Research articles for the 2021-02-02

A Prospect Theory Model for Predicting Cryptocurrency Returns
Thoma, Alexander
This paper investigates the risk and return properties of a trading strategy for the cryptocurrency market. The main predictive power for portfolio formation comes from a simple prospect theory model that only uses price information readily available. The dataset consists of a large body of cryptocurrencies from 2014 to 2020. I find a strong outperformance over the market, even after controlling for known predictors. Factor regressions with a cryptocurrency three-factor model further reveal significant alphas. Robustness test emphasize the legitimacy of the strategy. On average, cryptocurrencies with a high (low) prospect theory value earn low (high) subsequent returns. Interestingly, traders in the cryptocurrency market seem to assess the attractiveness of cryptocurrency in a way described by prospect theory. Mechanical tests of the model show that probability weighting is a main driver behind this assessment. Cryptocurrencies with a high prospect theory value tend to be highly positively skewed. This skewness could be the reason why the cryptocurrency seems attractive to traders, similar to lottery-like gambles.

A Stochastic Time Series Model for Predicting Financial Trends using NLP
Pratyush Muthukumar,Jie Zhong

Stock price forecasting is a highly complex and vitally important field of research. Recent advancements in deep neural network technology allow researchers to develop highly accurate models to predict financial trends. We propose a novel deep learning model called ST-GAN, or Stochastic Time-series Generative Adversarial Network, that analyzes both financial news texts and financial numerical data to predict stock trends. We utilize cutting-edge technology like the Generative Adversarial Network (GAN) to learn the correlations among textual and numerical data over time. We develop a new method of training a time-series GAN directly using the learned representations of Naive Bayes' sentiment analysis on financial text data alongside technical indicators from numerical data. Our experimental results show significant improvement over various existing models and prior research on deep neural networks for stock price forecasting.

A Study on Impact of Demonetization on Indian Stock Market and Selected Sectors of Indian Economy
Bantwa, Ashok
While addressing India in his historical speech on 8th November, 2016, prime minister of India, Narendra Modi announced that two highest denomination currency notes in India (500 rupee notes and 1000 rupee notes) will not remain legal tender. Demonetization is one of the most remarkable decisions of Indian government aimed at eradication of black and counterfeit money and control of terror funding. This decision is expected to bring significant change in mode of payment used by Indian people and will transit India towards the cashless economy. Undoubtedly the exact impact of demonetization on Indian economy can be figured out only in long run but in short run demonetization has considerable impact on people, businessmen, small and medium scale industries, companies and economy. This paper examines the impact of demonetization decision on Indian stock exchange as represented by NIFTY index of National Stock Exchange .The paper further examines the impact of demonetization decision on various sectors of Indian economy as represented by various sectoral indices of National Stock Exchange. These sectors include Automobile, Banking, FMCG, Information Technology, Media, Metals, Pharmaceuticals, Real estate, Infrastructure, Private sector banks, Public sector banks, Financial services and Service sector. The result of this study indicated that demonetization has statistically significant impact on all the indices under study. Considering the absolute percentage change in closing price of indices within 30 days of demonetization decision, demonetization has negative impact on all the indices except IT index. Except media index and pharma index the volatility of all other indices under study has increased considerably due to demonetization. Average closing price of NIFTY for 30 days after the demonetization is 5.78% lower than the average closing prices for 30 days before the demonetization. The absolute fall in NIFTY after one month of demonetization is 5.64%. Among the sectoral indices the highest impact of demonetization is on real estate sector followed by media and automobile sector whereas the lowest impact is on metal index followed by PSU banks and IT sector.

A Tale of Two Prices: Study of Relationship Between Underlying Shares and American Depository Receipts Issued by Firms of Indian Origin
Bantwa, Ashok
A growing number of companies from emerging economies are cross listing their shares on international exchanges in their effort to access the developed stock markets. The purpose of this paper is to look into the trends in prices of each dually listed stock that is being traded on the American and Indian stock exchanges. By taking the data for the period of five calendar years (January 2009 to December 2013) the annual absolute returns given by the ADRs and underlying equity shares of eight Indian companies, who have listed their ADRs on New York Stock Exchange (NYSE) are calculated and compared. The relationship between daily closing prices of underlying stocks and daily closing prices of ADRs is studied by using Carl Pearson‘s correlation coefficient. The relationship between daily closing prices of ADRs on NYSE and daily closing prices of underlying equity stocks on NSE during the study period five years is strong positive, implying that American Depository Receipts derive its price from price of its underlying shares and movement in ADR prices strongly follows the movement in prices of underlying shares.. The associated premia levels of each stock on the American Stock Exchange along with the movements of the underlying stock in the Indian stock market have been studied. ADRs of Infosys, Tata Motors, Dr. Reddy‘s Lab and ICICI traded at very thin premium level implying absence of arbitrage opportunities between ADRs and underlying stocks. ADRs of HDFC, MTNL and Sesa Sterlite traded at discount of 78.08, 0.96 and 37.64 per cent respectively implying the incentive for the ADR holders in USA to cancel ADRs, convert them into underlying shares and make arbitrage profit by selling the underlying shares in India. There is no significant difference between average stock returns and average ADR returns given by selected companies during the study period of five years

Adoption of E-wallets: A Post Demonetisation Study in Ahmedabad City
Padiya, Jasmin,Bantwa, Ashok
On 8th November 2016 honorable prime minister of India took a phenomenal step by declaring that two highest denomination currency notes in India (500 rupee notes and 1000 rupee notes) will not remain legal tender. Demonetization decision coupled with government’s initiative to make India a cashless economy is expected to bring a phenomenal transformation in the way people make payments and expected to increase inclination towards online payment. Among the various modes of online payments the mode gaining popularity during present time is E-wallets. In a nation such as India where larger part of clients still favors Cash-On-Delivery, it is difficult to fasten the pace of process of innovation diffusion such as digital wallets. This generates research interest to study the alacrity of people to use E-wallets and factors influencing the adoption of E-wallets including the factors refraining the usage of it, during the post demonetization period. This research paper is aimed at examining the adoption of E-wallets as a mode of payment in Ahmedabad City and to ascertain the factors encouraging and discouraging the usage of E-wallets during the post demonetization period. This paper also throws light on ancillary issues like impact of demonetization decision on preference for online payments, impact of various demographic factors on usage of E-wallets, problems faced by people while using E-wallets etc. The study is based on 318 valid responses received through a structured questionnaire. Data collected was analyzed by using percentages, cross tabulation and statistical tools like ANOVA. Through this study we found that, E-wallet users give very high level of importance to attributes like security, privacy concerns and pricing (Fees). The major problems frequently encountered by the respondents while using E-wallet are long transaction time taken by E-wallet for processing the transaction, security breach and delayed payment. Demonetization drive of government of India has contributed immensely towards awareness, usage and acceptance of online payment.

Are Cryptocurrency Markets, Efficient Markets?
Singh, Arjun
This paper examines the market efficiency of three key cryptocurrency markets namely: Bitcoin, Ethereum and Monero, before and during the COVID-19 pandemic. This research makes use of a Durbin-Watson test and a non-parametric runs test to test for weak-form efficiency, and two comprehensive event studies to test for semi-strong form and strong form efficiency. We conclude neither market can be considered efficient due to the presence of strong positive correlation, and inefficient reactions to our event studies. Despite this, each market became more efficient during the COVID-19 pandemic than before, due to the presence of weaker positive correlation during this timeframe, but inefficient, nonetheless. Thus, the study finds that of the tested cryptocurrency markets, none can be consider wholly efficient. This conclusion is consistent with the vast majority of existing literature.

Combined Custom Hedging: Optimal Design, Noninsurable Exposure, and Operational Risk Management
Guiotto, Paolo,Roncoroni, Andrea
We develop a normative framework for the optimal design, value assessment, and risk management integration of combined custom contingent claims. A risk averse firm faces a mix of financially insurable and noninsurable risk. The firm seeks optimal positioning in a pair of custom claims, one written on the insurable term, and another written on any listed index correlated to the noninsurable term. We prove that a unique optimum always exists unless the index is redundant, and show that the optimal payoff schedules satisfy a design integral equation. We assess the firm’s incremental benefit in terms of both an indifference value and an efficiency rating: this benefit increases with the correlation of the index to the noninsurable term, and it decreases with the correlation of the index to the insurable term. Our hedge proves empirically relevant for a highly risk averse firm facing a market shock (COVID-19 pandemic). In the context of a newsvendor model featuring random price and demand, we show that: (i) integrating our optimal combined custom hedge with the corresponding optimal procurement policy allows the firm to obtain a significant improvement in both risk and return; (ii) this gain may be traded off for a substantial enhancement in operational flexibility.

Concierge Treatment from Banks: Evidence from the Paycheck Protection Program
Duchin, Ran,Martin, Xiumin,Michaely, Roni,Wang, Hanmeng Ivy
We use the Paycheck Protection Program (PPP) as a laboratory to separate between favoritism and informational advantages in lending relationships. The PPP mutes information frictions because loans are fully guaranteed by the government and banks need not screen borrowers. We find that firms with prior lending relationships or personal connections to bank executives are more likely to obtain PPP loans. These effects lead to allocative distortions that force connected firms to return their loans. We also find that the role of connections weakens when monitoring is tighter. Overall, we offer clean estimates of the important role of favoritism in bank lending with implications for government program design.

Contagious Bank Runs and Committed Liquidity Support
Li, Zhao,Ma, Kebin
In a crisis, regulators and private investors can find it difficult, if not impossible, to tell whether banks facing runs are insolvent or merely illiquid. We introduce such an information constraint into a global-games-based bank run model with multiple banks and aggregate uncertainties. The information constraint creates a vicious cycle between contagious bank runs and falling asset prices and limits the effectiveness of traditional emergency liquidity assistance programs. We explain how a regulator can set up committed liquidity support to contain contagion and stabilize asset prices even without information on banks' solvency, rationalizing some recent developments in policy practices.

Correlation Breakdowns, Spread Positions, and CCP Margin Models
Li, David,Cerezetti, Fernando,Cheruvelil, Roy
The default of a participant at Nasdaq Clearing in 2018 and the recent COVID-19 events brought to the attention of risk managers at CCPs the importance of appropriately measuring correlation breakdowns. The sizable price dislocations registered on these occasions suggested that traditional risk models may not be fully equipped to capture the breakdowns. Because correlations are directly impacted by the statistical properties of each variable, any model that lacks the capacity to deal with non-stationarity may inappropriately represent correlation or its alterations. Using a GARCH-DCC approach to accommodate such properties, the objective of the paper is to study the correlation behaviour during adverse market conditions, and the potential subsequent impact to CCP margins. A study case for energy commodities is proposed, with the specific focus on spread positions for the electricity market. The analysis suggests that the correlation breakdowns are more frequent than traditionally expected. When different types of shocks are considered (i.e. September 2018 and March-May 2020), it becomes evident that while the magnitude of the breakdown may differ, its cycle presents a number of similarities. While elevated margin due to correlation breakdown may reduce breaching amount and improve margin coverage rate, this paper also recognizes the potentially increased margin procyclicality, and highlights the challenge of balancing margin responsiveness and stability during correlation breakdown for spread positions, and calls for further study in this area.

Dose Macroeconomic Factors Influence Cryptocurrencies Return?
Nakagawa, Kei,Sakemoto, Ryuta
This study investigates the relationship between expected returns on cryptocurrencies and macroeconomic fundamentals. We employ a dynamic factor model and summarize information as common factors. We observe that the common factors are strongly linked to the cryptocurrency expected returns at a quarterly frequency while we do not see that relationship using a few macroeconomic indicators such as inflation and money supply. This suggests that the macroeconomic information matters in a longer term, which contrasts to the study of Liu & Tsyvinski (2020) who explore a short-term relationship.

Dynamics of symmetric SSVI smiles and implied volatility bubbles
Mehdi El Amrani,Antoine Jacquier,Claude Martini

We develop a dynamic version of the SSVI parameterisation for the total implied variance, ensuring that European vanilla option prices are martingales, hence preventing the occurrence of arbitrage, both static and dynamic. Insisting on the constraint that the total implied variance needs to be null at the maturity of the option, we show that no model--in our setting--allows for such behaviour. This naturally gives rise to the concept of implied volatility bubbles, whereby trading in an arbitrage-free way is only possible during part of the life of the contract, but not all the way until expiry.

Effect of COVID-19 Pandemic on Global Stock Markets
Abba Ahmed, Bello,Abdu, Murtala
This paper examined the effect of COVID-19 pandemic on global stock markets. Daily closing prices were obtained from Wall Street data base from 1st January to 31st March 2020. For the analysis 80 trading days were used from 20th January, 2020, when the news of COVID-19 pandemic broke out to 11th March, 2020 when WHO declared it a global pandemic. Eighteen stock indices from the most affected countries were selected for the study based on the reports of Worldometers and event study was used for the analysis. The CARs and CAARs for each stock index were computed based on regional classification using four windows of analysis. The regional classifications comprised Europe, America (North and South) and Asian stock indices. A two-way fixed effect model was estimated to test for the effect of regional volatility. The study discovered that the worst hit stock index with the lowest AR a day to the event was Spain’s IBEX 35 Index while on the event day the lowest was MSCI Poland Index. And the worst hit on the day after the event was S&P 500 Composite Index of the US. It further found that the COVID-19 pandemic affected each region in different ways. The North and South American stocks suffered the highest volatility, whereas, the highest losses were observed in European stocks. The study recommends that governments should be very mindful of the policy responses and measures they adopt to address the COVID-19 pandemic. Internal policies should be designed in such a way to accommodate for external shocks.

Event-Driven LSTM For Forex Price Prediction
Ling Qi,Matloob Khushi,Josiah Poon

The majority of studies in the field of AI guided financial trading focus on purely applying machine learning algorithms to continuous historical price and technical analysis data. However, due to non-stationary and high volatile nature of Forex market most algorithms fail when put into real practice. We developed novel event-driven features which indicate a change of trend in direction. We then build long deep learning models to predict a retracement point providing a perfect entry point to gain maximum profit. We use a simple recurrent neural network (RNN) as our baseline model and compared with short-term memory (LSTM), bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU). Our experiment results show that the proposed event-driven feature selection together with the proposed models can form a robust prediction system which supports accurate trading strategies with minimal risk. Our best model on 15-minutes interval data for the EUR/GBP currency achieved RME 0.006x10^(-3) , RMSE 2.407x10^(-3), MAE 1.708x10^(-3), MAPE 0.194% outperforming previous studies.

Financial Literacy Education: Implication on Teachers' Economic and Social Life in Ghana
Matey, Juabin, Duut, Joseph Yennukua,Kombian, Mensah Felix
Financial literacy education is one of lifelong assets that every individual needs to function and fit well in modern-day society. It provides the financially savvy better decision making, best investment alternatives and family wellbeing. Unfortunately, consumers appear less active and less confident in participating meaningfully in the financial sector due to lack of knowledge about the complex nature of financial products and services and the risk that goes with them. This study looked at financial literacy education and its implication on the economic and social life of the teacher in the Upper East Region of Ghana. With a descriptive survey design, 118 participants responded to questionnaires. We found low levels of financial literacy among participants which can poorly affect their daily financial management. The financially literate has the advantage to undertake prudent retirement planning. Being financially illiterate adversely affects one’s life in relation to the culture of savings, expenditure pattern, investment decisions and budgeting skills, making one economically insecure. The study has policy frontier implications; policy makers, governments, non-governmental organizations and equity owners should come to the aid of teachers by way of introducing professional teacher development programmes specifically tailored at uplifting their financial literacy knowledge and skills.

Following the Money Trail: The Geographic Distribution of PPP Loans
Calem, Paul,Covas, Francisco,Freedman, Adam
This paper examines the distribution of Paycheck Protection Program loan dollars per employee across U.S. counties, using loan-level data released to the public by the U.S. Small Business Administration (SBA). Our multivariate analysis indicates a positive association between the degree of economic disruption (decline in visits to the workplace) and PPP dollars per employee. This association is especially strong for PPP lending by large banks. Additionally, we find that the PPP loan amount per employee is positively associated with the share of local small business employees at firms with fewer than twenty employees, and this association again is somewhat stronger for large banks. Specifically, we find that a county with twice the share of smaller firms receives 25 percent more PPP loans per employee from large banks compared to another county. This finding is particularly important because of concerns that smaller firms may have had greater difficulty accessing the program. Contrary to those concerns, the findings suggests that smaller firms received more benefit from the PPP program than their larger counterparts after controlling for other important factors.

Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Model Approach
Hallin, Marc,Trucíos, Carlos
Beyond their importance from a regulatory policy point of view, Value-at-Risk (VaR) and Expected Shortfall (ES) play an important role in risk management, portfolio allocation, capital level requirements, trading systems, and hedging strategies. Unfortunately, due to the curse of dimensionality, their accurate estimation in large portfolios is quite a challenge. To tackle this problem, we propose a filtered historical simulation method in which high-dimensional conditional covariance matrices are estimated via a general dynamic factor model with infinite-dimensional factor space and conditionally heteroscedastic factors. The procedure is applied to a panel with concentration ratio close to one. Backtesting and scoring results indicate that both VaR and ES are accurately estimated under our method, which outperforms alternative approaches available in the literature.

Forecasting Value-at-Risk and Expected Shortfall of Cryptocurrencies using Combinations based on Jump-Robust and Regime-Switching Models
Trucíos, Carlos,Taylor, James W.
Several procedures to estimate daily risk measures in cryptocurrency markets have been recently proposed in the literature. Among them, procedures taking into account the presence of extreme observations, as well as procedures that include more than a single regime, have performed substantially better than standard methods in terms of volatility and Value-at-Risk forecasting. Three of those procedures are revisited in this paper, and their Value-at-Risk forecasting performance is evaluated using recent cryptocurrency data that includes periods of turbulence. Those procedures are also extended to estimate the Expected Shortfall, and a comprehensive backtesting exercise based on both calibration tests and scoring functions is performed. In order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of forecast combinations strategies. In our empirical application, procedures that are robust to outliers performed slightly better than regime-switching models. We found some evidence that combining strategies can improve the forecasting of Value-at-Risk and Expected Shortfall, particularly for the 1% risk levels, making them an interesting alternative to be used by practitioners.

From optimal martingales to randomized dual optimal stopping
Denis Belomestny,John Schoenmakers

In this article we study and classify optimal martingales in the dual formulation of optimal stopping problems. In this respect we distinguish between weakly optimal and surely optimal martingales. It is shown that the family of weakly optimal and surely optimal martingales may be quite large. On the other hand it is shown that the Doob-martingale, that is, the martingale part of the Snell envelope, is in a certain sense the most robust surely optimal martingale under random perturbations. This new insight leads to a novel randomized dual martingale minimization algorithm that doesn't require nested simulation. As a main feature, in a possibly large family of optimal martingales the algorithm efficiently selects a martingale that is as close as possible to the Doob martingale. As a result, one obtains the dual upper bound for the optimal stopping problem with low variance.

Gold Demand Across Countries
Baur, Dirk G.,Gopalakrishnan, Balagopal,Mohapatra, Sanket
This paper analyzes the demand for jewelry, bars & coins, and gold ETFs across high-income and middle-income countries. We employ a simultaneous equations model that allows a joint determination of the various categories of gold demand and find significant differences across gold demand categories and across countries. Jewelry and bars & coins demand are positively related in middle-income countries but unrelated in high-income countries. Jewelry demand is negatively related to gold prices in middle-income countries but not in high-income countries. Bars and coins demand positively reacts to global risk (VIX) in high-income countries but not in middle-income countries. The findings indicate that not all gold is equal and serves different purposes in different countries. Gold is generally more integrated with financial markets in high-income countries than in middle-income countries and jewelry is the least integrated among the three categories of gold demand.

Information Asymmetry and the Pricing of Privately Placed Debt
Dinkelaker, Kilian,Mattig, Andreas Walter,Morkoetter, Stefan
Based on a dataset including 11,636 private debt placements issued globally between 1999 and 2016, we investigate the association between borrower-lender information asymmetry and the cost of debt for issuers. We observe that information asymmetry due to being a private or unrated firm is associated with higher cost of private debt. Our results equally inform corporate financing decisions and government initiatives aimed at promoting pri- vate debt markets in order to expand funding sources for the private sector.

Interacting Regional Policies in Containing a Disease
Arun G. Chandrasekhar,Paul Goldsmith-Pinkham,Matthew O. Jackson,Samuel Thau

Regional quarantine policies, in which a portion of a population surrounding infections are locked down, are an important tool to contain disease. However, jurisdictional governments -- such as cities, counties, states, and countries -- act with minimal coordination across borders. We show that a regional quarantine policy's effectiveness depends upon whether (i) the network of interactions satisfies a balanced-growth condition, (ii) infections have a short delay in detection, and (iii) the government has control over and knowledge of the necessary parts of the network (no leakage of behaviors). As these conditions generally fail to be satisfied, especially when interactions cross borders, we show that substantial improvements are possible if governments are outward-looking and proactive: triggering quarantines in reaction to neighbors' infection rates, in some cases even before infections are detected internally. We also show that even a few lax governments -- those that wait for nontrivial internal infection rates before quarantining -- impose substantial costs on the whole system. Our results illustrate the importance of understanding contagion across policy borders and offer a starting point in designing proactive policies for decentralized jurisdictions.

Investigating the Impact of COVID-19 Outbreak on Canada's Commodity Market
Destin, Achu Dobgima
SARS-Cov-2 was first reported in Wuhan, a town in Hubei Province of China with a population of 11 million in December 2019, following an outbreak of non-pneumonia of a clear cause. The virus has now spread across the globe considerably more than 200 countries and territories, and the world health organization (WHO) described it as a pandemic on 11 March 2020. On the economic front, COVID-19 has led more than 200 countries partially or totally lockdown. This situation disrupted global supply chain, and induced a significant fall in both economic activity and financial asset prices. Canada, being one of the country affected by the Virus with 731 000 Cases, and 18 622 Deaths as at the 21 of January 2020. Recently vaccines have been developed but the contamination wave keeps on increasing every day. On the socioeconomic front, COVID-19 has led more than 200 countries into partial or total lockdown, disrupted global supply chains, and induced a fall in both economic activity and financial asset prices. It’s of prime importance to study how COVID-19 impact’s on Canada’s commodity market. The commercial and financial effect of COVID-19. The impact of COVID-19 is unclear because the public health crisis is still unfolding. There is limited amount of studies concerning this topic, because the crisis is still unfolding. The objective of this study is to investigate the impact of Covid-19 on Canada’s Commodity markets. The author uses extreme bound analysis for market interpretation and sourced data from Bank of Canada and Our World in Data COVID-19, from January 1 to December 31 2020. The result of the study show that, Total deaths per million have an impact on the monthly Bank of Canada’s commodity price index of energy because of the robust relationship between them. Furthermore, the result also show that, commodity price index of energy and total deaths per million are determinants to total commodity price index. Furthermore, commodity price index excluding energy, commodity price index of energy, commodity price index of minerals and metals, commodity price index of forestry and total deaths per million are robust determinants of monthly Bank of Canada total commodity Prices. M.BCNE, M.ENER, M.MTLS, M.MTLS, M.FOPR, M.FOPR, M.AGRI, M.FISH, TC, TD have a positive relationship with MBCPI. M.BCPI, M.BCNE, M.MTLS, M.AGRI and M.FISH

Keep Calm and Do Nothing - Trading Behavior of Swedish Retirement Investors during the COVID-19 Pandemic
Hagen, Johannes,Malisa, Amedeus,Post, Thomas
How did investors in the Swedish Premium Pension System (PPS) react to the stock markets shock ignited in 2020 by the COVID-19 pandemic? The share of investors that traded more than doubled, and trades shifted capital from equity funds to low risk interest funds. In economic terms, however, trading activity stayed at very low levelsâ€"less than two percent of investors traded in March 2020 and there was no effect on pension withdrawals. Given the vast evidence on retail investors’ strongly increasing trading volume in crisis times, the reaction of PPS investors looks surprisingly smart, i.e., avoiding the many mistakes that investors incur when they try to outsmart the market. Potentially, the often-criticized choice architecture of the PPS that induces strong inertia provided positive side effects in times of a severe market shock.

Limiting Value of the Kolkata Index for Social Inequality and a Possible Social Constant
Asim Ghosh,Bikas K Chakrabarti

Based on some analytic structural properties of the Gini and Kolkata indices for social inequality, as obtained from a generic form of the Lorenz function, and some more new observations on the citation statistics of individual authors (including Nobel laureates), we make a conjecture that about $14\%$ of people or papers or social conflicts tend to earn or attract or cause about $86\%$ of wealth or citations or deaths in very competitive situations in markets, universities or wars. This is a modified form of the (more than a) century old $80-20$ law of Pareto in economy (not visible today because of various welfare and other strategies) and gives an universal value ($0.86$) of social (inequality) constant or number.

Man vs. Machine: Liquidity Provision and Market Fragility
Raman, Vikas,Robe, Michel A.,Yadav, Pradeep K.
We empirically investigate the participation and transactional liquidity provided by algorithmic vs. human traders during “abnormally” stressful periods, relative to what they do in “normal” periods, and the resultant implications for the quality and fragility of markets. We find strong evidence that, in periods of abnormal stress, algorithmic traders significantly reduce their participation and liquidity provision in trades; significantly reduce the extent to which they post new liquidity-supplying limit orders; significantly reduce the aggressiveness of these limit orders, and sharply increase the price at which they are willing to supply liquidity. We define abnormal stress based on persistently extreme levels of volatility, order-imbalances, and bid-offer spreads; and measures that proxy for “ambiguity” or complexity. This significantly greater withdrawal of algorithmic (relative to human) traders is directly associated with the disappearance of information advantages of algorithmic traders. We find that this has a significant propensity to generate feedback loops, and induce “contagion” through withdrawals in liquidity provision in related stocks, potentially making markets more “fragile”. Our results suggest that, in contrast to manual traders who adapt in (higher latency) real time, algorithmic trade execution appears less conducive to low impact adjustment of ambiguous information asymmetries or flows. Overall, our results reinforce regulatory concerns about the potential for systemic fragility in markets dominated by machine-based liquidity provision.

Measuring Purpose: An Integrated Framework
Barby, Clara,Barker, Richard,Cohen, Ronald,Eccles, Robert G.,Heller, Christian,Mayer, Colin,Roche, Bruno,Serafeim, George,Stroehle, Judith C.,Younger, Rupert,Zochowski, Thaddeus
This paper sets out a logical and inclusive approach to measuring purpose. We advocate a three-stage process, the first of which anchors the purpose, mission and vision of the organisation in its governance. The second stage identifies the business metrics that derive from purpose in relation to inputs, outputs, outcomes and impacts. In the final stage, these reporting metrics are converted into monetary values through enterprise cost-based accounting and societal valuation.This three-step approach provides a coherent reporting framework against which critical decisions can be made. These decisions may be internal â€" enabling management to allocate scarce resources appropriately â€" or external, allowing investors and other critical stakeholders to assess the performance of a company against its stated purpose. We conclude by categorising existing measurement initiatives in relation to the three stages.

Neighborhood Demographics and the Allocation of Paycheck Protection Program Funds
Calem, Paul,Freedman, Adam
Neighborhoods with a high percentage of racial and ethnic minorities received significantly more Paycheck Protection Program dollars per small business than other areas, BPI’s analysis demonstrates. In particular, the nation’s largest banks â€" those with more than $50 billion in assets â€" robustly channeled PPP credit into communities with a high percentage minority population. The pandemic relief program also directed a relatively large share of funds to neighborhoods encompassing Opportunity Zones as designated by the Treasury Department. The research shows a neighborhood made up entirely of Opportunity Zones that is in the top fifth of percent minority population would receive about 50 percent more PPP dollars per small business establishment than an area without those zones and with the bottom fifth percent minority population, according to BPI’s estimates.The analysis utilizes detailed loan-level data released by the SBA, merged at the ZIP code level with data from the U.S. Census Bureau and the Treasury Department, to examine the distribution of PPP funds in relation to the percent minority population of neighborhoods and presence of an Opportunity Zone. This is the first national study of which we are aware that analyzes the distribution of PPP funds across neighborhoods within counties.

New Frontiers of Robo-Advising: Consumption, Saving, Debt Management, and Taxes
D'Acunto, Francesco,Rossi, Alberto G.
Traditional forms of robo-advice were targeted to help individuals make portfolio allocation decisions. Based on the balance-sheet view of households, the scope for robo-advising has been expanding to many other personal-finance choices, such as households' saving and consumption decisions, debt management, mortgage uptake, tax management, and lending. This chapter reviews existing research on these new functions of robo-advising with a special emphasis on the questions that are still open for researchers across several disciplines. We also discuss the attempts to optimize jointly all personal-finance decisions, which we label ``holistic robo-advisors.'' We conclude by assessing fruitful avenues for research and practice in finance, computer science, marketing, decision science, information systems, law, and sociology.

Number of Numbers: Does Quantitative Disclosure Reduce Uncertainty in Quarterly Earnings Conference Calls?
Campbell, John L.,Zheng, Xin,Zhou, Dexin
Theoretical research argues that numbers convey more precise information than words. Based on this work, we hypothesize that when managers provide disclosure with a greater proportion of quantitative information in an earnings conference call, investor uncertainty around the call will be lower and, thus, short-window returns around the call will be higher. We offer three main findings. First, we find a positive association between the extent of hard information (i.e., numerical disclosure) in earnings conference calls and short-window stock returns around the call. This result suggests that investor uncertainty is lower when managers provide greater numerical disclosure. Second, we find that this positive association is larger when firms are smaller and have larger stock volatility or analyst forecast dispersion. These results suggest that the effect of numerical disclosure in reducing investor uncertainty is greater when the firm’s information environment is otherwise more uncertain. Finally, we find that this positive association is larger when firms issue a negative earnings surprise. This result suggests that the effect of numerical disclosure in reducing investor uncertainty is greater when the uncertainty of a firm’s performance is greater. Overall, our results suggest that investors react to the extent of hard information (i.e., numerical disclosure) in earnings conference calls.

Reinventing the Utility for DERs: A Proposal for a DSO-Centric Retail Electricity Market
Rabab Haider,David D'Achiardi,Venkatesh Venkataramanan,Anurag Srivastava,Anjan Bose,Anuradha M. Annaswamy

The increasing penetration of intermittent renewables, storage devices, and flexible loads is introducing operational challenges in distribution grids. The proper coordination and scheduling of these resources using a distributed approach is warranted, and can only be achieved through local retail markets employing transactive energy schemes. To this end, we propose a distribution-level retail market operated by a Distribution System Operator (DSO), which schedules DERs and determines the real-time distribution-level Locational Marginal Price (d-LPM). The retail market is built using a distributed Proximal Atomic Coordination (PAC) algorithm, which solves the optimal power flow model while accounting for network physics, rendering locationally and temporally varying d-LMPs. A numerical study of the market structure is carried out via simulations of the IEEE-123 node network using data from ISO-NE and Eversource in Massachusetts, US. The market performance is compared to existing retail practices, including demand response (DR) with no-export rules and net metering. The DSO-centric market increases DER utilization, permits continual market participation for DR, lowers electricity rates for customers, and eliminates the subsidies inherent to net metering programs. The resulting lower revenue stream for the DSO highlights the evolving business model of the modern utility, moving from commoditized markets towards performance-based ratemaking.

Resource Availability in the Social Cloud: An Economics Perspective
Pramod C. Mane,Nagarajan Krishnamurthy,Kapil Ahuja

This paper focuses on social cloud formation, where agents are involved in a closeness-based conditional resource sharing and build their resource sharing network themselves. The objectives of this paper are: (1) to investigate the impact of agents' decisions of link addition and deletion on their local and global resource availability, (2) to analyze spillover effects in terms of the impact of link addition between a pair of agents on others' utility, (3) to study the role of agents' closeness in determining what type of spillover effects these agents experience in the network, and (4) to model the choices of agents that suggest with whom they want to add links in the social cloud. The findings include the following. Firstly, agents' decision of link addition (deletion) increases (decreases) their local resource availability. However, these observations do not hold in the case of global resource availability. Secondly, in a connected network, agents experience either positive or negative spillover effect and there is no case with no spillover effects. Agents observe no spillover effects if and only if the network is disconnected and consists of more than two components (sub-networks). Furthermore, if there is no change in the closeness of an agent (not involved in link addition) due to a newly added link, then the agent experiences negative spillover effect. Although an increase in the closeness of agents is necessary in order to experience positive spillover effects, the condition is not sufficient. By focusing on parameters such as closeness and shortest distances, we provide conditions under which agents choose to add links so as to maximise their resource availability.

Risk, Return & Performance Evaluation of Selected Mutual Fund Schemes â€" A Study on Large & Mid Cap Funds
Bhuva, Krunal K.,Bantwa, Ashok
This paper studies the persistence of mutual fund performance. Academic research often focuses on fund returns. This study intends to examine the performance of selected Large cap and Mid cap mutual fund schemes of Indian Mutual fund industry during the study period 2007 to 2011. The performance of selected schemes is evaluated in terms of average returns, systematic risk, and unsystematic risk and by using different measures like: Sharpe, Jenson, Treynor and FAMA. After detailed analysis it is found that except two all the sampled schemes have performed better than market. Supporting the established relationship of high risk - high return, better performing schemes are exposed to higher risk. The findings also revealed that majority of the schemes were adequately diversified and about 60% of the schemes were able to beat the market with help of better stock selection skill of fund managers. Finding from the t-test calculations shows that there is no difference between returns from large cap mid cap mutual funds in long run. From the return comparison of mutual funds and market, in 2008 & 2011 large cap are underperforming than market and in 2011 only mid cap mutual funds are showing less return than market returns.

Smart(Phone) Investing? A within Investor-Time Analysis of New Technologies and Trading Behavior
Kalda, Ankit,Loos, Benjamin,Previtero, Alessandro,Hackethal, Andreas
Using transaction-level data from two German banks, we study the effects of smartphones on investor behavior. Comparing trades by the same investor in the same month across different platforms, we find that smartphones increase purchasing of riskier and lottery-type assets and chasing past returns. After the adoption of smartphones, investors do not substitute trades across platforms and buy also riskier, lottery-type, and hot investments on other platforms. Using smartphones to trade specific assets or during specific hours contributes to explain our results. Digital nudges and the device screen size do not mechanically drive our results. Smartphone effects are not transitory.

State Aid and Competition in the European Banking Markets
Laser, Falk Hendrik
I investigate whether bank bailouts since the outbreak of the financial crisis affected competition in the European banking markets. Using a unique dataset on large bank rescues I compare the development of market power of rescued and non-rescued banks between 2000 and 2018. I find that bank bailout coincides with a substantial drop of six percentage points in the Lerner index. Effects are heterogeneous and driven by bank rescues directly after the outbreak of the financial crisis in 2008 and not by bank rescues triggered during the European sovereign debt crisis starting in 2010. My findings cast positive light on European competition policy as banks do not appear to have capitalized on rescue money in terms of market power. Protecting competition in European banking markets remains a topical policy issue in light of rising levels of market power and potential public interventions in the course of the ongoing COVID-19 pandemic.

Stochastic Dominance Without Tears
Vinod, Hrishikesh D.
When does an entire income distribution f(x2) dominate f(x1)? When can we comprehensively say that f(x2) is ``richer'' than f(x1)? Anderson (1996) proposed a nonparametric quantification for pair-wise welfare-ordering of two countries by their entire income distributions. His algorithm readily computes index values for stochastic dominance of orders 1 to 4, denoted as SD1 to SD4. This paper suggests a new exact algorithm extending the orderings to many countries simultaneously while avoiding (a) cumbersome merging of data sets x1 and x2, and (b) the trapezoidal approximations subject to truncation errors. The new algorithm computes exact areas under step-functions defined by the empirical cumulative distribution functions, ECDF(xj). We provide intuitive examples and include bootstrap confidence intervals for inference on estimated SD1 to SD4 indexes provided for each f(xj), with j=1, 2, .. , n.

Sustainability Check of Global Stock Market Levels with Fundamental and Economic Analysis
Yeole, Tejas,Kothawade, Shubham
The COVID-19 pandemic has impacted the social as well as professional lives of individuals. The COVID-19 pandemic pushed economies into a Lockdown. However, it generated the worst recession since the Great Depression. This paper concentrates primarily on the most contributing factors to the worldwide stock market’s rise or fall. The worldwide economy’s current situation, rising debt, lowering GDP, increase in the bank’s bad loan, unemployment, and many more things contradict current market valuations. The market is at a lifetime high level. After studying behavioral patterns from historical data, we came to the hypothesis that the market will come to its original market valuation at some point in time, leading to a sudden market crash shortly. The data taken for GDP analysis includes the world’s top economic countries like Japan, Germany, the United States, and India. This paper primarily focuses on the United States and the Indian economy.

Tax Complexity, Tax Certainty, and the Pillar One and Two Blueprints Submission to the OECD Centre for Tax Policy and Administration in response to the Public Consultation Document on the Reports on the Pillar One and Pillar Two Blueprints, December 14, 2020
Colliard, Jean-Edouard,Eden, Lorraine,Georg, Co-Pierre
A key motivation underlying the Pillar One and Pillar Two Blueprints, released by the OECD/Inclusive Framework on 12 October 2020, is the desire to reduce tax complexity and improve tax certainty for taxpayers and tax authorities. Tax certainty is one of the underlying principles of a good tax system; tax complexity imposes compliance costs and distorts behaviors, making the principles of a good tax system impossible to achieve. Our goal in this paper is to assess the tax complexity of the Pillar One and Pillar Two Blueprints. Since tax complexity has different components, we focus on one specific type of tax complexity: rule complexity, which assesses the problems faced by taxpayers when interpreting written and unwritten tax rules. We use a novel method of measuring regulatory complexity, inspired by the extant literature on the complexity of algorithms and software. The key to our approach is to classify words in regulatory text as operators and operands. The complexity of one document (e.g., regulations, manuals, guidelines) is only meaningful when measured in a relative sense, either by comparison with another document or by examining changes in one document over time. For comparison purposes we use the 2017 OECD Transfer Pricing Guidelines (“TPG”) and the 2017 United Nations Practical Manual on Transfer Pricing for Developing Countries (“UNTPM”). Our goal is to compare the levels of tax complexity of four documents â€" the TPG, UNTPM, and Blueprints â€" and answer the question: Do the Pillar One and Pillar Two Blueprints exhibit more or less tax complexity when compared with the TPG and the UNTPM? We start by defining tax certainty and tax complexity, and provide a brief overview of the extant literature. We then explain our new measure of tax complexity, situate it within the existing literature, and outline our research methodology. We use this measure to assess the relative complexity of four transfer pricing documents: the TPG, the UNTPM, Pillar One, and Pillar Two. We find that both Blueprints exhibit greater tax complexity than either the TPG or UNTPM and provide some policy recommendations for reducing their level of rule complexity.

The Diversification Benefits of Cryptocurrencies in Multi-Asset Portfolios: Cross-Country Evidence
Colombo, Jefferson,Cruz, Fernando,Paese, Luis,Cortes, Renan
Using a sample of 21 developing and developed countries, we analyze whether a well-diversified investor of traditional assets (stocks, bonds, real estate, and commodities) may benefit from investing in cryptocurrencies. Country-specific analyses indicate that cryptocurrencies usually fit in the tangent portfolio (maximum Sharpe ratio) but no -- or very little -- in the minimum variance portfolio (MVP). Out-of-sample analysis indicates that even global portfolios that already benefits from international diversification may enjoy investing marginally in cryptocurrencies: mean-variance optimal and naive with cryptocurrencies outperformed otherwise identical portfolios in terms of risk-adjusted returns. Besides, exchange rate movements do not drive this better performance -- it occurs for both local (all returns denominated in the local currency) and global perspectives (all returns in U.S. Dollars). We also find that cryptocurrencies' diversification benefits occur both before and after the COVID-19 pandemics, with the 1/N portfolio with cryptocurrencies presenting the higher risk-adjusted returns. Our paper adds to the literature by analyzing the marginal effects of adding cryptocurrencies on a sample of developing and developed economies and considering up-to-date data following the COVID-19 crisis.

The Equity Market Implications of the Retail Investment Boom
van der Beck, Philippe,Jaunin, Coralie
Retail trading activity has soared during the COVID-19 pandemic. This paper quantifies the impact of the retail investment boom on the US stock market within a structural model. Using account holdings data from the online trading platform “Robinhood Markets Inc.” and 13F filings, we estimate retail and institutional demand curves and derive aggregate pricing implications via market clearing. The inelastic nature of institutional demand allows Robinhood investors to have a substantial effect on stock returns during the COVID-19 pandemic despite their negligible wealth share. We find that Robinhood traders account for over 7% of the cross-sectional variation in stock returns during the second quarter of 2020. We furthermore show that without the surge in retail trading activity the aggregate market capitalization of the smallest quintile of US stocks would have been over 30% lower. Lastly, Robinhood traders’ are able to affect the price of some large individual companies that are being held primarily by passive institutional investors.

The Macroeconomic Impacts of Entitlements
Ateeb Akhter Shah Syed,Kaneez Fatima,Riffat Arshad

The worries expressed by Alan Greenspan that the long run economic growth of the United States will fade away due to increasing burden of entitlements motivated us to empirically investigate the impact of entitlements of key macroeconomic variables. To examine this contemporary issue, we estimate a vector error-correction model is used to analyze the impact of entitlements on the price level, real output, and the long-term interest rate. The results show that a shock to entitlements leads to decrease in output and lends support to the assertion made by Alan Greenspan. Several robustness checks are conducted and the results of the model qualitatively remains unchanged.

Underpricing in the Cryptocurrency World: Evidence from Initial Coin Offerings
Felix, Thijn,von Eije, J. Henk
We analyze underpricing in listed Initial Coin Offerings (ICOs) by using a sample of 247 ICOs from September 2015 till January 2018. The results show an average level of underpricing of ICOs of 123% in the USA and 97% in the other countries. The results for the USA ICOs are significantly higher than for USA IPOs on average, and also significantly higher than USA IPOs at the beginning of the bubble. We also study the determinants of ICO underpricing. We use proxies based on asymmetric information from the IPO literature as well as ICO related variables. First-day trading volume and a good sentiment on the ICO market go together with more ICO underpricing. Moreover, hot markets make first day investors to benefit less. Finally, companies that use a large issue size or a pre-ICO (a sale of cryptocurrencies before the ICO) leave less money on the table.

What Does Academic Research Say about Short-Selling Bans?
Alderighi, Stefano,Gurrola-Perez, Pedro
As a reaction to higher market volatility due to the global COVID-19 pandemic, in March 2020 some financial regulators imposed short-selling bans on equity markets. Their argument is that short-selling exacerbates downward price movements, thus being responsible for heightened volatility and reduced market confidence. This paper reviews the academic literature on short-selling and short-selling bans, comparing the arguments against banning short-selling with the arguments in favour. We find that the evidence almost unanimously points towards short-selling bans being disruptive for the orderly functioning of markets, as they are found to reduce liquidity, increase price inefficiency and hamper price discovery. In addition, short-selling bans are found to have negative spillover effects on other markets, for example option markets. According to the literature, during periods of price decline and heightened volatility, short-sellers do not behave differently from any other traders, and contribute less to price declines than regular ‘long’ sellers. As research has shown that short-selling bans are more deleterious to markets characterized by a relatively high amount of small stocks, low levels of fragmentation, and fewer alternatives to short-selling, emerging markets should be particularly wary of bans on short-selling