Research articles for the 2020-03-11

A Climate Insidium with a Price on Warming
John F. Raffensperger

In this paper, I introduce a new emissions trading system (ETS) design to address the problems with existing ETSs and carbon taxes. First, existing ETS designs inhibit emissions but do not constrain warming to any set level. Existing ETSs have the indirect objective of reducing emissions instead of directly reducing warming. Even a global mechanism using an existing ETS cannot guarantee a particular warming path. Part 1: A Price on Warming addresses this. My proposed market trades contracts tied to temperature in a double-sided auction of emissions permits and sequestration contracts. Unlike existing ETSs, the mechanism has a consistent timescale and metric tied to warming, with explicit limits on global temperature in every period into the far future. Every auction finds prices for emissions into the far future. Second, if a jurisdiction does not require firms to manage their emissions, the firms have little incentive to do so. Part 2: A Climate Insidium addresses this. My design incentivizes firms to participate even if their jurisdictions do not join. With sanctions from member jurisdictions and participating firms, the design has bottom-up incentives for joining, and the incentives rise over time under realistic conditions, potentially resulting in a rush to join. Third, existing designs have high transaction costs for implementation, requiring international treaties to begin. Part 3: A Faster Path Forward addresses this. I propose a path without national or international action to begin. A coalition can implement these rules, creating political force to accelerate participation. Full implementation still requires national agreements. This design appears to be closer to "first best", with a lower cost of climate mitigation, than any in the literature, while increasing the certainty of avoiding catastrophic global warming. It might also provide a faster pathway to implementation.

A Mean-Field Game Approach to Equilibrium Pricing, Optimal Generation, and Trading in Solar Renewable Energy Certificate (SREC) Markets
Arvind Shrivats,Dena Firoozi,Sebastian Jaimungal

SREC markets are a market-based system designed to incentivize solar energy generation. A regulatory body imposes a lower bound on the amount of energy each regulated firm must generate via solar means, providing them with a certificate for each MWh generated. Regulated firms seek to navigate the market to minimize the cost imposed on them, by modulating their SREC generation and trading activities. As such, the SREC market can be viewed through the lens of a large stochastic game with heterogeneous agents, where agents interact through the market price of the certificates. We study this stochastic game by solving the mean-field game (MFG) limit with sub-populations of heterogeneous agents. Our market participants optimize costs accounting for trading frictions, cost of generation, SREC penalty, and generation uncertainty. Using techniques from variational analysis, we characterize firms' optimal controls as the solution of a new class of McKean-Vlasov FBSDE and determine the equilibrium SREC price. We numerically solve the MV-FBSDEs and conclude by demonstrating how firms behave in equilibrium using simulated examples.

A weighted finite difference method for American and Barrier options in subdiffusive Black-Scholes Model
Grzegorz Krzyżanowski,Marcin Magdziarz

This paper is focused on American option pricing in the subdiffusive Black Scholes model. Two methods for valuing American options in the considered model are proposed. The weighted scheme of the finite difference (FD) method is derived and the main properties of the method are presented. The Longstaff-Schwartz method is applied for the discussed model and is compared to the previous method. In the article it is also shown how to valuate wide range of Barrier options using the FD approach. The proposed FD method has $2-\alpha$ order of accuracy with respect to time, where $\alpha\in(0, 1)$ is the subdiffusion parameter, and 2 with respect to space. The paper is a continuation of [13], where the derivation of the governing fractional differential equation, similarly as the stability and convergence analysis can be found.

Anticipating Acquisitions
Macias, Antonio J.,Rau, P. Raghavendra,Stouraitis, Aris
Prior literature documents that acquirers earn declining returns to acquisitions as they continue acquiring. We show that subsequent acquisitions by acquirers are predictable ex ante. Controlling for market anticipation, there is little evidence that acquirers earn declining returns in their acquisitions sequence. We also find strong evidence of persistence in performance in acquirers, both for prior winners and prior losers. However persistent winners are not frequent acquirers. Persistent losers appear to be overvalued at the time of the acquisition and pay with overvalued stock, leaving them better off than if they had never acquired.

Applied Bachelier Option Pricing: Where Both Assets Are Price Uncertain, and Option Asset Value
Thomson, Ian
This paper builds on Louis Bachelier model for option pricing work by the author. The focus is to generalise the option model for where both assets are price uncertain, adapting the bivariate normal to the model; the paper also works on defining the underlying asset values based on this model, in doing so proposes the option definition be revised to recognise as a contract to ‘complete’ a purchase or sale transaction. In applying the model to the underlying asset values the question on attributing the instability coefficient arises and a solution is proposed implying a revision within the pricing model for this purpose, also the necessary question of the appropriate rate is addressed.

Banking Stability, Institutional Quality, Market Concentration, Competition and Political Conflict in MENA
Elfeituri, Hatem
This paper used panel data analysis via GMM estimation to investigate a number of contemporary issues regarding whether MENA banks’ stability and profitability are affected by market power and market concentration. Additionally, it investigates political issues that took place in late 2010 and 2011 as well as whether the quality of institutional environments contributed to shaping the financial performance of MENA banks and their stability. A large sample of banks have been selected for a period (1999-2016) that includes the recent global crisis period and Arab conflict. Findings confirm that the quality of institutional variables play a major role in explaining bank performance and stability. Furthermore, banks operating in more concentred markets are able to exercise their market power to obtain higher returns, confirming that less competition and higher concentration would lead to deterioration in banking stability. But, results showed that banking stability and profitability would be safeguarded if those banks with higher market power operate in better environments with high quality of regulatory, emphasising that quality of regulatory should be carefully considered to ensure the stability of financial systems and the national economy as whole.

Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods
Adamantios Ntakaris,Martin Magris,Juho Kanniainen,Moncef Gabbouj,Alexandros Iosifidis

Managing the prediction of metrics in high-frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high-frequency limit order markets for mid-price prediction. We extracted normalized data representations of time series data for five stocks from the NASDAQ Nordic stock market for a time period of ten consecutive days, leading to a dataset of ~4,000,000 time series samples in total. A day-based anchored cross-validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state-of-the-art methodologies. Performance of baseline approaches are also provided to facilitate experimental comparisons. We expect that such a large-scale dataset can serve as a testbed for devising novel solutions of expert systems for high-frequency limit order book data analysis.

Bond Liquidity and Dealer Inventories: Insights from US and European Regulatory Data
Ivanov, Plamen,Orlov, Alexei G.,Schihl, Michael
Most corporate bond research on liquidity and dealer inventories is based on the USD-denominated bonds transactions in the US reported to TRACE. Some of these bonds, however, are also traded in Europe, and those trades are not subject to the TRACE reporting requirements. Leveraging our access to both TRACE and ZEN, the UK's trade reporting system which is not publicly available, we find an overlap of about 30,000 bonds that are traded both in the US and in Europe. This paper examines how using the CUSIP-level information from TRACE and ZEN affects the computation of bond liquidity metrics, dealer inventories, and the relationship between the two. We find that in the combined dataset, the weekly volume traded and number of trades are significantly higher than in TRACE: e.g., the average unconditional number of trades in investment-grade (high-yield) bonds is 17% (20%) higher and the average unconditional volume traded is 15% (17%) higher when we incorporate the information from ZEN. We find a strong positive relationship between inventories and liquidity, as proxied by the trading activity metrics (i.e., number of trades, zero trading days, or par value traded) in TRACE data, and this result carries over to the combined dataset. When measuring bond liquidity with the Amihud ratio, we find strong relationships in both TRACE and ZEN but of opposite signs: greater (lagged) inventories result in higher liquidity in the US but lower liquidity in Europe. The two effects offset each other and significance disappears in the combined dataset. We conclude that (i) neither of the individual datasets paints a complete picture of the effects of dealer inventories on bond market liquidity, (ii) the measures based on the combined dataset appear more precise in describing the market characteristics, and (iii) data sharing across transaction reporting databases would allow a variety of stakeholders to gain a more accurate understanding of the liquidity and dealer inventories in global bond markets.

Complete and Competitive Financial Markets in a Complex World
Cassese, Gianluca
We investigate the possibility of completing financial markets in a model with no exogenous probability measure and market imperfections. A necessary and sufficient condition is obtained for such an extension to be possible.

Credit Ratings and Competition
Piccolo, Alessio
I analyze a model of competition between credit rating agencies (CRAs). In equilibrium, investors only buy assets that received high ratings from multiple CRAs. This has two contrasting effects on the quality of certification. On the one hand, the issuer needs to pass the screening of multiple CRAs; other things being equal, this improves certification. On the other hand, it has the perverse effect of incentivizing dishonest ratings, by introducing a team dimension that dilutes the CRAs' reputational concerns. When the perverse effect dominates, competition reduces the quality of certification. However, mandating disclosure of indicative ratings can restore the first-best outcome.

Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems
Ruimeng Hu

In this paper, we propose deep learning algorithms for ranking response surfaces, with applications to optimal stopping problems in financial mathematics. The problem of ranking response surfaces is motivated by estimating optimal feedback policy maps in stochastic control problems, aiming to efficiently find the index associated to the minimal response across the entire continuous input space $\mathcal{X} \subseteq \mathbb{R}^d$. By considering points in $\mathcal{X}$ as pixels and indices of the minimal surfaces as labels, we recast the problem as an image segmentation problem, which assigns a label to every pixel in an image such that pixels with the same label share certain characteristics. This provides an alternative method for efficiently solving the problem instead of using sequential design in our previous work [R. Hu and M. Ludkovski, SIAM/ASA Journal on Uncertainty Quantification, 5 (2017), 212--239].

Deep learning algorithms are scalable, parallel and model-free, i.e., no parametric assumptions needed on the response surfaces. Considering ranking response surfaces as image segmentation allows one to use a broad class of deep neural networks, e.g., UNet, SegNet, DeconvNet, which have been widely applied and numerically proved to possess high accuracy in the field. We also systematically study the dependence of deep learning algorithms on the input data generated on uniform grids or by sequential design sampling, and observe that the performance of deep learning is {\it not} sensitive to the noise and locations (close to/away from boundaries) of training data. We present a few examples including synthetic ones and the Bermudan option pricing problem to show the efficiency and accuracy of this method.

Did Firm-Exit Affect Prices During the Crisis?
Suveg, Melinda
This paper outlines a new mechanism that can explain the missing disinflation puzzle during the financial crisis and the sluggish recovery experienced after the recent crisis. The mechanism does not require consumer habits or an investment into firms' consumer base. Instead, it proposes a supply-side channel that works through changes in competition and financial constrains such that a price increase is caused by the additional exit following a financial shock. Micro-data evidence is provided for this new mechanism using Swedish firm-level data. The detail in the Swedish data makes it possible to measure the changes in each and every firm's market share in Sweden. To identify the effect of changes in industry-exit on firm-level prices, an instrumental variable approach is implemented. The results show that a one percent increase in the industry's relative exit increases firm-prices by 1.6 percent. A quantitative model is developed to formalise the mechanism and decompose the price-effect that stems from declining demand, declining competition and increasing financial costs. The model outlines oligopolistically competitive firms who face financial shocks via their connection to the banking sector.

Does Herding Exist in Lottery Stocks? Evidence From the Indian Stock Market
Ansari, Aleem,Aziz, Tariq,Ansari, Valeed Ahmad
In this paper, we investigate the presence of herd behaviour among lottery stocks using Max, skewness and idiosyncratic volatility in the Indian stock market during the period January 2000 to December 2018. We demonstrate that the herd behaviour is non-existent across proxies of lottery-stocks MAX and skewness and find that the herd behaviour is present among highly idiosyncratic stocks. This sheds light on why herding is not detected in the prior studies as it may be concentrated among stocks with certain characteristics. Further, it provides evidence of adverse herding.

Dynamic Portfolio Management and Market Anomalies (Presentation Slides)
Smirnov, Mikhail
We discuss performance of some known market anomalies like equal-weighted index, low volatility stock index, factor anomalies of Andrea Frazzini, Ronen Israel and Tobias J. Moskowitz. We suggest the utilization of these anomalies through dynamic risk allocation in portfolios based on these anomalies creating desired final fund value distribution. We introduce the notion of Dynamic Leverage as a VAR extending risk measure taking into account the investment time horizon. We introduce a modification of Black-Jones-Perold portfolio insurance. For an investment fund with dynamically controlled risk exposure and certain risk inertia, we demonstrate the existence of a critical NAV level below which the efficacy of de-leveraging is compromised.

Dynamics of Interest Rate Futures: A Comprehensive Study from The Sydney Futures Exchange
Gerace, Dionigi,Frino, Alex
This paper examines the dynamics of quoted bid-ask spreads, price volatility and percentage trading volume for the most liquid interest rate futures trading on the Sydney Futures Exchange. Using data for both the overnight and intraday markets of the Sydney Futures Exchange, patterns contrast the existing theory and prior research. During the Intra-night trading session, volume and volatility patterns show low trading activities between 5:30 and 9:30; while spreads show high asymmetric information during this interval. During the Intra-day trading session, both volume and volatility exhibits a significant increase at the opening and around 2.30pm, followed a significant decrease towards the end of the day session; Spreads are low at the opening and tendentially increase throughout the day up until the close of the day. We find that percentage Volume traded is higher during the day session; although spread significantly increases towards the end of the day session, it is tighter than the overnight spread. A number of tests are carried out documenting that these patterns are consistent with the effects of contagion from overseas markets, US versus Australia daylight savings, and major macro-economics information releases.

Exercising the ‘Governance Option’: Labour’s New Push to Reshape Financial Capitalism
Diamond, Stephen F.
New forms of stockholder activism call into question longstanding assumptions underpinning our system of corporate governance. Scholarship has largely failed to explain the basis for these new forms and, in particular, the differences among activists. Activists are not one undifferentiated mass. Both small activist hedge funds and large union-sponsored or -influenced pension funds use governance mechanisms to influence corporate behaviour. Pension funds, however, have a different set of incentives than hedge funds. The beneficiaries of these funds cannot easily switch between consumption and investment by buying or selling their holdings in firms. Thus, instead, institutional investors exercise an embedded ‘governance option’ found within shares of common stock to engage with firms. Organised labour, in particular, now uses its influence in pension funds to motivate progressive change by corporations. This form of activism has the potential to alter the balance of power between workers and capitalists in the era of financial capitalism.

Geometric Step Options with Jumps: Parity Relations, PIDEs, and Semi-Analytical Pricing
Farkas, Walter,Mathys, Ludovic
The present article studies geometric step options in exponential Lévy markets. Our contribution is manifold and extends several aspects of the geometric step option pricing literature. First, we provide symmetry and parity relations and derive various characterizations for both European-type and American-type geometric double barrier step options. In particular, we are able to obtain a jump-diffusion disentanglement for the early exercise premium of American-type geometric double barrier step contracts and its maturity-randomized equivalent as well as to characterize the diffusion and jump contributions to these early exercise premiums separately by means of partial integro-differential equations and ordinary integro-differential equations. As an application of our characterizations, we derive semi-analytical pricing results for (regular) European-type and American-type geometric down-and-out step call options under hyper-exponential jump-diffusion models. Lastly, we use the latter results to discuss the early exercise structure of geometric step options once jumps are added and to subsequently provide an analysis of the impact of jumps on the price and hedging parameters of (European-type and American-type) geometric step contracts.

Impact of Algorithmic Trading on Speed of Adjustment to New Information: Evidence from Interest Rate Derivatives
Zhou, Ivy,Garcia, Michael,Frino, Alex
This study examines the impact of algorithmic trading (AT) on the speed of adjustment and price discovery during scheduled macroeconomic announcements for interest rate derivatives. In February 2012, the Australian Securities Exchange (ASX) introduced co-location services for futures traders. This change in market structure increases AT and, in this study, we examine price efficiency for both exchange-traded futures and over-the-counter (OTC)-traded swaps in the pre- and post-AT periods. Our results demonstrate that, in the presence of AT, the speed of adjustment to new information has improved for both futures and swaps. In addition, we find that the price discovery contribution of the futures market improves in the post-AT period, with this improvement significant for announcement days. We conclude that AT stimulates market adjustment to new information and enhances the price discovery of futures on days with scheduled macroeconomic releases.

KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments
Shubhankar Mohapatra,Nauman Ahmed,Paulo Alencar

Cryptocurrencies, such as Bitcoin, are becoming increasingly popular, having been widely used as an exchange medium in areas such as financial transaction and asset transfer verification. However, there has been a lack of solutions that can support real-time price prediction to cope with high currency volatility, handle massive heterogeneous data volumes, including social media sentiments, while supporting fault tolerance and persistence in real time, and provide real-time adaptation of learning algorithms to cope with new price and sentiment data. In this paper we introduce KryptoOracle, a novel real-time and adaptive cryptocurrency price prediction platform based on Twitter sentiments. The integrative and modular platform is based on (i) a Spark-based architecture which handles the large volume of incoming data in a persistent and fault tolerant way; (ii) an approach that supports sentiment analysis which can respond to large amounts of natural language processing queries in real time; and (iii) a predictive method grounded on online learning in which a model adapts its weights to cope with new prices and sentiments. Besides providing an architectural design, the paper also describes the KryptoOracle platform implementation and experimental evaluation. Overall, the proposed platform can help accelerate decision-making, uncover new opportunities and provide more timely insights based on the available and ever-larger financial data volume and variety.

Machine Learning Treasury Yields
Zura Kakushadze,Willie Yu

We give explicit algorithms and source code for extracting factors underlying Treasury yields using (unsupervised) machine learning (ML) techniques, such as nonnegative matrix factorization (NMF) and (statistically deterministic) clustering. NMF is a popular ML algorithm (used in computer vision, bioinformatics/computational biology, document classification, etc.), but is often misconstrued and misused. We discuss how to properly apply NMF to Treasury yields. We analyze the factors based on NMF and clustering and their interpretation. We discuss their implications for forecasting Treasury yields in the context of out-of-sample ML stability issues.

Measurement of Factor Strenght: Theory and Practice
Bailey, Natalia,Kapetanios, George,Pesaran, M. Hashem
This paper proposes an estimator of factor strength and establishes its consistency and asymptotic distribution. The proposed estimator is based on the number of statistically significant factor loadings, taking account of the multiple testing problem. We focus on the case where the factors are observed which is of primary interest in many applications in macroeconomics and finance. We also consider using cross section averages as a proxy in the case of unobserved common factors. We face a fundamental factor identification issue when there are more than one unobserved common factors. We investigate the small sample properties of the proposed estimator by means of Monte Carlo experiments under a variety of scenarios. In general, we find that the estimator, and the associated inference, perform well. The test is conservative under the null hypothesis, but, nevertheless, has excellent power properties, especially when the factor strength is sufficiently high. Application of the proposed estimation strategy to factor models of asset returns shows that out of 146 factors recently considered in the finance literature, only the market factor is truly strong, while all other factors are at best semi-strong, with their strength varying considerably over time. Similarly, we only find evidence of semi-strong factors in an updated version of the Stock and Watson (2012) macroeconomic dataset.

Momentum and Contrarian Effects in the Ukrainian Stock Market: Case of Daily Overreactions
Plastun, Oleksiy,Strochenko, Nataliya,Zhmaylova, Olga,Sliusareva, Liudmyla,Bashlay, Sergiy
This paper examines momentum and contrarian effects in the Ukrainian stock market after one-day abnormal returns. To do this, UX futures data over the period 2010â€"2018 are used. The following hypotheses are tested: H1) hourly returns on overreaction days differ from hourly returns on normal days, H2) there are price patterns on overreaction days, and H3) to test these hypotheses, visual inspection and average analysis are used, as well as t-tests, cumulative abnormal returns, and trading simulation approaches. The results suggest that there are statistically significant differences between intraday dynamics during the usual days and the overreactions day. There is a strong momentum effect present on the day of overreaction: prices tend to change only in the direction of the overreaction during the whole day. The fact of the overreaction becomes clear after 13:00-14:00. This gives a lot of time to explore the momentum effect in the day of overreaction. On the day after the overreaction, prices tend to go in the opposite direction: contrarian pattern is detected, which is in line with the overreaction hypothesis. Based on detected price patterns, rules of trading and trading strategies for the Ukrainian stock market are developed. Momentum Strategy (based on price patterns on the day of overreaction) generates several successful trades; close to with 90%, and their number being is profitable (trading results differ from the random ones â€" confirmed by t-tests). Contrarian Strategy (based on price patterns on the day after the overreaction) demonstrates low efficiency, and results do not differ from random trading.

Multivariate High-Frequency-Based Factor Model
Bodilsen, Simon
We propose a new predictive model for large-dimensional realized covariance matrices. Using high-frequency data, we construct daily realized covariance matrices for the constituents of the S\&P 500 Index and a set of observable factors. Using a standard decomposition of the joint covariance matrix we express the covariance matrix of the individual assets similar to an approximate factor model. A novel feature of the model, is the use of the hierarchical clustering algorithm to determine the structure of the idiosyncratic covariance matrices. To construct a conditional covariance model, we suggest to model the components of the covariance structure separately using autoregressive time series regressions. In an out-of-sample portfolio selection exercise, we find that the proposed model outperforms other commonly used multivariate volatility models in extant literature.

Off-Market Block Trades: New Evidence on Transparency and Information Efficiency
Frino, Alex
This paper examines the price impact of off-market block trades in futures markets. Consistent with previous literature based on stock markets [Gemmill, 1996, Journal of Finance], a statistically significant price reaction is documented around the time that the trades are executed. This paper extends previous work by documenting a further statistically significant price reaction at the time that block trades are later reported. Contrary to previous research, these findings imply that delaying the reporting of off-market trades has an impact on markets by delaying the speed of adjustment to the information conveyed by block trades and therefore market price efficiency.

On the structure of the world economy: An absorbing Markov chain approach
Olivera Kostoska,Viktor Stojkoski,Ljupco Kocarev

The expansion of global production networks has raised many important questions about the interdependence among countries and how future changes in the world economy are likely to affect the countries' positioning in global value chains. We are approaching the structure and lengths of value chains from a completely different perspective than has been available so far. By assigning a random endogenous variable to a network linkage representing the number of intermediate sales/purchases before absorption (final use or value added), the discrete-time absorbing Markov chains proposed here shed new light on the world input/output networks. The variance of this variable can help assess the risk when shaping the chain length and optimize the level of production. Contrary to what might be expected simply on the basis of comparative advantage, the results reveal that both the input and output chains exhibit the same quasi-stationary product distribution. Put differently, the expected proportion of time spent in a state before absorption is invariant to changes of the network type. Finally, the several global metrics proposed here, including the probability distribution of global value added/final output, provide guidance for policy makers when estimating the resilience of world trading system and forecasting the macroeconomic developments.

Preparing Fertile Ground: How Does the Business Environment Affect Outcomes From Microfinance?
Fu, Jonathan,Krauss, Annette
Transformative effects on microfinance client business growth are expected in financial inclusion's traditional theory of change, yet have largely been unrealized in most randomized controlled trial studies on microcredit. While these studies have been quite instructive, their broader policy relevance may be limited by their localized geographic coverage and invariance of business environments in which client businesses operate. In this paper, we draw on longer-term nationwide administrative data from a leading Cambodian financial service provider and exploit a quasi-natural experiment to test if and to what extent differences in business environments affect clients’ business growth. Our main finding is that positive shocks to the business environment lead to significantly increased employment in exposed client enterprises compared to client enterprises in contextually similar districts but that are unexposed. The effect is particularly strong for small (rather than micro-) enterprises and clients located in districts with larger local economic markets. Furthermore, factors relating to enterprises’ business environment are more predictive for growth than those relating to business characteristics. To broaden our argument and policy relevance, we then pool data from past microcredit impact evaluations to demonstrate how a number of related business environment factors help explain the heterogeneity found in their results. In those data, we observe, for example, that subnational differences in levels of trust in formal institutions are particularly strongly related to greater levels of employment in client businesses, and that areas with access to better infrastructure and larger markets show signs of particularly higher client profits. Our results suggest that financial access in isolation is less likely to trigger the desired transformative effects, or may not be fulfilling its real potential, unless coupled with the right opportunities in the business environment. Policy implications are that microfinance clientele respond to opportunities provided by local business environments and that national and subnational governments can play more active roles in improving business framework conditions.

Private Company Lies
Pollman, Elizabeth
Rule 10b-5’s antifraud catch-all is one of the most consequential pieces of American administrative law and most highly developed areas of judicially-created federal law. Although the rule broadly prohibits securities fraud in both public and private company stock, the vast majority of jurisprudence, and the voluminous academic literature that accompanies it, has developed through a public company lens. This Article illuminates how the explosive growth of private markets has left huge portions of U.S. capital markets with relatively light securities fraud scrutiny and enforcement. Some of the largest private companies by valuation grow in an environment of extreme information asymmetry and with the pressure, opportunity, and rationalizing culture that can foster misconduct and deception. Many investors in the private markets are sophisticated and can bear high levels of risk and significant losses from securities fraud. It is increasingly evident, however, that private company lies can harm a broader range of shareholders and stakeholders as well as the efficiency of allocating billions of dollars for innovation and new business. In response to this underappreciated problem, this Article explores a range of mechanisms to improve accountability in the private markets and ultimately argues for greater public oversight and enforcement.

Prospect Theory and Market Response to Public Announcements
Niebuhr, Nikolaj Kirkeby
In this paper I develop a noisy-rational-expectations model with a risk-neutral market maker and speculators who display prospect theory-inspired preferences and who can acquire private information about a public announcement prior to its release. Private information acquisition decreases the market response to the public announcement. Increasing loss-aversion increases the market response to the public announcements. Increasing the preferences for risk-seeking in losses decreases the change in informativeness but increases price variance, providing an ambiguous effect on the market response. Overall, prospect theory-inspired preferences in the economy increase the market response to the public signal as loss-aversion dominates the effect of risk-seeking in losses for any reasonable parameter choices. These results are important for regulators deciding on accounting standards, on hindering insider trading and for both managers' and investors' decisions surrounding public announcements, when the market can be characterized by prospect theory-inspired preferences.

Providing a Systematic Model to Determine the Efficiency of Urban Transport Organizations, Focusing on Quality of Service and Customer Satisfaction, to Rank Citizens Satisfaction by DEA Method
Shoushtari, Reza
Despite the growing trend of IT-based systems, the implementation of the CSM system in urban transport services companies such as Tehran bus service can greatly assist in the efficient management of maintenance and repair activities, thereby reducing equipment failure and increasing reliability, increasing productivity, and most importantly increase the efficiency and productivity of its staff. CRM is one of the fast-growing management approaches that leads to improved competitive position, greater market share and profitability. The purpose of this paper is to evaluate and explain the level of satisfaction of passengers with urban transport system, measure the efficiency of staff, identify efficient and inefficient systems, and rank the degree of satisfaction, over different time periods, and finally to compare and evaluate the effect of effective factors on their efficiency to enhance the organization's productivity with a customer satisfaction approach. In order to implement CRM in DEA model, input and output variables are respectively service quality and customer satisfaction. For this purpose, after identifying the factors affecting customer satisfaction through questionnaires distributed among 2500 passengers over 17 six-month periods, a DEA model including 3 inputs and 2 outputs is defined. By solving the model using the CCR method, the periods of efficient and inefficient are identified and the results of sensitivity analysis on the model inputs show the effect of each input on the output according to the rankings performed.

Regulators Nurturing FinTech Innovation: Global Evolution of the Regulatory Sandbox as Opportunity Based Regulation
Ahern, Deirdre M.
The regulatory sandbox is a real world alternative to regulatory lag. Its emergence as a novel regulatory development responds to challenges faced by FinTech innovators in navigating an unwieldy regulatory landscape not designed with FinTech in mind. Regulatory sandboxes are in operation in developed countries including Australia, Canada, Denmark, Hong Kong, Singapore, Switzerland, the Netherlands, the United Arab Emirates, the United Kingdom, and the United States. Within the EU they are seen in Denmark, Hungary, Lithuania, Poland and the Netherlands. The concept has also gained traction with regulators in developing countries such as India, Indonesia, Malaysia, Mauritius and Thailand.Not only is the regulatory sandbox an experimental space for firms testing innovative FinTech products and services, it is also a novel regulatory experiment for regulators. This article advances the available literature through focusing on the contradictions inherent in the role of the regulator in administering a regulatory sandbox. It characterises the regulatory sandbox as a form of agile, opportunity-based regulation, distinguished by a regulatory approach that is concerned with actively supporting innovators in nurturing cutting-edge innovation to benefit innovators, consumers, investors, and the wider economy. This is path-breaking regulatory territory. In its provision and design a regulatory sandbox phenomenon performs a crucial positioning function in relation to a given financial system’s receptivity to FinTech business. An economic, pro-innovation agenda is at work. Distinct policy questions are therefore raised regarding the legitimate role of public gatekeeper financial services regulators operating regulatory sandboxes. The role of a regulatory sandbox in nurturing and expanding competition suggests a public interest role in the interests of consumer choice, price and efficiency rather than simply on risk minimisation. However, pressure on regulators to produce sandbox successes and to compete with other sandboxes may influence the exercise of regulatory discretion and produce regulatory distortions that affect competition in FinTech markets.

Regulatory Cooperation in Securities Market Regulation: Perspectives from Australia
Hill, Jennifer G.
The global financial crisis highlighted the interconnectedness of international financial markets and the risk of contagion it posed. The crisis also emphasized the importance of supranational regulation and regulatory cooperation to address that risk. Yet, although capital flows are global, securities regulation is not. As a 2019 report by IOSCO notes, the regulatory challenges revealed during the global financial crisis have by no means dissipated over the last decade. Lack of international standards, or differences in the way jurisdictions implement such standards, can often result in regulatory-driven market fragmentation. This article considers a range of cooperative techniques designed to achieve international regulatory harmonization and effective cross-border financial market supervision. It discusses three major techniques: (i) transgovernmental networks of financial regulators; (ii) complex multilateral arrangements; and (iii) mutual recognition agreements, and considers the benefits and downsides of each of these regulatory mechanisms. The article focuses particularly on developments in Australia. It examines, for example, a high profile cross-border supervisory experiment, the US-Australian Mutual Recognition Agreement, which the SEC and Australia’s business conduct regulator, ASIC, signed in 2008. This was the first agreement of its kind for the SEC. The article also considers some key regulatory developments in Australia and Asia since the time of the US-Australian Mutual Recognition Agreement.

Retaining Alpha: The Effect of Trade Size and Rebalancing Frequency on FX Strategy Returns
Melvin, Michael,Pan, Wenqiang,Wikstrom, Petra
The literature on currency investing that incorporates transaction costs uses costs relevant for small trade sizes. Using the entire order book of the major electronic brokerages for FX, we compute sweep-to-fill costs for trades of different sizes and illustrate the reduction in post-cost returns as trade size increases. Researchers should consider trade size and frequency to create realistic forecasts of post-tcost returns to gauge the capacity of a strategy. We show how incorporating tcosts in the construction of a portfolio improves performance for both high and low frequency strategies and retains a larger portion of the alpha.

Reversals, Market Capitalization, and the Illiquidity Premium
Busse, Jeffrey A.,Wu, Yanbin
We find that the illiquidity premium is mainly attributable to recent loser stocks, consistent with the possibility that it is driven, in part, by return reversals in these stocks. Among illiquid, loser stocks, stocks that were recently sold by mutual funds subsequently show significantly greater returns than stocks that were recently purchased, suggesting that the effect relates to a price rebound following negative price pressure. We further find that when accounting for the fact that stock liquidity is highly correlated with market capitalization, there is little evidence to suggest that an illiquidity premium extends beyond the small cap anomaly.

Teaching the Bloomberg Financial Information System Using Online Videos: An Action Research Study
Godfrey, Chris
This paper discusses an Action Research approach to the teaching of the Bloomberg financial information system, within a Master’s level Portfolio Management module for accounting and finance students. Bloomberg was used to help deliver a ‘fantasy fund manager’ investment simulation which aims to teach macroeconomic forecasting, portfolio analytics and employability skills. I discuss the challenges and benefits of teaching Bloomberg using online videos, and how successive cycles of action research were used to improve the module and promote deep learning. This resulted in better delivery of the intended learning outcomes: survey evidence showed that students were achieving deeper learning as they engaged with the online videos, and the group coursework reports showed an increase in higher-order learning, with students using Bloomberg for themselves in creative and unexpected ways. I draw lessons about creating online videos in the teaching of accounting information systems, especially where teaching resources are constrained.

The Heterogenous Effects of Savings and Capital Inflows on Capital Outflows: A Quantile Regression Approach
Suh, Jaehyun
Purpose â€" The paper aims to investigate the main driver of gross capital outflows in emerging market economies. Accordingly, it tests the following two hypotheses: first, capital outflows are mostly fueled by capital inflows, rather than by domestic savings, and second, the causal impact of capital inflows is stronger in the upper quantiles of capital outflows. Design/Methodology/Approach â€" We estimate the impacts of domestic private savings and gross capital inflows on gross capital outflows in 56 emerging market economies over 1990 - 2014 using Powell’s (2015) quantile regression methodology. Findings â€" According to the results, the response of capital outflows to capital inflows and domestic savings is similar if capital outflows are below the median. However, if they are above the median, the impact of external loans is stronger than that of savings. Furthermore, a country tends to borrow from foreign countries to purchase debts rather than equities in the short run. This is consistent with several stylized facts, such as pro-cyclical capital inflows and outflows, the high leverage ratio, and high probability of serial default and sudden stops during short-term booms. Research Implications â€" The results suggest capital flight is not a market-exiting behavior by domestic agents because they use borrowings rather than savings to increase foreign asset holdings. Therefore, it is unlikely that capital flight significantly decreases domestic agents’ domestic asset holdings.

The Volume and Behaviour of Crowds (Media Coverage)
Shi, Leilei,Webb, Andy
Volume is a comparatively neglected variable in academic finance â€" price and return usually attract far more research interest. An interesting recent exception to this rule, which examines the interaction of volume with behavioural finance, is “Market crowd trading conditioning, agreement price, and volume implications” by a group of Chinese researchers. Automated Trader discusses the paper with its lead author, Leilei Shi of the University of Science and Technology of China.

Timely Persuasion
Basak, Deepal,Zhou, Zhen
We propose a simple dynamic information disclosure policy that eliminates panic. Panic occurs when some agents take an undesirable action (attack) because they fear that other agents will behave similarly, and thus causing regime change even though it is unwarranted. We consider a mass of privately informed agents who can attack a regime at any point within a time window. The attack is irreversible, waiting is costly, and the waiting cost is continuous. The policy we propose is called “disaster alert,” which, at a given time, publicly discloses whether the regime is going to change regardless of the future actions of agents. We show that a timely alert persuades agents to wait for the alert and not attack if the alert is not triggered, regardless of their private information, thus eliminating panic. We apply this theory to demonstrate how forward-looking stress tests can help stop bank runs.

Web Appendix of 'Structural VAR and Financial Networks: A Minimum Distance Approach to Spatial Modeling'
Scida, Daniela
In this paper, I interpret a time series spatial model (T-SAR) as a constrained Structural Vector Autoregressive (SVAR) model. Based on these restrictions, I propose a Minimum Distance approach to estimate the (row-standardized) network matrix and the overall network influence parameter of the T-SAR from the SVAR estimates. I also develop a Wald-type test to assess the distance between these two models. To implement the methodology, I discuss machine learning methods as one possible identification strategy of SVAR models. The methodology is illustrated through an application to volatility spillovers across major stock markets based on daily realized volatility data for 2003-2015.

Whos Ditching the Bus?
Simon J. Berrebi,Kari E. Watkins

This paper uses stop-level passenger count data in four cities to understand the nation-wide bus ridership decline between 2012 and 2018. The local characteristics associated with ridership change are evaluated in Portland, Miami, Minneapolis/St-Paul, and Atlanta. Poisson models explain ridership as a cross-section and the change thereof as a panel. While controlling for the change in frequency, jobs, and population, the correlation with local socio-demographic characteristics are investigated using data from the American Community Survey. The effect of changing neighborhood demographics on bus ridership are modeled using Longitudinal Employer-Household Dynamics data. At a point in time, neighborhoods with high proportions of non-white, carless, and most significantly, high-school-educated residents are the most likely to have high ridership. Over time, white neighborhoods are losing the most ridership across all four cities. In Miami and Atlanta, places with high concentrations of residents with college education and without access to a car also lose ridership at a faster rate. In Minneapolis/St-Paul, the proportion of college-educated residents is linked to ridership gain. The sign and significance of these results remain consistent even when controlling for intra-urban migration. Although bus ridership is declining across neighborhood characteristics, these results suggest that the underlying cause of bus ridership decline must be primarily affecting the travel behavior of white bus riders.