Research articles for the 2020-06-10

A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction
Christoph Berninger,Almond Stöcker,David Rügamer

Motivated by the application to German interest rates, we propose a timevarying autoregressive model for short and long term prediction of time series that exhibit a temporary non-stationary behavior but are assumed to mean revert in the long run. We use a Bayesian formulation to incorporate prior assumptions on the mean reverting process in the model and thereby regularize predictions in the far future. We use MCMC-based inference by deriving relevant full conditional distributions and employ a Metropolis-Hastings within Gibbs Sampler approach to sample from the posterior (predictive) distribution. In combining data-driven short term predictions with long term distribution assumptions our model is competitive to the existing methods in the short horizon while yielding reasonable predictions in the long run. We apply our model to interest rate data and contrast the forecasting performance to the one of a 2-Additive-Factor Gaussian model as well as to the predictions of a dynamic Nelson-Siegel model.

A Monthly Indicator of Economic Growth for Low Income Countries
Stanger, Michael
Monthly economic indicators support policy analysis of current economic developments and forecasting. This paper presents an overview of the data and statistical requirements to develop those indicators taking into account resource constraints that LIC typically face. We review statistical procedures for developing these indicators under the System of National Accounts and propose a general procedure to derive a monthly composite indicator of economic growth in low income economies.

A Public-Private Insurance Model for Natural Risk Management: an Application to Seismic and Flood Risks on Residential Buildings in Italy
Selene Perazzini,Giorgio Stefano Gnecco,Fabio Pammolli

This paper proposes a public-private insurance scheme for earthquakes and floods in Italy in which property-owners, the insurer and the government co-operate in risk financing. Our model departs from the existing literature by describing a public-private insurance intended to relieve the financial burden that natural events place on governments, while at the same time assisting individuals and protecting the insurance business. Hence, the business is aiming at maximizing social welfare rather than profits. Given the limited amount of data available on natural risks, expected losses per individual have been estimated through risk-modeling. In order to evaluate the insurer's loss profile, spatial correlation among insured assets has been evaluated by means of the Hoeffding bound for r-dependent random variables. Though earthquakes generate expected losses that are almost six times greater than floods, we found that the amount of public funds needed to manage the two perils is almost the same. We argue that this result is determined by a combination of the risk aversion of individuals and the shape of the loss distribution. Lastly, since earthquakes and floods are uncorrelated, we tested whether jointly managing the two perils can counteract the negative impact of spatial correlation. Some benefit from risk diversification emerged, though the probability of the government having to inject further capital might be considerable. Our findings suggest that, when not supported by the government, private insurance might either financially over-expose the insurer or set premiums so high that individuals would fail to purchase policies.

A Resource-based View of Corporate Social Irresponsibility: Evidence from Shareholder Value Destruction in China
Harjoto, Maretno A.,Hoepner, Andreas G. F.,Li, Qian
Barney (1991) states a resource can be a potential source of sustained competitive advantage if it is rare, valuable, inimitable and non-substitutable. We invert his definition to conceptualise the emerging corporate social irresponsibility (CSI) literature from the resource-based view. CSI episodes are quite rare and no sane competitor would aim to imitate or substitute them. However, the degree to which CSI episodes are impacting shareholder value is an empirical question, whose result implies if CSI can be conceptualised as a ‘resource damaging factor’. We differentiate CSI domains based on the firm’s power to substitute offended stakeholders without long-term implications (i.e., employees or suppliers) versus those where such option is not available (i.e. investors or customers or regulators). We conceptualise the impact of CSI episodes as metaphorical 3-dimensional objects and hypothesize that the negative long-term valuation impact of CSI episodes is bigger for closer proximity of the observer to the CSI episode (“breadth”), more severe CSI episodes (“depth”), or more prominent disclosure (“height”). We focus on Chinese firms with their unique shareholder split between local and foreign investors and employ RepRisk, a curated negative news radar database, to identify disclosures of CSI episodes. We find evidence in support of a significant value destruction of CSI episodes, which is amplified for severe episodes of high profile as seen by local investors. The value impact is concentrated on domains where Chinese firms have little power to substitute stakeholders potentially offended by CSI, namely the local investors, the customers, and the Chinese regulator.

A Study on Working Capital Strategies of Indian ITes Industry
Shroff, Sumita J
Working capital strategies are amongst the significant ones for every business enterprise. In this context, the current study examined the working capital strategy in terms of current asset structure, current liability structure, working capital policy comprising current asset investment, financing, inventory policy, and credit policy of the Indian ITes industry over a period of 15 years. The analysis revealed that the asset structure of ITes industry is liquid with a higher proportion of current assets. Receivables comprised the major component in the current asset structure and Trade Credit dominated the current liability structure. The industry pursued an aggressive working capital financing strategy.

A framework for modeling interdependencies among households, businesses, and infrastructure systems; and their response to disruptions
Mateusz Iwo Dubaniowski,Hans R. Heinimann

Urban systems, composed of households, businesses, and infrastructures, are continuously evolving and expanding. This has several implications because the impacts of disruptions, and the complexity and interdependence of systems, are rapidly increasing. Hence, we face a challenge in how to improve our understanding about the interdependencies among those entities, as well as their responses to disruptions. The aims of this study were to (1) create an agent that mimics the metabolism of a business or household that obtains supplies from and provides output to infrastructure systems; (2) implement a network of agents that exchange resources, as coordinated with a price mechanism; and (3) test the responses of this prototype model to disruptions. Our investigation resulted in the development of a business/household agent and a dynamically self-organizing mechanism of network coordination under disruption based on costs for production and transportation. Simulation experiments confirmed the feasibility of this new model for analyzing responses to disruptions. Among the nine disruption scenarios considered, in line with our expectations, the one combining the failures of infrastructure links and production processes had the most negative impact. We also identified areas for future research that focus on network topologies, mechanisms for resource allocation, and disruption generation.

A multi-factor polynomial framework for long-term electricity forwards with delivery period
Xi Kleisinger-Yu,Vlatka Komaric,Martin Larsson,Markus Regez

We propose a multi-factor polynomial framework to model and hedge long-term electricity contracts with delivery period. This framework has several advantages: the computation of forwards, risk premium and correlation between different forwards are fully explicit, and the model can be calibrated to observed electricity forward curves easily and well. Electricity markets suffer from non-storability and poor medium- to long-term liquidity. Therefore, we suggest a rolling hedge which only uses liquid forward contracts and is risk-minimizing in the sense of F\"ollmer and Schweizer. We calibrate the model to over eight years of German power calendar year forward curves and investigate the quality of the risk-minimizing hedge over various time horizons.

An Application of Deep Reinforcement Learning to Algorithmic Trading
Thibaut Théate,Damien Ernst

This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets. It proposes a novel DRL trading strategy so as to maximise the resulting Sharpe ratio performance indicator on a broad range of stock markets. Denominated the Trading Deep Q-Network algorithm (TDQN), this new trading strategy is inspired from the popular DQN algorithm and significantly adapted to the specific algorithmic trading problem at hand. The training of the resulting reinforcement learning (RL) agent is entirely based on the generation of artificial trajectories from a limited set of stock market historical data. In order to objectively assess the performance of trading strategies, the research paper also proposes a novel, more rigorous performance assessment methodology. Following this new performance assessment approach, promising results are reported for the TDQN strategy.

An Artificial Intelligence Solution for Electricity Procurement in Forward Markets
Thibaut Théate,Sébastien Mathieu,Damien Ernst

Retailers and major consumers of electricity generally purchase a critical percentage of their estimated electricity needs years ahead on the forward markets. This long-term electricity procurement task consists of determining when to buy electricity so that the resulting energy cost is minimised, and the forecast consumption is covered. In this scientific article, the focus is set on a yearly base load product, named calendar (CAL), which is tradable up to three years ahead of the delivery period. This research paper introduces a novel algorithm providing recommendations to either buy electricity now or wait for a future opportunity based on the history of CAL prices. This algorithm relies on deep learning forecasting techniques and on an indicator quantifying the deviation from a perfectly uniform reference procurement strategy. Basically, a new purchase operation is advised when this mathematical indicator hits the trigger associated with the market direction predicted by the forecaster. On average, the proposed approach surpasses benchmark procurement strategies and achieves a reduction in costs of 1.65% with respect to the perfectly uniform reference procurement strategy achieving the mean electricity price. Moreover, in addition to automating the electricity procurement task, this algorithm demonstrates more consistent results throughout the years compared to the benchmark strategies.

An elementary approach to the Merton problem
Martin Herdegen,David Hobson,Joseph Jerome

In this article we consider the infinite-horizon Merton investment-consumption problem in a constant-parameter Black - Scholes - Merton market for an agent with constant relative risk aversion R. The classical primal approach is to write down a candidate value function and to use a verification argument to prove that this is the solution to the problem. However, features of the problem take it outside the standard settings of stochastic control, and the existing primal verification proofs rely on parameter restrictions (especially, but not only, R<1), restrictions on the space of admissible strategies, or intricate approximation arguments.

The purpose of this paper is to show that these complications can be overcome using a simple and elegant argument involving a stochastic perturbation of the utility function.

An overall view of key problems in algorithmic trading and recent progress
Michaël Karpe

We summarize the fundamental issues at stake in algorithmic trading, and the progress made in this field over the last twenty years. We first present the key problems of algorithmic trading, describing the concepts of optimal execution, optimal placement, and price impact. We then discuss the most recent advances in algorithmic trading through the use of Machine Learning, discussing the use of Deep Learning, Reinforcement Learning, and Generative Adversarial Networks.

Are Sustainability-Oriented Investors Different? Evidence from Equity Crowdfunding
Hornuf, Lars,Stenzhorn, Eliza,Vintis, Tim
In this article, we examine how investor motives affect investment behavior in equity crowdfunding. In particular, we compare the investment behavior of sustainability-oriented with ordinary crowd investors on six leading equity crowdfunding platforms in Austria and Germany and investigate whether they suffer from a default shock that was recently identified by Dorfleitner et al. (2019). In general, we find evidence of a default shock in equity crowdfunding that occurs immediately after the event and if investors experience more than two insolvencies. Moreover, we find that sustainability-oriented investors pledge larger amounts of money and invest in more campaigns than ordinary crowd investors. The results also suggest that sustainability-oriented crowd investors care about non-financial returns, as they react more sensitively after experiencing a default in their equity crowdfunding portfolios, which indicates that they suffer beyond the pure financial loss. These findings contribute to recent literature on equity crowdfunding, socially responsible investing, and how individual investment motives and personal experiences affect investment decisions.

Bailout Stigma
Yeon-Koo Che,Chongwoo Choe,Keeyoung Rhee

We develop a model of bailout stigma where accepting a bailout signals a firm's balance-sheet weakness and worsens its funding prospect. To avoid stigma, a firm with high-quality legacy assets either withdraws from subsequent financing after receiving a bailout or refuses a bailout altogether to send a favorable signal. The former leads to a short-lived stimulation with subsequent market freeze even worse than if there were no bailout. The latter revives the funding market, albeit with delay, to the level achievable without any stigma. Strikingly, a bailout offer is most effective when many firms reject it (to build a favorable reputation) rather than accept it.

Board of Directors Network Centrality and Environmental, Social and Governance (ESG) Performance
Harjoto, Maretno A.,Wang, Yan
Drawing from social capital, social network theory of stakeholder influence, and stakeholder management, this study examines the relationship between board network centrality and firms’ environmental, social and governance (ESG) performance. Using social network analysis, we construct five board network centrality, namely degree centrality (the number of connections), closeness centrality (distance among firms), eigenvector centrality (the quality of connections), betweenness centrality (how often a firm sits between two other firms), and the information centrality (the speed and reliability of information), as measures of board access for social capital and timely information. Using a sample of non-financial firms listed in the UK FTSE 350 index from 2007 to 2018, we find that board networks, measured by degree, closeness, eigenvector, betweenness, and information centrality, have positive influence on firms’ environmental, social and governance (ESG) performance. Furthermore, our findings show that there is a non-linear relationship between board networks and ESG performance and this relationship is stronger in the sectors where firms that have high product market concentration and high percentage of women board members.This study unveils that strong board network centrality brings higher social (reputational) capital and information advantages to the firm to effectively, timely, and accurately deal with the pressures from stakeholders (stakeholder management), which leads to better environmental, social, and governance (ESG) performance.

C\`adl\`ag semimartingale strategies for optimal trade execution in stochastic order book models
Julia Ackermann,Thomas Kruse,Mikhail Urusov

We analyze an optimal trade execution problem in a financial market with stochastic liquidity. To this end we set up a limit order book model in continuous time. Both order book depth and resilience are allowed to evolve randomly in time. We allow for trading in both directions and for c\`adl\`ag semimartingales as execution strategies. We derive a quadratic BSDE that under appropriate assumptions characterizes minimal execution costs and identify conditions under which an optimal execution strategy exists. We also investigate qualitative aspects of optimal strategies such as, e.g., appearance of strategies with infinite variation or existence of block trades and discuss connections with the discrete-time formulation of the problem. Our findings are illustrated in several examples.

Coastal Flood Risk in the Mortgage Market: Storm Surge Models' Predictions vs. Flood Insurance Maps
Amine Ouazad

Prior literature has argued that flood insurance maps may not capture the extent of flood risk. This paper performs a granular assessment of coastal flood risk in the mortgage market by using physical simulations of hurricane storm surge heights instead of using FEMA's flood insurance maps. Matching neighborhood-level predicted storm surge heights with mortgage files suggests that coastal flood risk may be large: originations and securitizations in storm surge areas have been rising sharply since 2012, while they remain stable when using flood insurance maps. Every year, more than 50 billion dollars of originations occur in storm surge areas outside of insurance floodplains. The share of agency mortgages increases in storm surge areas, yet remains stable in the flood insurance 100-year floodplain. Mortgages in storm surge areas are more likely to be complex: non-fully amortizing features such as interest-only or adjustable rates. Households may also be more vulnerable in storm surge areas: median household income is lower, the share of African Americans and Hispanics is substantially higher, the share of individuals with health coverage is lower. Price-to-rent ratios are declining in storm surge areas while they are increasing in flood insurance areas. This paper suggests that uncovering future financial flood risk requires scientific models that are independent of the flood insurance mapping process.

Coronavirus Perceptions And Economic Anxiety
Thiemo Fetzer,Lukas Hensel,Johannes Hermle,Christopher Roth

We provide one of the first systematic assessments of the development and determinants of economic anxiety at the onset of the coronavirus pandemic. Using a global dataset on internet searches and two representative surveys from the US, we document a substantial increase in economic anxiety during and after the arrival of the coronavirus. We also document a large dispersion in beliefs about the pandemic risk factors of the coronavirus, and demonstrate that these beliefs causally affect individuals' economic anxieties. Finally, we show that individuals' mental models of infectious disease spread understate non-linear growth and shape the extent of economic anxiety.

Credit Default Swaps and Non-GAAP Earnings Disclosure
Black, Dirk E.,Kolev, Kalin S.,Zhao, Binghao (Jimmy)
We examine the effect of credit default swap (CDS) coverage on voluntary disclosure using firm provided non-GAAP earnings as a laboratory. For a large sample of U.S. firms, we find that for companies with CDS coverage, the persistence of non-GAAP exclusions is lower, implying higher disclosure quality. The effect manifests across a range of performance measures and measurement windows and is strongest among non-investment-grade firms, entities for which monitors are more likely to rely on public accounting reports. This improvement in quality comes as a counterpoint to a decrease in the frequency of non-GAAP disclosure among firms that experience CDS coverage initiation. Collectively, our findings suggest firms counteract the perceived negative externalities associated with CDS coverage with higher-quality voluntary disclosure.

Deep Stock Predictions
Akash Doshi,Alexander Issa,Puneet Sachdeva,Sina Rafati,Somnath Rakshit

Forecasting stock prices can be interpreted as a time series prediction problem, for which Long Short Term Memory (LSTM) neural networks are often used due to their architecture specifically built to solve such problems. In this paper, we consider the design of a trading strategy that performs portfolio optimization using the LSTM stock price prediction for four different companies. We then customize the loss function used to train the LSTM to increase the profit earned. Moreover, we propose a data driven approach for optimal selection of window length and multi-step prediction length, and consider the addition of analyst calls as technical indicators to a multi-stack Bidirectional LSTM strengthened by the addition of Attention units. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA, while the addition of analyst call does improve the performance for certain datasets.

Dynamic Banking and the Value of Deposits
Bolton, Patrick,Li, Ye,Wang, Neng,Yang, Jinqiang
Deposits are inside money issued by banks, serving as means of payment for the rest of the economy. Depositors value the payment function and assign a money premium on deposits that reduces banks’ cost of financing, so deposits create value for banks. However, driven by payment flows, deposits are essentially debts with random maturities that cannot be fully controlled. Banks can adjust deposit flows by setting the deposit rate, but once the deposit rate hits the zero lower bound, banks lose control of leverage. Under equity issuance costs, deposits be- come burden and destroy value when banks are significantly undercapitalized. Outside money issued by the government can liberate undercapitalized banks by absorbing the money demand.

Energy Contagion in the Covid-19 Crisis
Heinlein, Reinhold,Legrenzi, Gabriella Deborah,R. Mahadeo, Scott M.
We investigate the relationship between oil prices and stock markets of selected oil importers and oil exporters at the time of the COVID-19 pandemic. We provide evidence in favour of energy contagion, in term of significantly higher correlations between oil and stock markets returns during turbulent phases in the oil market, for all countries in our sample. Our results are robust to different crisis datings and consistent across different segments of the assets return distributions.

Financial Crisis Prediction Capability of Financial Ratios
Islam, Md Saiful
Bankruptcy of a business firm is an event which results substantial losses to creditors and stockholders. A model which is capable of predicting an upcoming business failure will serve as a very useful tool to reduce such losses by providing warning to the interested parties. This was the main motivation for Beaver (1966) and Altman (1968) to construct bankruptcy prediction models based on the financial data (Deakin 1972).
This research study also initiated with a great interest on this subject to investigate the predictive capability of financial ratios for forecasting of corporate distress and bankruptcy events. The current global financial climate demands even the best international companies to constantly monitor their financial situation and their related companies with which they cooperate. Globalization process has delivered a complex network of relationships in the business environment. Due to increase in complexity of related business environment, forecasting the financial health of companies nowadays became increasingly important and worthwhile to analyse (Korol 2013). Bankruptcy is a continuous process, which can be distinguished into several stages, starting from the emergence of the first signs of financial crisis, through blindness and ignorance towards the financial and nonfinancial symptoms of crisis in a firm, to inappropriate activities that lead to the final phase of the crisis, which is bankruptcy. The Bankruptcy process cycle may take up to 5â€"6 years which is not a sudden phenomenon and impossible to predict, however the earlier warning signals can be detected and corrective measures may avoid the ultimate bankruptcy event depending on the preparation and reactions of the management to tackle the bankruptcy (Korol 2013). Due to the recent worldwide corporate financial crisis the need to reform the existing financial architecture has been intensified. Objective of business crisis prediction is to build models that can read the risk factors from the past observations and evaluate business crisis risk of companies with a much broader scope (Lin et al. 2011). Ozkan cited in Lin et al. 2011 mentioned that financial indicators has been reviewed by number of researchers as a major basis for predicting financial distress and some common methodologies including peer group analysis, comprehensive risk assessment systems, and statistical and econometric analysis. Premachandra (2009) argued that bankruptcy prediction is important because corporate failure imposes significant direct and indirect costs on stakeholders. Warner cited in Premachandra (2009), evidence suggests that direct bankruptcy costs (such as court costs, lawyers and accountants fees) may be as low as 5%, or (Altman cited in Premachandra 2009) can shoot up to 28% when both direct and indirect costs (such as lost sales, lost profits, higher cost of credit, inability to issue new securities and lost investment opportunities) are considered. Therefore the early detection of potential bankruptcy is very important due to corporate decision makers make their decisions in a world of dynamic technology development, imperfect knowledge and uncertainty (Premachandra 2009). Niewrzedowski cited in Korol (2013) indicated that as per statistical analysis by Huler-Hermes, the number of potential bankruptcy has been increased in USA by 54%, in Spain by 118% and in the UK by 56%. Therefore the importance of early warning of potential bankruptcy has been increased along with the overall increase of bankruptcy risk in companies around the world.

Global Economic Impact of COVID-19: Evidence from Insider Trades
Anginer, Deniz,Donmez, Anil,Seyhun, H. Nejat,Zhang, Ray
We examine insider trades around the onset of the COVID-19 pandemic. Insiders purchased shares in record numbers after the stock market decline that began in late February 2020. We find that insider purchases were more pronounced for larger firms, value firms, firms with high levels of leverage as well as firms in the finance, energy and consumer nondurable sectors. These results suggest that insiders believe the impact of COVID-19 on global economic activity and the stock prices of their companies to be temporary. We also find some evidence of opportunistic insider selling in January and February 2020 prior to the stock market decline, suggesting that some insiders anticipated the decline. Finally, we find similar patterns in insider trading in Canada, Italy, Spain and South Korea, but a more muted response in China. Our results indicate that insiders’ private information became especially valuable during this period of significant market disruption.

Heterogeneous Effects of Job Displacement on Earnings
Afrouz Azadikhah Jahromi,Brantly Callaway

This paper considers how the effect of job displacement varies across different individuals. In particular, our interest centers on features of the distribution of the individual-level effect of job displacement. Identifying features of this distribution is particularly challenging -- e.g., even if we could randomly assign workers to be displaced or not, many of the parameters that we consider would not be point identified. We exploit our access to panel data, and our approach relies on comparing outcomes of displaced workers to outcomes the same workers would have experienced if they had not been displaced and if they maintained the same rank in the distribution of earnings as they had before they were displaced. Using data from the Displaced Workers Survey, we find that displaced workers earn about $157 per week less than they would have earned if they had not been displaced. We also find that there is substantial heterogeneity. We estimate that 42% of workers have higher earnings than they would have had if they had not been displaced and that a large fraction of workers have experienced substantially more negative effects than the average effect of displacement. Finally, we also document major differences in the distribution of the effect of job displacement across education levels, sex, age, and counterfactual earnings levels. Throughout the paper, we rely heavily on quantile regression. First, we use quantile regression as a flexible (yet feasible) first step estimator of conditional distributions and quantile functions that our main results build on. We also use quantile regression to study how covariates affect the distribution of the individual-level effect of job displacement.

Implied Equity Duration: A Measure of Pandemic Shut-Down Risk
Dechow, Patricia,Erhard, Ryan,Sloan, Richard G.,Soliman, Mark T.
Implied equity duration was originally developed to analyze the sensitivity of equity prices to discount rate changes. We demonstrate that implied equity duration is also well suited to analyzing the sensitivity of equity prices to pandemic shutdowns. Pandemic shutdowns primarily impact short-term cash flows and thus have a greater impact on low duration equities. We show that implied equity duration has a strong positive relation to US equity returns during the onset of the 2020 coronavirus lockdown. Our analysis also demonstrates that the underperformance of ‘value’ stocks during this period is a rational response to their lower durations.

Macro-Structural Obstacles to Firm Performance: Evidence from 2,640 Firms in Nigeria
Hosny, Amr
A recent World Bank enterprise survey identified access to finance as the top constraint to Doing Business in Nigeria. In this context, the objective of this paper is two-fold: (i) study firm characteristics associated with more access to finance and export diversification; and (ii) quantify the impact of these structural obstacles on firm performance. Results suggest that (i) larger and export-oriented firms are about 40 percentage points less likely to report access to finance as a business obstacle, while firms perceiving access to finance as a constraint are, on average, about 10-40 percentage points less likely to be export-oriented diversified firms; and (ii) better access to finance and export diversification can help firm employment -as much as 80 percent higher- and capacity utilization. Results are largely robust to different specifications and estimation methods.

Macroprudential Policies, Economic Growth, and Banking Crises
Belkhir, Mohamed,Ben Naceur, Sami,Candelon, Bertrand,Wijnandts, Jean-Charles
Using a sample that covers more than 100 countries over the 2000-2017 period, we assess the impact of macroprudential policies on financial stability. In particular, we examine whether the activation of macroprudential policies is conducive to a lower incidence of systemic banking crises. Our empirical setup is designed to account for the potential direct and indirect effects that macroprudential policies can have on banking crises. We find that while macro-prudential policies exert a direct stabilizing effect, they also have an indirect destabilizing effect, which works through the depressing of economic growth. A Generalized Impulse Response Function analysis of a dynamic system composed of the probability of a banking crisis and economic growth reveals, however, that macroprudential policies have a positive net effect on financial stability (lower likelihood of systemic banking crises).

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 enormous challenges for the development and validation of optimal execution strategies. We propose a model-free approach by training Reinforcement Learning (RL) agents in a realistic market simulation environment with multiple agents. First, we have configured a multi-agent historical order book simulation environment for execution tasks based on an Agent-Based Interactive Discrete Event Simulation (ABIDES) [arXiv:1904.12066]. Second, we formulated the problem of optimal execution in an RL setting in which an intelligent agent can make order execution and placement decisions based on market microstructure trading signals in HFT. Third, we developed and trained 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 evaluated the simulation with our RL agent by comparing the simulation on the actual market Limit Order Book (LOB) characteristics.

Non-Resident Holdings of Domestic Debt in Nigeria: Internal or External Driven?
Hosny, Amr
Foreign holdings of domestic debt instruments in Nigeria have been increasing. Using data over 2007M1-2019M1, we show that, on average, global factors (global interest rates, oil prices) seem to carry more weight than domestic factors (treasury bills rate and domestic risk) in foreign portfolio invetsors' decisions in Nigeria. Specifically, we show that foreign participation is, in the long run, positively correlated with oil prices and profitable rates of return on local-currency instruments, but negatively correlated with exchange rate depreciation pressures. In the short run, oil prices, opportunity cost of funds and perception of Nigeria-specific risks also play a role. These results highlight the volatile short-term nature of such flows and call for a package of policy reforms to attract longer term direct investments.

Noncompliance with SEC Regulations: Evidence from Timely Loan Disclosures
Caskey, Judson,Huang, Kanyuan (Kevin),Saavedra, Daniel
We use required 8-K filings around major borrowings to shed light on firms’ choices of whether to comply with SEC disclosure rules. We first develop a simple model in which the firm weighs the benefit of obscuring the disclosure of an adverse event, against the cost of failing to comply with a rule that mandates disclosure. In the context of loan disclosures, the model predicts that the benefit of nondisclosure increases in the loan spread, and decreases in the ex ante probability of borrowing and in investors’ ability to infer the loan information from a subsequent 10-K or 10-Q. Consistent with the model, and exploiting within-firm variation, we find that firms are more likely to hide loans with high spreads. Firms are less likely to hide loans when investors anticipate borrowing during asset acquisition and when firms are followed by more equity analysts. Lastly, we provide evidence that the SEC does not rigorously enforce compliance with 8-K loan disclosures.

Optimal trade execution in an order book model with stochastic liquidity parameters
Julia Ackermann,Thomas Kruse,Mikhail Urusov

We analyze an optimal trade execution problem in a financial market with stochastic liquidity. To this end we set up a limit order book model in which both order book depth and resilience evolve randomly in time. Trading is allowed in both directions and at discrete points in time. We derive an explicit recursion that, under certain structural assumptions, characterizes minimal execution costs. We also discuss several qualitative aspects of optimal strategies, such as existence of profitable round trips or closing the position in one go, and compare our findings with the literature.

Predicting Downside Risks to House Prices and Macro-Financial Stability
Deghi, Andrea,Katagiri, Mitsuru,Shahid, Sohaib,Valckx, Nico
This paper predicts downside risks to future real house price growth (house-prices-at-risk or HaR) in 32 advanced and emerging market economies. Through a macro-model and predictive quantile regressions, we show that current house price overvaluation, excessive credit growth, and tighter financial conditions jointly forecast higher house-prices-at-risk up to three years ahead. House-prices-at-risk help predict future growth at-risk and financial crises. We also investigate and propose policy solutions for preventing the identified risks. We find that overall, a tightening of macroprudential policy is the most effective at curbing downside risks to house prices, whereas a loosening of conventional monetary policy reduces downside risks only in advanced economies and only in the short-term.

Protecting Investors in Equity Crowdfunding: An Empirical Analysis of the Small Investor Protection Act
Goethner, Maximilian,Hornuf, Lars,Regner, Tobias
During the past decade, equity crowdfunding (ECF) has emerged as an alternative funding channel for startup firms. In Germany, the Small Investor Protection Act became binding in July 2015, with the legislative goal to protect investors engaging in this new asset class. Since then, investors pledging more than 1,000 EUR now must self-report their income and wealth. Investing more than 10,000 EUR in a single ECF issuer is only possible through a corporate entity. We examine how the Small Investor Protection Act has affected investor behavior at Companisto, Germany's largest ECF portal for startup firms. The results show that after the new law became binding, sophisticated investors invest less on average while casual investors invest more. Moreover, the signaling capacity of large investments has disappeared.

Public-Private Partnership in the Management of Natural Disasters: A Review
Selene Perazzini

Natural hazards can considerably impact the overall society of a country. As some degree of public sector involvement is always necessary to deal with the consequences of natural disasters, central governments have increasingly invested in proactive risk management planning. In order to empower and involve the whole society, some countries have established public-private partnerships, mainly with the insurance industry, with satisfactorily outcomes. Although they have proven necessary and most often effective, the public-private initiatives have often incurred high debts or have failed to achieved the desired risk reduction objectives. We review the role of these partnerships in the management of natural risks, with particular attention to the insurance sector. Among other country-specific issues, poor risk knowledge and weak governance have widely challenged the initiatives during the recent years, while the future is threatened by the uncertainty of climate change and unsustainable development. In order to strengthen the country's resilience, a greater involvement of all segments of the community, especially the weakest layers, is needed and the management of natural risks should be included in a sustainable development plan.

Quais os Fatores de Risco Relevantes aos Investidores? Evidências no Mercado de Fundos Brasileiro (Which Factors Matter to Investors? Evidence from Brazilian Mutual Funds)
Cavalcante Filho, Elias,De-Losso, Rodrigo,Santos, Jose Carlos
Portuguese Abstract: Neste artigo investiga-se o que determina o fluxo de recursos para fundos de investimentos brasileiros. Constata-se que investidores são mais atentos ao risco de mercado (beta) ao avaliar fundos, enquanto tendem a atribuir o retorno de fatores como tamanho, valor, momentum, iliquidez e risco de indústrias ao alfa. Usando medidas de variação da sofisticação de investidores, constata-se também que investidores mais sofisticados tendem a avaliar fundos com base em critérios mais complexos. O resultado é aderente ao observado para os EUA. Adicionalmente, é observado que investidores menos sofisticados demonstram ser mais sensíveis a todas métricas de retorno passado, porém, ao decompor os alfas dos fundos em componente persistente e componente aleatório, evidencia-se que essa sensibilidade está concentrada no componente aleatório dos alfas.English Abstract: In this article we investigate the drivers of investment flows to Brazilian mutual funds. Investors pay most attention to market risk (beta) when evaluating funds, while they attribute returns to size, value, momentum, and industry factors to alpha. Using measures for investor sophistication, we also find that more sophisticated investors tend to evaluate funds using more sophisticated criteria. These results are consistent with those reported for the US stock market. We moreover document that less sophisticated investors are relatively more sensitive to all past return metrics. When fund alphas are decomposed into a persistent component and a random component, however, the greater sensitivity is concentrated in the random component of alphas.

Quant Bust 2020
Zura Kakushadze

We explain in a nontechnical fashion why dollar-neutral quant trading strategies, such as equities Statistical Arbitrage, suffered substantial losses (drawdowns) during the COVID-19 market selloff. We discuss: (i) why these strategies work during "normal" times; (ii) the market regimes when they work best; and (iii) their limitations and the reasons for why they "break" during extreme market events. An accompanying appendix (with a link to freely accessible source code) includes backtests for various strategies, which put flesh on and illustrate the discussion in the main text.

Real Exchange Rate Overshooting in Large Depreciations: Determinants and Consequences
Culiuc, Alexander
The consequences of large depreciations on economic activity depend on the relative strength of the contractionary balance sheet and expansionary expenditure switching effects. However, the two operate over different time horizons: the balance sheet effect hits almost immediately, while expenditure switching is delayed by nominal rigidities and other frictions. The paper hypothesizes that the overshooting phase-observed early in the depreciation episode and driven by the balance sheet effect-is largely irrelevant for expenditure switching, which is more closely aligned with ex-post equilibrium depreciation. Given this, larger real exchange rate overshooting should signal a relatively stronger balance sheet effect. Empirical findings support this hypothesis: (i) overshooting is driven by factors associated with the balance sheet effect (high external debt, low reserves, low trade openness), (ii) overshooting-based measures of the balance sheet effect foreshadow post-depreciation output losses, and (iii) the balance sheet effect is strongest early on, while expenditure switching strengthens over the medium term.

Relative utility bounds for empirically optimal portfolios
Dmitry B. Rokhlin

We consider a single-period portfolio selection problem for an investor, maximizing the expected ratio of the portfolio utility and the utility of a best asset taken in hindsight. The decision rules are based on the history of stock returns with unknown distribution. Assuming that the utility function is Lipschitz or H\"{o}lder continuous (the concavity is not required), we obtain high probability utility bounds under the sole assumption that the returns are independent and identically distributed. These bounds depend only on the utility function, the number of assets and the number of observations. For concave utilities similar bounds are obtained for the portfolios produced by the exponentiated gradient method. Also we use statistical experiments to study risk and generalization properties of empirically optimal portfolios. Herein we consider a model with one risky asset and a dataset, containing the stock prices from NYSE.

Reusing Natural Experiments
Heath, Davidson,Ringgenberg, Matthew,Samadi, Mehrdad,Werner, Ingrid M.
After a natural experiment is first used, other researchers often reuse the setting, examining different outcome variables. We examine the consequences of reusing an experimental setting using two extensively studied natural experiments, business combination laws and the Regulation SHO pilot. We apply multiple hypothesis corrections and our findings suggest many results in the existing literature are false positives. We provide guidelines for inference when an experiment is reused using simulation evidence for several popular empirical settings including difference-in-differences regressions, instrumental variables regressions, and regression discontinuity designs.

Short-Sale Deregulation and Corporate Tax Avoidance: Evidence From the Chinese Market
Cao, Yue,Dong, Yizhe ,Guo, Tianxiao,Ma, Diandian
We study how short selling affects corporate tax avoidance. By exploiting staggered short-sale deregulation on the Chinese stock market as a source of variation in market pressure and monitoring, our difference-in-differences estimates show that the introduction of a short-selling scheme significantly discourages pilot firms from engaging in tax avoidance. We also find that the negative effect of short selling on tax avoidance is more pronounced for firms that have high advertising costs and high institutional holdings and are located in weak tax law-enforcement regions. We further reveal that short selling has an indirect effect on tax avoidance through the additional external pressure exerted by auditors, media, and financial analysts. Our evidence highlights the monitoring and discipline roles that short sellers play in determining the level of corporate tax avoidance.

Striking Oil in the Boardroom: Overpaying Executives through Manipulating Performance Metrics
Atanasov, Vladimir A.,Boutchkova, Maria
Using hand-collected proxy statement data, we examine the distribution of performance metrics used to calculate executive compensation in 86 US oil and gas firms. We find that the distribution of achievedâ€"target differences is significantly discontinuous at zero over the 13-year period 2006-2018. Executives are three times more likely to just beat than to just miss their performance targets. When we split metrics into GAAP-like and non-GAAP-like measures, we find discontinuities only in the non-GAAP-like group. The discontinuities also disappear when firms are financially distressed or have better governance. Our findings suggest that managers can routinely manipulate performance metrics in order to increase their performance-based compensation. Our framework can help analysts and investors in their firm governance assessments and the US Securities and Exchange Commission in its current efforts to redesign disclosure rules for non-GAAP performance metrics. The discontinuity detection approach we introduce can help SEC pre-filter filings and focus its review process.

Tech in Fin Before FinTech: Blessing or Curse for Financial Stability?
Pierri, Nicola,Timmer, Yannick
Motivated by the world-wide surge of FinTech lending, we analyze the implications of lenders' information technology adoption for financial stability. We estimate bank-level intensity of IT adoption before the global financial crisis using a novel dataset that provides information on hardware used in US commercial bank branches after mapping them to their parent bank. We find that higher intensity of IT-adoption led to significantly lower non-performing loans when the crisis hit: banks with a one standard deviation higher IT-adoption experienced 10% lower non-performing loans. High-IT-adoption banks were not less exposed to the crisis through their geographical footprint, business model, funding sources, or other observable characteristics. Loan-level analysis indicates that high-IT-adoption banks originated mortgages with better performance and did not offload low-quality loans. We apply a simple text-analysis algorithm to the biographies of top executives and find that banks led by more 'tech-oriented' managers adopted IT more intensively and experienced lower non-performing loans during the crisis. Our results suggest that technology adoption in lending can enhance financial stability through the production of more resilient loans.

The Anatomy of the Transmission of Macroprudential Policies
Acharya, Viral,Bergant, Katharina,Crosignani, Matteo,Eisert, Tim,McCann, Fergal
We analyze how regulatory constraints on household leverage-in the form of loan-to-income and loan-to-value limits-a?ect residential mortgage credit and house prices as well as other asset classes not directly targeted by the limits. Supervisory loan level data suggest that mortgage credit is reallocated from low-to high-income borrowers and from urban to rural counties. This reallocation weakens the feedback loop between credit and house prices and slows down house price growth in 'hot' housing markets. Consistent with constrained lenders adjusting their portfolio choice, more-a?ected banks drive this reallocation and substitute their risk-taking into holdings of securities and corporate credit.

The Implications of the COVID-19 Pandemic for Pensions
Sutcliffe, Charles
COVID-19 and the lockdowns have had a big global economic effect, as well as increasing mortality. We examine the effects of COVID-19 and the resulting relaxations of pension regulations on pension schemes. Those who transfer their pension or withdraw cash from their pension pot while asset prices are depressed by COVID-19 are losers; as are members of defined benefit schemes with a deficit whose employer fails due to COVID-19. The increased mortality from covid-19 will have a minimal effect on pensions. If economies recover to pre-covid-19 levels, the long run effects on pensions should be small.

The Treasury Bill Risk Premium: Why T-Bills Are About as Risky as Stocks in the Long Term
White, James,Haghani, Victor
These days it’s become convention (reinforced by the media’s treatment of wealth) to assess our net worth by tallying up the market value of our financial assets, even though it’s more natural and useful to think of our wealth as a stream of dollars over time given the nature of our income and spending. Once we entertain the idea that what we really care about is the long-term, inflation-adjusted purchasing power of our financial assets (what we’ll call the Real Annuity Value of our wealth), we’ll need to make some big changes to how we save and invest. Most significantly, we’ll have to abandon the notion that T-bills and other cash proxies, such as money market funds and bank deposits, are the lowest-risk assets we can own. While it’s true that the nominal value of T-bills doesn’t go up or down much day to day, we’ll see them as dramatically more risky once we focus on their Real Annuity Value.

Trading Privacy for the Greater Social Good: How Did America React During COVID-19?
Anindya Ghose,Beibei Li,Meghanath Macha,Chenshuo Sun,Natasha Ying Zhang Foutz

Digital contact tracing and analysis of social distancing from smartphone location data are two prime examples of non-therapeutic interventions used in many countries to mitigate the impact of the COVID-19 pandemic. While many understand the importance of trading personal privacy for the public good, others have been alarmed at the potential for surveillance via measures enabled through location tracking on smartphones. In our research, we analyzed massive yet atomic individual-level location data containing over 22 billion records from ten Blue (Democratic) and ten Red (Republican) cities in the U.S., based on which we present, herein, some of the first evidence of how Americans responded to the increasing concerns that government authorities, the private sector, and public health experts might use individual-level location data to track the COVID-19 spread. First, we found a significant decreasing trend of mobile-app location-sharing opt-out. Whereas areas with more Democrats were more privacy-concerned than areas with more Republicans before the advent of the COVID-19 pandemic, there was a significant decrease in the overall opt-out rates after COVID-19, and this effect was more salient among Democratic than Republican cities. Second, people who practiced social distancing (i.e., those who traveled less and interacted with fewer close contacts during the pandemic) were also less likely to opt-out, whereas the converse was true for people who practiced less social-distancing. This relationship also was more salient among Democratic than Republican cities. Third, high-income populations and males, compared with low-income populations and females, were more privacy-conscientious and more likely to opt-out of location tracking.

Understanding the Effects of Tennessee's Open Covid-19 Testing Policy: Bounding Policy Effects with Nonrandomly Missing Data
Brantly Callaway,Tong Li

Increased testing for Covid-19 is seen as one of the most important steps to re-open the economy. The current paper considers Tennessee's "open-testing" policy where the state substantially increased testing while removing symptom requirements for individuals to be tested. To understand the effect of the policy, we combine standard identifying assumptions with additional weak assumptions to deal with non-random testing that lead to bounds on policy effects of interest. Our results suggest that Tennessee's open-testing policy has reduced total cases (which are not fully observed), confirmed cases, and trips to work among counties with a fast-growing number of confirmed cases.

Who Gets Duped? The Impact of Economics and Finance Education on Skepticism in an Investment Task
Blackwell, Calvin,Maynard, Norman,Malm, James,Pyles, Mark,Snyder, Marcia S.,Witte, Mark David
Many financial scandals appear to depend on a lack of skepticism on the part of their victims. For example, sophisticated investors trusted Bernie Madoff, despite early warning signs of implausible returns. Our study investigates how education and personality explain skeptical behavior in financial decision making. In a simple survey, economics and finance students are asked to make an investment recommendation from among four different hypothetical funds, including one based on Madoff’s fund. We find that perceptions of the suspiciousness and ethicality of the Madoff fund affect a participant’s recommendation. In turn, these perceptions are affected by education and personality, with more education leading to higher suspicions.

Working Capital Management in Indian Steel Industry
Shroff, Sumita J
Working capital management is a very significant decision aspect of every business entity as it facilitates smooth functioning, revenue generation, and efficient asset utilization. In this context, the current study examined the structure of current assets, working capital policy comprising of current asset investment, financing, inventory policy, credit policy, and working capital leverage of the Indian Steel Industry over a period of 12 years from 2000-01 to 2011-12. Further, the impact of working capital leverage on Return on Total Assets is also examined. The analysis revealed that the asset structure of the steel industry is liquid with a higher proportion of current assets. Receivables comprised of the major component in the current asset structure. The results of the regression analysis revealed that the industry is pursuing a conservative current asset investment and aggressive current asset financing policy over the study period. A significant downtrend was observed for CR and QR indicating deterioration of liquidity position in the Indian Steel industry.

Working Capital Management of Market Leaders
Shroff, Sumita J
Considering the significance of working capital management, the current study attempts to examine various aspects of working capital management, i.e., current asset structure, liquidity, working capital policy, and working capital management efficiency as represented by working capital ratios of the BSE Listed firms for a period of twelve years from 2000 to 2012. The analysis revealed that market leaders pursue a moderate working capital investment policy whereas an aggressive working capital financing policy. Loans and Advances had the highest share in the structure of their current assets. Market leaders enjoy sound liquidity position and manage their inventories andreceivables in an efficient manner. They effectively utilize the investment of their current assets. The results of time trend analysis revealed a significant uptrend in CATAR, CLTAR, CLTDR, CASR, ALR, CBBTCAR, ITR and DTR whereas a significant downtrend in ITCAR, DTCAR, OC, and NTC.