Research articles for the 2020-09-14

A Computational Approach to Hedging Credit Valuation Adjustment in a Jump-Diffusion Setting
T. van der Zwaard,L.A. Grzelak,C.W. Oosterlee

This study contributes to understanding Valuation Adjustments (xVA) by focussing on the dynamic hedging of Credit Valuation Adjustment (CVA), corresponding Profit & Loss (P&L) and the P&L explain. This is done in a Monte Carlo simulation setting, based on a theoretical hedging framework discussed in existing literature. We look at hedging CVA market risk for a portfolio with European options on a stock, first in a Black-Scholes setting, then in a Merton jump-diffusion setting. Furthermore, we analyze the trading business at a bank after including xVAs in pricing. We provide insights into the hedging of derivatives and their xVAs by analyzing and visualizing the cash-flows of a portfolio from a desk structure perspective. The case study shows that not charging CVA at trade inception results in an expected loss. Furthermore, hedging CVA market risk is crucial to end up with a stable trading strategy. In the Black-Scholes setting this can be done using the underlying stock, whereas in the Merton jump-diffusion setting we need to add extra options to the hedge portfolio to properly hedge the jump risk. In addition to the simulation, we derive analytical results that explain our observations from the numerical experiments. Understanding the hedging of CVA helps to deal with xVAs in a practical setting.

A Remedy for Soaring Executive Pay: Focus Less on it
Wong, Simon C. Y.
This article argues that the increasing focus on pay by policymakers, shareholders, and the media has magnified its importance as a gauge of success for top executives and created a vicious cycle of ever-higher pay demands. It suggests ways for policymakers, shareholders, and boards of directors to de-emphasize monetary incentives, with the view to moderating the rapid ascent of executive compensation.

AI Adoption and Firm Performance: Management versus IT
Alekseeva, Liudmila,Gine, Mireia,Samila, Sampsa,Taska, Bledi
We examine the impact of AI adoption on firm growth, productivity, and investment decisions and explore whether the impact on firm size and policies stems from AI adoption among management ranks or IT specialists. We measure the firm-level AI adoption using the demand for AI-related skills in online job postings. First, we document a positive association between the firm-level AI adoption and the firm's size, Capex, R&D, and total investments. We do not find robust relationships with productivity measures. Second, we find that the adoption of AI skills among managers drives the positive association with growth in sales and market capitalization, as well as with R&D and Capex. AI adoption among IT specialists does not show any robust association with firm outcomes.

Application of a system of indicatirs for assessing the socio-economic situation of a subject based on digital shadows
Olga G. Lebedinskaya

The development of Digital Economy sets its own requirements for the formation and development of so-called digital doubles and digital shadows of real objects (subjects/regions). An integral element of their development and application is a multi-level matrix of targets and resource constraints (time, financial, technological, production, etc.). The volume of statistical information collected for a digital double must meet several criteria: be objective, characterize the real state of the managed object as accurately as possible, contain all the necessary information on all managed parameters, and at the same time avoid unnecessary and duplicate indicators ("information garbage"). The relevance of forming the profile of the "digital shadow of the region" in the context of multitasking and conflict of departmental and Federal statistics predetermined the goal of the work-to form a system of indicators of the socio-economic situation of regions based on the harmonization of information resources. In this study, an inventory of the composition of indicators of statistical forms for their relevance and relevance was carried out on the example of assessing the economic health of the subject and the level of provision of banking services

Availability Bias of Urban and Rural Investors: Relationship Study of the Gujarat State of India
Sachan, Abhishek,Chugan, Pawan K.
Biases challenge ability of investors to make rational decisions. The knowledge of concentration of biases based on demographics of investors may have implications for wealth managers and policy makers. This study focuses on relationship between availability bias and urban-rural residence of individual investors. The study reports that place of residence significantly relates to availability bias. A person belonging to rural areas has higher probability to be susceptible to availability bias. Indian rural population has lower per capita incomes and has lower cushion to absorb financial losses, in such a scenario, cost of being biased is very high, for which this study implicates the requirement of credible and sufficient information sources to reduce the availability bias of investors. Wealth managers, hence, are required to develop different communication skills for rural clients in order to build consensus for optimum investment decisions.

Bank Regulation and Stock Market Stability across Countries
Elkelish, Walaa Wahid,Tucker, Jon
The purpose of this paper is to investigate whether bank capital strength and external auditing requirements influenced international stock market stability during the 2007/2008 global financial crisis. Bank mandatory regulation data are obtained from the World Bank database, while stock market stability is gauged for 385 listed banks across 43 countries by means of generalised least squares regression models. The authors find that mandatory capital strength requirements and the existence of mandatory audit increase stock market stability across countries. Further, more profitable banks increase stock market stability. The results are robust to both country institutional settings and economic freedom characteristics. This paper provides evidence of the impact of bank regulations on stock market stability during the global financial crisis, thereby providing a useful insight for stakeholders to enhance financial regulation and policy.

Conditional Conservatism, Regulation, and Insolvency Risk
Zhang, Juan
We develop a new method of assessing conditional conservatism using more detailed data available from the insurance industry. We look at how conditional conservatism affects insolvency risk and the financial strength rating of property-liability (P&L) insurance companies. We also investigate how a change to accounting rules affects conditional conservatism. The P&L insurance industry is a perfect setting for studying accruals because we have specific and detailed firm-year level information about loss accrual development. The data enable us to develop a new method of measuring conditional conservatism, which is based on the concavity of the loss development curve. We study U.S. domiciled P&L insurance companies from 1995 to 2015. We find that the greater the degree of conditional conservatism, the lower is insolvency probability, and the better is the financial strength rating, with other things being constant. The result indicates that regulators and rating agencies reward insurers that voluntarily utilize conditional conservatism accounting strategy. Moreover, we find that the level of conditional conservatism is reduced after the enactment of the Model Audit Rule (MAR). MAR, like the Sarbanes-Oxley Act Section 404, increased board oversight of internal risk management. Our result provides evidence that complying with additional disclosure requirements reduces P&L insurers’ incentives to use conditional conservatism to mitigate regulatory monitoring costs.

Covid-19 impact on cryptocurrencies: evidence from a wavelet-based Hurst exponent
M. Belén Arouxet,Aurelio F. Bariviera,Verónica E. Pastor,Victoria Vampa

Cryptocurrency history begins in 2008 as a means of payment proposal. However, cryptocurrencies evolved into complex, high yield speculative assets. Contrary to traditional financial instruments, they are not (mostly) traded in organized, law-abiding venues, but on online platforms, where anonymity reigns. This paper examines the long term memory in return and volatility, using high frequency time series of eleven important coins. Our study covers the pre-Covid-19 and the subsequent pandemia period. We use a recently developed method, based on the wavelet transform, which provides more robust estimators of the Hurst exponent. We detect that, during the peak of Covid-19 pandemic (around March 2020), the long memory of returns was only mildly affected. However, volatility suffered a temporary impact in its long range correlation structure. Our results could be of interest for both academics and practitioners.

Crime Aggregation, Deterrence, and Witness Credibility
Harry Pei,Bruno Strulovici

We present a model for the equilibrium frequency of offenses and the informativeness of witness reports when potential offenders can commit multiple offenses and witnesses are subject to retaliation risk and idiosyncratic reporting preferences. We compare two ways of handling multiple accusations discussed in legal scholarship: (i) When convictions are based on the probability that the defendant committed at least one, unspecified offense and entail a severe punishment, potential offenders induce negative correlation in witnesses' private information, which leads to uninformative reports, information aggregation failures, and frequent offenses in equilibrium. Moreover, lowering the punishment in case of conviction can improve deterrence and the informativeness of witnesses' reports. (ii) When accusations are treated separately to adjudicate guilt and conviction entails a severe punishment, witness reports are highly informative and offenses are infrequent in equilibrium.

Do Institutional Investors Mitigate Social Costs of Privatization? Evidence from Prisons
Yegen, Eyub
This paper examines whether institutional investors mitigate the social trade-off that arises when government services are outsourced to private enterprises. Using hand-collected data on U.S. prisons, I find that a 1% increase in institutional ownership of public companies that manage prisons (PMCs) reduces prisoner suicides by up to 1.2%. These effects are stronger for institutional investors sensitive to long-term firm value since they are more attentive to social outcomes. Using a novel tax reform that exogenously changed institutional ownership of PMCs, I show that there is a causal effect of ownership on social outcomes. Lastly, I find that institutional investors have an incentive to be socially responsible due to litigation and reputation risks.

Dynamic indifference pricing via the G-expectation
Qian Lin

We study the dynamic indifference pricing with ambiguity preferences. For this, we introduce the dynamic expected utility with ambiguity via the nonlinear expectation--G-expectation, introduced by Peng (2007). We also study the risk aversion and certainty equivalent for the agents with ambiguity. We obtain the dynamic consistency of indifference pricing with ambiguity preferences. Finally, we obtain comparative statics.

Effects of Low Audit Quality Revelation on Audit Effort, Audit Quality, and Auditor Tenure
Calvin, Christopher,Kim, Byungki,Park, You-il (Chris)
We provide evidence that the revelation of low-quality audits is associated with auditors exerting greater effort on clients, which received low-quality audits as well as other clients of the low-quality audit office. In a sample of 15,934 public firm-years over the period 2004âˆ'2016, we find client restatements (a proxy for low audit quality) in an audit office are positively associated with concurrent audit lag, audit fees, and a common factor of two audit effort proxies on both restating and non-restating clients. We also find that, in spite of greater auditor effort, clients which previously received a low-quality audit are subject to additional increased misstatement risk. Unsurprisingly, auditors have a higher likelihood of subsequently being dismissed by these clients. Surprisingly, auditors also have a higher likelihood of subsequently being dismissed by clients with low underlying financial reporting quality, even if those clients are not subject to additional increased misstatement risk. Our results suggest that, in contrast to prior literature demonstrating poor audit quality being contagious among clients of the same audit office, low audit quality disclosure (in the form of restatements) results in an audit effort benefit across all clients of the low-quality audit office. However, clients with low financial reporting quality appear to take advantage of public revelations of low auditor quality to shift focus away from themselves, regardless of the auditors’ subsequent effort.

Globalization? Trade War? A Counterbalance Perspective
Xingwei Hu

During the past few decades, globalization and protectionism have ebbed and flowed from time to time among economies. The movements demand formal analytics that can help countries make better trade policies. They also imply that the best trade policies could be time-varying and country-specific. Economies and their imports and exports constitute a counterbalanced network where conflict and cooperation are two sides of the same coin. A country could improve its relative strength in the network by embracing globalization, protectionism, or trade wars. This paper provides necessary conditions for globalization and trade wars, evaluates their side effects, identifies the right targets for conflict or collaboration, and recommends fair resolutions for trade wars. Data and events from the past twenty years support these conditions.

High-Resolution Poverty Maps in Sub-Saharan Africa
Kamwoo Lee,Jeanine Braithwaite

Up-to-date poverty maps are an important tool for policymakers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of the 25 countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many Sub-Saharan African countries and other low- and middle-income countries.

How to Talk When a Machine is Listening: Corporate Disclosure in the Age of AI
Cao, Sean,Jiang, Wei,Yang, Baozhong,Zhang, Alan L.
This paper analyzes how corporate disclosure has been reshaped by machine processors, employed by algorithmic traders, robot investment advisors, and quantitative analysts. Our findings indicate that increasing machine and AI readership, proxied by machine downloads, motivates firms to prepare filings that are more friendly to machine parsing and processing. Moreover, firms with high expected machine downloads manage textual sentiment and audio emotion in ways catered to machine and AI readers, such as by differentially avoiding words that are perceived as negative by computational algorithms as compared to those by human readers, and by exhibiting speech emotion favored by machine learning software processors. The publication of Loughran and McDonald (2011) is instrumental in attributing the change in the measured sentiment to machine and AI readership. While existing research has explored how investors and researchers apply machine learning and computational tools to quantify qualitative information from disclosure and news, this study is the first to identify and analyze the feedback effect on corporate disclosure decisions, i.e., how companies adjust the way they talk knowing that machines are listening.

How to build a cross-impact model from first principles: Theoretical requirements and empirical results
Mehdi Tomas,Iacopo Mastromatteo,Michael Benzaquen

Trading a financial instrument induces a price response on itself and on other correlated instruments, a phenomenon known as cross-impact. Unfortunately, empirical measures of cross-impact are affected by a large estimation error due to both the large number of interactions to infer and the strongly fluctuating nature of price returns. In this study we propose a principled approach that leverages simple consistency criteria (symmetries, no-arbitrage conditions, correlation and liquidity limit-case properties) in order to impose ex-ante properties that might be required for practical applications. We validate our approach on empirical data for several asset classes, thus determining which properties are desirable across multiple markets. In particular, our results show that two cross-impact models perform well in all markets studied but only one is suitable for other applications, such as optimal execution.

Initial Coin Offerings, Corporate Finance and Financial Regulation
Allen, Franklin
Initial Coin Offerings (ICOs) have grown substantially in recent years. They involve issuing coins that are recorded on a block-chain. These can be used to purchase the service or good that the firm they finance produces. The coins can be exchanged for currency on cryptocurrency exchanges. Although many ICOs are fraudulent, most studies find positive average and median returns. Theoretical analyses suggest they can have several advantages compared to Initial Public Offerings (IPOs). They are regulated in widely differing ways with the UK, Switzerland and Singapore having regimes that make them easier to undertake than other countries.

Inversion-free Leontief inverse: statistical regularities in input-output analysis from partial information
Silvia Bartolucci,Fabio Caccioli,Francesco Caravelli,Pierpaolo Vivo

We present a baseline stochastic framework for assessing inter-sectorial relationships in a generic economy. We show that - irrespective of the specific features of the technology matrix for a given country or a particular year - the Leontief multipliers (and any upstreamness/downstreamness indicator computed from the Leontief inverse matrix) follow a universal pattern, which we characterize analytically. We formulate a universal benchmark to assess the structural inter-dependence of sectors in a generic economy. Several empirical results on World Input-Output Database (WIOD, 2013 Release) are presented that corroborate our findings.

Is It Expected Volatility or Expected Precision?
Gonzalez-Perez, Maria T.,Guerrero, David E.
We implement data analyses of daily closing VIX in 2017-19 and conclude that the value of the lambda parameter, in the one-parameter Box & Cox (1964) family, appropriate for VIX to be normal, is minus one (expected precision), which is very far from one (no transformation) and zero (logarithm), the values typical of most of the literature. We show how to write the VIX likelihood functions as a function of the expected precision statistical moments, report a stronger leverage effect at low-expected-precision days, and define the precision risk premium (PRP), which relates negatively to market returns. We advise using expected precision to study VIX and return dynamics.

Machine Learning for Temporal Data in Finance: Challenges and Opportunities
Jason Wittenbach,Brian d'Alessandro,C. Bayan Bruss

Temporal data are ubiquitous in the financial services (FS) industry -- traditional data like economic indicators, operational data such as bank account transactions, and modern data sources like website clickstreams -- all of these occur as a time-indexed sequence. But machine learning efforts in FS often fail to account for the temporal richness of these data, even in cases where domain knowledge suggests that the precise temporal patterns between events should contain valuable information. At best, such data are often treated as uniform time series, where there is a sequence but no sense of exact timing. At worst, rough aggregate features are computed over a pre-selected window so that static sample-based approaches can be applied (e.g. number of open lines of credit in the previous year or maximum credit utilization over the previous month). Such approaches are at odds with the deep learning paradigm which advocates for building models that act directly on raw or lightly processed data and for leveraging modern optimization techniques to discover optimal feature transformations en route to solving the modeling task at hand. Furthermore, a full picture of the entity being modeled (customer, company, etc.) might only be attainable by examining multiple data streams that unfold across potentially vastly different time scales. In this paper, we examine the different types of temporal data found in common FS use cases, review the current machine learning approaches in this area, and finally assess challenges and opportunities for researchers working at the intersection of machine learning for temporal data and applications in FS.

Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation
Michaël Karpe,Jin Fang,Zhongyao Ma,Chen Wang

Optimal order execution is widely studied by industry practitioners and academic researchers because it determines the profitability of investment decisions and high-level trading strategies, particularly those involving large volumes of orders. However, complex and unknown market dynamics pose significant challenges for the development and validation of optimal execution strategies. In this paper, we propose a model-free approach by training Reinforcement Learning (RL) agents in a realistic market simulation environment with multiple agents. First, we configure a multi-agent historical order book simulation environment for execution tasks built on an Agent-Based Interactive Discrete Event Simulation (ABIDES) [arXiv:1904.12066]. Second, we formulate the problem of optimal execution in an RL setting where an intelligent agent can make order execution and placement decisions based on market microstructure trading signals in High Frequency Trading (HFT). Third, we develop and train an RL execution agent using the Double Deep Q-Learning (DDQL) algorithm in the ABIDES environment. In some scenarios, our RL agent converges towards a Time-Weighted Average Price (TWAP) strategy. Finally, we evaluate the simulation with our RL agent by comparing it with a market replay simulation using real market Limit Order Book (LOB) data.

News-Based Peers and Stock Returns
Tao, Ran,Yim, Andrew,Han, Tian
Applying a “co-coverage” concept to the Dow Jones Newswire articles, we propose to identify each firm’s news-based peers (NBPs) and thereby construct a time-varying firm-centric grouping aimed to augment existing industry classification schemes. The advantage of NBP-augmented schemes over traditional industry classifications is to better capture the up-to-date relative importance of the economic links between a base firm and its peers. We show that the base firm’s share price responds more favorably to shocks to the stock returns of its NBPs than of its traditional industry peers that are not NBPs. The response persists for several months, suggesting that the up-to-date relative importance of the economic links is not immediately clear to investors. Additional empirical tests show that the persistent response, referred to as NBP momentum, can partially unify several lead-lag return momentum previously documented and is consistent with the investor attention hypothesis. Taken together, our results suggest that monitoring news co-coverage is a key to understand the lead-lag return momentum documented in the literature.

Online Appendix for 'External Financing and Customer Capital: A Financial Theory of Markups'
Dou, Winston,Ji, Yan
This is the supplemental material to the paper titled "External Financing and Customer Capital: A Financial Theory of Markups." It includes additional empirical, theoretical, and quantitative results. It also includes illustration for the numerical algorithm for our model solution.

Optimal market making under partial information and numerical methods for impulse control games with applications
Diego Zabaljauregui

The topics treated in this thesis are inherently two-fold. The first part considers the problem of a market maker optimally setting bid/ask quotes over a finite time horizon, to maximize her expected utility. The intensities of the orders she receives depend not only on the spreads she quotes, but also on unobservable factors modelled by a hidden Markov chain. This stochastic control problem under partial information is solved by means of stochastic filtering, control and PDMPs theory. The value function is characterized as the unique continuous viscosity solution of its dynamic programming equation and numerically compared with its full information counterpart. The optimal full information spreads are shown to be biased when the exact market regime is unknown, as the market maker needs to adjust for additional regime uncertainty in terms of PnL sensitivity and observable order flow volatility.

The second part deals with numerically solving nonzero-sum stochastic impulse control games. These offer a realistic and far-reaching modelling framework, but the difficulty in solving such problems has hindered their proliferation. A policy-iteration-type solver is proposed to solve an underlying system of quasi-variational inequalities, and it is validated numerically with reassuring results.

Eventually, the focus is put on games with a symmetric structure and an improved algorithm is put forward. A rigorous convergence analysis is undertaken with natural assumptions on the players strategies, which admit graph-theoretic interpretations in the context of weakly chained diagonally dominant matrices. The algorithm is used to compute with high precision equilibrium payoffs and Nash equilibria of otherwise too challenging problems, and even some for which results go beyond the scope of the currently available theory.

Path Dependent Optimal Transport and Model Calibration on Exotic Derivatives
Ivan Guo,Gregoire Loeper

In this paper, we introduce and develop the theory of semimartingale optimal transport in a path dependent setting. Instead of the classical constraints on marginal distributions, we consider a general framework of path dependent constraints. Duality results are established, representing the solution in terms of path dependent partial differential equations (PPDEs). Moreover, we provide a dimension reduction result based on the new notion of "semifiltrations", which identifies appropriate Markovian state variables based on the constraints and the cost function. Our technique is then applied to the exact calibration of volatility models to the prices of general path dependent derivatives.

Price mediated contagion through capital ratio requirements
Tathagata Banerjee,Zachary Feinstein

We develop a framework for price-mediated contagion in financial systems where banks are forced to liquidate assets to satisfy a risk-weight based capital adequacy requirement. In constructing this modeling framework, we introduce a two-tier pricing structure: the volume weighted average price that is obtained by any bank liquidating assets and the terminal mark-to-market price used to account for all assets held at the end of the clearing process. We consider the case of multiple illiquid assets and develop conditions for the existence and uniqueness of clearing prices. We provide a closed-form representation for the sensitivity of these clearing prices to the system parameters, and use this result to quantify: (1) the cost of regulation, in stress scenarios, faced by the system as a whole and the individual banks, and (2) the value of providing bailouts to consider when such notions are financially advisable. Numerical case studies are provided to study the application of this model to data.

Set-Valued Risk Measures as Backward Stochastic Difference Inclusions and Equations
Çağın Ararat,Zachary Feinstein

Scalar dynamic risk measures for univariate positions in continuous time are commonly represented as backward stochastic differential equations. In the multivariate setting, dynamic risk measures have been defined and studied as families of set-valued functionals in the recent literature. There are two possible extensions of scalar backward stochastic differential equations for the set-valued framework: (1) backward stochastic differential inclusions, which evaluate the risk dynamics on the selectors of acceptable capital allocations; or (2) set-valued backward stochastic differential equations, which evaluate the risk dynamics on the full set of acceptable capital allocations as a singular object. In this work, the discrete time setting is investigated with difference inclusions and difference equations in order to provide insights for such differential representations for set-valued dynamic risk measures in continuous time.

Small Firms amidst COVID-19: Financial Constraints and Role of Government Support
Chundakkadan, Radeef,Raj, Rajesh,Sasidharan, Subash
The coronavirus epidemic has severely affected the small and medium enterprises (SMEs), which are more financially constrained compared with large companies. Waking up to this challenge, various countries employed short-run and long-run policies to support SMEs. Using rich firm-level data from 13 countries, this paper explores the impact of the pandemic-led crisis on cash-strapped SMEs and the role that governments played in offsetting the losses in the sector. Our results unambiguously suggest that financially constrained firms are more likely to shut down their operations. The results are robust to concerns arising from endogeneity of finance constraints and also to alternative measures of firm closure and specifications. We also find that government support programmes are more inclusive as they target mostly financially constrained firms. Our final set of analysis reveals that financially constrained firms are more likely to sack workers; and there is clear evidence of gender bias in layoffs.

Spearman's footrule and Gini's gamma: Local bounds for bivariate copulas and the exact region with respect to Blomqvist's beta
Damjana Kokol Bukovšek,Tomaž Košir,Blaž Mojškerc,Matjaž Omladič

Copulas are becoming an essential tool in analyzing data and knowing local copula bounds with a fixed value of a given measure of association is turning into a prerequisite in the early stage of exploratory data analysis. These bounds have been computed for Spearman's rho, Kendall's tau, and Blomqvist's beta. The importance of another two measures of association, Spearman's footrule and Gini's gamma, has been reconfirmed recently. It is the main purpose of this paper to fill in the gap and present the mentioned local bounds for these two measures as well. It turns out that this is a quite non-trivial endeavor as the bounds are quasi-copulas that are not copulas for certain values of the two measures. We also give relations between these two measures of association and Blomqvist's beta.

Stablecoins 2.0: Economic Foundations and Risk-based Models
Ariah Klages-Mundt,Dominik Harz,Lewis Gudgeon,Jun-You Liu,Andreea Minca

Stablecoins are one of the most widely capitalized type of cryptocurrency. However, their risks vary significantly according to their design and are often poorly understood. We seek to provide a sound foundation for stablecoin theory, with a risk-based functional characterization of the economic structure of stablecoins. First, we match existing economic models to the disparate set of custodial systems. Next, we characterize the unique risks that emerge in non-custodial stablecoins and develop a model framework that unifies existing models from economics and computer science. We further discuss how this modeling framework is applicable to a wide array of cryptoeconomic systems, including cross-chain protocols, collateralized lending, and decentralized exchanges. These unique risks yield unanswered research questions that will form the crux of research in decentralized finance going forward.

Supervised learning for the prediction of firm dynamics
Falco J. Bargagli-Stoffi,Jan Niederreiter,Massimo Riccaboni

Thanks to the increasing availability of granular, yet high-dimensional, firm level data, machine learning (ML) algorithms have been successfully applied to address multiple research questions related to firm dynamics. Especially supervised learning (SL), the branch of ML dealing with the prediction of labelled outcomes, has been used to better predict firms' performance. In this contribution, we will illustrate a series of SL approaches to be used for prediction tasks, relevant at different stages of the company life cycle. The stages we will focus on are (i) startup and innovation, (ii) growth and performance of companies, and (iii) firms exit from the market. First, we review SL implementations to predict successful startups and R&D projects. Next, we describe how SL tools can be used to analyze company growth and performance. Finally, we review SL applications to better forecast financial distress and company failure. In the concluding Section, we extend the discussion of SL methods in the light of targeted policies, result interpretability, and causality.

Targeting Weather Insurance Markets
Cole, Shawn Allen,Tobacman, Jeremy,Mukherjee, Anita
The suitability of insurance products often depends greatly on individual circum- stances. This paper examines the challenges of heterogeneity in a relatively new product, weather-indexed insurance. This index insurance product has been launched in over a dozen countries, with the goal of enabling households engaged in agricultural ac- tivity a means to manage risk. Using data from a large-scale field experiment, we build and calibrate a model which accounts for household investment decisions, including the scope for self-insurance via labor markets to (risky) wage work. Our results show that insurance is most valuable to households with reduced access to wage labor, or to those who face wages that are sensitive to rainfall risk. These findings have important implications for areas where index insurance is most effective.

The Phases and Catalysts of Mini Flash Crashes
Shearer, Megan
Mini flash crashes are rapid volatility events reminiscent of the May 6, 2010 Flash Crash in the US stock market. The frequency of mini flash crashes and their similarity to the Flash Crash provide an avenue to study patterns in order and trade activity during smaller volatility events to gain greater insight into potential activity during larger volatility events. I find a set of potential mini flash crashes using a two-step process. I then divide each mini flash crash into phases, where I define a phase as a stage with the same price directionality. Splitting each mini flash crash into phases facilitates a more dynamic and granular examination of order and trade activity during different stages of a mini flash crash. Using Daily TAQ data, I find that the proportion of sell Inter-market Sweep Orders increases before the price begins to drop. This result suggests that some participants may exacerbate mini flash crashes with certain order and trade practices. Using off-exchange sub-penny trades as a proxy for retail activity, I find a significant increase in volume potentially initiated by retail sell orders executed at or near the lowest price of the mini flash crash, which may indicate that retail investors could be particularly subject to harm during these events.

The Seven-League Scheme: Deep learning for large time step Monte Carlo simulations of stochastic differential equations
Shuaiqiang Liu,Lech A. Grzelak,Cornelis W. Oosterlee

We propose an accurate data-driven numerical scheme to solve Stochastic Differential Equations (SDEs), by taking large time steps. The SDE discretization is built up by means of a polynomial chaos expansion method, on the basis of accurately determined stochastic collocation (SC) points. By employing an artificial neural network to learn these SC points, we can perform Monte Carlo simulations with large time steps. Error analysis confirms that this data-driven scheme results in accurate SDE solutions in the sense of strong convergence, provided the learning methodology is robust and accurate. With a variant method called the compression-decompression collocation and interpolation technique, we can drastically reduce the number of neural network functions that have to be learned, so that computational speed is enhanced. Numerical results show the high quality strong convergence error results, when using large time steps, and the novel scheme outperforms some classical numerical SDE discretizations. Some applications, here in financial option valuation, are also presented.

The Usefulness of Accrual Accounting in Forming Analysts’ Forecasts of Accruals and Cash Flows from Operations
Sankaraguruswamy, Srinivasan
Recent work shows that the role of accrual accounting in mitigating the timing differences between cash flows and operating performance has been disappearing over time (Bushman, Lerman, and Zhang 2016). We argue that even though there is noise in the accrual accounting process, sophisticated users of financial information â€" financial analysts, are able to extract useful information from the reported accrual accounting process, and use them in their forecasting process, are able to smooth the timing differences in forecasted cash flows, and also predict future reported cash flows. We find that the analysts’ forecasts of accruals and cash flows are negatively correlated with each other, and this negative correlation has not changed over time. We also find that the ability of analysts’ accruals forecasts to predict future cash flows has not declined over time.

To the Moon: A History of Bitcoin Price Manipulation
Peterson, Timothy
Unmolested prices have been shown to exhibit an expected, natural distribution characterized by Benford’s law. Deviations from this distribution indicate an anomaly, and typically that anomaly is caused by some type of fraud. With bitcoin, we conducted an analysis for the entire period of daily closing prices from July 2010 through May 2020. We also conducted analyses for calendar years 2011-2019. We can say with near 100% confidence that bitcoin’s price has been fraudulently manipulated at some point in its lifespan since 2010. We can say with 95% confidence that bitcoin was manipulated in 2013; 95% confidence that bitcoin was manipulated in 2017; and 98% confidence that bitcoin was manipulated in 2019.We believe this is the first application of Benford’s law to bitcoin. Our ultimate aim is to raise the level of awareness such that future illicit behavior in the bitcoin marketplace is more easily identified and mitigated, either through market forces or regulatory oversight. Substantial mitigation of bitcoin price manipulation would increase bitcoin’s value by about 40%. Lastly, our findings imply that both technical and fundamental approaches to value bitcoin over the suspect periods are likely meaningless because bitcoin’s price did not reflect equally motivated buyers and sellers.

Unexpected Bond Ratings and the Cost of Municipal Debt
Beck, Amanda,Parsons, Linda M.,Sorensen, Trevor
We investigate whether credit rating agencies (CRAs) and investors price the extent to which municipal bond ratings are explainable using public information. We use an ordinal logistic regression to estimate the expected and unexpected portions of bond ratings, and find that both CRA fees and yield premiums are higher for negative unexplained (i.e., pessimistic) ratings. CRA fees are not generally associated with positive unexplained (i.e., optimistic) ratings, although we document a positive association when financial reporting quality or investor sophistication is low. While creditors price both pessimistic and optimistic unexplained ratings, yield premiums for pessimistic ratings are significantly greater than yield discounts for optimistic ratings. These findings manifest after the passage of the Dodd-Frank Act, among sophisticated investors, and among municipalities with high financial reporting quality. We contribute to research that examines the determinants of municipal debt costs, CRA fees, and the information content of bond ratings.

Western Ideological Homogeneity in Entrepreneurial Finance Research: Evidence From Highly Cited Publications
Nguyen, Minh-Hoang,Nguyen, Huyen Thanh T.,Pham, Thanh-Hang,Ho, Manh-Toan,Vuong, Quan Hoang
Entrepreneurs play crucial roles in global sustainable development, but limited financial resources constrain their performance and survival rate. Entrepreneurial finance discipline is, therefore, born to explore the connection between finance and entrepreneurship. Despite the global presence of entrepreneurship, the literature of entrepreneurial finance is suspected to be Western ideologically homogeneous. Thus, the objective of this study is to examine the existence of Western ideological homogeneity in entrepreneurial finance literature. Employing the mind-sponge mechanism and biblio-metric analyses (Y-index and social structure), we analyze 412 highly cited publications extracted from Web of Science database and find Western ideological dominance as well as weak tolerance towards heterogeneity in the set of core ideologies of entrepreneurial finance. These results are consistent across author-, institution-, and country-levels, which reveals strong evidence for the existence of Western ideological homogeneity in the field. We recommend editors, reviewers, and authors to have proactive actions to diversify research topics and enhancing knowledge exchange to avoid the shortfalls of ideological homogeneity. Moreover, the synthesis of mind-sponge mechanism and biblio-metric analyses are suggested as a possible way to evaluate the state of ideological diversity in other scientific disciplines.