Research articles for the 2021-05-26

A Note on the CAPM With Endogenously Consistent Market Returns
Krause, Andreas
I demonstrate that with the market return determined by the equilibrium returns of the CAPM, expected returns of an asset are affected by the risks of all assets jointly. Another implication is that the range of feasible market returns will be limited and dependent on the distribution of weights in the market portfolio. A large and well diversified market with no dominating asset will only return zero while a market dominated by a small number of assets will only return the risk-free rate. In the limiting case of atomistic assets, we recover the properties of the standard CAPM.

A Perturbation Approach to Optimal Investment, Liability Ratio, and Dividend Strategies
Zhuo Jin,Zuo Quan Xu,Bin Zou

We study an optimal dividend problem for an insurer who simultaneously controls investment weights in a financial market, liability ratio in the insurance business, and dividend payout rate. The insurer seeks an optimal strategy to maximize her expected utility of dividend payments over an infinite horizon. By applying a perturbation approach, we obtain the optimal strategy and the value function in closed form for log and power utility. We conduct an economic analysis to investigate the impact of various model parameters and risk aversion on the insurer's optimal strategy.

Anatomy of Systematic Internalizers and Price Efficiency
Aramian, Fatemeh
The promotion of systematic internalizers (SIs) as qualified off-exchange venues led to the proliferation of SIs run by distinct firms, including high-frequency traders (HFTs) and banks, and consequently a significant increase in SIs’ market share. This paper investigates whether execution of orders away from exchanges on SI platforms, and on each SI type affects price efficiency on exchanges. The paper further addresses the characteristics of SIs and factors leading a trading firm to become one. The results show that SI trading is beneficial to price efficiency and leads to more efficient prices on exchanges. The improved price efficiency is linked to the segmentation of informed and uninformed order flow across the two trading systems, which leaves informed traders concentrated on exchanges. The analysis further reveals that the positive association between SIs and price efficiency is driven by both HFT- and bank-SIs as the activity of each type results in more informative prices. Finally, the findings indicate that the tendency of trading firms to operate as SIs is higher for firms with dominant market making and principal trading strategies.

Anonymous Equity Research
Dyer, Travis,Kim, Eunjee
Crowdsourced financial information platforms often allow content contributors to publish equity research anonymously. This study examines whether investors value or discount information in anonymous equity research. In the short window around research releases, we find that investors’ stock price reaction to anonymous research is muted in comparison to non-anonymous research. Consistent with credibility concerns influencing investor response, we document that this discount to anonymous research dissipates as the monitoring of content contributors intensifies and as authors develop a reputation for high-quality reporting. In addition, we perform a content analysis on the research reports and find that the muted market reaction to anonymous equity research is robust to controlling for textual attributes of information content, further supporting our inference that investors’ are concerned about the credibility of anonymous equity research.

Assessing asset-liability risk with neural networks
Patrick Cheridito,John Ery,Mario V. Wüthrich

We introduce a neural network approach for assessing the risk of a portfolio of assets and liabilities over a given time period. This requires a conditional valuation of the portfolio given the state of the world at a later time, a problem that is particularly challenging if the portfolio contains structured products or complex insurance contracts which do not admit closed form valuation formulas. We illustrate the method on different examples from banking and insurance. We focus on value-at-risk and expected shortfall, but the approach also works for other risk measures.

Avoiding COVID-19 Related Foreclosures by Implementing Cost-Effective Mortgage Modifications for Federally-Backed Loans
Bhagat, Kanav
Millions of homeowners are having difficulty paying their mortgage because of the COVID-19 emergency, and those suffering from a sustained income loss will be unable to afford their original monthly payments. Research based on post-Great Recession defaults provide compelling evidence that a mortgage modification that delivers substantial payment reduction is the right tool to keep borrowers in their home and avoid foreclosures. Based on this research, I establish the steps of an “optimized” mortgage modification waterfall that offers substantial payment reduction to the borrower while minimizing the financial impact on the lender. I then compare the optimized waterfall to the GSE, FHA, and VA mortgage modification options and provide specific recommendations that, if implemented, would allow the Federal agencies to offer deeper payment reductions and broaden the availability to modifications to help more homeowners in need while minimizing the cost to lenders.

Bitcoin: Like a Satellite or Always Hardcore? - A Core-Satellite Identification in the Cryptocurrency Market
Börner, Christoph J.,Hoffmann, Ingo,Krettek, Jonas,Kuerzinger, Lars,Schmitz, Tim
Cryptocurrencies (CCs) become more interesting for institutional investors’ strategic asset allocation and will be a fixed component of professional portfolios in future. This asset class differs from established assets especially in terms of the severe manifestation of statistical parameters. The question arises whether CCs with similar statistical key figures exist. On this basis, a core market incorporating CCs with comparable properties enables the implementation of a tracking error approach. A prerequisite for this is the segmentation of the CC market into a core and a satellite, the latter comprising the accumulation of the residual CCs remaining in the complement. Using a concrete example, we segment the CC market into these components, based on modern methods from image / pattern recognition.

Bitcoin: Like a Satellite or Always Hardcore? A Core-Satellite Identification in the Cryptocurrency Market
Christoph J. Börner,Ingo Hoffmann,Jonas Krettek,Lars M. Kürzinger,Tim Schmitz

Cryptocurrencies (CCs) become more interesting for institutional investors' strategic asset allocation and will be a fixed component of professional portfolios in future. This asset class differs from established assets especially in terms of the severe manifestation of statistical parameters. The question arises whether CCs with similar statistical key figures exist. On this basis, a core market incorporating CCs with comparable properties enables the implementation of a tracking error approach. A prerequisite for this is the segmentation of the CC market into a core and a satellite, the latter comprising the accumulation of the residual CCs remaining in the complement. Using a concrete example, we segment the CC market into these components, based on modern methods from image / pattern recognition.

Can a Signal Mitigate a Dilemma? Quality Management Standards, Corruption, and Business Ethics
Ullah, Barkat ,Wei, Zuobao,Zhu, Yicheng
Firms operating in corrupt environments routinely face an ethical dilemma. On the one hand, bribery can be used as an efficient strategy to “get things done”. On the other hand, corruption is unethical, illegal, and a social ill that people detest. A corrupt environment is also informationally opaque, as the illegality of bribery is necessarily linked to secrecy. Hence, business strategies conducive to reducing information asymmetry help mitigate the ethical dilemma. One such strategy is signaling through adopting internationally recognized quality management standards such as ISO certifications. We examine the World Bank Enterprise Survey (WBES) data for almost 100 thousand firms from 2006-2019 across 141 mostly developing countries where corruption is rampant. We find that certified firms exhibit higher sales growth than uncertified firms. More importantly, we find that ISO adoption has a moderating effect on corruption activities and that the effect is stronger where corruption is more severe. Our findings have two important strategic implications. First, firms in developing countries can use ISO certification as a signal to overcome the inherent information asymmetry and facilitate growth. Second, ISO adoption also sends a signal to bureaucrats that certified firms have a more ethical culture, stronger internal controls and are not easy picks, thus mitigating the ethical conundrum.

Charting By Machines
Murray, Scott,Xiao, Houping,Xia, Yusen
We test the efficient market hypothesis by using machine learning to forecast future stock returns from historical performance. These forecasts strongly predict the cross section of future stock returns. The predictive power holds in most subperiods, is strong among the largest 500 stocks, and is distinct from momentum and reversal. The forecasting function has important nonlinearities and interactions and is remarkably stable through time. Our research design ensures that our findings are not a result of data mining. These findings question the efficient market hypothesis and indicate that investment strategies based on technical analysis and charting may have merit.

Corporate Liquidity and Solvency in Europe During COVID-19: The Role of Policies
Ebeke, Christian,Jovanovic, Nemanja,Valderrama, Laura,Zhou, Jing
The spread of COVID-19, containment measures, and general uncertainty led to a sharp reduction in activity in the first half of 2020. Europe was hit particularly hardâ€"the economic contraction in 2020 is estimated to have been among the largest in the worldâ€"with potentially severe repercussions on its nonfinancial corporations. A wave of corporate bankruptcies would generate mass unemployment, and a loss of productive capacity and firm-specific human capital. With many SMEs in Europe relying primarily on the banking sector for external finance, stress in the corporate sector could easily translate into pressures in the banking system (Aiyar et al., forthcoming).

Cross-Currency Swap Market through the Lens of OTC Derivative Transaction Data: Impact of COVID-19 and Subsequent Recovery
Maruyama, Rinto,Washimi, Kazuaki
Cross-currency swaps are one of the major US dollar funding tools for non-US banks. While their developments have attracted international attention, data for gauging transaction details are limited since these swaps are over-the-counter transactions, not trades on an exchange. This report provides an overview of the Japan's cross-currency swap market with over-the-counter derivative transaction data collected in Japan. Then it briefly reviews the impact of the COVID-19 crisis on these transactions around the spring of 2020. A data analysis indicates that major banks continued transactions as a market maker by breaking trades into smaller blocks and diversifying the counterparties, while smaller banks who do not actively engage in normal times were found to have participated in trading.

Deep Kernel Gaussian Process Based Financial Market Predictions
Yong Shi,Wei Dai,Wen Long,Bo Li

The Gaussian Process with a deep kernel is an extension of the classic GP regression model and this extended model usually constructs a new kernel function by deploying deep learning techniques like long short-term memory networks. A Gaussian Process with the kernel learned by LSTM, abbreviated as GP-LSTM, has the advantage of capturing the complex dependency of financial sequential data, while retaining the ability of probabilistic inference. However, the deep kernel Gaussian Process has not been applied to forecast the conditional returns and volatility in financial market to the best of our knowledge. In this paper, grid search algorithm, used for performing hyper-parameter optimization, is integrated with GP-LSTM to predict both the conditional mean and volatility of stock returns, which are then combined together to calculate the conditional Sharpe Ratio for constructing a long-short portfolio. The experiments are performed on a dataset covering all constituents of Shenzhen Stock Exchange Component Index. Based on empirical results, we find that the GP-LSTM model can provide more accurate forecasts in stock returns and volatility, which are jointly evaluated by the performance of constructed portfolios. Further sub-period analysis of the experiment results indicates that the superiority of GP-LSTM model over the benchmark models stems from better performance in highly volatile periods.

Effects of COVID-19 Vaccine Developments and Rollout on the Capital Market -- A Case Study
Maximilian Vierlboeck,Roshanak Rose Nilchiani

Various companies have developed vaccines to combat the pandemic caused 2020 by the virus COVID-19. Such vaccines and the distribution can have a major impact on the success of pharmaceutical companies, which in turn can show itself in their valuation and stock price. This poses the question if and how the trends or popularity of the companies might be connected to the value and stock price of said entities. To gain some insight into these questions, the work at hand looks at five COVID vaccine development companies and evaluates their correlations over the development of the vaccine as well as after the rollout start. The process was conducted by using python including various libraries. The result of this analysis was that there is a significant correlation between the Google Trend data and the respective stock prices (retrieved from yahoo! Finance) of the companies on average, where the time during the development of the drugs is more positively correlated and the post-rollout periods show a shift to a slightly negative inclining correlation. Furthermore, it was found that the smaller companies based on their market cap show a higher price volatility overall. In addition, higher average trend scores and thus popularity values were found after the rollout of the respective companies. In conclusion, a correlations between the trend data and the financial values have been found and corroborate the plots of the data. Due to the small size of the sample, the result cannot yet be considered statistically significant, but possibility for expansion exists and is already being worked on.

Effects of limited and heterogeneous memory in hidden-action situations
Patrick Reinwald,Stephan Leitner,Friederike Wall

Limited memory of decision-makers is often neglected in economic models, although it is reasonable to assume that it significantly influences the models' outcomes. The hidden-action model introduced by Holmstr\"om also includes this assumption. In delegation relationships between a principal and an agent, this model provides the optimal sharing rule for the outcome that optimizes both parties' utilities. This paper introduces an agent-based model of the hidden-action problem that includes limitations in the cognitive capacity of contracting parties. Our analysis mainly focuses on the sensitivity of the principal's and the agent's utilities to the relaxed assumptions. The results indicate that the agent's utility drops with limitations in the principal's cognitive capacity. Also, we find that the agent's cognitive capacity limitations affect neither his nor the principal's utility. Thus, the agent bears all adverse effects resulting from limitations in cognitive capacity.

Efficient Estimation of Pricing Kernels and Market-Implied Densities
Dalderop, Jeroen
This paper studies the nonparametric identification and estimation of projected pricing kernels implicit in European option prices and underlying asset returns using conditional moment restrictions. The proposed series estimator avoids computing ratios of estimated risk-neutral and physical densities. Instead, we consider efficient estimation based on the conditional Euclidean empirical likelihood or continuously-updated GMM criterion, which takes into account the informativeness of option prices of varying strike prices beyond observed conditioning variables. In a second step, we convert the implied probabilities into predictive densities by matching the informative part of cross-sections of option prices. Empirically, pricing kernels tend to be U-shaped in the S&P 500 index return given high levels of the VIX, and call and ATM options are more informative about their payoff than put and OTM options.

Financing the extension of social insurance to informal economy workers: The role of remittances
Kolev, Alexandre,La, Justina
Informal employment, defined through the lack of employment-based social protection, constitutes the bulk of employment in developing countries, and entails a level of vulnerability to poverty and other risks that are borne by all who are dependent on informal work income. Results from the Key Indicators of Informality based on Individuals and their Households database (KIIbIH) show that a disproportionately large number of middle‑class informal economy workers receive remittances. Such results confirm that risk management strategies, such as migration, play a part in minimising the potential risks of informal work for middle‑class informal households who may not be eligible to social assistance. They further suggest that middle‑class informal workers may have a solvent demand for social insurance so that, if informality-robust social insurance schemes were made available to them, remittances could potentially be channelled to finance the extension of social insurance to the informal economy. L'emploi informel, défini par l'absence de protection sociale basée sur l'emploi, constitue la majeure partie de l'emploi dans les pays en développement, et entraîne un niveau de vulnérabilité à la pauvreté et à d'autres risques qui sont supportés par tous ceux qui dépendent des revenus du travail informel. Les résultats de la base de données des Indicateurs clés de l'informalité en fonction des individus et leurs ménages (KIIbIH) montrent qu'un nombre disproportionné de travailleurs de l'économie informelle de la classe moyenne reçoivent des transferts de fonds. Ces résultats confirment que les stratégies de gestion des risques, telles que la migration, jouent un rôle dans la minimisation des risques potentiels du travail informel pour les ménages informels de la classe moyenne qui peuvent ne pas être éligibles à l'aide sociale. Ils suggèrent en outre que les travailleurs informels de classe moyenne peuvent avoir une demande solvable d'assurance sociale, de sorte que, si des régimes d'assurance sociale respectueux de l'informalité leur étaient accessibles, les transferts de fonds pourraient potentiellement être canalisés pour financer l'extension de l'assurance sociale à l'économie informelle.

Getting easy when trustworthy? Evidence from financial analyst
Chen, Kejing,Guo, Wenqi,Pu, Mijia,Xiong, Xiong
We study whether financial analysts' trustworthiness is associated with their performance. Using a dataset of the analysts’ facial features, we find that analysts’ facial trustworthiness significantly promotes the probability to conduct site visits and acquire more information, which makes it possible that trustworthy analysts are more likely to make accurate earnings forecasts. Moreover, the stock recommendations made by trustworthy analysts are also more informative in the short term and in the long run. The results are robust with a battery of robustness checks and after excluding potential alternative explanations. Further analyses reveal that trustworthy analysts with privileged access to information can significantly improve forecast accuracy when corporate transparency is low and social trust is weak. Overall, our study shows that analysts' facial trustworthiness has a significant effect on their access to information, hence affect their forecast accuracy.

Green Urban Development: The Impact Investment Strategy of Canadian Pension Funds
Beath, Alexander,Betermier, Sebastien,Van Bragt, Maaike,Spehner, Quentin,Liu, Yuedan
This paper investigates the investment strategy that large Canadian pension funds implement in the private real estate market. Even though they manage just 6% of global pension assets in our data, Canadian pension funds are responsible for 60% of the total value of direct real estate deals involving a pension fund. Their portfolio strategy combines global asset diversification with a local impact strategy that consists of internally developing and greening urban properties. Using a common benchmarking methodology across funds, we show that this strategy delivers superior performance net of fees and drives the green development of major city centers.

Handle with Care: Regulatory Easing in Times of COVID-19
Valencia, Fabián,Varghese, Richard,Yao, Weija,Yépez, Juan F.
The policy response to the COVID-19 shock included regulatory easing across many jurisdictions to facilitate the flow of credit to the economy and mitigate a further ampli-fication of the shock through tighter financial conditions. Using an intraday event study,this paper examines how stock pricesâ€"a key driver in financial conditionsâ€"reacted to regulatory easing announcements in a sample of 18 advanced economies and 8 emerging markets. The paper finds that overall, regulatory easing announcements contributed to looser financial conditions, but effects varied across sectors and tools. Financial regulatory easing led to lower valuations for financial sector stocks, and higher valuations for non-financial sector stocks, particularly for industries that are more dependent on bank financing. Furthermore, valuations declined and financial conditions tightened following announcements related to easier bank capital regulation while equity valuation rose and financial conditions loosened after those about liquidity regulation. Effects from non-regulatory financial measures appear to be generally more muted.

How Have COVID-19 Confirmed Cases and Deaths Affected Stock Markets? Evidence from Nigeria
Abu, Nurudeen,Gamal, Awadh Ahmed Mohammed,Sakanko, Musa Abdullahi,Mateen, Ana,David, Joseph,Amaechi, Ben-Obi Onyewuchi
This study assesses the effect of COVID-19 proxied by the number of confirmed cases of the infection and deaths on Nigeria’s stock market over the 23rd March to 11th September 2020 period using the autoregressive distributed lag (ARDL), canonical cointegrating regression (CCR), dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS) techniques. The bounds test to cointegration result reveals that a long-run relationship exists between COVID-19 and Nigeria’s stock market (along with oil prices and exchange rate). The results of the various estimations demonstrate that COVID-19 (proxied by the number of confirmed cases of infection) has a negative and significant impact on stock market performance, while the number deaths has a positive and significant impact on the market in the long-run. In addition, oil prices and exchange rate have a significant and positive effect on stock market performance in the long-run. Similar results were found for sub-sectors including consumer goods and healthcare sub-sectors of the stock market. The study recommends policies to curb the spread of the virus.

Impact Investing in Social Sector Organizations: A Systematic Review and Research Agenda
Islam, Syrus
Impact investing has great potential to contribute to achieving the Sustainable Development Goals by financing the growth of social sector organizations. This paper conducts a systematic literature review to develop cumulative insights into impact investing in social sector organizations. It identifies four streams of impact investing research â€" impact investment decision making, impact evaluation in impact investing, behavioral issues in impact investing, and impact investing ecosystem. This paper also identifies nine main research focus areas within these research streams and discusses key insights into each of them. Finally, building on prior research, this article offers a comprehensive future research agenda.

India's Approach to Open Banking: Some Implications for Financial Inclusion
Carriere-Swallow, Yan,Haksar, Vikram,Patnam, Manasa
We examine how the development of the digital infrastructure known as the “India Stack”â€"including an interoperable payments system, a universal digital ID, and other featuresâ€"is delivering on the government’s objective to expand the provision of financial services. While each individual component of the India Stack is important, we argue that its key overarching feature is a foundational approach of providing extensive public infrastructures and standards that generates important synergies across the layers of the Stack. Until recently, a large share of India’s population lacked access to formal banking services and was largely reliant on cash for financial transactions. The expansion of mobile-based financial services that enable simple and convenient ways to save and conduct financial transactions has provided a novel alternative for expanding the financial net. The Stack’s improved digital infrastructures have already allowed for a rapid increase in the use of digital payments and the entry of a range of competitors including fintech and bigtech firms.

Institutional Ownership, Peer Pressure and Voluntary Disclosures
Lin, Yupeng,Mao, Ying,Wang, Zheng
We document peer effect as an important factor in determining corporate voluntary disclosure policies. Our identification strategy relies on a discontinuity in the distribution of institutional ownership caused by the annual Russell 1000/2000 index reconstitution. Around the threshold of the Russell 1000/2000 index, the top Russell 2000 index firms experience a significant jump in institutional ownership compared with their closely-neighbored bottom Russell 1000 index firms due to index funds' benchmarking strategies. The increase in institutional ownership and resultant improvement in the information environment of the top Russell 2000 index firms create pressures on their industry peers to increase voluntary disclosures. Consistent with this prediction, we find that the discontinuously higher institutional ownership of the top Russell 2000 index firms significantly increases industry peers' likelihood and frequency of issuing management forecasts. Further analyses show that such an effect could be driven by firms' incentive to compete for capital.

Labor and Product Market Reforms and External Imbalances: Evidence from Advanced Economies
Duval, Romain,Furceri, Davide,Jalles, João Tovar
We explore the impact of major labor and product market reforms on current account dynamics using a new “narrative” database of major changes in employment protection for regular workers and product market regulation for non-manufacturing industries covering 26 advanced economies over the past four decades. Our main finding is that product market deregulation is associated with a weakening of the current account, while labor market deregulation is associated with an improvement. These effects are transitory and driven by both saving and investment responses. Labor and product market reforms both have a more positive impact on the current account balance when implemented under weak macroeconomic conditions. Our results are broadly consistent with predictions from recent DSGE models with endogenous producer entry and labor market frictions.

Macroeconomic Effects of Uncertainty: A Big Data Analysis for India
Priyaranjan, Nalin,Pratap, Bhanu
Uncertainty about the current state and near-term outlook of an economy as wellas the likely course of future policy actions can prompt economic agents to altertheir decisions to spend, save, invest and hire. In this paper, we construct threealternative indices to measure the level of uncertainty for the Indian economy. Thefirst two uncertainty indices are constructed by applying text mining and naturallanguage processing (NLP) techniques on a dataset compiled from leading Indianbusiness newspapers. The third index is based on internet search intensity dataavailable from Google Trends. Empirical findings from a Local Projections-basedeconometric framework suggest that uncertainty shocks influence financial marketsas well as the real economy in India. Our results indicate that both investmentactivity and real GDP growth slow down when uncertainty increases in theeconomy. Such uncertainty indices can help strengthen policy simulation exercisesto study the impact of low/high uncertainty scenarios and also improve near-termprojection of macroeconomic variables which exhibit high degree of sensitivity touncertainty.

Major Insurance Sector Digitalization Trends in 2021
Nebolsina, Elena
The article looks into key changes on the insurance market pursuant to accelerating the implementation of digital resources and technologies including, but not limited to the impact of the COVID-19 pandemic.

Managing Manufacturing and Delivery of Personalised Medicine: Current and Future Models
Andreea Avramescu,Richard Allmendinger,Manuel López-Ibáñez

With almost 50% of annual commercial drug approvals being Personalised Medicine (PM) and its huge potential to improve quality of life, this emerging medical sector has received increased attention from the industry and medical research, driven by health and care services, and us, the patients. Notwithstanding the power of Advanced Therapy Medicinal Products (ATMPs) to treat progressive illnesses and rare genetic conditions, their delivery on large scale is still problematic. The biopharmaceutical companies are currently struggling to meet timely delivery and, given high prices of up to $2 million per patient, prove the cost-effectiveness of their ATMP. The fragility of ATMPs combined with the impossibility for replacements due to the nature of the treatment and the advanced stages of the patient's condition are some of the bottlenecks added to a generally critical supply chain. As a consequence, ATMPs are currently used in most cases only as a last resort. ATMPs are at the intersection of multiple healthcare logistic networks and, due to their novelty, research around their commercialisation is still in its infancy from an operations research perspective. To accelerate technology adoption in this domain, we characterize pertinent practical challenges in a PM supply chain and then capture them in a holistic mathematical model ready for optimisation. The identified challenges and derived model will be contrasted with literature of related supply chains in terms of model formulations and suitable optimisation methods. Finally, needed technological advancements are discussed to pave the way to affordable commercialisation of PM.

Measuring Global Macroeconomic Uncertainty
Moramarco, Graziano
This paper provides a new index of global macroeconomic uncertainty and investigates the cross-country transmission of uncertainty using a global vector autoregressive (GVAR) model. The index measures the dispersion of forecasts resulting from parameter uncertainty in the GVAR. Over the period 2000Q1-2019Q4, global macroeconomic uncertainty is highly correlated with financial market volatility. We quantify global spillover effects by decomposing uncertainty into the contributions from individual countries. On average, about 40\% of a country's domestic uncertainty is accounted for by spillovers from abroad.

Multi-Dimensional Screening: Buyer-Optimal Learning and Informational Robustness
Rahul Deb,Anne-Katrin Roesler

A monopolist seller of multiple goods screens a buyer whose type is initially unknown to both but drawn from a commonly known distribution. The buyer privately learns about his type via a signal. We derive the seller's optimal mechanism in two different information environments. We begin by deriving the buyer-optimal outcome. Here, an information designer first selects a signal, and then the seller chooses an optimal mechanism in response; the designer's objective is to maximize consumer surplus. Then, we derive the optimal informationally robust mechanism. In this case, the seller first chooses the mechanism, and then nature picks the signal that minimizes the seller's profits. We derive the relation between both problems and show that the optimal mechanism in both cases takes the form of pure bundling.

On the Return Distributions of a Basket of Cryptocurrencies and Subsequent Implications
Christoph J. Börner,Ingo Hoffmann,Jonas Krettek,Lars M. Kürzinger,Tim Schmitz

This paper evaluates and assesses the risk associated with capital allocation in cryptocurrencies (CCs). In this regard, we take a basket of 27 CCs and the CC index EWCI$^-$ into account. After considering a series of statistical tests we find the stable distribution (SDI) to be the most appropriate to model the body of CCs returns. However, as we find the SDI to possess less favorable properties in the tail area for high quantiles, the generalized Pareto distribution is adapted for a more precise risk assessment. We use a combination of both distributions to calculate the Value at Risk and the Conditional Value at Risk, indicating two subgroups of CCs with differing risk characteristics.

Optimal Bidding Strategy for Maker Auctions
Michael Darlin,Nikolaos Papadis,Leandros Tassiulas

The Maker Protocol is a decentralized finance application that enables collateralized lending. The application uses open-bid, second-price auctions to complete its loan liquidation process. In this paper, we develop a bidding function for these auctions, focusing on the costs incurred to participate in the auctions. We then optimize these costs using parameters from historical auction data, and compare our optimal bidding prices to the historical auction prices. We find that the majority of auctions end at higher prices than our recommended optimal prices, and we propose several theories for these results.

Orange is the New Black: Changing Landscapes of Earnings Surprises and the Market Reaction
Heater, John C.,Liu, Ye,Tan, Qin,Zhang, Frank
In this paper, we document strikingly opposite time-series patterns of earnings surprises and associated market reaction. Earnings surprises have been increasing over time, with the mean analyst forecast error rising from negative 8 cents in 1990 to positive 2 cents in 2019. However, average earnings announcement returns have declined from 0.30% in 1990 to -0.35% in 2019, turning negative in the past 15 years. As firms strive to meet or beat earnings expectations, strong past performance of a firm and that of other firms raise the market’s earnings expectation above analysts’ consensus forecast, leading to investors’ disappointment upon earnings announcements. Our evidence has broad implications for appropriate earnings benchmarking, for empirical design when examining the earnings-return relation, for a disappearing earnings announcement premium, and for disappearing discontinuity in the earnings surprise distribution around zero.

Overconfidence and the Political and Financial Behavior of a Representative Sample
Ahrens, Steffen,Bosch-Rosa, Ciril,Kassner, Bernhard
We study the relationship between overconfidence and the political and financial behavior of a nationally representative sample. To do so, we introduce a new method of eliciting overconfidence that is simple to understand, quick to implement, and captures respondents\' excess confidence in their own judgment. Our results show that, in line with theoretical predictions, an excessive degree of confidence in one\'s judgment is correlated with lower portfolio diversification, larger stock price forecasting errors, and more extreme political views. Additionally, we find that overconfidence is correlated with voting absenteeism. These results appear to validate our method and show how overconfidence is a bias that permeates several aspects of peoples\' life.

Predictable Price Pressure
Hartzmark, Samuel M.,Solomon, David H.
We present evidence that stock returns, both at the market level and the individual stock level, can be predicted by the timing of uninformed inflows and outflows of cash that are known in advance. Aggregate dividend payments to investors predict higher value-weighted market returns on the day of payment and the day afterwards, by 13 b.p. for the top five days per year, and 5 b.p. for the top fifty days. This effect holds in the US and internationally. Effects are weaker in months when mutual funds pay out dividends to investors (and so are less likely to reinvest). Industries with greater past exposure to dividend price pressure significantly underperform those with less exposure, consistent with an eventual partial reversal. Predictable selling pressure leads to significantly lower returns after earnings announcements for firms with higher stock compensation. Back of the envelope calculations suggest price multipliers of each dollar invested in the aggregate market ranging from 1.5 to 2.3. These results suggest that predictable price pressure is a widespread result of money flows, rather than an anomaly.

Responsible Investing and Stock Allocation
Briere, Marie,Ramelli, Stefano
We analyze the portfolio choices of approximately 913,000 active participants inemployee saving plans in France. Looking at the cross-section of equity exposure, wefind that the inclusion of responsible equity options in the menu of available fundsis associated with a 2.1% higher equity allocation by plan participants. Comparedto an average equity asset allocation of 12.1%, it represents a material increase (17%in relative terms). Difference-in-differences analyses confirm that the introductionof a responsible equity option to a saving plan is followed by an increase of 7.2% inparticipants' appetite for stocks, contrary to what happens with conventional equityfunds.

Robust Optimal Macroprudential Policy
Montamat, Giselle,Roch, Francisco
We consider how fear of model misspecification on the part of the planner and/or the households affects welfare gains from optimal macroprudential taxes in an economy with occasionally binding collateral constraints as in Bianchi (2011). On the one hand, there exist welfare gains from internalizing how borrowing decisions in good times affect the value of collateral during a crisis. On the other hand, interventions by a robust planner that has in mind a model far from the true underlying distribution of shocks, can result in negligible welfare gains, or even losses. This is because a policy that is robust to misspecification, as in Hansen and Sargent (2011), is optimal under a "worst-case'' scenario but not under alternative distributions of the state. A robust planner introduces taxes that are 5 percentage points higher but does not achieve a significant increase in welfare gains compared to a non-robust planner when the true underlying model is not the worst-case. If households also make choices that are robust to model misspecification, the gains are significantly reduced and a highly-robust planner "underborrows" and induces welfare losses. If, however, the worst-case scenario is indeed realized, then welfare gains are the largest possible.

Robust fundamental theorems of asset pricing in discrete time
Huy N. Chau

This paper is devoted to the study of robust fundamental theorems of asset pricing in discrete time and finite horizon settings. The new concept "robust pricing system" is introduced to rule out the existence of model independent arbitrage opportunities. Superhedging duality and strategy are obtained.

Stay Competitive in the Digital Age: The Future of Banks
Liu, Estelle
The latest advancement in financial technology has posed unprecedented challenges for incumbent banks. This paper analyzes the implications of these challenges on bank competitveness, and explores the factors that could support digital advancement in banks. The analysis shows that the traditionally leading role of banks in advancing financial technology has diminished in recent years, and suggests that onoing efforts to catch up to the digital frontier could lead to a more concentrated banking industry, as smaller and less tech-savvy banks struggle to survive. Cross-country evidence has suggested that banks in high-income economies appear to have been the digital leaders, likely benefiting from a sound digital infrastructure, a strong legal and business environment, and healthy competition. Nonetheless, some digital leaders may fall behind in the coming years in adopting newer technologies due to entrenched consumer behavior favoring older technologies, less active fintech and bigtech companies, and weak bank balance sheets.

Testing capacity of the EU banking sector to finance the transition to a sustainable economy
Eley, Slavka
Amidst increased attention on climate change, which is characterised by high uncertainties and long-term time horizons, the financial sector and supervisors are developing methods and approaches for the evaluation of climate-related financial risks. This paper proposes a transition capacity testing system based on the EU Taxonomy for environmentally sustainable economic activities as a useful tool for banks and supervisors to identify the exposures that are most vulnerable to climate change, to improve understanding of the transition financing needs and to support the greening of the financial sector.

The Community Partnership Program BUMDES Catu Graha Mandiri in Gumbrih Tourism Village, Pekutatan, Jembrana, Bali
Junaedi, I Wayan Ruspendi,Feoh, Gerson,Utama, I Gusti Bagus Rai Utama
The partnership program between Dhyana Pura University and Catu Graha Mandiri BUMDES was applied by socialization activity, management training, mentoring, and evaluation. The program used a business management indicator that aims to improve the knowledge and skills of the Catu Graha Mandiri BUMDES community business group members. The partnership programs were measured by Pre-Test and Post-Test Method so that the effectiveness and output of the program can be measured accurately. The program offered the solution in terms of providing assistance and training on the use of appropriate technology in increasing the quantity and quality of production of BUMDES Catu Graha Mandiri Gumbrih Village, as well as making financial reports, providing assistance and training on management, education on ethics and consumer behavior, and training product marketing using social media. The results of this partnership program have been able to improve the services of BUMDES Catu Graha Mandiri Gumbrih to consumers and be able to make financial reports accurately. The sales turnover of partners BUMDES has also increased and developed so that job opportunities are open to the community as well as the addition of new entrepreneurs in Gumbrih Village. The increase in human resources in the application of professional management has also increased, an increase in business production has also occurred, marketing has also increased and financial reports can also be done regularly, and promotions on social media have increased so that the sales turnover of BUMDES partners Catu Graha Mandiri Desa Gumbrih more increasing.

The Death of Stock Splits: An Increase in the Costs to Split
Heater, John C.,Liu, Ye,Tan, Qin,Zhang, Frank
The percentage of firms engaging in stock splits has declined from 8.6% in the 1980s to just 1.0% in the 2010s. In this paper, we document a previously unknown cost of stock splits: failure to sufficiently beat earnings targets and its associated capital markets punishment. We show that both firms’ earnings announcement returns and likelihood of beating analysts’ expectations by at least two cents decline post-stock split. This patterned decline in both split activity and post-split returns only occurs for publicly-listed firms, whereas abnormal returns for exchange-traded and closed-end funds do not consistently vary over time. Overall, the results suggest that declining signaling benefits and increasing costs led to fewer stock splits in recent years.

The Effect of Energy Cryptos on Efficient Portfolios of Key Energy Listed Companies in the S&P Composite 1500 Energy Index
Gurrib, Ikhlaas,Elshareif, Elgilani E.,Kamalov, Firuz
This paper investigates if energy block chain based cryptocurrencies can help diversify equity portfolios consisting primarily of leading energy companies of the US S&P Composite 1500 Energy Index. Key contributions are in terms of assessing the importance of energy cryptos as alternative investments in portfolio management, and whether different volatility models such as Autoregressive Moving Average â€" Generalized Autoregressive Heteroskedasticity (ARMA-GARCH) and Machine Learning (ML) can help investors make better investment decisions. The methodology utilizes the traditional Markowitz mean-variance framework to obtain optimized portfolio combinations. Volatility measures, derived from the Cornish-Fisher adjusted variance, ARMA family classes and machine learning models are used to compare efficient portfolios. The study also analyses the effect of adding cryptos to equity portfolios with non-positive excess returns. Different models are assessed using the Sharpe performance measure. Daily data is used, spanning from 21st November 2017 to 31st January 2019. Findings suggest that energy based cryptos do not have a significant impact on energy equity portfolios, despite the use of different risk measures. This is attributable to the relatively poor performance of energy cryptos which did not contribute in improving the excess return per unit of risk of efficient portfolios based on the leading US energy stocks.

The Effect of Providing Peer Information on Evaluation for Gender Equalized and ESG Oriented Firms: An Internet Survey Experiment
Eiji Yamamura

Internet survey experiment is conducted to examine how providing peer information of evaluation about progressive firms changed individual's evaluations. Using large sample including over 13,000 observations collected by two-step experimental surveys, I found; (1) provision of the information leads individuals to expect higher probability of rising of stocks and be more willing to buy it. (2) the effect on willingness to buy is larger than the expected probability of stock price rising, (3) The effect for woman is larger than for man. (4) individuals who prefer environment (woman's empowerment) become more willing to buy stock of pro-environment (gender-balanced) firms than others if they have the information. (5) The effect of the peer information is larger for individuals with "warm -glow" motivation.

The Influence of Central Bank's Digital Currency on Electronic Payment
LYU, Yunzhen
On September 24, 2019, the Governor of the People’s Bank of China, Yi Gang, said at an event celebrating the 70th anniversary of the People’s Republic of China that the People’s Bank of China has made positive progress in research on digital currencies. The People’s Bank of China plans to combine the central bank’s digital currency (hereinafter referred to as DCEP) with electronic payment tools, calling it a digital currency and electronic payment package. This paper aims to discuss the current business model and structure of our country's electronic payment industry and related electronic authentication and financial network security industries, as well as the possible impact of the central bank's digital currency after it enters the market.

The Value of Internal Sources of Funding Liquidity: U.S. Broker-Dealers and the Financial Crisis
Caglio, Cecilia,Copeland, Adam M.,Martin, Antoine
We use confidential and novel data to measure the benefit to broker-dealers of being affiliated with a bank holding company and the resulting access to internal sources of funding. We accomplish this by comparing the balance sheets of broker-dealers that are associated with bank holding companies to those that are not and we find that the latter dramatically re-structured their balance sheets during the 2007-09 financial crisis, pivoting away from trading illiquid assets and toward more liquid government securities. Specifically, we estimate that broker-dealers that are not associated with bank holding companies both increased repo as a share of total assets by 10 percentage points and also increased the share of long inventory devoted to government securities by 15 percentage points, relative to broker-dealers associated with bank holding companies.

“Salvation and Profit”: Deconstructing the Clean-Tech Bubble
Giorgis, Vincent,Huber, Tobias,Sornette, Didier
From 2004 to 2008, a bubble formed in clean technologies, such as solar, biofuels, batteries, and other renewable energy sources. In this paper, we analyze this clean-tech bubble through the lens of the Social Bubble Hypothesis, which holds that strong social interactions between enthusiastic supporters weave a network of reinforcing feedbacks that lead to widespread endorsement and extraordinary commitment by those involved. We present a detailed synthesis of the development of the clean-tech bubble, its history, and the role of venture capital and government funding in catalyzing it. In particular, we dissect the underlying narrative that was fueling the bubble. As bubbles can be essential in the process of accelerating the development of emerging technologies and diffusion of technological innovations, we present evidence that the clean-tech bubble constituted an example of an innovation-accelerating process.