own local systems. This reduces the risk of exposing sensitive data, but it is still possible to reverse engineer information about a client's private data set from communicated model parameters. Most federated learning systems therefore use differential privacy to introduce noise to the parameters. This adds uncertainty to any attempt to reveal private client data, but also reduces the accuracy of the shared model, limiting the useful scale of privacy-preserving noise. A system can further reduce the coordinating server's ability to recover private client information, without additional accuracy loss, by also including secure multiparty computation. An approach combining both techniques is especially relevant to financial firms as it allows new possibilities for collaborative learning without exposing sensitive client data. This could produce more accurate models for important tasks like optimal trade execution, credit origination, or fraud detection. The key contributions of this paper are: We present a privacy-preserving federated learning protocol to a non-specialist audience, demonstrate it using logistic regression on a real-world credit card fraud data set, and evaluate it using an open-source simulation platform which we have adapted for the development of federated learning systems.

Earnings Prediction with Deep Learning
Lars Elend,Sebastian A. Tideman,Kerstin Lopatta,Oliver Kramer

In the financial sector, a reliable forecast the future financial performance of a company is of great importance for investors' investment decisions. In this paper we compare long-term short-term memory (LSTM) networks to temporal convolution network (TCNs) in the prediction of future earnings per share (EPS). The experimental analysis is based on quarterly financial reporting data and daily stock market returns. For a broad sample of US firms, we find that both LSTMs outperform the naive persistent model with up to 30.0% more accurate predictions, while TCNs achieve and an improvement of 30.8%. Both types of networks are at least as accurate as analysts and exceed them by up to 12.2% (LSTM) and 13.2% (TCN).

Electoral Politics and the Allocation of Government Capital
Duchin, Ran,Hackney, John
We construct novel measures of political importance that capture swing and base voters using data from Facebook ad spending, independent political expenditures, the Cook Political Report, and campaign contributions. We find that businesses in politically important states, districts, and sectors receive more government stimulus funds following the onset of the COVID-19 crisis, controlling for funding demand and both health and economic conditions. Survey evidence shows that the tilt in government funding weakens the adverse effects of COVID-19 on employment and small business activity. Similarly, difference-in-difference evidence shows that government funding attenuates the decline in employment and business applications. Overall, we provide novel evidence on the allocative distortions and real effects of electoral politics.

Equity warrant pricing under subdiffusive fractional Brownian motion of the short rate
Foad Shokrollahi,Marcin Marcin Magdziarz

In this paper we propose an extension of the Merton model. We apply the subdiffusive mechanism to analyze equity warrant in a fractional Brownian motion environment, when the short rate follows the subdiffusive fractional Black-Scholes model. We obtain the pricing formula for zero-coupon bond in the introduced model and derive the partial differential equation with appropriate boundary conditions for the valuation of equity warrant. Finally, the pricing formula for equity warrant is provided under subdiffusive fractional Brownian motion model of the short rate.

Identifying causal channels of policy reforms with multiple treatments and different types of selection
Annabelle Doerr,Anthony Strittmatter

We study the identification of channels of policy reforms with multiple treatments and different types of selection for each treatment. We disentangle reform effects into policy effects, selection effects, and time effects under the assumption of conditional independence, common trends, and an additional exclusion restriction on the non-treated. Furthermore, we show the identification of direct- and indirect policy effects after imposing additional sequential conditional independence assumptions on mediating variables. We illustrate the approach using the German reform of the allocation system of vocational training for unemployed persons. The reform changed the allocation of training from a mandatory system to a voluntary voucher system. Simultaneously, the selection criteria for participants changed, and the reform altered the composition of course types. We consider the course composition as a mediator of the policy reform. We show that the empirical evidence from previous studies reverses when considering the course composition. This has important implications for policy conclusions.

Impact of the COVID-19 Pandemic on Insurance Companies in Europe - First Signals
Puławska, Karolina
Action to prevent the spread of the Coronavirus Disease 2019 has been taken internationally. Service companies have been restricted, and a number of sports and cultural events have been postponed or canceled. As a result, the current pandemic has led to global socio-economic disruption. The current economic situation, caused by the pandemic, might significantly affect the functioning of insurance companies in Europe, as the insurance companies are in the delicate position of balancing a claims load with their capital and solvency stability. In this study, we evaluate the effects of the CoronaCrisis on the insurance companies. We use financial statements of insurance companies comprising European insurance companies during 2010-2020. The results unambiguously demonstrate that CoronaCrisis negatively affects the insurance sector’s stability. However, we do not see the effect of CoronaCrisis on the Z-Score ratio. Moreover, our estimation results demonstrate that the CoronaCrisis increase the value of receivables owed to the insurance companies. Therefore, in light of the above, European legislators should discuss how to manage probable financial problems of insurance companies. A lack of proper management would certainly endanger the customers' safety and stability of the sector. Therefore, we confirm that government interventions in European countries needed to prevent the insurance sector from collapse.

Infinite horizon utility maximisation from inter-temporal wealth
Michael Monoyios

We develop a duality theory for the problem of maximising expected lifetime utility from inter-temporal wealth over an infinite horizon, under the minimal no-arbitrage assumption of No Unbounded Profit with Bounded Risk (NUPBR). We use only deflators, with no arguments involving equivalent martingale measures, so do not require the stronger condition of No Free Lunch with Vanishing Risk (NFLVR). Our formalism also works without alteration for the finite horizon version of the problem. As well as extending work of Bouchard and Pham to any horizon and to a weaker no-arbitrage setting, we obtain a stronger duality statement, because we do not assume by definition that the dual domain is the polar set of the primal space. Instead, we adopt a method akin to that used for inter-temporal consumption problems, developing a supermartingale property of the deflated wealth and its path that yields an infinite horizon budget constraint and serves to define the correct dual variables. The structure of our dual space allows us to show that it is convex, without forcing this property by assumption. We proceed to enlarge the primal and dual domains to confer solidity to them, and use supermartingale convergence results which exploit Fatou convergence, to establish that the enlarged dual domain is the bipolar of the original dual space. The resulting duality theorem shows that all the classical tenets of convex duality hold. Moreover, at the optimum, the deflated wealth process is a potential converging to zero. We work out examples, including a case with a stock whose market price of risk is a three-dimensional Bessel process, so satisfying NUPBR but not NFLVR.

Inflation, ECB and short-term interest rates: A new model, with calibration to market data
F. Antonacci,C. Costantini,F. D'Ippoliti,M. Papi

We propose a new model for the joint evolution of the European inflation rate, the European Central Bank official interest rate and the short-term interest rate, in a stochastic, continuous time setting.

We derive the valuation equation for a contingent claim and show that it has a unique solution. The contingent claim payoff may depend on all three economic factors of the model and the discount factor is allowed to include inflation.

Taking as a benchmark the model of Ho, H.W., Huang, H.H. and Yildirim, Y., Affine model of inflation-indexed derivatives and inflation risk premium, (European Journal of Operational Researc, 2014), we show that our model performs better on market data from 2008 to 2015.

Our model is not an affine model. Although in some special cases the solution of the valuation equation might admit a closed form, in general it has to be solved numerically. This can be done efficiently by the algorithm that we provide. Our model uses many fewer parameters than the benchmark model, which partly compensates the higher complexity of the numerical procedure and also suggests that our model describes the behaviour of the economic factors more closely.

Interpretable Neural Networks for Panel Data Analysis in Economics
Yucheng Yang,Zhong Zheng,Weinan E

The lack of interpretability and transparency are preventing economists from using advanced tools like neural networks in their empirical work. In this paper, we propose a new class of interpretable neural network models that can achieve both high prediction accuracy and interpretability in regression problems with time series cross-sectional data. Our model can essentially be written as a simple function of a limited number of interpretable features. In particular, we incorporate a class of interpretable functions named persistent change filters as part of the neural network. We apply this model to predicting individual's monthly employment status using high-dimensional administrative data in China. We achieve an accuracy of 94.5% on the out-of-sample test set, which is comparable to the most accurate conventional machine learning methods. Furthermore, the interpretability of the model allows us to understand the mechanism that underlies the ability for predicting employment status using administrative data: an individual's employment status is closely related to whether she pays different types of insurances. Our work is a useful step towards overcoming the "black box" problem of neural networks, and provide a promising new tool for economists to study administrative and proprietary big data.

Is There a Macro-Announcement Premium?
Ghaderi, Mohammad,Seo, Sang Byung
The VIX barely drops at macro-announcements. This is at odds with virtually all models that attempt to explain the "macro-announcement premium." We point out that the macro-announcement sample is too small, considering the high volatility and fat tail of daily returns. Our small-sample argument jointly explains the return and VIX patterns of macro-announcement days. The estimation based on a statistical model shows that high macro-announcement returns are not a manifestation of high conditional equity premiums, but return innovations that are not averaged out in-sample. Non-announcement days with similar drops in the VIX obtain similar excess returns through asymmetric volatility. Our analysis suggests that the large average macro-announcement return might not be a compensation for perceived uncertainty.

Lessons from Pandemics: Computational agent-based model approach for estimation of downstream and upstream measures to achieve requisite societal behavioural changes
Pradipta Banerjee,Subhrabrata Choudhury

Pandemics such as COVID-19 have lethal potential for inflicting long-lasting cyclic devastations if required preventive, curative and reformative steps are not taken up in time which puts forth mammoth multi-dimensional challenges for survival before mankind. Scientists and policymakers all around are striving to achieve R $\leq$ 1 alongside having less number of CoVID-19 patients. Lockdowns all across the globe have been implemented for the sake of social physical distancing. However, even if the desired R value status is achieved it becomes nowhere near safe. As normal social activity and inter-regional travel resumes, danger of contraction of the virus from undetected asymptomatic carriers and reactivation of the virus in previously affected patients looms over. The virus poses further threat due to its chances of resurgence, its mutative and adaptive nature thereby giving limited medical respite. The problems intensify with increasing population density whilst varying with several socio-economic-geo-cultural and human activity parameters. Such zoonotic pandemics unravel the primary challenges of all countries in securing the general wellbeing of the society. Ensuring a mechanism for policy designs envisaging crisis scenarios through continuous analysis of real-time region-specific data about societal activities and disease/health indicators can be the only solution. An approach perspective is discussed for addressing the tightly-coupled UN Sustainable goals (2, 3, 6, 12 and 13) for developing a general-scale computational agent-based model to estimate the downstream and upstream measures for achieving requisite societal behavioural changes with the prognostic knowledge concerning the conditions and options for future scenarios of stable sustainability.

Limit Order Markets under Asymmetric Information
Bhattacharya, Ayan,Saar, Gideon
We develop a model of dynamic limit order markets under asymmetric information that can be simplified enough to be solved analytically. We find that informed traders tend to “make” liquidity in illiquid markets and “take” liquidity from more liquid markets. Time between arrivals of limit or market orders conveys information, resulting in repricing of orders in the book and generating the frequent cancellations and resubmissions that have become a staple of modern markets. A novel methodological contribution of our approach is its demonstration of the recursive nature of time-to-execution of limit orders that can be used to characterize equilibrium prices.

Off the Grid... and Back Again? The Recent Evolution of American Street Network Planning and Design
Geoff Boeing

This morphological study identifies and measures recent nationwide trends in American street network design. Historically, orthogonal street grids provided the interconnectivity and density that researchers identify as important factors for reducing vehicular travel and emissions and increasing road safety and physical activity. During the 20th century, griddedness declined in planning practice alongside declines in urban form compactness, density, and connectivity as urbanization sprawled around automobile dependence. But less is known about comprehensive empirical trends across US neighborhoods, especially in recent years. This study uses public and open data to examine tract-level street networks across the entire US. It develops theoretical and measurement frameworks for a quality of street networks defined here as griddedness. It measures how griddedness, orientation order, straightness, 4-way intersections, and intersection density declined from 1940 through the 1990s while dead-ends and block lengths increased. However, since 2000, these trends have rebounded, shifting back toward historical design patterns. Yet, despite this rebound, when controlling for topography and built environment factors all decades post-1939 are associated with lower griddedness than pre-1940. Higher griddedness is associated with less car ownership - which itself has a well-established relationship with vehicle kilometers traveled and greenhouse gas emissions - while controlling for density, home and household size, income, jobs proximity, street network grain, and local topography. Interconnected grid-like street networks offer practitioners an important tool for curbing car dependence and emissions. Once established, street patterns determine urban spatial structure for centuries, so proactive planning is essential.

Optimal Trading with Differing Trade Signals
Ryan Donnelly,Matthew Lorig

We consider the problem of maximizing portfolio value when an agent has a subjective view on asset value which differs from the traded market price. The agent's trades will have a price impact which affect the price at which the asset is traded. In addition to the agent's trades affecting the market price, the agent may change his view on the asset's value if its difference from the market price persists. We also consider a situation of several agents interacting and trading simultaneously when they have a subjective view on the asset value. Two cases of the subjective views of agents are considered, one in which they all share the same information, and one in which they all have an individual signal correlated with price innovations. To study the large agent problem we take a mean-field game approach which remains tractable. After classifying the mean-field equilibrium we compute the cross-sectional distribution of agents' inventories and the dependence of price distribution on the amount of shared information among the agents.

Pandemic Exposure, Credit Market Reactions, and Corporate Default Risk
Imerman, Michael B.,Zhao, Ran
This paper provides an innovative theoretical model and empirical evidence for how firm-level pandemic exposure, as an informational shock, increases a firm's credit spread and default risk. We find a positive relationship between pandemic exposure and single-name CDS spreads, and this empirical relationship remains under robustness checks and after controlling for endogeneity. Furthermore, we find that this effect of pandemic risk is more pronounced for firms with higher leverage. COVID-19 exposure has a much more significant economic impact on credit spread than the past pandemics. We also find firm-level pandemic risk reduces CDS spread slope and increases credit spread volatility which indicates that pandemic risk tends to manifests as as short-term shock that results in short term credit spreads trading higher relative to longer term credit spreads and more uncertainty around credit status.

Pay Regulation â€" Is More Better?
Chu, Jenny,Gupta, Aditi,Livne, Gilad
From October 2013, UK law and regulations (the Reform) require periodic binding shareholders’ approval of executive directors’ remuneration policy, as well as enhanced disclosure in remuneration reports. These requirements supplement an ongoing requirement for an annual non-binding vote on compensation outcomes that are detailed in the remuneration report. Using a large sample of listed companies from 2010â€"2017 we investigate whether the Reform has affected pay levels, pay-performance sensitivity, the pay gap between the CEO and other employees, the amount of cash returned to shareholders, and dissent voting on the remuneration report. We find little evidence that the Reform has affected these variables in our sample firms. Using market-based tests we find that market participants anticipated an improvement in corporate governance for some key dates before the Reform came into force. Taken together, the paper’s evidence suggests the Reform has not met its stated objectives.

Prior knowledge distillation based on financial time series
Jie Fang,Jianwu Lin

One of the major characteristics of financial time series is that they contain a large amount of non-stationary noise, which is challenging for deep neural networks. People normally use various features to address this problem. However, the performance of these features depends on the choice of hyper-parameters. In this paper, we propose to use neural networks to represent these indicators and train a large network constructed of smaller networks as feature layers to fine-tune the prior knowledge represented by the indicators. During back propagation, prior knowledge is transferred from human logic to machine logic via gradient descent. Prior knowledge is the deep belief of neural network and teaches the network to not be affected by non-stationary noise. Moreover, co-distillation is applied to distill the structure into a much smaller size to reduce redundant features and the risk of overfitting. In addition, the decisions of the smaller networks in terms of gradient descent are more robust and cautious than those of large networks. In numerical experiments, we find that our algorithm is faster and more accurate than traditional methods on real financial datasets. We also conduct experiments to verify and comprehend the method.

Securities Lending and Trading by Active and Passive Funds
Honkanen, Pekka
I study the market for lending and borrowing securities in the United States. I find that by making securities available for borrowing, mutual funds acquire information about short selling, which they exploit for trading. Funds with discretion in their investment choices rebalance their portfolios away from borrowed stocks, avoiding capital losses on stocks with decreasing prices. Funds also trade more aggressively on stocks with stronger signals. Finally, active funds charge lower lending fees than passive funds, consistent with funds paying for the information with lower fees.

Semi-analytic pricing of double barrier options with time-dependent barriers and rebates at hit
Andrey Itkin,Dmitry Muravey

We continue a series of papers devoted to construction of semi-analytic solutions for barrier options. These options are written on underlying following some simple one-factor diffusion model, but all the parameters of the model as well as the barriers are time-dependent. We managed to show that these solutions are systematically more efficient for pricing and calibration than, eg., the corresponding finite-difference solvers. In this paper we extend this technique to pricing double barrier options and present two approaches to solving it: the General Integral transform method and the Heat Potential method. Our results confirm that for double barrier options these semi-analytic techniques are also more efficient than the traditional numerical methods used to solve this type of problems.

Specilized day trading -- a new view on an old game
V Simovic,V Simovic

After the U.S market earned strong returns in 2003, day trading made a comeback and once again became a popular trading method among traders. Although there is no comprehensive empirical evidence available to answer the question do individual day traders make money, there is a number of studies that point out that only few are able to consistently earn profits sufficient to cover transaction costs and thus make money. The day trading concept of buying and selling stocks on margin alone suggests that it is more risky than the usual going long way of making profit. This paper offers a new approach to day trading, an approach that eliminates some of the risks of day trading through specialization. The concept is that the trader should specialize himself in just one (blue chip) stock and use existing day trading techniques (trend following, playing news, range trading, scalping, technical analysis, covering spreads) to make money.

Static Replication of European Standard Dispersion Options
Bossu, Sebastien,Carr, Peter,Papanicolaou, Andrew
The replication of any European contingent claim by a static portfolio of calls and puts with strikes forming a continuum, formally proven by Carr and Madan (1998), extends to "standard dispersion" options written on the Euclidean norm of a vector of n asset performances. With the help of fractional calculus techniques we derive replicating portfolios for calls, puts and indeed any claim contingent on standard dispersion using vanilla basket calls whose basket weights span an n-dimensional continuum. Consequently multi-asset standard dispersion options admit a "model-free" price enforced by arbitrage, just as single-asset European claims do.

The Costs of Corporate Debt Overhang
Blickle, Kristian,Santos, João A. C.
Firms with debt overhang, measured as total borrowing to cash-flow, experience 2% slower asset growth during ordinary times and up to 3% slower growth during a crisis, compared to similar firms without debt overhang. These patterns extend to a firm's growth in employment and capital expenditures. The effects of debt overhang during the great recession are more pronounced for firms with greater need for external funding, including those that had to refinance during the crisis and those with fewer unused funds in their credit lines. We account for debt-structure endogeneity by showing that overhang correlated with credit line cuts after the failure of a syndicated member bank. The effects of the debt overhang during the great recession, together with early data on the revenue contractions following the COVID-19 outbreak, suggest that the increase in debt overhang could lead to an up to 10% decrease in growth for firms in industries most affected by the economic lock-down.

The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data
Yucheng Yang,Yue Pang,Guanhua Huang,Weinan E

The current knowledge system of macroeconomics is built on interactions among a small number of variables, since traditional macroeconomic models can mostly handle a handful of inputs. Recent work using big data suggests that a much larger number of variables are active in driving the dynamics of the aggregate economy. In this paper, we introduce a knowledge graph (KG) that consists of not only linkages between traditional economic variables but also new alternative big data variables. We extract these new variables and the linkages by applying advanced natural language processing (NLP) tools on the massive textual data of academic literature and research reports. As one example of the potential applications, we use it as the prior knowledge to select variables for economic forecasting models in macroeconomics. Compared to statistical variable selection methods, KG-based methods achieve significantly higher forecasting accuracy, especially for long run forecasts.

The Rise and Fall of Banks: What is the Future for Banks?
Bonaparte, Yosef
The market cap (value) for Facebooks is about $835.2 Billion, while the sum market caps of arguably top 5 Banks combined (J.P Morgan, Morgan Stanley, Bank of America, Wells Fargo and Citi Group) is less; stands at $816.7 Billion. In fact, Facebook cap is 10 times larger than Morgan Stanley, the lead underwriter on the Facebook IPO. What happens? How the value of a young (IPO in 5/2012) and such an average Tech company (less than half of Apple cap $2.21 Billion) exceeds the values of large banks with estimated asset management of $8.29 Trillion combined? Why a company with a relatively small revenue of only $75 Billion exceeds the value of these Banks with a combined revenue of $405 Billion (2019 revenues). The answer is simple, welcome to the tech revolution and good-bye to the industrial revolution. Facebook revenue growth in 2019 was 26.6%, while banks barely 3%; this is the entire story. With an easy access to raise capital via the stock market, and when investors can have zero commission fees via Robinhood, banks struggle. From the agriculture to the industrial revolutions, we are witnessing the fall of the banks, and the rise of the tech revolution. Banks are no longer value stocks; henceforth we brand them as ‘Sunset’ stocks: passed their prime time. This study also enhance our understanding of the valuation companies like Tesla, Amazon and PayPal stocks.

The loss optimisation of loan recovery decision times using forecast cash flows
Arno Botha,Conrad Beyers,Pieter de Villiers

A theoretical method is empirically illustrated in finding the best time to forsake a loan such that the overall credit loss is minimised. This is predicated by forecasting the future cash flows of a loan portfolio up to the contractual term, as a remedy to the inherent right-censoring of real-world `incomplete' portfolios. Two techniques, a simple probabilistic model as well as an eight-state Markov chain, are used to forecast these cash flows independently. We train both techniques from different segments within residential mortgage data, provided by a large South African bank, as part of a comparative experimental framework. As a result, the recovery decision's implied timing is empirically illustrated as a multi-period optimisation problem across uncertain cash flows and competing costs. Using a delinquency measure as a central criterion, our procedure helps to find a loss-optimal threshold at which loan recovery should ideally occur for a given portfolio. Furthermore, both the portfolio's historical risk profile and forecasting thereof are shown to influence the timing of the recovery decision. This work can therefore facilitate the revision of relevant bank policies or strategies towards optimising the loan collections process, especially that of secured lending.

The relationship between driving volatility in time to collision and crash injury severity in a naturalistic driving environment
Behram Wali,Asad Khattak,Thomas Karnowski

As a key indicator of unsafe driving, driving volatility characterizes the variations in microscopic driving decisions. This study characterizes volatility in longitudinal and lateral driving decisions and examines the links between driving volatility in time to collision and crash injury severity. By using a unique real-world naturalistic driving database from the 2nd Strategic Highway Research Program (SHRP), a test set of 671 crash events featuring around 0.2 million temporal samples of real world driving are analyzed. Based on different driving performance measures, 16 different volatility indices are created. To explore the relationships between crash-injury severity outcomes and driving volatility, the volatility indices are then linked with individual crash events including information on crash severity, drivers' pre crash maneuvers and behaviors, secondary tasks and durations, and other factors. As driving volatility prior to crash involvement can have different components, an indepth analysis is conducted using the aggregate as well as segmented (based on time to collision) real world driving data. To account for the issues of observed and unobserved heterogeneity, fixed and random parameter logit models with heterogeneity in parameter means and variances are estimated. The empirical results offer important insights regarding how driving volatility in time to collision relates to crash severity outcomes. Overall, statistically significant positive correlations are found between the aggregate (as well as segmented) volatility measures and crash severity outcomes. The findings suggest that greater driving volatility (both in longitudinal and lateral direction) in time to collision increases the likelihood of police reportable or most severe crash events... ...

Tight Bounds for a Class of Data-Driven Distributionally Robust Risk Measures
Derek Singh,Shuzhong Zhang

This paper expands the notion of robust moment problems to incorporate distributional ambiguity using Wasserstein distance as the ambiguity measure. The classical Chebyshev-Cantelli (zeroth partial moment) inequalities, Scarf and Lo (first partial moment) bounds, and semideviation (second partial moment) in one dimension are investigated. The infinite dimensional primal problems are formulated and the simpler finite dimensional dual problems are derived. A principal motivating question is how does data-driven distributional ambiguity affect the moment bounds. Towards answering this question, some theory is developed and computational experiments are conducted for specific problem instances in inventory control and portfolio management. Finally some open questions and suggestions for future research are discussed.

Torpedo Your Competition: Strategic Reporting and Peer Firm IPO
Billett, Matthew T.,Ma, Mark (Shuai),Yu, Xiaoyun
A firm’s initial public offering (IPO) generates negative externalities for its industry competitors. We show that to mitigate this threat, incumbent firms manage their earnings downwards when industry peers file for an IPO. This lowers incumbent firm valuation multiples, and results in lower offer prices, smaller amounts of capital raised, and a higher withdrawal probability for the IPO firms. Completed IPO firms invest less, hoard more cash, and experience lower profitability, while incumbents experience higher profitability and market share growth. Our results highlight the role of strategic reporting on product market competition and identify a new cost of going public.

Towards Self-Regulating AI: Challenges and Opportunities of AI Model Governance in Financial Services
Eren Kurshan,Hongda Shen,Jiahao Chen

AI systems have found a wide range of application areas in financial services. Their involvement in broader and increasingly critical decisions has escalated the need for compliance and effective model governance. Current governance practices have evolved from more traditional financial applications and modeling frameworks. They often struggle with the fundamental differences in AI characteristics such as uncertainty in the assumptions, and the lack of explicit programming. AI model governance frequently involves complex review flows and relies heavily on manual steps. As a result, it faces serious challenges in effectiveness, cost, complexity, and speed. Furthermore, the unprecedented rate of growth in the AI model complexity raises questions on the sustainability of the current practices. This paper focuses on the challenges of AI model governance in the financial services industry. As a part of the outlook, we present a system-level framework towards increased self-regulation for robustness and compliance. This approach aims to enable potential solution opportunities through increased automation and the integration of monitoring, management, and mitigation capabilities. The proposed framework also provides model governance and risk management improved capabilities to manage model risk during deployment.

Twin Estimates of the Effects of Prenatal Environment, Child Biology, and Parental Bias on Sex Differences in Early Age Mortality
Roland Pongou

Sex differences in early age mortality have been explained in prior literature by differences in biological make-up and gender discrimination in the allocation of household resources. Studies estimating the effects of these factors have generally assumed that offspring sex ratio is random, which is implausible in view of recent evidence that the sex of a child is partly determined by prenatal environmental factors. These factors may also affect child health and survival in utero or after birth, which implies that conventional approaches to explaining sex differences in mortality are likely to yield biased estimates. We propose a methodology for decomposing these differences into the effects of prenatal environment, child biology, and parental preferences. Using a large sample of twins, we compare mortality rates in male-female twin pairs in India, a region known for discriminating against daughters, and sub-Saharan Africa, a region where sons and daughters are thought to be valued by their parents about equally. We find that: (1) prenatal environment positively affects the mortality of male children; (2) biological make-up of the latter contributes to their excess mortality, but its effect has been previously overestimated; and (3) parental discrimination against female children in India negatively affects their survival; but failure to control for the effects of prenatal and biological factors leads conventional approaches to underestimating its effect by 237 percent during infancy, and 44 percent during childhood.

Using Information to Amplify Competition
Wenhao Li

I characterize the consumer-optimal market segmentation in competitive markets with differentiated products. I show that this segmentation is public---in that each firm observes the same market segments---and takes a simple form: in each market segment, there is a dominant firm favored by all consumers in that segment. By segmenting the market, all but the dominant firm maximally compete to poach the consumer's business, setting price to equal marginal cost. Information, thus, is being used to amplify competition. This segmentation simultaneously generates an efficient allocation and delivers to each firm its minimax profit.

Valid t-ratio Inference for IV
David L. Lee,Justin McCrary,Marcelo J. Moreira,Jack Porter

In the single IV model, current practice relies on the first-stage F exceeding some threshold (e.g., 10) as a criterion for trusting t-ratio inferences, even though this yields an anti-conservative test. We show that a true 5 percent test instead requires an F greater than 104.7. Maintaining 10 as a threshold requires replacing the critical value 1.96 with 3.43. We re-examine 57 AER papers and find that corrected inference causes half of the initially presumed statistically significant results to be insignificant. We introduce a more powerful test, the tF procedure, which provides F-dependent adjusted t-ratio critical values.