Research articles for the 2020-08-27

A Deep Learning Approach to Estimate Forward Default Intensities
Divernois, Marc-Aurèle
This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous literature is to allow the estimation of non-linear forward intensities by using neural networks instead of classical maximum likelihood estimation. The model specification allows an easy replication of previous literature using linear assumption and shows the improvement that can be achieved.

A Spatial Analysis of Disposable Income in Ireland: A GWR Approach
Paul Kilgarriff,Martin Charlton

This paper examines the spatial distribution of income in Ireland. Median gross household disposable income data from the CSO, available at the Electoral Division (ED) level, is used to explore the spatial variability in income. Geary's C highlights the spatial dependence of income, highlighting that the distribution of income is not random across space and is influenced by location. Given the presence of spatial autocorrelation, utilising a global OLS regression will lead to biased results. Geographically Weighted Regression (GWR) is used to examine the spatial heterogeneity of income and the impact of local demographic drivers on income. GWR results show the demographic drivers have varying levels of influence on income across locations. Lone parent has a stronger negative impact in the Cork commuter belt than it does in the Dublin commuter belt. The relationship between household income and the demographic context of the area is a complicated one. This paper attempts to examine these relationships acknowledging the impact of space.

A comprehensive analysis of soccer penalty shootout designs
László Csató,Dóra Gréta Petróczy

The standard rule of soccer penalty shootouts has received serious criticism due to its bias towards the team kicking the first penalty in each round. The rule-making body of the sport has decided in 2017 to try alternative designs. This paper offers an extensive overview of eight penalty shootout mechanisms, one of them first introduced here. Their fairness is analysed under three possible mathematical models of psychological pressure. We also consider the probability of reaching the sudden death stage, as well as the complexity and strategy-proofness of the rules. Some designs are found to be inferior because they do not lead to a substantial gain in fairness compared to simpler mechanisms. Changing the standard rule by reversing the shooting order in the sudden death stage improves fairness, while it remains less complicated than the regulation of field hockey shootouts. Our work has the potential to impact decision-makers who can save resources by choosing only theoretically competitive policy options for field experiments.

Accounting Conservatism and Corporate Social Responsibility
Anagnostopoulou, Seraina C.,Tsekrekos, Andrianos E.,Voulgaris, Georgios
We examine the association between accounting conservatism, expressed in the form of asymmetric timeliness of recognition of economic gains and losses, and corporate social responsibility (CSR). We provide evidence that, under unfavorable macroeconomic conditions and financial constraints, as well as increased levels of outside pressure from debt-holders and equity holders, catering for capital providers through conservative reporting becomes a managerial priority over engagement in CSR. Our results overall indicate that, for our whole sample period (starting in the early 2000s), higher levels of conservatism are negatively associated with a CSR orientation shown by firms; however, our analysis also indicates a significant reversing trend regarding the effect of conservatism on CSR, coinciding with the post-financial-crisis period. The findings are robust to a number of specifications and tests, including the use of an instrumental variable approach explicitly addressing endogeneity biases related to reverse causality concerns. Our study suggests that, under monitoring pressure from financial stakeholders, firms prioritize commitment to accounting conservatism over the needs of non-financial stakeholders and other interest groups.

Artificial Intelligence, Firm Growth, and Industry Concentration
Babina, Tania,Fedyk, Anastassia,He, Alex Xi,Hodson, James
Which firms invest in artificial intelligence (AI) technologies, and how do these investments affect individual firms and industries? We provide a comprehensive picture of the use of AI technologies and its impact among US firms over the last decade, using a unique combination of job postings and individual-level employment profiles. We introduce a novel measure of investments in AI technologies based on human capital and document that larger firms with higher sales, markups, and cash reserves tend to invest in AI more. Firms that invest more in AI experience faster growth in both sales and employment, which translates into analogous growth at the industry level. The positive effects are concentrated among the largest firms, leading to a positive correlation between AI investments and an increase in industry concentration. However, increases in concentration are not accompanied by either increased mark-ups or increased productivity. Our results are robust to instrumenting firm-level AI investments with foreign industry-level AI investments and local variation in industry-level AI investments, and we document consistent patterns across measures of AI using firms' demand for AI talent (job postings) and actual AI talent (resumes). Overall, our findings support the view that new technologies, such as AI, increase the scale of the most productive firms and contribute to the rise of superstar firms.

Bankruptcy Process for Sale
Ayotte, Kenneth,Ellias, Jared A.
The lenders that fund Chapter 11 reorganizations exert significant influence over the bankruptcy process through the contract associated with the debtor-in-possession (“DIP”) loan. In this Article, we study a large sample of DIP loan contracts and document a trend: over the past three decades, DIP lenders have steadily increased their contractual control of Chapter 11. In fact, today’s DIP loan agreements routinely go so far as to dictate the very outcome of the restructuring process. When managers sell control over the bankruptcy case to a subset of the creditors in exchange for compensation, we call this transaction a “bankruptcy process sale.” We model two situations where process sales raise bankruptcy policy concerns: (1) when a senior creditor leverages the debtor’s need for financing to lock in a preferred outcome at the outset of the case (“plan protection”); and (2) when a senior creditor steers the case to protect its claim against litigation (“entitlement protection”). We show that both scenarios can lead to bankruptcy outcomes that fail to maximize the value of the firm for creditors as a whole. We study a new dataset that uses the text of 1.5 million court documents to identify creditor conflict over process sales, and our analysis offers evidence consistent with the predictions of the model.

Bayesian Filtering for Multi-period Mean-Variance Portfolio Selection
Shubhangi Sikaria,Rituparna Sen,Neelesh S. Upadhye

For a long investment time horizon, it is preferable to rebalance the portfolio weights at intermediate times. This necessitates a multi-period market model in which portfolio optimization is usually done through dynamic programming. However, this assumes a known distribution for the parameters of the financial time series. We consider the situation where this distribution is unknown and needs to be estimated from the data that is arriving dynamically. We applied Bayesian filtering through dynamic linear models to sequentially update the parameters. We considered uncertain investment lifetime to make the model more adaptive to the market conditions. These updated parameters are put into the dynamic mean-variance problem to arrive at optimal efficient portfolios. Extensive simulations are conducted to study the effect of varying underlying parameters and investment horizon on the performance of the method. An implementation of this model to the S&P500 illustrates that the Bayesian updating is strongly favored by the data and that it is practically implementable.

Can Decentralized Markets Be More Efficient?
Glode, Vincent,Opp, Christian C.
Decentralized markets attract large amounts of trade volume, even though they exhibit frictions absent in centralized exchanges. We develop a model with asymmetric information and expertise acquisition where some traders try to exploit any market structure to inefficiently screen their counterparties. In this environment, frictions characteristic of decentralized markets, such as time consuming search, can promote higher efficiency. First, screening behavior may be less aggressive when traders reach fewer counterparties. Second, for asset classes where information improves allocative efficiency, decentralized markets with predictable trading encounters may dominate by encouraging expertise acquisition. In contrast, when information causes adverse selection, centralized markets dominate.

Changes in mobility and socioeconomic conditions in Bogot\'a city during the COVID-19 outbreak
Marco Dueñas,Mercedes Campi,Luis Olmos

We analyze mobility changes following the implementation of containment measures aimed at mitigating the spread of COVID-19 in Bogot\'a, Colombia. We characterize the mobility network before and during the pandemic and analyze its evolution and changes between January and July 2020. We then link the observed mobility changes to socioeconomic conditions, estimating a gravity model to assess the effect of socioeconomic conditions on mobility flows. We observe an overall reduction in mobility trends, but the overall connectivity between different areas of the city remains after the lockdown, reflecting the mobility network's resilience. We find that the responses to lockdown policies depend on socioeconomic conditions. Before the pandemic, the population with better socioeconomic conditions shows higher mobility flows. Since the lockdown, mobility presents a general decrease, but the population with worse socioeconomic conditions shows lower decreases in mobility flows. We conclude deriving policy implications.

Cross-Border Buyout Pricing
Hammer, Benjamin,Janssen, Nils,Schwetzler, Bernhard
Using a dataset of 1,149 global private equity transactions, we find that cross-border buyouts are associated with significantly higher valuation multiples than domestic ones. We attribute this finding to informational disadvantages of foreign acquirers. Consistent with this idea, we find that the spread in valuation multiples becomes smaller when the target operates in a country with high accounting standards, when it was publicly listed prior to the buyout, and when information production is facilitated due to large firm size. Further results suggest that local partnering in a syndicate serves as an effective remedy to avoid adverse pricing effects. The spread in valuation multiples is also less pronounced for large buyout funds, presumably because they draw on sufficient organizational resources to cope with cross-border-related transaction costs.

Deep Learning for Constrained Utility Maximisation
Ashley Davey,Harry Zheng

This paper proposes two algorithms for solving stochastic control problems with deep reinforcement learning, with a focus on the utility maximisation problem. The first algorithm solves Markovian problems via the Hamilton Jacobi Bellman (HJB) equation. We solve this highly nonlinear partial differential equation (PDE) with a second order backward stochastic differential equation (2BSDE) formulation. The convex structure of the problem allows us to describe a dual problem that can either verify the original primal approach or bypass some of the complexity. The second algorithm utilises the full power of the duality method to solve non-Markovian problems, which are often beyond the scope of stochastic control solvers in the existing literature. We solve an adjoint BSDE that satisfies the dual optimality conditions. We apply these algorithms to problems with power, log and non-HARA utilities in the Black-Scholes, the Heston stochastic volatility, and path dependent volatility models. Numerical experiments show highly accurate results with low computational cost, supporting our proposed algorithms.

Deep learning Profit & Loss
Pietro Rossi,Flavio Cocco,Giacomo Bormetti

Building the future profit and loss (P&L) distribution of a portfolio holding, among other assets, highly non-linear and path-dependent derivatives is a challenging task. We provide a simple machinery where more and more assets could be accounted for in a simple and semi-automatic fashion. We resort to a variation of the Least Square Monte Carlo algorithm where interpolation of the continuation value of the portfolio is done with a feed forward neural network. This approach has several appealing features not all of them will be fully discussed in the paper. Neural networks are extremely flexible regressors. We do not need to worry about the fact that for multi assets payoff, the exercise surface could be non connected. Neither we have to search for smart regressors. The idea is to use, regardless of the complexity of the payoff, only the underlying processes. Neural networks with many outputs can interpolate every single assets in the portfolio generated by a single Monte Carlo simulation. This is an essential feature to account for the P&L distribution of the whole portfolio when the dependence structure between the different assets is very strong like the case where one has contingent claims written on the same underlying.

DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data
Aiusha Sangadiev,Rodrigo Rivera-Castro,Kirill Stepanov,Andrey Poddubny,Kirill Bubenchikov,Nikita Bekezin,Polina Pilyugina,Evgeny Burnaev

This work proposes DeepFolio, a new model for deep portfolio management based on data from limit order books (LOB). DeepFolio solves problems found in the state-of-the-art for LOB data to predict price movements. Our evaluation consists of two scenarios using a large dataset of millions of time series. The improvements deliver superior results both in cases of abundant as well as scarce data. The experiments show that DeepFolio outperforms the state-of-the-art on the benchmark FI-2010 LOB. Further, we use DeepFolio for optimal portfolio allocation of crypto-assets with rebalancing. For this purpose, we use two loss-functions - Sharpe ratio loss and minimum volatility risk. We show that DeepFolio outperforms widely used portfolio allocation techniques in the literature.

Exchange-Traded Confusion: How Industry Practices Undermine Product Comparisons in Exchange Traded Funds
Clements, Ryan
Despite their incredible popularity and importance to modern capital markets, exchange traded funds (ETFs) are extremely difficult to compare side-by-side. Investors who successfully navigate the initial challenges of product choice overload, and opaque index construction methodology, soon encounter a wide array of discretionary operational, management, marketing, and financial practices of ETF sponsors that combine to undermine simple product and performance comparisons. This dilemma is compounded by disclosure effectiveness challenges given investor cognitive limitations and behavioral tendencies. This article is the first scholarly work, amongst a growing body of ETF studies, to illustrate why accurate “apples to apples” product comparisons in ETFs are so challenging (at times even impossible) to perform. It presents a variety of ETF case studies to demonstrate this challenge including recent performance instabilities in the coronavirus pandemic. It advocates for continued positive momentum around investor-focused reforms in ETFs, building on encouraging steps undertaken by the U.S. Securities & Exchange Commission in its recent “Rule 6c-11” under the Investment Company Act of 1940. It makes several recommendations to improve ETF product comparisons including standardizing website formats and layouts for information presentation, uniform calculation methodologies of key ETF variables, an ETF naming convention, and standard terms in sustainable investing. ETF investors would also greatly benefit from a systematized and structured electronic reporting mechanism whereby standardized data is provided by ETF sponsors to a centrally controlled public repository. Additional studies are warranted on strategic ETF disclosure ordering, digital enhancement, and added contextual discussion around critical concepts like arbitrage and index composition methodology. The ETF “model portfolio” industry is also an emerging concern that should be assessed, and the article provides suggestions to reduce informational opacity and improve comparative assessments.

Financial Crisis, Excessive Pay And Fat Cats: Why Employment Scholars Should Start Reflecting on Regulation of Executive Remuneration
Gaudio, Giovanni
In the aftermath of the 2007-2008 financial crisis, flawed variable pay structures of executives were blamed by many for contributing to the build-up of the global financial turmoil, as they allegedly incentivized them to engage in excessive risk-taking. Legislators around the globe decided to regulate remuneration structures of the fat cats in the financial industry with a view to better align their compensation with effective risk management practices. Since 2010, several Directives have been adopted at EU level, imposing on financial institutions a combination of mandatory norms regarding how the variable part of remuneration is to be paid out. Although this topic has been widely investigated by corporate governance researchers, it has been largely neglected by labour law scholars. This article tries to fill this gap, analysing the issues of mandatory pay structure in the financial industry through the lenses of employment law. These peculiar mandatory norms can be interesting for labour lawyers for the following two reasons. First, because they establish a structural participative nexus between employers and their executives, contributing to set up novel possible forms of managerial democracy in financial institutions. Second, because these mandatory norms are characterized, as many employment norms, by inderogability. However, their structure is reversed. Rather than being unidirectional in favour of employees, they protect employers, due to the predominant public interest of safeguarding the stability and soundness of the financial system. The article concludes by analysing the role that norms regarding pay structure can play in the evolutionary process of employment norms’ inderogability.

Financing a Sustainable Energy Transiton
Cornell, Bradford ,Cicchetti, Charles
We begin by identifying the four major obstacles for financing the transition to a sustainable energy world: the immense size of the required financing, the need to coordinate investments in dozens of interrelated projects required for an orderly transition, the need to get the investment incentive right and to avoid crony capitalism, and the need to manage the political economics of electricity regulation and pricing. We then show that these obstacles cannot be overcome by sole reliance on either public or private finance. What is need is a creative combination of the two. In this regard, we suggest several possible policies. These include an expanded role of government guarantees of the private financing of sustainable energy projects, a focus on the funding of basic research, and an entire rethinking of the role of electricity pricing and regulation.

Go Preventive or Go Home - A New Role of MREL
Martino, Edoardo,Parchimowicz, Katarzyna
Bank Resolution is considered a cornerstone of the post-crisis financial regulation; however, it is also widely considered ineffective and inefficient in handling bank failures. This article analyses the preventive potential of the resolution framework, specifically focusing on the minimum requirement for own funds and eligible liabilities (MREL). We argue that MREL has a double nature. On the one hand, it should ensure the feasibility of resolution in case of a bank failure. On the other hand, it aims at restricting the funding model of banks, similarly to the other (preventive) capital requirements. By analysing the 2019 reform of the EU banking regulation, we contend that MREL represents an important complement to the rest of the preventive regulatory framework and that the latest reform unleashes such potential. We demonstrate that the new rules on MREL determination and enforcement allows the resolution authority to look after the build-up of systemic risk. The analysis reveals that MREL can serve both micro- and macro-prudential purposes. Finally, we argue that the current institutional architecture represents the main impeding factor for the new regulation to efficiently work, curbing the positive preventive potential of MREL.

Hidden and self-excited attractors in an economic system of integer and fractional order
Marius-F. Danca

In this paper the dynamics of an economic system with foreign financing, of integer and fractional order are analyzed. The symmetry of the system determines the existence of two pairs of coexisting attractors. The integer-order variant of the system proves to have coexisting of several combinations of hidden attractors with self-excited attractors. Because one of the system variables represents the foreign capital inflow of the system, the presence of hidden attractors could be of a real interest in economic models. The fractional-order variant presents too an interesting fractional-order coexistence of attractors in the space of the fractional order.

How Much Ad Viewability is Enough? The Effect of Display Ad Viewability on Advertising Effectiveness
Christina Uhl,Nadia Abou Nabout,Klaus Miller

A large share of all online display advertisements (ads) are never seen by a human. For instance, an ad could appear below the page fold, where a user never scrolls. Yet, an ad is essentially ineffective if it is not at least somewhat viewable. Ad viewability - which refers to the pixel percentage-in-view and the exposure duration of an online display ad - has recently garnered great interest among digital advertisers and publishers. However, we know very little about the impact of ad viewability on advertising effectiveness. We work to close this gap by analyzing a large-scale observational data set with more than 350,000 ad impressions similar to the data sets that are typically available to digital advertisers and publishers. This analysis reveals that longer exposure durations (>10 seconds) and 100% visible pixels do not appear to be optimal in generating view-throughs. The highest view-through rates seem to be generated with relatively lower pixel/second-combinations of 50%/1, 50%/5, 75%/1, and 75%/5. However, this analysis does not account for user behavior that may be correlated with or even drive ad viewability and may therefore result in endogeneity issues. Consequently, we manipulated ad viewability in a randomized online experiment for a major European news website, finding the highest ad recognition rates among relatively higher pixel/second-combinations of 75%/10, 100%/5 and 100%/10. Everything below 75\% or 5 seconds performs worse. Yet, we find that it may be sufficient to have either a long exposure duration or high pixel percentage-in-view to reach high advertising effectiveness. Our results provide guidance to advertisers enabling them to establish target viewability rates more appropriately and to publishers who wish to differentiate their viewability products.

Hybrid quantum-classical optimization for financial index tracking
Samuel Fernández-Lorenzo,Diego Porras,Juan José García-Ripoll

Tracking a financial index boils down to replicating its trajectory of returns for a well-defined time span by investing in a weighted subset of the securities included in the benchmark. Picking the optimal combination of assets becomes a challenging NP-hard problem even for moderately large indices consisting of dozens or hundreds of assets, thereby requiring heuristic methods to find approximate solutions. Hybrid quantum-classical optimization with variational gate-based quantum circuits arises as a plausible method to improve performance of current schemes. In this work we introduce a heuristic pruning algorithm to find weighted combinations of assets subject to cardinality constraints. We further consider different strategies to respect such constraints and compare the performance of relevant quantum ans\"{a}tze and classical optimizers through numerical simulations.

Information Leakages, Distribution of Profits from Informed Trading, and Last Mover Advantage
Pankratov, Andrey
I model a market in which a trader with superior information about an asset is subject to careful scrutiny by another agent who immediately observes the trading decisions of the informed agent with some noise and engages in (klepto)parasitic behavior by imicking the informed trader and trading on her own behalf (this can be interpreted as a broker or a high-frequency trader).I show that if the precision with which the parasitic trader observes the informed trader’s decisions is high enough, then the parasitic trader absorbs a dominant fraction of the expected abnormal profits coming from informed trading.My theory is able to explain why the percentage abnormal returns on the trades of corporate insiders are high while dollar returns on these trades can be quite moderate.Additionally, I explain through my model a sudden upsurge of HFT activity during a five-year period 2004-2009.

Investor Sentiment and Stock Return Draw-downs
Divernois, Marc-Aurèle
This paper develops a model to extract a firm-individual time-serie Bullishness measure out of 60 million tweets. Tweets are scraped from over 10 years and classified into bullish, bearish or neutral classes using a 3-gram logit model on TFIDF vectorized pre-processed messages. Logit regressions on stock returns draw-downs, draw-ups and combined show the significant predictive powers of the 3-month Bullishness measure on next period uncertainty.

Is the Stock Market Becoming More Bayesian?
Cornell, Bradford
One simple way for a hypothetical investor to update an estimate of expected returns is to apply the Bayes rule. In its simplest form this involves no information other than an estimate of the prior distribution and historical data on stock returns. However, such a simple method of updating expectations is inconsistent with much of finance theory. This short paper draws out the distinction and asks if the market is becoming more Bayesian and, if so, what are the implications?

Machine Learning from the COVID-19 Pandemic About the Value of the NYSE Floor in Market Closing Time
Kye, Hyungil
The spread of COVID-19 in the New York City area forced the New York Stock Exchange (NYSE) to temporarily shut down its iconic trading floor. Using a recently developed machine learning approach, I study the effects of this COVID-19 closure on market quality during the market closing time, 3:50 - 4:00 pm. Investigating NYSE- and Nasdaq-listed stocks in the Russell 3000 index for March 23 through April 30 stock-by-stock day-by-day, I find that there is little statistical evidence showing that the closure of the NYSE floor harms market quality for the NYSE-listed stocks, gauged by percentage quoted spread, consolidated displayed depth, and volatility. Looking at average effects, though, market quality for the NYSE-listed stocks deteriorates relative to the Nasdaq-listed stocks for a first few weeks after the closure. Within about a month, however, the average difference of market quality between the NYSE- and Nasdaq-listed stocks disappears. As far as those three market quality measures are concerned, the findings suggest that the role of the NYSE floor in the market closing time is replaceable by the electronic trading setup configured for the COVID-19 closure.

Market-making with reinforcement-learning (SAC)
Alexey Bakshaev

The paper explores the application of a continuous action space soft actor-critic (SAC) reinforcement learning model to the area of automated market-making. The reinforcement learning agent receives a simulated flow of client trades, thus accruing a position in an asset, and learns to offset this risk by either hedging at simulated "exchange" spreads or by attracting an offsetting client flow by changing offered client spreads (skewing the offered prices). The question of learning minimum spreads that compensate for the risk of taking the position is being investigated. Finally, the agent is posed with a problem of learning to hedge a blended client trade flow resulting from independent price processes (a "portfolio" position). The position penalty method is introduced to improve the convergence. An Open-AI gym-compatible hedge environment is introduced and the Open AI SAC baseline RL engine is being used as a learning baseline.

On the Preferences of CoCo Bond Buyers and Sellers
Caporale, Guglielmo Maria,Kang, Woo-Young
This paper estimates the preference scores of CoCo bond buyers and sellers by running multinomial logistic regressions taking into account both bond and issuing banks’ characteristics; it also provides evidence on the role of countryâˆ'specific CoCo bond market concentration. Buyers are defined as having a preference for CoCo bonds if their returnâˆ'toâˆ'risk is higher than the corresponding 25th, 50th and 75th annual percentile values; the preferences of buyers and sellers are assumed to be mutually exclusive. We find that the sellers’ needs to have bankruptcy protection and to comply with the Basel III financial regulations play a more important role than the buyers’ desire to increase their income from this fixedâˆ'income instrument. Sellers prefer to issue CoCo bonds when they are not financially sound whilst buyers prefer CoCo bonds with low risk; therefore, these two categories can be characterised as being riskâˆ'loving and riskâˆ'averse respectively, especially in the higher percentiles. Globally, buyers are most responsive to CoCo bonds with higher coupon rates issued by banks with a higher valuation while sellers are most attracted by the mechanical conversion mechanism, which makes CoCo bonds cheaper to issue than the principal write-down feature; the strongest responses are found in the UK and China.

Psychological Ownership of (Borrowed) Money
Sharma, Eesha,Tully, Stephanie,Cryder, Cynthia
The current research introduces the concept of psychological ownership of borrowed money, a construct that represents how much consumers feel that borrowed money is their own. We observe both individual-level and contextual-level variation in the degree to which consumers feel psychological ownership of borrowed money, and variation on this dimension predicts willingness to borrow money for discretionary purchases. At an individual level, psychological ownership of borrowed money is distinct from other individual factors such as debt aversion, financial literacy, income, inter-temporal discounting, materialism, propensity to plan, self-control, spare money, and tightwad-spendthrift tendencies, and, it predicts willingness to borrow above and beyond these factors. At a contextual level, we document systematic differences in psychological ownership between different debt types. We show that these differences in psychological ownership manifest in consumers’ online search behavior and explain consumers’ differential interest in borrowing across debt types. Finally, we demonstrate that psychological ownership of borrowed money is malleable, such that framing debt in terms of low psychological ownership can reduce consumers’ propensity to borrow.

Quantifying the impact of Covid-19 on the US stock market: An analysis from multi-source information
Asim Kumer Dey,Toufiqul Haq,Kumer Das

We investigate the impact of Covid-19 cases and deaths, local spread spreads of Covid-19, and Google search activities on the US stock market. We develop a temporal complex network to quantify US county level spread dynamics of Covid-19. We conduct the analysis by using the following sequence of methods: Spearman's rank correlation, Granger causality, Random Forest (RF) model, and EGARCH (1,1) model. The results suggest that Covid-19 cases and deaths, its local spread spreads, and Google searches have impacts on the abnormal stock price between January 2020 to May 2020. However, although a few of Covid-19 variables, e.g., US total deaths and US new cases exhibit causal relationship on price volatility, EGARCH model suggests that Covid-19 cases and deaths, local spread spreads of Covid-19, and Google search activities do not have impacts on price volatility.

Retail Investors’ Disposition Effect and Order Choices
De Winne, Rudy,Luong, Nhung,Palan, Stefan
Retail investors are prone to the disposition effect and submit many more limit orders than market orders. Mechanical effects stemming from the price-contingency conditions for order executions can lead these limit orders to inflate an investor's measured disposition effect (Linnainmaa 2010). Our paper is the first to demonstrate that the relationship between the disposition effect and order choices is bi-directional. Using trading data of thousands of investors, we show that investors who are prone to the disposition effect differ from others in their use of limit orders and in their choice of limit prices.

Seeking Analyst Coverage: Steering User-Generated Content Using Monetary Incentives
Claussen, Jörg,Litterscheidt, Rouven,Streich, David
We study how monetary incentive structures affect the selection of stocks covered by non-professional analysts (NPA), as well as the quality of the published research articles. Specifically, we use two exogenous incentive structure changes on a peer-to-peer financial analysis platform as a natural experiment with professional analysts (PA) as the control group. Our results suggest that monetary incentive structures are an effective tool to increase and steer NPA research support. The incentive structure changes increased coverage in the targeted market capitalization segments and primarily affected contributors who joined the platform more recently. We further show that NPA coverage affects market liquidity to a similar extent as PA coverage. The incentive structure changes did not deteriorate the quality of NPA research support as measured by its impact on liquidity. In summary, our findings suggest that NPA coverage may be a suitable complement to, if not substitute for, PA coverage.

Semimartingale price systems in models with transaction costs beyond efficient friction
Christoph Kühn,Alexander Molitor

A standing assumption in the literature on proportional transaction costs is efficient friction. Together with robust no free lunch with vanishing risk, it rules out strategies of infinite variation, as they usually appear in frictionless markets. In this paper, we show how the models with and without transaction costs can be unified.

The bid and the ask price of a risky asset are given by c\'adl\'ag processes which are locally bounded from below and may coincide at some points. In a first step, we show that if the bid-ask model satisfies "no unbounded profit with bounded risk" for simple strategies, then there exists a semimartingale lying between the bid and the ask price process.

In a second step, under the additional assumption that the zeros of the bid-ask spread are either starting points of an excursion away from zero or inner points from the right, we show that for every bounded predictable strategy specifying the amount of risky assets, the semimartingale can be used to construct the corresponding self-financing risk-free position in a consistent way. Finally, the set of most general strategies is introduced, which also provides a new view on the frictionless case.

Sentiment Analysis and Gender Differences in Earnings Conference Calls
De Amicis, Chiara,Falconieri, Sonia,Tastan, Mesut
We apply textual analysis to the transcripts of a sample of nearly 78,000 earnings conference calls between 2004 and 2018, comparing the difference in sentiment between female and male senior managers (CEOs and CFOs). We focus on two main measures of sentiment: tone and vagueness. Our contribution is twofold. Firstly, we show that, ceteris paribus, female executives employ a more positive and less vague tone than their male colleagues during the two sessions that make up the calls. The more positive and less vague tone of female executives does not imply any incremental information content and appears to be a purely linguistic feature that distinguishes female from male executives. Secondly, we find that the financial analysts participating in the calls exhibit gender bias in that they are on average much less positive and more vague when faced with a female executive. In contrast, the stock market reaction, measured by the CAR one day after the call, responds only to the sentiment of the call, not the executive’s gender.

Share Price Prediction of Aerospace Relevant Companies with Recurrent Neural Networks based on PCA
Linyu Zheng,Hongmei He

The capital market plays a vital role in marketing operations for aerospace industry. However, due to the uncertainty and complexity of the stock market and many cyclical factors, the stock prices of listed aerospace companies fluctuate significantly. This makes the share price prediction challengeable. To improve the prediction of share price for aerospace industry sector and well understand the impact of various indicators on stock prices, we provided a hybrid prediction model by the combination of Principal Component Analysis (PCA) and Recurrent Neural Networks. We investigated two types of aerospace industries (manufacturer and operator). The experimental results show that PCA could improve both accuracy and efficiency of prediction. Various factors could influence the performance of prediction models, such as finance data, extracted features, optimisation algorithms, and parameters of the prediction model. The selection of features may depend on the stability of historical data: technical features could be the first option when the share price is stable, whereas fundamental features could be better when the share price has high fluctuation. The delays of RNN also depend on the stability of historical data for different types of companies. It would be more accurate through using short-term historical data for aerospace manufacturers, whereas using long-term historical data for aerospace operating airlines. The developed model could be an intelligent agent in an automatic stock prediction system, with which, the financial industry could make a prompt decision for their economic strategies and business activities in terms of predicted future share price, thus improving the return on investment. Currently, COVID-19 severely influences aerospace industries. The developed approach can be used to predict the share price of aerospace industries at post COVID-19 time.

Stochastic Interest Rate Model and Its Applications: A Case for India
Poddar, Saharsh
In this dissertation the main focus area was to use one factor interest rate models in India to obtain the descriptive nature and risk profile of money markets in India. For the period of 2012-2020 we found that CIR (1985) model fits and describes the interest rate path for India much better than Vasicek model. Both of these models were calibrated to make each of their parameters pseudo time varying and maximum likelihood estimation (MLE) was used to find optimal model parameters. We used daily weighted average of call money market rates as input data for calibration. Under the assumptions of constant relative risk aversion (CRRA) and decreasing absolute risk aversion (DARA) of CIR model, we used the closed form solutions to obtain bond prices and yields on 3 month T-bill. Average 10 year yield curve for the period 2012-20 was also forecasted and it was observed that Indian yield curve has a hump shape, with higher yields on longer dated bonds. We find that there exists higher term premium for longer dated bonds, for example, term premium on 10 year G-Sec has increased at a faster rate, relative to shorter maturity bonds, with the trend getting stronger since 2016 with an exception in year 2019. For risk profiling, the paper uses Expected-shortfall (ES) and Value-at-Risk (VaR) on 3 month bonds. It was found that expected shortfall normalized for yield stood at 25% on average for the period 2012-20. This means that traders in the money market need to constantly look out for price risk, to hedge for this, we propose that the trader uses call options on bonds. We find that fair prices of call options for CIR distribution function under Blacks-Scholes model will be an expensive hedging strategy. But that said, the Greeks for these call options (Rho, Delta and Gamma) show that the call option will not be sensitive to interest rate and bond price changes and hence will be a stable hedging strategy. The paper concludes that this is because of very high speed of mean reversion and low volatility in interest rate paths. This result can be associated with credibility, transparency and clear policy decisions made by the Indian central bank and newly formed Monetary Policy Committee (MPC). It was also seen that MPC has been successful to lower volatility by 9 times since its inception while maintaining the same high levels of speed of reversion in interest rates.

Stock Market Wealth and Worker Output
Li, Teng,Qian, Wenlan,Xiong, Wei A.,Zou, Xin
This paper uses individual-level data linking stock investments to work performance to examine how changes in stock market wealth affect worker output. Exploiting large return variations over time and across investors, we document a 10% increase in monthly stock investment returns is associated with a decrease of 3.9% in the same investor’s next-month work output. The negative output response is not driven by concurrent economic conditions, pronounced when focusing on more idiosyncratic stock investment returns, and moreover is unexplained by investor-specific liquidity needs. Consistent with the wealth-effect interpretation, the effect is stronger for higher-income workers. In the negative-return domain, interestingly, a decline in stock investment returns is followed by lower output, especially for male, younger, less educated, and lower-income workers. Overall, our results highlight a novel channel of transmitting stock market fluctuation to the real economy through labor supply.

Testing for Asset Price Bubbles using Options Data
Fusari, Nicola,Jarrow, Robert,Lamichhane, Sujan
We present a new approach to identifying asset price bubbles based on options data. Given their forward-looking nature, options are ideal instruments with which to investigate market expectations about the future evolution of asset prices, which are key to understanding price bubbles. By exploiting the di˙erential pricing between put and call options, we can detect and quantify bubbles in the prices of underlying asset. We apply our methodology to two stock market indexes, the S&P 500 and the Nasdaq-100, and two technology stocks, Amazon and Facebook, over the 2014-2018 sample period. We find that, while indexes exhibit rare and modest bubbles, Amazon and Facebook show more frequent and much larger bubbles. Since our approach can be implemented in real time, it is useful to both policy-makers and investors.

The Dynamics of Cross-Boundary Fire - Financial Contagion between the Oil and Stock Markets
Wang, Haiying,Yuan, Ying,Wang, Tianyang
Motivated by the complex dynamics between the oil and stock markets, this study develops a dynamic Markov regime switching-copula-extreme value theory (MRS-copula-EVT) model to quantitatively investigate financial contagion and its characteristics between these two markets. The proposed model, which characterizes the complex dynamics in lower tail dependence, is applied to daily returns on the crude oil price and major stock indices in both the United States and China's markets over a series of major extreme downside risk events, including the most recent 2020 oil and stock market crashes after the outbreak of COVID-19 pandemic. The proposed model outperforms the alternative model in detecting financial contagion, showing that such contagion is shorter but stronger in the United States than in China; moreover, financial contagion is more susceptible to extreme downside shocks between the oil and stock markets in the United States than in China. In addition, the COVID-19 crisis shows the largest financial contagion compared with previous crises. Our results remain consistent following extensive robustness tests. These findings shed new light on our understanding of financial contagion and provide important insights and guidance for asset allocation and risk management.

The MREL Framework Under the Banking Reform Package
Maragopoulos, Nikos
The adoption of the “Banking Reform Package” (CRR2, CRD4, BRRD2, SRMR2) in 2019 led to material changes, among others, in the regulatory framework governing the Minimum Requirement for Own funds and Eligible Liabilities (MREL). The revised framework aimed at introducing the rules adopted by the Financial Stability Board (FSB) on the Total Loss-Absorbing Capacity (TLAC) to the Union law and addressing the deficiencies of the former framework.As regards the novelties brought in by the new framework, the most significant one pertains to the introduction of a minimum and fixed requirement (Pillar 1 MREL) for Global Systemically Important Banks (G-SIBs) and other large banks. At the same time, all banks are still subject to the bank-specific requirement (Pillar 2 MREL) for which no material amendment in the determination approach is introduced in the revised framework. In practice, the MREL target that banks must meet is the higher of the Pillar 1 MREL and the bank-specific Pillar 2 MREL.Other key elements of the revised framework are related to the obligation for banks to cover a significant part of the MREL with instruments subordinated to liabilities excluded (or likely to be excluded) from bail-in (subordination requirement), the introduction of the internal MREL for subsidiaries of resolution entities and the establishment of harmonized criteria for MREL-eligible liabilities. Furthermore, the revised rules clarify several aspects of the former framework, including the deadline for banks to meet the MREL, the redemption of MREL-eligible liabilities and the measures to address a breach of the MREL. The present paper analyses the aforementioned key elements of the revised MREL framework and assesses its conformity with the TLAC standard, as well as the implications that may arise as a result of its complexity and the discretions provided to resolution authorities. As regards the first point, the analysis indicates that the transposition of the TLAC rules into the Union law not only achieves the target of conformity, but also in some cases the MREL extends beyond the scope and the level required under the international standard. In relation to the nature of the requirement, the MREL framework is characterized by a significant degree of complexity and the discretionary powers assigned upon resolution authorities. Excluding the Pillar 1 MREL applicable to G-SIBs and “top-tier banks”, there is no other single MREL-related requirement that is clear about which banks it applies to and what the level thereof is. All other requirements are subject to decisions taken by resolution authorities, mostly based on unclear criteria. Hence, resolution authorities have extensive powers to decide on the scope of requirements (e.g. Pillar 1 MREL, Pillar 2 MREL, subordination requirement, internal MREL) and other critical aspects (e.g. transitional period for Pillar 2 MREL, redemption of MREL-eligible liabilities, measures to address a breach of the MREL). The discretionary character of the framework allows resolution authorities to translate the MREL rules into their policies in a different way resulting in a divergent application of the MREL across the EU. In addition, discretions foster ambiguity regarding the requirements that banks are obliged to meet, even if they are under the remit of the same resolution authority. As a result, these arrangements cause uncertainty to both banks and investors over the applicable requirements, the MREL needs and the resources required to meet those needs.

The Passing from the Classical to the Extended Form of the Holiday Effect on the Euronext
Stefanescu, Razvan,Dumitriu, Ramona
In some circumstances, the classical form of the Holiday Effect, consisting in abnormal returns that occur one trading day before and one trading day after a public holiday, could be replaced by an extended form, in which abnormal returns are found in an enlarged time interval. This paper explores the presence of the classical and the extended form of the Holiday Effect on four indexes of the Euronext capital market: AEX, CAC 40, ISEQ 20 and PSI 20. We perform this investigation for two periods: January 2000 - December 2011 and January 2012 - June 2020. For the first period the results suggest that classical form of the Holiday Effect predominated. Instead, for the second period we found abnormal returns in enlarged time intervals.

The Shape of Green Fixed Income Investing to Come
Schumacher, Kim
This paper serves as an introduction to the Journal of Environmental Investing’s issue (Vol. 10, No. 1, 2020) on climate and green bonds. Fixed income securities that integrate environmental, social, and governance (ESG) factors have become a crucial component of most sustainable investment and climate-related risk management strategies. Global green bond issuance has grown from USD87.2bn in 2016 to USD257.7bn in 2019. However, this issue is addressing some of the challenges of rapid market scaling. The first pertains to the labeling of green bonds. Albeit the term “green bond” becoming synonymous for all ESG-aligned fixed income securities, there now exists a plethora of labels, names, and designations for green bonds, often resulting in confusion of what exactly constitutes a green bond. By providing the most comprehensive overview to date of all green bond variants, this issue explores the core attributes of green bonds, such as their potential returns from financial and non-financial angles, taxonomical and underlying conceptual considerations, and academic assessment of the market as a whole. In conclusion, this paper and the corresponding issue provide contemporary insights and an up-to-date snapshot of the evolving characteristics of climate and green bonds.

The Tesla Stock Split Experiment
Cornell, Bradford
On August 11, 2019 at 16:59 EDT, Tesla announced a 5-for-1 stock split. The trading in the after market and during the subsequent two days amounts to a unique financial economic experiment. Although stock splits have no fundamental impact on value, Tesla’s stock price rose 17.94% in the two days following the split â€" adding almost $50 billion in market value. This paper examines that price increase in detail and concludes there is no rational explanation for the size of the run-up following Tesla’s stock split announcement.

The time function of stock price
Shengfeng Mei,Hong Gao

This paper tends to define the quantitative relationship between the stock price and time as a time function. Based on the empirical evidence that the log-return of a stock is the series of white noise, a mathematical model of the integral white noise is established to describe the phenomenon of stock price movement. A deductive approach is used to derive the auto-correlation function, displacement formula and power spectral density of the stock price movement, which reveals not only the characteristics and rules of the movement but also the predictability of the stock price. The deductive fundamental is provided for the price analysis, prediction and risk management of portfolio investment.

Trans-Atlantic Equity Volatility Connectedness: U.S. and European Financial Institutions, 2004-2014
Diebold, Francis X.,Yilmaz, Kamil
We characterize equity return volatility connectedness in the network of major American and European financial institutions, 2004-2014. Our methods enable precise characterization of the timing and evolution of key aspects of the financial crisis. First, we find that during 2007-2008 the direction of connectedness was clearly from the U.S. to Europe, but that connectedness became bi-directional starting in late 2008. Second, we find an unprecedented surge in directional connectedness from European to U.S. financial institutions in June 2011, consistent with massive deterioration in the health of EU financial institutions. Third, we identify particular institutions that played disproportionately important roles in generating connectedness during the U.S. and the European crises.

Variance Contracts
Chi, Yichun,Zhou, Xun Yu,Zhuang, Sheng Chao
We study the design of an optimal insurance contract in which the insured maximizes her expected utility and the insurer limits the variance of his risk exposure while maintaining the principle of indemnity and charging the premium according to the expected value principle. We derive the optimal policy semi-analytically, which is coinsurance above a deductible when the variance bound is binding. This policy automatically satisfies the incentive-compatible condition, which is crucial to rule out ex post moral hazard. We also find that the deductible is absent if and only if the contract pricing is actuarially fair. Focusing on the actuarially fair case, we carry out comparative statics on the effects of the insured's initial wealth and the variance bound on insurance demand. Our results indicate that the expected coverage is always larger for a wealthier insured, implying that the underlying insurance is a normal good, which supports certain recent empirical findings. Moreover, as the variance constraint tightens, the insured who is prudent cedes less losses, while the insurer is exposed to less tail risk.

When Does Corporate Social Responsibility Backfire in Acquisitions? Signal Incongruence and Acquirer Returns
Zhang, Tingting,Zhang, Zhengyi,Yang, Jingyu
This study examines whether an acquirer’s pre-announcement corporate social responsibility (CSR) engagement can provide an insurance-like effect to preserve acquirer returns during the announcement of an acquisition event. Drawing on stakeholder theory and signaling theory, we posit that CSR engagement accrues positive moral capital for an acquirer and sends a positive signal indicating the acquirer’s altruism, both of which temper stakeholders’ negative responses and prevent a reduction in market returns around the announcement of an acquisition. However, high-CSR engagement could backfire when the acquirer makes a hostile takeover announcement. In-congruent signals between high-CSR engagement and the hostile practice are a sign of hypocrisy in the eyes of stakeholders, which can worry investors and hurt acquirer returns. By analysing 1,310 acquisition transactions from 2002â€"2012, the results of our event study show that high-CSR acquirers generally enjoy positive acquirer returns during their acquisition announcements, but negative returns when the acquisitions are hostile. These findings support the idea that CSR engagement can provide insurance-like benefits during an event that is often seen as “negative”, while also identifying signal in-congruence as an important boundary condition.