Research articles for the 2021-06-22

A class of recursive optimal stopping problems with applications to stock trading
Katia Colaneri,Tiziano De Angelis

In this paper we introduce and solve a class of optimal stopping problems of recursive type. In particular, the stopping payoff depends directly on the value function of the problem itself. In a multi-dimensional Markovian setting we show that the problem is well posed, in the sense that the value is indeed the unique solution to a fixed point problem in a suitable space of continuous functions, and an optimal stopping time exists. We then apply our class of problems to a model for stock trading in two different market venues and we determine the optimal stopping rule in that case.

A systems framework for remedying dysfunction in U.S. democracy
Samuel S.-H. Wang,Jonathan Cervas,Bernard Grofman,Keena Lipsitz

Democracy often fails to meet its ideals, and these failures may be made worse by electoral institutions. Unwanted outcomes include polarized institutions, unresponsive representatives, and the ability of a faction of voters to gain power at the expense of the majority. Various reforms have been proposed to address these problems, but their effectiveness is difficult to predict against a backdrop of complex interactions. Here we outline a path for systems-level modeling to help understand and optimize repairs to U.S. democracy. Following the tradition of engineering and biology, models of systems include mechanisms with dynamical properties that include nonlinearities and amplification (voting rules), positive feedback mechanisms (single-party control, gerrymandering), negative feedback (checks and balances), integration over time (lifetime judicial appointments), and low dimensionality (polarization). To illustrate a systems-level approach we analyze three emergent phenomena: low dimensionality, elite polarization, and anti-majoritarianism in legislatures. In each case, long-standing rules now contribute to undesirable outcomes as a consequence of changes in the political environment. Theoretical understanding at a general level will also help evaluate whether a proposed reform's benefits will materialize and be lasting, especially as conditions change again. In this way, rigorous modeling may not only shape new lines of research, but aid in the design of effective and lasting reform.

Bitcoin's Crypto Flow Newtork
Yoshi Fujiwara,Rubaiyat Islam

How crypto flows among Bitcoin users is an important question for understanding the structure and dynamics of the cryptoasset at a global scale. We compiled all the blockchain data of Bitcoin from its genesis to the year 2020, identified users from anonymous addresses of wallets, and constructed monthly snapshots of networks by focusing on regular users as big players. We apply the methods of bow-tie structure and Hodge decomposition in order to locate the users in the upstream, downstream, and core of the entire crypto flow. Additionally, we reveal principal components hidden in the flow by using non-negative matrix factorization, which we interpret as a probabilistic model. We show that the model is equivalent to a probabilistic latent semantic analysis in natural language processing, enabling us to estimate the number of such hidden components. Moreover, we find that the bow-tie structure and the principal components are quite stable among those big players. This study can be a solid basis on which one can further investigate the temporal change of crypto flow, entry and exit of big players, and so forth.

COVID-19 Restrictions and UK GDP Growth
Milas, Costas
This paper discusses an empirical model of UK GDP growth in the context of the COVID-19 pandemic. The model estimates that social distancing and lockdown restrictions reduced, on average, annual UK growth by 9.7 percentage points compared to the scenario of no government action. At the other extreme, had government stringency remained at its April 2020 ‘lockdown level’ throughout the pandemic, annual UK growth would have been lower by (a further) 1.9 percentage points on average compared to the impact of the imposed restrictions. The economic impact of the continuous strict lockdown scenario is slightly worse than what actual lockdown policies deliver which arguably shows the ability of the economy to adjust and get on during lockdown restrictions. The paper also finds that the pace at which lockdown restrictions have been eased since early 2021 appeared to be more responsive to the proportion of the UK's population who had been fully inoculated than the number of people who had received a single dose of vaccine.

Changing the Student Loan Dischargeability Framework: How the Department of Education Can Ease the Path for Borrowers in Bankruptcy
Foohey, Pamela,Ament, Aaron,Zibel, Daniel
Our nation’s consumer bankruptcy system supposedly gives “honest but unfortunate” individuals “a new opportunity in life with a clear field for future effort, unhampered by the pressure and discouragement of preexisting debt.” Access to bankruptcy’s discharge of debt is especially important in the wake of the COVID-19 pandemic, which has resulted in a once-in-a-century economic crisis that is projected to increase consumer bankruptcy filings. The people who file bankruptcy will find a system that is already difficult to navigate and has long-recognized racial and gender disparities in access and outcomes. Student loan borrowers will find a system with even more barriers to relief from their education debt. These barriers are two-fold: those put up by bankruptcy laws and those put up by loan holders, including the United States Department of Education (“Department”). This Essay focuses on how the Department should update its policies for how it responds to borrowers who seek to discharge their student loans in bankruptcy. It details two options for how the Department can update its approach to handling bankruptcies to ensure that it calibrates its actions to make the promise of a fresh start more real for student borrowers. Importantly, the Department can implement the framework set forth in this Essay without substantially negatively impacting the net amount of money that it is likely to recover from borrowers who file bankruptcy and seek to discharge their student loans.

Competition in Exchanges and Reputational Concerns
Boussetta, Selma
This paper proposes a theoretical model to analyse the effect of competition on the quality of the certification process offered by stock exchanges. If the stock exchange truthfully certifies the quality of a new issue, then it would list only the good projects, which would alleviate information asymmetries and generate gains from trade. However, it may be more profitable for exchanges be too lax in its listing requirements. The trade-off between short-term profits and reputation effects induces strategic behaviour. The results show that overestimating the quality of a project is an equilibrium despite the presence of reputation costs. Counterintuitively, introducing competition leads to more lax requirements than in the monopolistic case and reduces welfare as long as the reputation of the competitor is higher than that of the monopolistic stock exchange.

Decentralized Stablecoins and Collateral Risk
Kozhan, Roman,Viswanath-Natraj, Ganesh
In this paper, we study the mechanisms that govern price stability of MakerDAO's DAI token, the first decentralized stablecoin. DAI works through a set of autonomous smart contracts, in which users deposit cryptocurrency collateral, typically Ethereum, and borrow a fraction of their positions as DAI tokens. Using data on the universe of collateralized debt positions, we show that DAI price covaries negatively with returns to risky collateral. The peg-price volatility is related to collateral risk, while the stability rate has little ability to stabilize the coin. The introduction of safe collateral types has led to an increase in peg stability.

Discovering the Drivers of Market Volatility: Asset Allocation Applications
Cho, Hoon,Chun, Dohyun,Ryu, Doojin
This study investigates the economic and financial drivers of volatility changes and integrates them into stock market volatility forecasting. We first collect a diverse set of predictor variables and analyze them within a unified framework. We discover that only a small number of variables contain significant predictive information, and that the Chinese stock market return significantly predicts U.S. stock market volatility. Using the HAR-LASSO procedure, we integrate the drivers’ predictive information and forecast short-term, medium-term, and long-term market volatilities. Through various volatility timing strategies, we verify that HAR-LASSO-based portfolios lead to outstanding investment performance, regardless of the strategies and forecasting horizons. These results not only economically justify our procedure, but also provide meaningful financial implications of accurate volatility forecasting.

Do Macroprudential Measures Increase Inequality? Evidence from the Euro Area Household Survey
Georgescu, Oana Maria,Martín, Diego Vila
Borrower-based macroprudential (MP) policies - such as caps on loan-to-value (LTV) ratios and debt-service-to-income (DSTI) limits - contain the build-up of systemic risk by reducing the probability and conditional impact of a crisis. While LTV/DSTI limits can increase inequality at introduction, they can dampen the increase in inequality under adverse macroeconomic conditions. The relative size of these opposing effects is an empirical question. We conduct counterfactual simulations under different macroeconomic and macroprudential policy scenarios using granular income and wealth data from the Households Finance and Consumption Survey (HFCS) for Ireland, Italy, Netherlands and Portugal. Simulation results show that borrower-based measures have a moderate negative welfare impact in terms of wealth inequality and a negligible impact on income inequality.

Do Stocks Become More Liquid When Exchanges Demutualize?
Boussetta, Selma
This paper empirically investigates the effects of stock exchange demutualization on listed firms. In particular, this work examines how exchange demutualization affects stock liquidity and how the effect varies by country development level and exchange operating performance. We document the positive effect of exchange demutualization on stock liquidity, while gains in trading activity as measured by turnover are concentrated among exchanges across developed countries. The liquidity improvement in terms of transaction costs and market activity is more valuable for exchanges that exhibit higher levels of market profitability, tend to diversify their income revenue and invest in technology. Further, demutualization is associated with an increase in the market share of exchanges from developed countries, where the increase is drawn from the domestic order flow. Overall, our findings highlight the important role of exchange and country development settings in shaping the impact of demutualization on stock liquidity in international markets.

Efficient Detection in Large-Scale Testing
Zhu, Min
Large-scale inference has become increasingly popular in financial economics nowadays. I explore an empirical Bayes approach for large-scale multiple testing. The proposed approach bases its inferences on the posterior probability that the null is true given the observed data. It establishes a data-driven t-statistic hurdle that can be associated with any pre-specified false discovery rate, or weighted combination of false discovery rate and false nondiscovery rate. Applying the method to meta-analysis of market anomalies, the factor zoo which is characterized by both multiple testing and p-hacking, and fund performance evaluation, I find that t-hurdle needs to be raised substantially in all cases.

How do Investors React to Biased Information? Evidence from Chinese IPO Auctions
He, Jingbin,Liu, Bo,Wang, Yiyao,Wu, Fei
We study how institutional investors utilize potentially biased information by analyzing the effect of IPO underwriters' earnings forecasts on investors' bidding behaviors in Chinese IPO auctions. Despite the presence of upward biases in underwriters' earnings forecasts, we nd that investors' bid prices are higher in IPOs with higher earnings forecasts. The investors' positive reaction to biased information can be explained in a rational expectation model where the underwriter has valuable information about the IPO but has a biased incentive in presenting the information to investors. Consistent with the model's predictions, we find that an investor's bid price is more sensitive to the underwriter's earnings forecast when the forecast bias is expected to be smaller, when the relative precision of the underwriter's information over the investor's information is higher, and when the investor has a higher valuation of the IPO.

Impacts of the Covid-19 Crisis: Evidence from 2 Million UK SMEs
Hurley, James,Karmakar, Sudipto,Markoska, Elena,Walczak, Eryk,Walker, Daniel
We introduce a novel data set to analyze the impact of the Covid-19 crisis on SME cash flows. The crisis led to a sharp drop in economic activity in the UK, which hit SMEs harder than larger businesses. The data set comprises monthly information on all 2 million SMEs that have current accounts or debt with nine major banking groups, with roughly 5 billion data points in total. We document a few basic facts on UK SME cash flows during Covid-19. (1) The virus and the public health interventions coincided with a 30 percentage point reduction in turnover growth for the average SME. (2) There was significant heterogeneity in the turnover shock across SMEs, with the biggest reductions for younger SMEs in consumer-facing sectors in Scotland and London. (3) Cash flows were broadly flat on average and there was much less heterogeneity across SMEs. (4) SMEs with average turnover growth in 2020 were most likely to use the main government-guaranteed loan scheme for SMEs, as well as those in the hospitality sector in more affluent areas of the country. Our analysis provides a framework to monitor SMEs as the sector recovers from the pandemic.

Indirect Investor Protection: The Investment Ecosystem and Its Legal Underpinnings
Spamann, Holger
This paper argues that the key mechanisms protecting retail investors’ financial stake in their portfolio investments are indirect. They do not rely on actions by the investors or by any private actor directly charged with looking after investors’ interests. Rather, they are provided by the ecosystem that investors (are legally forced to) inhabit, as a byproduct of the mostly self-interested, mutually and legally constrained behavior of third parties without a mandate to help the investors (e.g., speculators, activists). This elucidates key rules, resolves the mandatory vs. enabling tension in corporate/securities law, and exposes passive investing’s fragile reliance on others’ trading.

Information of income position and its impact on perceived tax burden and preference for redistribution: An Internet Survey Experiment
Eiji Yamamura

A customized internet survey experiment is conducted in Japan to examine how individuals' relative income position influences preferences for income redistribution and individual perceptions regarding income tax burden. I first asked respondents about their perceived income position in their country and their preferences for redistribution and perceived tax burden. In the follow-up survey for the treatment group, I provided information on their true income position and asked the same questions as in the first survey. For the control group, I did not provide their true income position and asked the same questions. I gathered a large sample that comprised observations of the treatment group (4,682) and the control group (2,268). The key findings suggest that after being informed of individuals' real income position, (1) individuals who thought their income position was higher than the true one perceived their tax burden to be larger, (2) individuals' preference for redistribution hardly changes, and (3) irreciprocal individuals perceive their tax burden to be larger and are more likely to prefer redistribution. However, the share of irreciprocal ones is small. This leads Japan to be a non-welfare state.

Learning versus Unlearning: An Experiment on Retractions
Duarte Gonçalves,Jonathan Libgober,Jack Willis

Widely discredited ideas nevertheless persist. Why do people fail to ``unlearn''? We study one explanation: beliefs are resistant to retractions (the revoking of earlier information). Our experimental design identifies unlearning -- i.e., updating from retractions -- and enables its comparison with learning from equivalent new information. Across different kinds of retractions -- for instance, those consistent or contradictory with the prior, or those occurring when prior beliefs are either extreme or moderate -- subjects do not fully unlearn from retractions and update less from them than from equivalent new information. This phenomenon is not explained by most of the well-studied violations of Bayesian updating, which yield differing predictions in our design. However, it is consistent with difficulties in conditional reasoning, which have been documented in other domains and circumstances.

Leverage and Cash Dynamics
DeAngelo, Harry,Gonçalves, Andrei,Stulz, René M.
This paper documents new and empirically important interactions between cash-balance and leverage dynamics. Cash ratios typically vary widely over extended horizons, with dynamics remarkably similar to (and complementary with) those of capital structure. Leverage and cash dynamics interact approximately as predicted by the internal-versus-external funding regimes in Myers and Majluf (1984). Leverage is quite volatile when cash ratios are stable and vice-versa, while net-debt ratios are almost always volatile. Most firms increase leverage sharply as cash balances (internal funds) become scarce. Capital structure models that extend Hennessy and Whited (2005) to include cash-balance dynamics explain some, but not all, aspects of the observed relation between cash squeezes and leverage increases.

Mid-Day Call Auctions
Brogaard, Jonathan,Hagströmer, Björn,Xu, Caihong
In illiquid and fragmented limit order book markets, asynchronously arriving buyers and sellers have a coordination problem. This problem is particularly strong mid-day, when trading is generally thin. We evaluate a market structure reform at Nasdaq Nordic, where the continuous trading session is replaced mid-day by a five-minute call auction. We find that the mid-day call auction works as a coordination device, reducing transitory price impact. The call auction attracts end investors rather than intermediaries. Stocks with greater end investor flows show stronger benefits of the call auction. The results indicate that mid-day auctions can improve continuous markets.

Once Bitten, Twice Shy: Learning From Corporate Fraud and Corporate Governance Spillovers
Nguyen, Trung
This paper finds that investors learn from their experience with corporate fraud and financial misconduct and modify their investment behavior to avoid suspicious firms and increase corporate governance efforts. More specifically, mutual funds that experienced corporate fraud at one of their portfolio firms subsequently chose firms with lower probabilities of fraud and financial misconduct, compared to otherwise similar funds that did not experience any corporate malfeasance incidents. Furthermore, mutual funds that experienced corporate fraud intensify their corporate governance activities and vote significantly more against management at other firms in their portfolios, compared to the voting behavior at the same firms by otherwise similar funds but that did not experience any fraud, especially on issues related to director election, audit, and financial statement. I find that fraud-experienced investors are significantly less likely to vote for problematic directors. Finally, I find that firms held by more fraud-experienced investors observe a significant drop in the propensity to get an accounting fraud sanction in subsequent years. Taken together, my results show that learning and experience play a critical role in corporate governance spillovers, fraud detection, and deterrence.

Quantifying the Impact of Human Capital, Job History, and Language Factors on Job Seniority with a Large-scale Analysis of Resumes
Austin P Wright,Caleb Ziems,Haekyu Park,Jon Saad-Falcon,Duen Horng Chau,Diyi Yang,Maria Tomprou

As job markets worldwide have become more competitive and applicant selection criteria have become more opaque, and different (and sometimes contradictory) information and advice is available for job seekers wishing to progress in their careers, it has never been more difficult to determine which factors in a r\'esum\'e most effectively help career progression. In this work we present a novel, large scale dataset of over half a million r\'esum\'es with preliminary analysis to begin to answer empirically which factors help or hurt people wishing to transition to more senior roles as they progress in their career. We find that previous experience forms the most important factor, outweighing other aspects of human capital, and find which language factors in a r\'esum\'e have significant effects. This lays the groundwork for future inquiry in career trajectories using large scale data analysis and natural language processing techniques.

Reaching for Influence: Do Banks Use Loans to Establish Political Connections?
Kaviani, Mahsa,Maleki, Hosein,Savor, Pavel G.
We explore whether banks use loans as a tool for political influence. Using close elections as our setting, we show that firms linked to members of Congress receive lower interest rates on new loans, which are also larger and have fewer covenants. Such firms, however, do not experience improvements in future performance or reductions in default risk. The impact of political connections is greater for important politicians, for firms that are large contributors, and when both are from the same state. Consistent with banks seeking influence through preferential lending to politically connected firms, the effect is also stronger for banks with regulatory problems, during the TARP period, and for banks with a history of bailouts. Furthermore, such banks lend more frequently to connected firms.

Real Cash Flow Expectations and Asset Prices
De la O, Ricardo,Myers, Sean
Using survey forecasts, we find that systematic errors in expectations of long-term inflation and short-term nominal earnings growth are the main driver of prices and return puzzles for bonds and stocks. We demonstrate this by deriving and testing a single necessary and sufficient condition based on accounting identities. Errors in expectations of short-term inflation and long-term nominal earnings growth do not play a role in either asset market. Because of these systematic errors, real cash flow expectations closely match aggregate bond and stock prices, leaving little room for time-varying discount rates. These expectations also accurately match key return puzzles for bonds and stocks: the rejection of the expectations hypothesis and stock return predictability. These results are consistent with a simple model in which agents believe the persistences of inflation and nominal earnings growth are magnified versions of the objective persistences.

Recent Innovation in Benchmark Rates (BMR): Evidence from Influential Factors on Turkish Lira Overnight Reference Interest Rate with Machine Learning Algorithms
Depren, Özer,Kartal, Mustafa Tevfik,Depren, Serpil Kilic
Some countries have announced national benchmark rates while some others have been working on the recent trend in which the London Interbank Offered Rate (LIBOR) will be retired at the end of 2021. Considering that Turkey announced the Turkish Lira Overnight Reference Interest Rate (TLREF), this study examines the determinants of TLREF. In this context, three global determinants, five country-level macroeconomic determinants, and COVID-19 pandemic are considered by using daily data between December 28, 2018 and December 31, 2020 by performing machine learning algorithms and Ordinary Least Square. The empirical results show that (i) the most significant determinant is the amount of securities bought by Central Bank; (ii) country-level macroeconomic factors have a higher impact whereas global factors are less important, and the pandemic does not have a significant effect; (iii) Random Forest is the most accurate prediction model. Taking actions by considering the findings of the study, can be beneficial to support economic growth by achieving low-level benchmark rates.

Sectoral portfolio optimization by judicious selection of financial ratios via PCA
Vrinda Dhingra,Amita Sharma,Shiv K. Gupta

Embedding value investment in portfolio optimization models has always been a challenge. In this paper, we attempt to incorporate it by first employing principal component analysis (PCA) sector wise to filter out dominant financial ratios from each sector and thereafter, use the portfolio optimization model incorporating second order stochastic dominance (SSD) criteria to derive the final optimal investment. We consider a total of 11 well known financial ratios corresponding to each sector representing four categories of ratios, namely liquidity, solvency, profitability, and valuation. PCA is then applied sector wise over a period of 10 years from April 2004 to March 2014 to extract dominant ratios from each sector in two ways, one from the component solution and other from each category on the basis of their communalities. The two step Sectoral Portfolio Optimization (SPO) model integrating the SSD criteria in constraints is then utilized to build an optimal portfolio. The strategy formed using the former extracted ratios is termed as PCA-SPO(A) and the latter one as PCA-SPO(B).

The results obtained from the proposed strategies are compared with the SPO model and two nominal SSD models, with and without financial ratios for computational study. Empirical performance of proposed strategies is assessed over the period of 6 years from April 2014 to March 2020 using a rolling window scheme with varying out-of-sample time line of 3, 6, 9, 12 and 24 months for S&P BSE 500 market. We observe that the proposed strategy PCA-SPO(B) outperforms all other models in terms of downside deviation, CVaR, VaR, Sortino ratio, Rachev ratio, and STARR ratios over almost all out-of-sample periods. This highlights the importance of value investment where ratios are carefully selected and embedded quantitatively in portfolio selection process.

Show Me the Money! Dividend Policy in Countries with Weak Institutions
Ellahie, Atif,Kaplan, Zachary
We hypothesize that, in weak-institution countries, firms adjust the ‘timing’ of dividend payments by committing to distribute a percentage of current earnings as dividends, revealing the extent of firm-level agency conflicts to future investors and facilitating the raising of external capital. Consistent with this hypothesis, we find that, on average, firms in weak-institution countries have a higher speed of adjustment (SOA) to their target payout ratio, pay dividends earlier in the life cycle, and are more likely to disclose a dividend policy committing to pay a minimum percentage of earnings. Within-country tests show that, in weak-institution countries, the firms with the highest SOA dividend policies have fewer agency problems and an increased ability to raise external capital. Finally, returns tests around earnings announcements show that high-SOA dividend policies are associated with larger market reactions to earnings in weak-institution countries. Collectively, our findings suggest that dividend policy helps to alleviate agency conflicts in weak-institution countries between firms and (future) investors.

Sub- and Super-solution Approach to Accuracy Analysis of Portfolio Optimization Asymptotics in Multiscale Stochastic Factor Market
Jean-Pierre Fouque,Ruimeng Hu,Ronnie Sircar

The problem of portfolio optimization when stochastic factors drive returns and volatilities has been studied in previous works by the authors. In particular, they proposed asymptotic approximations for value functions and optimal strategies in the regime where these factors are running on both slow and fast timescales. However, the rigorous justification of the accuracy of these approximations has been limited to power utilities and a single factor. In this paper, we provide an accuracy analysis for cases with general utility functions and two timescale factors by constructing sub- and super-solutions to the fully nonlinear problem such that their difference is at the desired level of accuracy. This approach will be valuable in various related stochastic control problems.

The COVID-19 Impact on Corporate Leverage and Financial Fragility
Haque, Sharjil,Varghese, Richard
We study the impact of the COVID-19 recession on capital structure of US public firms. Our estimates suggest leverage (Net Debt/Asset) decreased by 5.3 percentage points relative to the pre-shock mean of 19.6 percent, while debt maturity increased moderately. This de-leveraging effect is stronger for firms exposed to significant rollover risk (which we define as financial risk), while firms whose businesses were most vulnerable to social distancing (which we define as business risk) did not change their debt levels. The data also shows firms exposed to financial (business) risk raised (lowered) debt maturity. We rationalize our evidence through a structural model that shows decline in expected growth rate and surge in volatility of cash flows following COVID reduced optimal levels of corporate leverage, consistent with our benchmark regressions. However, comparing model-implied optimal leverage with the data shows firms which did not de-lever (i.e. those exposed to business risk) became over-leveraged and experienced greater spike in default probability. We simulate stress tests and confirm these over-leveraged firms are more likely to default if growth slows down or uncertainty increases, with risks concentrated among the largest and most vulnerable firms. Consequently, leverage dynamics during COVID-19 raise important implications for systemic risk and financial stability.

The Dark Side of Globalization: Evidence from the Impact of COVID-19 on Multinational Companies
Guedhami, Omrane,Knill, April M.,Megginson, William L.,Senbet, Lemma W.
The COVID-19 pandemic has led to economic and health crises (“twin crises”) worldwide. Using a sample of firms from 74 countries over the period January to August 2020, we examine stock price reactions of multinational corporations (MNCs) and purely domestic companies (DCs) to the crisis. We find that, on average, MNCs suffer a significantly larger decline in firm value relative to DCs during the stock market crisis caused by the pandemic, with their significant post-crisis recovery not fully offseting the decline. The relative underperformance of MNCs holds across all regions, except North America and Latin America & the Caribbean, and across all Famaâ€"French industries, except chemicals, healthcare/drugs, and finance. We then examine to the effect of government responses on the valuation gap and find that stronger government responses exacerbate the MNCunder-performance. Finally, we show that a stronger financial system mitigates negative crisis returns, especially under stronger government reponses, while real factors, such as the firm’s supply chain, investments in human capital, research and development exacerbate negative crisis returns. Our findings have important implications for managers of MNCs and government policymakers alike and contribute to studies on the international diversificationâ€"performance relation by demonstrating a dark side of internationalization during a tail-risk event.

The Governance of Money and Payment Systems. Rationale and Aims of a Study
Gimigliano, Gabriella
This is the introduction to the research monograph and gives and overview of the aims and strcuture of the book.

Time Series Momentum Predictability via Dynamic Binary Classification
Bruno P. C. Levy,Hedibert F. Lopes

Time series momentum strategies are widely applied in the quantitative financial industry and its academic research has grown rapidly since the work of Moskowitz, Ooi and Pedersen (2012). However, trading signals are usually obtained via simple observation of past return measurements. In this article we study the benefits of incorporating dynamic econometric models to sequentially learn the time-varying importance of different look-back periods for individual assets. By the use of a dynamic binary classifier model, the investor is able to switch between time-varying or constant relations between past momentum and future returns, dynamically combining different look-back periods and improving trading signals accuracy and portfolio performance. Using data from 56 future contracts we show that a mean-variance investor will be willing to pay a considerable managment fee to switch from the traditional naive time series momentum strategy to the dynamic classifier approach.

Two Price Regimes in Limit Order Books: Liquidity Cushion and Fragmented Distant Field
Sebastian M. Krause,Edgar Jungblut,Thomas Guhr

The distribution of liquidity within the limit order book is essential for the impact of market orders on the stock price and the emergence of price shocks. Hence it is of great interest to improve the understanding of the time-dependent dynamics of the limit order book. In our analysis we find a broad distribution of limit order lifetimes. Around the quotes we find a densely filled regime with mostly short living limit orders, far away from the quotes we find a sparse filling with mostly long living limit orders. We determine the characteristics of those two regimes and point out the main differences. Based on our research we propose a model for simulating the regime around the quotes.

Uncovering the Iceberg from Its Tip: A Model of Publication Bias and p-Hacking
Harvey, Campbell R.,Liu, Yan
Harvey, Liu, and Zhu (2016) argue that a large proportion of published asset-pricing factors are likely false. Researchers may try many variables and report only the significant ones, so-called p-hacking. Some recent work challenges the prevalence of p-hacking and argues that the amount of shrinkage necessary for reported results is trivial. We present a model where there are true anomalies and false anomalies. Our model does a good job of fitting the observed population. Our evidence is consistent with the idea that a large proportion of anomalies are false and reinforces the need to raise the thresholds for statistical significance.

Why Bitcoin Fails as Money: An Operational Risk Analysis
Walch, Angela
[NOTE: This paper was written in late 2014 and early 2015. It is relevant given the continued movement of Bitcoin toward the mainstream, exemplified by El Salvador's adoption of Bitcoin as legal tender in June 2021.]After a slow beginning in 2009, the digital currency Bitcoin has edged closer to the mainstream, and regulators are scrambling to determine what to do with it. So far, they have focused on harms that its use creates, such as easy money laundering and sales of illicit goods. But Bitcoin’s ability to grease the wheels of crime is not the only risk we should worry about. Rather, due to its status as decentralized, open-source software, Bitcoin poses a risk that money has not historically been subject to â€" the risk that the money will just stop working one day due to a technology or basic governance problem. Illuminating the importance of reliable money to our society, this paper unpacks the operational risks generated by Bitcoin’s very structure, such as the inherent vulnerabilities of software to bugs and attacks, the governance problems spawned by its decentralized structure and open-source nature, and the lack of monetary expertise of the coders who run the currency. Explicitly considering how each operational risk impacts Bitcoin’s status as money, I conclude that the aggregation of Bitcoin’s operational risks means that it is simply not durable enough to serve as money â€" even if it becomes widely accepted and achieves a stable value. With hundreds of millions of dollars in investments now pouring into Bitcoin and the larger virtual currency ecosystem, and with more and more prominent individuals jumping daily on the Bitcoin bandwagon, this paper urges regulators and policy-makers to specifically address Bitcoin’s critical operational risks as they design the soon-to-come regulations for virtual currencies.