Research articles for the 2020-11-30

A Frequency-Specific Factorization to Identify Commonalities with an Application to the European Bond Markets
Boffelli, Simona,Novotny, Jan,Urga, Giovanni
We propose a frequency-specific framework to link the common features in the multivariate high-frequency price jumps with the low-frequency exogenous factors. We introduce the measure of commonality and the measure of multiplicity based on high-frequency data and define the notions of co-arrivals and co-jumps to explore the contribution of individual assets. We employ the framework to study the 10-year high-frequency European government bond yields over June 2009-April 2019 as a function of macro-factors, macro-announcements, bond auctions and unconventional monetary policy announcements. Both idiosyncratic and common jump arrivals are significant, with the idiosyncratic arrivals being more sensitive to financial distress as characterized by a low level of commonality in jump arrivals.

A Review of Nudges: Definitions, Justifications, Effectiveness
Congiu, Luca,Moscati, Ivan
In an influential book published in 2008, Thaler and Sunstein suggested a novel approach to policy making based on the notion of a ‘nudge.’ Roughly speaking, a nudge is defined as an aspect of the decisional context that steers people’s decisions by acting on their cognitive biases. The book generated an intense debate, over the course of which concerns were raised about: (1) the exact definition of nudges, (2) their ethical justifiability, and (3) their effectiveness. In this paper, we review the nudge literature by focusing on these three concerns.

An Impact Measure for News: It's Use in (Daily) Trading Strategies
Yu, Xiang,Mitra, Gautam,Arbex-Valle, Cristiano,Sayer, Tilman
We investigate how “news sentiment” in general and the “impact of news” in particular can be utilized in designing equity trading strategies. News is an event that moves the market in a small way or a big way. We have introduced a derived measure of news impact score which takes into consideration news flow and decay of sentiment. Since asset behavior is characterized by return, volatility and liquidity we first consider a predictive analytic model in which market data and impact scores are the inputs and also the independent variables of the model. We finally describe the trading strategies which take into consideration the three important characteristics of an asset, namely, return, volatility and liquidity. The minute-bar market data as well as intraday news sentiment metadata have been provided by Thomson Reuters.

An authenticated and secure accounting system for international emissions trading
Chenxing Li,Yang Yu,Andrew Chi-Chih Yao,Da Zhang,Xiliang Zhang

Expanding multi-country emissions trading system is considered as crucial to fill the existing mitigation gap for the 2\degree C climate target. Trustworthy emissions accounting is the cornerstone of such a system encompassing different jurisdictions. However, traditional emissions measuring, reporting, and verification practices that support data authenticity might not be applicable as detailed data from large utilities and production facilities to be covered in the multi-country emissions trading system are usually highly sensitive and of severe national security concern. In this study, we propose a cryptographic framework for an authenticated and secure emissions accounting system that can resolve this data dilemma. We demonstrate that integrating a sequence of cryptographic protocols can preserve data authenticity and security for a stylized multi-country emissions trading system. We call for more research to promote applications of modern cryptography in future international climate governance to build trust and strengthen collaboration.

Assessing Systemic Risk Based on the CARE System
Chuang, Hui-Ching,Chuang, O-Chia,Huang, Zhenhong
Expectile is a coherent and elicitable risk measure that responds to the catastrophic losses more properly than a quantile-based approach. In this paper, we extend the univariate conditional autoregressive expectile (CARE) model of Kuan et al. (2009) to a multivariate CARE system to model the systemic risk of financial institutions. We propose an estimation method and derive its asymptotic properties. In the empirical study, we model the 12-variate CARE system on the global systemically important banks' (G-SIBs') returns for the period Jan 2015 to Jun 2020. We show that the CARE system identifies the asymmetric transmissions of the banks' systemic risk, i.e., the square positive and the square negative lagged returns generate different patterns of associations in the expectile connectedness. In addition, the out-of-sample Expectile-based Value-at-Risk prediction captures the 2020 mid-March US trading curb turbulence.

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

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

Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19
Nick James,Max Menzies,Jennifer Chan

This paper introduces new methods for analysing the extreme and erratic behaviour of time series to evaluate the impact of COVID-19 on cryptocurrency market dynamics. Across 51 cryptocurrencies, we examine extreme behaviour through a study of distribution extremities, and erratic behaviour through structural breaks. First, we analyse the structure of the market as a whole and observe a reduction in self-similarity as a result of COVID-19, particularly with respect to structural breaks in variance. Second, we compare and contrast these two behaviours, and identify individual anomalous cryptocurrencies. Tether (USDT) and TrueUSD (TUSD) are consistent outliers with respect to their returns, while Holo (HOT), NEXO (NEXO), Maker (MKR) and NEM (XEM) are frequently observed as anomalous with respect to both behaviours and time. Even among a market known as consistently volatile, this identifies individual cryptocurrencies that behave most irregularly in their extreme and erratic behaviour and shows these were more affected during the COVID-19 market crisis.

Collective dynamics of stock market efficiency
Luiz G. A. Alves,Higor Y. D. Sigaki,Matjaz Perc,Haroldo V. Ribeiro

Summarized by the efficient market hypothesis, the idea that stock prices fully reflect all available information is always confronted with the behavior of real-world markets. While there is plenty of evidence indicating and quantifying the efficiency of stock markets, most studies assume this efficiency to be constant over time so that its dynamical and collective aspects remain poorly understood. Here we define the time-varying efficiency of stock markets by calculating the permutation entropy within sliding time-windows of log-returns of stock market indices. We show that major world stock markets can be hierarchically classified into several groups that display similar long-term efficiency profiles. However, we also show that efficiency ranks and clusters of markets with similar trends are only stable for a few months at a time. We thus propose a network representation of stock markets that aggregates their short-term efficiency patterns into a global and coherent picture. We find this financial network to be strongly entangled while also having a modular structure that consists of two distinct groups of stock markets. Our results suggest that stock market efficiency is a collective phenomenon that can drive its operation at a high level of informational efficiency, but also places the entire system under risk of failure.

Complex risk statistics with scenario analysis
Fei Sun,Yichuan Dong

Complex risk is a critical factor for both intelligent systems and risk management. In this paper, we consider a special class of risk statistics, named complex risk statistics. Our result provides a new approach for addressing complex risk, especially in deep neural networks. By further developing the properties related to complex risk statistics, we are able to derive dual representation for such risk.

Construction of Minimum Spanning Trees from Financial Returns using Rank Correlation
Tristan Millington,Mahesan Niranjan

The construction of minimum spanning trees (MSTs) from correlation matrices is an often used method to study relationships in the financial markets. However most of the work on this topic tends to use the Pearson correlation coefficient, which relies on the assumption of normality and can be brittle to the presence of outliers, neither of which is ideal for the study of financial returns. In this paper we study the inference of MSTs from daily US, UK and German financial returns using Pearson and two rank correlation methods, Spearman and Kendall's $\tau$. MSTs constructed using these rank methods tend to be more stable and maintain more edges over the dataset than those constructed using Pearson correlation. The edge agreement between the Pearson and rank MSTs varies significantly depending on the state of the markets, but the rank MSTs generally show strong agreement at all times. Deviation from univariate normality can be related to changes in the correlation matrices but is more difficult to connect to changes in the MSTs. Irrelevant of coefficient, the trees tend to have similar topologies. Portfolios constructed from the MST correlation matrices have a smaller turnover than those from the full covariance matrix for the larger markets, but not for the smaller German market. Using a bootstrap method we find that the correlation matrices constructed using the rank correlations are more robust, but there is little difference between the robustness of the MSTs.

Corporate Takeovers and Non-Financial Stakeholders
Greene, Daniel,Kini, Omesh,Shen, Mo ,Shenoy, Jaideep
A large body of work has examined the impact of corporate takeovers on the financial stakeholders (shareholders and bondholders) of the merging firms. Since the late 2000s, empirical research has increasingly highlighted the crucial role played by the non-financial stakeholders (labor, suppliers, customers, government, and communities) in these transactions. This article surveys studies that examine the interplay between corporate takeovers and the non-financial stakeholders of the firm. Financial economists have long viewed the firm as a nexus of contracts between various stakeholders connected to the firm. Corporate takeovers not only play an important role in redefining the broad boundaries of the firm, they also result in major changes to corporate ownership and structure. In the process, takeovers can significantly alter the contractual relationships with non-financial stakeholders. Because the firm’s relationships with these stakeholders are governed by implicit and explicit contracts, circumstances can arise that allow acquiring firms to, fully or partially, abrogate these contracts and extract rents from non-financial stakeholders after deal completion. In contrast, non-financial stakeholders can also potentially benefit from a takeover if they get to share in any efficiency gains that are generated in the deal. Given this framework, the ex ante importance of these contractual relationships can have a bearing on the efficacy of takeovers. The ability to alter contractual relationships ex post can affect the propensity of a takeover and merging firms’ shareholders, and, in turn, impact non-financial stakeholders. Non-financial stakeholders will be more vested in post-takeover success if they can trust the acquiring firm to not take actions that are detrimental to them. The big picture that emerges from the surveyed literature is that non-financial stakeholder considerations affect takeover decisions and post-takeover outcomes. Moreover, takeovers also have an impact on non-financial stakeholders. The directions of all these effects, however, are dependent on the economic environment in which the merging firms operate.

Decoding the Pricing of Uncertainty Shocks
Chen, Zhanhui,Gallmeyer, Michael F.,Kim, Baek-Chun
Uncertainty affects business cycles and asset prices. We estimate firm-level productivity and decompose total uncertainty risk measured as cross-sectional productivity dispersion into macro uncertainty (an aggregate component) and micro uncertainty (an idiosyncratic component). We find that macro uncertainty is strongly counter-cyclical and priced among stocks, but micro uncertainty is acyclical and not priced. Moreover, we show that the expected investment growth factor proposed in Hou et al. (2020) captures macro uncertainty risk, which helps us understand the success of the q5-model.

Did Expected Credit Loss (ECL) fair for Banks in COVID-19?
Kristanto, Septian Bayu,Permatasari, Widowati Dian,Zulkarnain, Nizar,Hambali, Ahmad
In this paper, we explore the impact of the COVID-19 crisis on the accounting practices associated with the ECL approach by PSAK 71. Given the complexity of the pandemic, the neutral application of existing accounting standards is of more importance than ever as it ensures objective decision-useful information that serves comparability, maintenance of a level playing field and transparency. Worldwide interventions by banking regulators, however, have considerable potential to interfere with these fundamental contributions of financial statements. The result is that for banks under PSAK 71 it is not even entirely clear what assessments banks can and will use in their calculations estimating the effects of COVID-19.

Did Hurricane Katrina Reduce Mortality?
Robert Kaestner

In a recent article in the American Economic Review, Tatyana Deryugina and David Molitor (DM) analyzed the effect of Hurricane Katrina on the mortality of elderly and disabled residents of New Orleans. The authors concluded that Hurricane Katrina improved the eight-year survival rate of elderly and disabled residents of New Orleans by 3% and that most of this decline in mortality was due to declines in mortality among those who moved to places with lower mortality. In this article, I provide a critical assessment of the evidence provided by DM to support their conclusions. There are three main problems. First, DM generally fail to account for the fact that people of different ages, races or sex will have different probabilities of dying as time goes by, and when they do allow for this, results change markedly. Second, DM do not account for the fact that residents in New Orleans are likely to be selected non-randomly on the basis of health because of the relatively high mortality rate in New Orleans compared to the rest of the country. Third, there is considerable evidence that among those who moved from New Orleans, the destination chosen was non-random. Finally, DM never directly assessed changes in mortality of those who moved, or stayed, in New Orleans before and after Hurricane Katrina. These problems lead me to conclude that the evidence presented by DM does not support their inferences.

Did Tax Reform Influence the Prevalence of Nonfinancial Compensation Incentives?
Fox, Zackery D.
In this study, I examine whether taxes influence the structure of executive compensation incentives. Recently, the Tax Cuts and Jobs Act (TCJA) removed the requirement that bonus plans be tied to objective and verifiable performance measures for the bonus to be tax deductible. A potential consequence of this removal is that firms may begin to rely more heavily on nonfinancial performance measures in their bonus arrangements. I find an increase, post-TCJA, in both the number of and the weight applied to nonfinancial performance measures. These changes impacted important outcomes â€" I find an increase in both investor and information uncertainty for firms increasing their use of nonfinancial performance metrics following the TCJA. I also find that the increase in nonfinancial metrics leads to improvements in sustainability outputs (actions aimed at environmental, social, and governance activities). Overall, the results suggest that the recent tax reform facilitated the inclusion of additional nonfinancial performance measures in executive bonus plans, leading to greater investor and information uncertainty.

Do You Hear the People’s Saying? - The Voice of Individual Investors
Chau, Jacky,Lai, Shufang,Yang, Yong George
In a setting where regulators provide a curated online platform to facilitate management access by individual investors, we examine the “voice” of individual shareholders in their interaction with firm management. We find that the timing and content of online postings directed to management from individual investors are generally responsive to significant corporate events. Besides information acquisition, individual investors sometimes convey criticisms and suggestions to management. Individual investors collectively turn more active in their online engagements with management when there are signs of weaker external monitoring from institutional investors or weaker managerial incentive alignment with shareholders. Individual investors also tend to step up their engagement efforts after their followed firm experiences poor performance. Additionally, we observe increased engagements by individual investors during securities rules violations and these engagements are associated with sooner enforcement actions on the violations. Our findings suggest that, empowered by a conducive regulatory environment, individual investors could manifest active monitoring incentives when the perceived benefits are sufficiently high.

Does ESG Disclosure Transparency Help Mitigate the COVID-19 Pandemic Shock? An Empirical Analysis of Listed Firms in the UK
Hoang, Thi-Hong-Van,Segbotangni, Elysé A.,Lahiani, Amine
This paper examines whether the transparency in ESG reporting helps listed firms in the UK better mitigate the impact of the COVID-19 pandemic. We investigate 179 listed firms from August 2019 to May 2020. The results show that the performance spread of firms with high ESG disclosure score is less negatively impacted by firm internal and external factors during the COVID-19 pandemic. Furthermore, the ESG transparency helps reduce the volatility. However, we find no evidence that the ESG reporting transparency helps improve the stock performance. Therefore, ESG reporting transparency helps firms better resist to extreme shocks like the COVID-19 pandemic.

ESG Ratings: The Road Ahead
Lopez, Claude,Contreras, Oscar,Bendix, Joseph
In this report, we show that a standard set of variables would partially resolve inconsistencies and lack of uniform standards among rating providers, which often confuses investors. Furthermore, we dissociate the impact of the rating agencies’ different focus on E, S, or G from that of using non-standardized data. While the former, if properly disclosed, can be useful as it allows investors to choose what rating will align more with their preferences, the latter necessarily requires harmonization of the data.

Efficient Market Managers
Atanasov, Vladimir A.,Pirinsky, Christo A.,Wang, Qinghai
We examine the effect of the Efficient Market Hypothesis (EMH) on the investment behavior of mutual fund managers. We show that managers who are more likely to be exposed to the ideas of EMH throughout their higher education are more “passive” than their unexposed peers: they are more likely to manage index funds, and when managing active funds, they hold portfolios with larger numbers of stocks and deviate less from their investment benchmarks. Exposed managers, however, take more systematic risks. Although academic exposure to the EMH does not result in better performance, it helps professional investors generate capital inflows.

Evaluating Range Value at Risk Forecasts
Tobias Fissler,Johanna F. Ziegel

The debate of what quantitative risk measure to choose in practice has mainly focused on the dichotomy between Value at Risk (VaR) -- a quantile -- and Expected Shortfall (ES) -- a tail expectation. Range Value at Risk (RVaR) is a natural interpolation between these two prominent risk measures, which constitutes a tradeoff between the sensitivity of the latter and the robustness of the former, turning it into a practically relevant risk measure on its own. As such, there is a need to statistically validate RVaR forecasts and to compare and rank the performance of different RVaR models, tasks subsumed under the term 'backtesting' in finance. The predictive performance is best evaluated and compared in terms of strictly consistent loss or scoring functions. That is, functions which are minimised in expectation by the correct RVaR forecast. Much like ES, it has been shown recently that RVaR does not admit strictly consistent scoring functions, i.e., it is not elicitable. Mitigating this negative result, this paper shows that a triplet of RVaR with two VaR components at different levels is elicitable. We characterise the class of strictly consistent scoring functions for this triplet. Additional properties of these scoring functions are examined, including the diagnostic tool of Murphy diagrams. The results are illustrated with a simulation study, and we put our approach in perspective with respect to the classical approach of trimmed least squares in robust regression.

Everyone Has an Opinion: The Informativeness of Social Media’s Response to Management Guidance
Campbell, John L.,D'Adduzio, Jenna,Moon, James
We examine whether the social media reaction to an important firm disclosure provides a signal of the quality of that disclosure and whether capital market participants’ reactions to the disclosure are consistent with the social media reaction. Specifically, we examine the sentiment of posts on StockTwits immediately following management forecasts issued between 2010 to 2017 and offer three main findings. First, we document that the relation between StockTwits sentiment and forecast news is stronger when the forecast is later revealed to be more accurate and less biased, suggesting that StockTwits provides an early signal of forecast quality. Second, we find a positive association between the extent to which social media sentiment agrees with the forecast news and stock price reaction to the management forecast. This suggests that when social media sentiment agrees with management forecast news, investors do too. Finally, we find a positive association between the extent to which social media sentiment agrees with the forecast news and subsequent analyst forecast revisions, particularly when forecast news is positive. This suggests that when social media sentiment agrees with positive management forecast news, analysts do too. Additional analysis suggests that investors appear to underreact to the signal provided by social media sentiment, while analysts appear to overreact to the signal. Overall, our results suggest that the social media reaction to management forecasts provides a timely and accurate reflection of not only the forecast’s quality, but also of how the forecast will be received by important capital market participants.

Extreme Price Co-Movement of Commodity Futures and Industrial Production Growth: An Empirical Evaluation
Wen, Xiaoqian,Xie, Yuxin,Pantelous, Athanasios A.
This paper studies how the extreme price co-movement of commodity futures indicates industrial production (IP) growth. In this regard, we model synchronized movements and large price changes into one measure by characterizing upside and downside price extremes. We find that the derived price extremes are positively associated with IP growth over the next quarter. We further conclude that such impact is not symmetric, as the impact led by downside extremes is robust whereas that of upside extremes is not persistent. Our results reinforce the informational friction theory as well as those financial studies that emphasize downside risk.

Finding Optimal Cancer Treatment using Markov Decision Process to Improve Overall Health and Quality of Life
Navonil Deb,Abhinandan Dalal,Gopal Krishna Basak

Markov Decision Processes and Dynamic Treatment Regimes have grown increasingly popular in the treatment of diseases, including cancer. However, cancer treatment often impacts quality of life drastically, and people often fail to take treatments that are sustainable, affordable and can be adhered to. In this paper, we emphasize the usage of ambient factors like profession, radioactive exposure, food habits on the treatment choice, keeping in mind that the aim is not just to relieve the patient of his disease, but rather to maximize his overall physical, social and mental well being. We delineate a general framework which can directly incorporate a net benefit function from a physician as well as patient's utility, and can incorporate the varying probabilities of exposure and survival of patients of varying medical profiles. We also show by simulations that the optimal choice of actions often is sensitive to extraneous factors, like the financial status of a person (as a proxy for the affordability of treatment), and that these actions should be welcome keeping in mind the overall quality of life.

Fools Rush In: Competitive Effects of Reaction Time in Automated Trading
Henry Hanifan,John Cartlidge

We explore the competitive effects of reaction time of automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. A large body of research conducted over several decades has been devoted to trading agent design and simulation, but the majority of this work focuses on pricing strategy and does not consider the time taken for these strategies to compute. In real-world financial markets, speed is known to heavily influence the design of automated trading algorithms, with the generally accepted wisdom that faster is better. Here, we introduce increasingly realistic models of trading speed and profile the computation times of a suite of eminent trading algorithms from the literature. Results demonstrate that: (a) trading performance is impacted by speed, but faster is not always better; (b) the Adaptive-Aggressive (AA) algorithm, until recently considered the most dominant trading strategy in the literature, is outperformed by the simplistic Shaver (SHVR) strategy - shave one tick off the current best bid or ask - when relative computation times are accurately simulated.

Forecasting financial markets with semantic network analysis in the COVID-19 crisis
A. Fronzetti Colladon,S. Grassi,F. Ravazzolo,F. Violante

This paper uses a new textual data index for predicting stock market data. The index is applied to a large set of news to evaluate the importance of one or more general economic related keywords appearing in the text. The index assesses the importance of the economic related keywords, based on their frequency of use and semantic network position. We apply it to the Italian press and construct indices to predict Italian stock and bond market returns and volatilities in a recent sample period, including the COVID-19 crisis. The evidence shows that the index captures the different phases of financial time series well. Moreover, results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility.

Gender diversity in research teams and citation impact in Economics and Management
Abdelghani Maddi,Yves Gingras

The aim of this paper is twofold:1)contribute to a better understanding of the place of women in Economics and Management disciplines by characterizing the difference in levels of scientific collaboration between men and women at the specialties level;2) Investigate the relationship between gender diversity and citation impact in Economics and Management. Our data, extracted from the Web of Science database, cover global production as indexed in 302 journals in Economics and 370 journals in Management, with respectively 153 667 and 163 567 articles published between 2008 and 2018. Results show that collaborative practices between men and women are quite different in Economics and Management. We also find that there is a positive and significant effect of gender diversity on the academic impact of publications. Mixed-gender publications (co-authored by men and women) receive more citations than non-mixed papers (written by same-gender author teams) or single-author publications. The effect is slightly stronger in Management. The regression analysis also indicates that there is, for both disciplines, a small negative effect on citations received if the corresponding author is a woman.

Hang in There: Stock Market Reactions to Withdrawals of COVID-19 Stimulus Measures
Chan-Lau, Jorge A.,Zhao, Yunhui
The COVID-19 pandemic crisis has triggered unprecedented stimulus policy responses by countries worldwide, particularly fiscal stimulus measures. Given the high fiscal costs, some countries have withdrawn such measures, and other countries are contemplating doing so. In this paper, we empirically examine the impact of the withdrawal of fiscal stimulus policies on stock markets using daily data. To this end, we construct a database of withdrawal events and examine the difference between the pre- and post-event stock price returns using event study analysis and cross-country regressions. The results show a significant negative reaction when stimulus is withdrawn prematurely, i.e., when the daily COVID cases were still high relative to the historical pattern, a reaction which can be compounded by social unrest. The results suggest that markets are concerned about the negative impact of early withdrawals of stimulus on the economic recovery prospect, a risk that policymakers have to account for while contemplating the exit strategy from the exceptional crisis-fighting policies.

High-Frequency Estimates of the Natural Real Rate and Inflation Expectations
Aronovich, Alex,Meldrum, Andrew
We propose a new method of estimating the natural real rate and long-horizon inflation expectations, using nonlinear regressions of survey-based measures of short-term nominal interest rates and inflation expectations on U.S. Treasury yields. We find that the natural real rate was relatively stable during the 1990s and early 2000s, but declined steadily after the global financial crisis, before dropping more sharply to around 0 percent during the recent COVID-19 pandemic. Long-horizon inflation expectations declined steadily during the 1990s and have since been relatively stable at close to 2 percent. Our estimates are available at whatever frequency we observe bond yields, making them ideal for intraday event-study analysis---for example, we show that the natural real rate and long-horizon inflation expectations are not affected by temporary shocks to the stance of monetary policy.

How to Re-Conceptualize and Re-Integrate Climate Finance Into Society Through Ecological Accounting?
Rambaud, Alexandre,Chenet, Hugues
In this paper, we argue that current finance, and the prevailing fair value accounting system, is disconnected from companies and from strong sustainability requirements, making it difficult to develop a climate finance system that is operational and aligned with the challenges of climate preservation. Based on this observation, we propose an exploratory and theoretical study which introduces how and why a particular and innovative ecological accounting approach, the CARE model, currently called upon by a growing number of practitioners and researchers, is a relevant framework to re-conceptualize the issue of climate finance. From a theoretical point of view, CARE offers a suitable language for structuring the issues of ecological costs, debts and conservation and associated financing. From a practical point of view, it offers a methodological support that can be used to address these issues, from an accounting and management point of view as well as from an investor's point of view, ensuring compliance with the Paris Agreements 2°C goal in particular.

Interpretable Neural Networks for Panel Data Analysis in Economics
Yucheng Yang,Zhong Zheng,Weinan E

The lack of interpretability and transparency are preventing economists from using advanced tools like neural networks in their empirical research. In this paper, we propose a class of interpretable neural network models that can achieve both high prediction accuracy and interpretability. The model can be written as a simple function of a regularized number of interpretable features, which are outcomes of interpretable functions encoded in the neural network. Researchers can design different forms of interpretable functions based on the nature of their tasks. In particular, we encode a class of interpretable functions named persistent change filters in the neural network to study time series cross-sectional data. We apply the model to predicting individual's monthly employment status using high-dimensional administrative data. We achieve an accuracy of 94.5% in the test set, which is comparable to the best performed conventional machine learning methods. Furthermore, the interpretability of the model allows us to understand the mechanism that underlies the prediction: an individual's employment status is closely related to whether she pays different types of insurances. Our work is a useful step towards overcoming the black-box problem of neural networks, and provide a new tool for economists to study administrative and proprietary big data.

Intraday Pricing and Liquidity of Italian and German Treasury Auctions
Bellia, Mario
This paper examines how the bond supply influences the price and the liquidity in the secondary market during the primary auction days. The focus is on the intraday behavior of the primary dealers, or market makers, to capture their risk aversion before and after the auction. Using quote data from the Mercato Telematico dei titoli di Stato (MTS), I find evidence of an intraday pronounced inverted V-Shape on the yield difference, which goes up with a maximum at the auction time, and the recovers more than two hours after. This indicates a strong price pressure around the auction time. The analysis of liquidity shows that the bid-ask spread is usually better on the auction days, but rise sharply at the time of the auction. Dealers withdraw their quotes just before the auction and start quoting again from ten to twenty minutes later. A portion of the dealers are very risk-averse and prefers not to expose themselves to the secondary market. The sovereign bond crisis exacerbates the dry-up of liquidity for Italy and the price pressure for Germany. However, the ECB intervention through the Public Sector Purchase Program (PSPP) appears to restore the market makers confidence, especially for Italy

Investing in the year of Corona: The Modified Risk Parity Portfolios
Maewal, Akhilesh,Bock, Joel R.
Extended examples of long-term, annually rebalanced portfolio performance using the modified risk parity (MRP) approach are presented. The analysis considers three distinct diversified portfolio holdings, comprising index funds, sector funds and a blended portfolio of index and sector funds.The analysis considers the time period 2000-mid November 2020, which includes drawdown from the March 2020 worldwide outbreak of coronavirus 2019 disease.Comparisons of MRP return performance versus competitive portfolios of index-following or balanced funds are presented. Results indicate that for sufficiently long holding periods, substantial out-performance of MRP allocation strategies relative to passive buy-and-hold benchmarks can be realized.

Managing Climate Change Risks: Sea Level Rise and Mergers and Acquisitions
Bai, John (Jianqiu),Chu, Yongqiang,Shen, Chen,Wan, Chi
Using a large sample over the period 1986 to 2017, we show that companies with higher exposure to climate change risk induced by sea-level rise (SLR) tend to acquire firms that are unlikely to be directly affected by SLR. We find that acquirers with higher SLR exposure experience significantly higher announcement-period abnormal stock returns. Post-merger, analyst forecasts become more accurate and environmental-related ESG score improves.

Martingale optimal transport duality
Patrick Cheridito,Matti Kiiski,David J. Prömel,H. Mete Soner

We obtain a dual representation of the Kantorovich functional defined for functions on the Skorokhod space using quotient sets. Our representation takes the form of a Choquet capacity generated by martingale measures satisfying additional constraints to ensure compatibility with the quotient sets. These sets contain stochastic integrals defined pathwise and two such definitions starting with simple integrands are given. Another important ingredient of our analysis is a regularized version of Jakubowski's $S$-topology on the Skorokhod space.

Mispricing or Risk Premium? An Explanation of the R&D-to-Market Anomaly
Lee, Jangwook,Lee, Jiyoon
Two contrasting explanations are offered in the literature for the R&D-to-market anomaly: limited investor attention to R&D spending that is expensed under generally accepted accounting principles or failure of conventional risk factors to completely capture the risk associated with R&D. Exploiting accounting treatments in Korea, in which R&D expenditures are capitalized under certain conditions, we show that both the expensed and capitalized portions of R&D are positively associated with returns. The positive R&D-return relationship weakens with the extent of progress toward completion of R&D projects, consistent with Berk, Green, and Naik's (2004) risk-based theoretical prediction. In addition, the decrease in returns following progress toward R&D completion is fully explained by conventional risk factors. The results suggest overall that the positive R&D-return relationship is attributable to compensation for bearing risk.

Modeling Non-Maturing Demand Deposits: On the Determination of the Threshold of Separation Between Volatile and Stable Deposit Volumes
Döpp, Sophie,Horovitz, Andre,Szimayer, Alexander
This paper aims to develop a methodology for the determination of the threshold of separation between volatile and stable deposit volumes in accordance with the deposit taking institutions idiosyncratic credit and liquidity risk tolerances and liquidity management preferences. The effort is motivated by Principle 9 of the BIS Basel III recommendations for managing liquidity risk. We develop the methodology by including the new liquidity constraints from the Basel III regulatory recommendations introduced in 2013 (LCR and NSFR). We determine the two main variables that affect the threshold of separation: the confidence level and the length of the data time series of daily deposit variations, both bank-specific, in accordance with the institution's specific risk profile and internal liquidity management policies. For the determination of the specific confidence level we use established methodological results stemming from the evaluation of default probabilities of portfolios exhibiting very low default observations by \cite{pluto2011estimating} while for the determination of the institution specific minimal data time series we use the properties of the power statistics associated with the Kolmogorov-Smirnov test applied to the distributions of deposit volumes time series. We further illustrate the application of the methodology via data time series for a fictional European savings bank. Furthermore, we point to other ancillary applications of these procedures in the financial risk management practices.

On Classifying the Effects of Policy Announcements on Volatility
Giampiero M. Gallo,Demetrio Lacava,Edoardo Otranto

The financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Multiplicative Error Model, we propose a model--based classification of the dates of a Central Bank's announcements to distinguish the cases where the announcement implies an increase or a decrease in volatility, or no effect. In detail, we propose two na\"ive classification methods, obtained as a by--product of the model estimation, which provide very similar results to those coming from a classical k--means clustering procedure. The application on four Eurozone market volatility series shows a successful classification of 144 European Central Bank announcements.

On the effectiveness of the European Central Bank's conventional and unconventional policies under uncertainty
Niko Hauzenberger,Michael Pfarrhofer,Anna Stelzer

In this paper, we investigate the effectiveness of conventional and unconventional monetary policy measures by the European Central Bank (ECB) conditional on the prevailing level of uncertainty. To obtain exogenous variation in central bank policy, we rely on high-frequency surprises in financial market data for the euro area (EA) around policy announcement dates. We trace the dynamic effects of shocks to the short-term policy rate, forward guidance and quantitative easing on several key macroeconomic and financial quantities alongside survey-based measures of expectations. For this purpose, we propose a Bayesian smooth-transition vector autoregression (ST-VAR). Our results suggest that transmission channels are impaired when uncertainty is elevated. While conventional monetary policy is less effective during such periods, and sometimes also forward guidance, quantitative easing measures seem to work comparatively well in uncertain times.

Only Blunt Tools Left? How IFRS 9 Affects the Earnings and Capital Management of European Banks
Kund, Arndt-Gerrit,Neitzert, Florian
Previous research establishes the discretionary nature of Loan Loss Provisions as a prominent tool for Earnings and Capital Management in banks. However, the transition from the Incurred Credit Loss model of IAS 39 to the Expected Credit Loss model of IFRS 9 has marginalized this leeway. We investigate the implications of this shift in accounting on the Earnings and Capital Management in European banks by drawing inference from the bank stress test data. Doing so generates two distinct advantages for our identification strategy: first, we have homogeneous incentives, as all banks want to apply Earnings and Capital Management in order to appear resilient in the stress test. Second, it allows us to obtain unpublished data for IFRS 9, such that we can create a panel spanning the old and the new accounting standard. The conjunction of these two attributes makes this setting predestine for investigating the true impact of the novel accounting standard. We find that the analyzed banks employ both Earnings and Capital Management. The level of impairments grows under IFRS 9, in line with the hypothesis of a more objective loan loss provisioning process. Moreover, we show that risk-sensitive capital requirements are proactively managed, whereas the opposite is true for risk-insensitive metrics. Our results are robust to different models, parametrizations, and definitions of required capital.

Portfolio Optimization on Multivariate Regime Switching GARCH Model with Normal Tempered Stable Innovation
Cheng Peng,Young Shin Kim

We propose a Markov regime switching GARCH model with multivariate normal tempered stable innovation to accommodate fat tails and other stylized facts in returns of financial assets. The model is used to simulate sample paths as input for portfolio optimization with risk measures, namely, conditional value at risk and conditional drawdown. The motivation is to have a portfolio that avoids left tail events by combining models that incorporates fat tail with optimization that focuses on tail risk. In-sample test is conducted to demonstrate goodness of fit. Out-of-sample test shows that our approach yields higher performance measured by Sharpe-like ratios than the market and equally weighted portfolio in recent years which includes some of the most volatile periods in history. We also find that suboptimal portfolios with higher return constraints tend to outperform optimal portfolios.

Privacy versus Convenience: Customer Response to Data Breaches of Their Information
Agarwal, Sumit,Ghosh, Pulak,Ruan, Tianyue,Zhang, Yunqi
Cybersecurity breaches pose a substantial privacy concern in the digital era. We investigate how customers respond to privacy leakage in multiple unexpected data breaches. Difference-in-differences estimates show that digital payments declined by 4.6% ~ 7.5% relative to cash payments immediately after an unexpected data breach in a food delivery platform, but the gap disappeared three months later. Customer entry and exit also exhibit weak, short-lived changes. Additional analyses on bank and online grocery data breaches uncover even weaker direct and spillover effects of data breaches. Our findings imply that the perceived benefit of convenience overweighs the cost of privacy leakages.

Production Networks and War
Vasily Korovkin,Alexey Makarin

How do severe shocks, such as war, alter the economy? We study how a country's production network is affected by a devastating but localized conflict. We use novel transaction-level data on Ukrainian railway shipments, complemented by administrative data on firms, to document the effect of war on firms and interfirm trade. First, we document substantial propagation effects-trade declines even between firms outside the conflict areas if one of them had traded with the conflict areas before the war. Our estimates suggest that the magnitude of the second-degree effect of conflict is one-third of the first-degree effect. Second, we study firm-level consequences of a change in production network structure. Firms that, for exogenous reasons, become more central in the production network after the start of the conflict receive a lasting boost to their revenues and a temporary one to their profits. A temporary increase in markups suggests a rise in market power as one of the mechanisms. Finally, in a production networks model, we separate the effects of exogenous firm removal and subsequent endogenous network adjustment on firm revenue distribution. At the median of the distribution, network adjustment compensates for 72% of network destruction.

Quantile Diffusions
Holly Brannelly,Andrea Macrina,Gareth W. Peters

We propose a novel approach for the construction of quantile processes governing the stochastic dynamics of quantiles in continuous time. Two classes of quantile diffusions are identified: The first, which we largely focus on, features a random quantile level and allows for direct interpretation of model parameters such as skewness and kurtosis. The second type are function-valued quantile diffusions and are driven by stochastic parameter processes, which determine the entire quantile function at each point in time. The quantile processes are obtained by transforming the marginals of a diffusion process under a composite map consisting of a distribution and a quantile function. Such maps, analogous to rank transmutation maps, produce the marginals of the resulting quantile process. We discuss the relationship and differences between our approach and existing methods and characterisations of quantile processes in discrete and continuous time. As an example of an application of quantile diffusions, we show how measure distortions, often found in financial mathematics and actuarial science, may be induced.

Quantile Risk Premiums
Brinkmann, Felix,Dörries, Julian,Korn, Olaf
This paper studies quantile-based moment premiums. The quantile-based approach delivers robust and flexible alternatives to premiums for variance, skewness and kurtosis risk and enhances our understanding of the pricing of risks in derivatives markets. To quantify these premiums, the paper introduces a new class of synthetic derivatives contracts: quantile swaps. Such contracts mimic quantile-based moment measures from robust statistics. An empirical study of index options detects two distinct premiums for dispersion and asymmetry, but no premium for steepness. This finding is in clear contrast to results obtained through traditional moment swaps and warns us to interpret moment premiums carefully.

Reconstruction Rating Model of Sovereign Debt by Logical Analysis of Data
Elnaz Gholipour,Béla Vizvári,Zoltán Lakner

Sovereign debt ratings provided by rating agencies measure the solvency of a country, as gauged by a lender or an investor. It is an indication of the risk involved in investment, and should be determined correctly and in a well timed manner. The present study reconstructs sovereign debt ratings through logical analysis of data, which is based on the theory of Boolean functions. It organizes groups of countries according to twenty World Bank defined variables for the period 2012 till 2015. The Fitch Rating Agency, one of the three big global rating agencies, is used as a case study. An approximate algorithm was crucial in exploring the rating method, in correcting the agencys errors, and in determining the estimated rating of otherwise non rated countries. The outcome was a decision tree for each year. Each country was assigned a rating. On average, the algorithm reached almost ninety eight percentage matched ratings in the training set, and was verified by eighty four percentage in the test set. This was a considerable achievement.

Reform Reversal in Former Transition Economies (FTEs) of the European Union: Areas, Circumstances and Motivations
Ward-Warmedinger, Melanie E.,Székely, István P.
The rapid journey from central planning to EU (euro area) membership stress-tested the social learning processes of the Former Transition Economies (FTEs). The desire for a higher standard of living, to be anchored to the West, and to enter the EU, spurred major reform waves and led to the very rapid introduction of best-practice institutions. Although social learning accompanied this process, in many FTEs it was not fast enough to keep pace with the rapid reforms, leaving best-practice institutions with social norms that were not sufficiently strong to maintain them. As a result, wide-spread reform reversals emerged in the region. Such reform reversals appeared as formal reversals, which changed legislation (or formal rules), and behavioral reversals, which eroded the quality of an institution by materially changing the way it worked. It was frequently the interaction of reversals in different sectors that created a full-blown reform reversal episode, with the financial sector particularly prone to behavioral reversals, both in public and private institutions. External anchors such as the Washington institutions played a dominant role in shaping the transition process. Along with the EU accession process, the EU acted as a strong anchor that could prevent or reverse formal reform reversals in areas covered by EU law, but could play a much weaker role in the case of behavioral reversals. Our analysis naturally leads to the conclusion that the ultimate solution to prevent reform reversals is to accelerate social learning processes that strengthen the national ownership of reforms. It is also important to focus on the quality and internal coherence of reforms and newly created institutions.

Slađana Babić,Christophe Ley,Lorenzo Ricci,David Veredas

Economic and financial crises are characterised by unusually large events. These tail events co-move because of linear and/or nonlinear dependencies. We introduce TailCoR, a metric that combines (and disentangles) these linear and non-linear dependencies. TailCoR between two variables is based on the tail inter quantile range of a simple projection. It is dimension-free, it performs well in small samples, and no optimisations are needed.

The Effect of Education on Smoking Decisions in the United States
Sang T. Truong

This paper explores the link between education and the decision to start smoking as well as the decision to quit smoking. Data is gathered from IPUMS CPS and Centers for Disease Control and Prevention. Probit analysis (with the use of probability weight and robust standard error) indicates that every additional year of education will reduce the 2.3 percentage point of the smoking probability and will add 3.53 percentage point in quitting likelihood, holding home restriction, public restriction, cigarette price, family income, age, gender, race, and ethnicity constant. I believe that tobacco epidemic is a serious global issue that may be mitigated by using careful regulations on smoking restriction and education.

The Financial Determinants of Corporate Cash Holdings: Does Shariah-Compliance Matter?
Alnori, Faisal,Bugshan, Abdullah salem,Bakry, Walid
Purpose: The purpose of this study is to explore the difference between the determinants of cash holdings of shariah-compliant and non-shariah-compliant firms, for non-financial corporations in the Gulf Cooperation Council (GCC). Methodology: The data includes all non-financial firms listed in six GCC markets over the period 2005 to 2016. The Ideal Ratings database is used to identify shariah-compliant firms in the GCC. To examine the determinants of cash holdings, static model is used. To confirm the applicability of the method applied, the Breusch-Pagan Lagrange Multiplier (LM) and Hausman (1978) are employed to choose the most efficient and consistent static panel regression. Findings: The Results show that, for shariah-complaint firms, the relevant determinants of cash holdings are leverage, profitability, capital expenditure, and net working capital. For non-shariah-compliant firms, the only relevant determinants of cash holdings are leverage and net working capital. The findings suggest that cash holding decisions of shariah-complaint firms can be best explained using the pecking order theory. This reveals that shariah-compliant firms use liquid assets as their first financing option since, due to the shariah regulations.Research limitations/implications: Future studies may investigate the optimal levels of cash holdings and compare the adjustment speeds toward target cash holdings of both the shariah-compliant firms and their conventional counterparts.Originality/value: This study is the first to investigate the difference between the determinants of cash holdings of shariah-compliant and non-shariah-compliant firms.

The Impact of Social Media on Venture Capital Financing: Evidence from Twitter Interactions
Bayar, Onur,Kesici, Emre
We examine how information acquisition through social media affects venture capital (VC) investments into VC-backed startups. We collect a unique data set from Twitter API to measure the impact of portfolio companies owned social media (OSM) and earned social media (ESM) on the structure of VC investments. We find evidence consistent with the hypothesis that startup firms’ social media engagement affects the staging of VC financing, the VC syndicate structure, and the probability of a successful exit. If a portfolio company’s social media accounts are more active and the company has a higher engagement volume with its followers, VC firms reduce the extent of stage financing and are less likely to syndicate with each other in financing such a portfolio company. In particular, our results demonstrate that entrepreneurial firms with higher OSM and ESM engagement volume have fewer VC financing rounds, a smaller number of VCs in their VC syndicates, a lower probability of VC syndication, a higher probability of an IPO exit and a higher amount total funding across all rounds. Our findings are robust to a variety of alternative model specifications, subsamples, controlling for endogeneity in VC staging and syndication, selection biases and machine learning approaches.

The Politicization of the U.S. Supreme Court: Danger to Democracy
Trautman, Lawrence J.
Our democracy is experiencing increased danger by virtue of the continued politicization of the U.S. Supreme Court. The author believes that without a neutral judiciary, our tripartite system cannot function as the founders intended. The death of Justice Ruth Bader Ginsburg during September 2020 leaves a historic vacancy and presents yet another opportunity for a permanent and dramatic precedent to be established for the Supreme Court nomination process. With voting for the 2020 presidential election having already started in many states, the decision by President Donald Trump and Senate Majority Leader Mitch McConnell to accelerate the nomination process, only weeks before a presidential election may result in changing the political makeup of the Court for many decades. The nomination of Judge Amy Coney Barrett is inextricability tied to issues immediately before the Court, including the 2020 election and future of the Affordable Care Act (ACA), scheduled to be heard just one week following the presidential election. The author thanks the Cardozo Law Review for this opportunity to comment and discuss the positions and merits for and against postponing the nomination and confirmation of a new Supreme Court justice until after the presidential election.This Article proceeds in seven parts. First, I examine the life, death, and impact of Justice Ruth Bader Ginsburg. Second, is a brief review of the four-years of the Trump presidency. Third, is a discussion about the Supreme Court nomination process. Fourth, is a look at the eighteen Supreme Court nominations during the past 35 years. Fifth, I explore what is now known about nominee Amy Coney Barrett. Sixth, is my discussion of the likely implications of this nomination for the future of the Court. And last, I conclude.

The Resilience of Islamic Equity Funds during COVID-19: Evidence from Risk Adjusted Performance, Investment Styles and Volatility Timing
Yarovaya, Larisa,Mirza, Nawazish,Rizvi, Syed Kumail Abbas,Saba, Irum,Naqvi, Bushra
This paper analyses the risk-adjusted performance of Islamic and conventional equity funds during three stages of the COVID-19 pandemic. We show that Islamic equity funds demonstrated differentials in risk-adjusted performance, investment styles, and volatility timing compared to their conventional counterparts. Specifically, the results revealed that Islamic equity funds are more resilient to COVID-19 shock, since they outperformed non-Islamic counterparts during the peak months of the pandemic. These findings confirm the safe haven properties of Islamic equity funds, which is useful for investors aiming to hedge pandemic risks. The style analysis reveals investment drift from riskier styles to more prudent options in response to the uncertainties underlying each stage. The results suggest policy makers should further investigate Islamic financial assets and their underlying principles to improve the resilience of financial systems in any future black swan events.

The Solution of the Equity Premium Puzzle
Atilla Aras

In this paper, the solution of the equity premium puzzle was given. First, the Arrow-Pratt measure of relative risk aversion for detecting the risk behavior of investors was questioned, and then a new tool was developed to study the risk behavior of investors. This new tool in the new formulated model was tested for the equity premium puzzle for a solution. The results show that the calculated value of the coefficient of relative risk aversion is 2.201455 which is compatible with the empirical studies and as investors who invest in risk-free asset place disutility on the not sure wealth value, investors who invest in equity place utility on the not sure wealth value.

The Unintended Consequences of Stay-at-Home Policies on Work Outcomes: The Impacts of Lockdown Orders on Content Creation
Xunyi Wang,Reza Mousavi,Yili Hong

The COVID-19 pandemic has posed an unprecedented challenge to individuals around the globe. To mitigate the spread of the virus, many states in the U.S. issued lockdown orders to urge their residents to stay at their homes, avoid get-togethers, and minimize physical interactions. While many offline workers are experiencing significant challenges performing their duties, digital technologies have provided ample tools for individuals to continue working and to maintain their productivity. Although using digital platforms to build resilience in remote work is effective, other aspects of remote work (beyond the continuation of work) should also be considered in gauging true resilience. In this study, we focus on content creators, and investigate how restrictions in individual's physical environment impact their online content creation behavior. Exploiting a natural experimental setting wherein four states issued state-wide lockdown orders on the same day whereas five states never issued a lockdown order, and using a unique dataset collected from a short video-sharing social media platform, we study the impact of lockdown orders on content creators' behaviors in terms of content volume, content novelty, and content optimism. We combined econometric methods (difference-in-differences estimations of a matched sample) with machine learning-based natural language processing to show that on average, compared to the users residing in non-lockdown states, the users residing in lockdown states create more content after the lockdown order enforcement. However, we find a decrease in the novelty level and optimism of the content generated by the latter group. Our findings have important contributions to the digital resilience literature and shed light on managers' decision-making process related to the adjustment of employees' work mode in the long run.

To Target a Date or Not to Target a Date? That is the Question: The Unintended Consequences of Investing for the Long Run
Massa, Massimo,Moussawi, Rabih,Simonov, Andrei
We study how asset managers behave when shielded from the liquidity constraints and their investors' short-term needs. We directly test whether extending the horizon is beneficial for the investors' optimal portfolio allocation or if it also allows asset managers to use it as a shield to put in place policies detrimental to the investors. Using the universe of all US Target Date Funds (TDFs) funds, which are created to invest for the long-run, we document that investor attention is lower when a TDF fund is farther away from the target horizon. Both multivariate and portfolio analyses document that asset managers exploit lower investor attention and deliver lower performance (around 2.9 bp per year of distance from the target horizon). The long term performance reduction is economically significant at -21% for an average investor holding the fund for 50 years. The negative impact on performance is more pronounced for retail funds when investors are less sensitive to performance, and in families with higher flow volatility when the fund family has more need to use the TDFs, it manages to smooth performance. It is also less negative for bigger families, and when the family has the expertise to invest in equity and less need to smooth the flows. Moreover, the longer the horizon, the higher the total fees the asset managers charge partly due to TDFs investing in the most expensive fund share classes of underlying funds. We use the Pension Protection Act of 2006 as an exogenous shock that allowed firms to offer TDFs as default investment options within 401(k) retirement plans.

What Determines the Asset Allocation of UK Defined Benefit Pension Funds?
Zhao, Zucheng,Sutcliffe, Charles
The asset allocation decision is one of the most important decisions made by defined benefit pension schemes. We investigate the determinants of the equity allocation of UK pension funds by analyzing panel data on 125 FTSE 100 companies over the 2003-2019 period. We find that nine variables have a significant effect on the allocation between equities and bonds â€" maturity (as measured by effective duration), default risk, leverage, size of the sponsor (or the scheme), whether the scheme is closed or has significant overseas liabilities, the strength of its response to the credit crunch, and equity returns exceeding bond returns.

What Explains Price Momentum and 52-Week High Momentum When They Really Work?
Barroso, Pedro,Wang, Haoxu
Price momentum and 52-week high are two robust relative strength stock market anomalies. Recent studies argue both anomalies are well explained by the q-factor model. We compare this explanation with a behavioral mispricing model and fundamental momentum. Unconditionally, all models subsume the anomalies. Yet, the bulk of momentum profits is known to be predictable and occurs after periods of low-risk and bull markets. In this study, we compare models explaining the anomalies in a conditional setting, focusing on when the anomalies actually work, and document that: i) 52-week high has predictability similar to momentum; ii) factor models that explain both anomalies unconditionally generally fail to capture them after low-risk months; iii) time-varying loadings to the investment CAPM are inconsistent with the observed pattern of predictability. The best model to fit these conditional patterns is fundamental momentum of Novy-Marx (2015a) but it also does not fully explain observed time-variation in risk-adjusted returns.

What Makes Private Stablecoins Stable?
Bellia, Mario,Schich, Sebastian
Stability of crypto assets, in terms of their exchange rate vis-à-vis fiat currency, is an elusive goal. This note argues that for privately issued stablecoins to be successful in terms of delivering stability vis-à-vis current fiat money, they need to “piggy-back” from the credibility of the prevailing fiat money systems. The latter in turn are backed by publicly supported financial safety net. One way to “piggy-back” on the credibility of the latter is for the private issuer to suggest that stablecoins issued are collateralized by existing fiat money. Using data for the prices of 31 different stablecoins and the fiat currency or other assets the former promise to be pegged to, we apply a set of dynamic panel regression in order to investigate what determines relative stability. We find evidence that the design of stablecoin stabilization mechanisms, and in particular the extent to which fiat money is used as collateral, as well as the presence of external auditors of such backing to enhance the credibility of such backing is crucial for the stability in terms of exchange rate of privately issued stablecoins, controlling for other factors of price developments of stablecoins.

When Sentiment Is News: The Polarity Pattern Approach
Babolmorad, Nazanin,Massoud, Nadia
We present a new approach and set of dictionaries for the analysis of sentiments in financial and business news headlines at the firm level. The proposed Polarity Pattern Approach (PPA) captures the deep structure of context and syntax and reduces measurement errors evident in previous approaches for classifying the tone of words and sentences in news headlines. We demonstrate the superior predictive validity of PPA using an out-of-sample dataset. As expected, negative news headlines are negatively related to stock returns and positively related to trading volumes, while positive news headlines are positively related to both, especially for finance news. Prior studies report a non-significant or, counter intuitively, negative relationship between positive news and stock returns, which we attribute to measurement error in previous approaches. Additionally, using market activities, we jointly estimate cross-sectional regression to compare the explanatory power of the PPA approach with commonly used methods. We show how PPA’s tone assignment significantly outperforms other dictionaries and approaches, including the commercial package (RavenPack) that uses both machine learning and deep learning. The PPA, including dictionaries, provides finance researchers with a new, more valid method of sentiment analysis for media news.Link to PPA tone assignment dataset: