# Research articles for the 2021-01-28

A Study on India Volatility Index (VIX) and its Performance as Risk Management Tool in Indian Stock Market
Bantwa, Ashok
SSRN
This study is aimed at examining the relationship between India VIX and NIFTY and to examine the usefulness of volatility index as risk management tool for stock market trading. It is found that relationship between NIFTY and VIX is strong when market is moving down and vice a versa. I observed that two indices moved in opposite direction. The linear association between the India VIX and NIFTY is statistically significant. The result indicated that statistically significant relationship exist between percentage change in the level of India VIX and rates of returns for various holding periods on index future contract on CNX Nifty Index and CNX midcap 50 Index. In terms of trading strategies it is found that portfolio performance can be improved by shifting the portfolio from midcap to large cap stocks when India VIX goes up and shifting the portfolio from large-cap to mid-cap when India VIX goes down.

Automatic Order Matching and Latency in the Dissemination of U.S. Equity Data
Schwenk-Nebbe, Sander
SSRN
This study reveals that 64-65% of all trades on the NYSE and Nasdaq trigger concurrent top-of-book updates by virtue of automatic order matching. Because of latency in dissemination, concurrent events need not be published on the consolidated data feeds at the same time. In consequence, up to 79% of quote updates are disseminated ahead of trades that triggered them. This impedes the identification of prevailing quotes and induces material errors in trading cost and price impact measures. The issue can be avoided using secondary timestamps that are assigned before dissemination and could be prevented entirely with a revised dissemination procedure.

Bitcoin's future carbon footprint
Shize Qin,Lena Klaaßen,Ulrich Gallersdörfer,Christian Stoll,Da Zhang
arXiv

The carbon footprint of Bitcoin has drawn wide attention, but Bitcoin's long-term impact on the climate remains uncertain. Here we present a framework to overcome uncertainties in previous estimates and project Bitcoin's electricity consumption and carbon footprint in the long term. If we assume Bitcoin's market capitalization grows in line with the one of gold, we find that the annual electricity consumption of Bitcoin may increase from 60 to 400 TWh between 2020 and 2100. The future carbon footprint of Bitcoin strongly depends on the decarbonization pathway of the electricity sector. If the electricity sector achieves carbon neutrality by 2050, Bitcoin's carbon footprint has peaked already. However, in the business-as-usual scenario, emissions sum up to 2 gigatons until 2100, an amount comparable to 7% of global emissions in 2019. The Bitcoin price spike at the end of 2020 shows, however, that progressive development of market capitalization could yield an electricity consumption of more than 100 TWh already in 2021, and lead to cumulative emissions of over 5 gigatons by 2100. Therefore, we also discuss policy instruments to reduce Bitcoin's future carbon footprint.

Central Bank Communication and Asset Correlation
Hoang, Duc Hong
SSRN
In this paper, I investigate the effect of Fed and ECB communications on the long-run stock-bond and stock-FX correlations. I find that a negative tone in central banksâ€™ communication results to a lower stock-bond correlation, which supports the flight to quality phenomenon in both US and Euro area. In contrast, results for stock-FX correlation are mixed. Fedâ€™s negative sentiment leads to opposite movements of US stock prices and USD, while ECBâ€™s negative sentiment drives Eurozone stock prices and EUR moving in the same direction. This result documents for safe haven property of USD.

Community Membership and Reciprocity in Lending: Evidence from Informal Markets
Tomy, Rimmy E.,Wittenberg Moerman, Regina
SSRN
We study how wholesalers extend trade credit to retailers in economies where formal market institutions, such as financial reporting systems, auditing, and courts, are nonexistent or function poorly. Using the setting of a large market in the northeastern part of India, we find that community membership plays a strong role in the access to trade credit. Wholesalers are more likely to provide trade credit and offer less restrictive credit terms to within-community retailers, are more lenient when these retailers default, and are less likely to experience defaults from them. We show that this cooperation between same-community wholesalers and retailers is achieved through a reciprocity mechanism, which provides insurance against income shocks.

Cryptocurrency Shocks
SSRN
In this paper, we use a bivariate structural VAR to investigate risk spillovers from the cryptocurrency market to standard financial markets. We investigate the effects of cryptocurrency shocks on key financial markets, including the stock, bond, gold, and foreign exchange markets. The results show that cryptocurrency shocks do not have statistically significant effects on standard financial markets except for the bond market. This is consistent with most of the existing literature that argues that cryptocurrencies are mostly a new and different asset class, not related to standard factors.

Customer Loyalty and the Persistence of Revenues and Earnings
Jin, Hengda,Stubben, Stephen,Ton, Karen
SSRN
We use big data on customer shopping patterns to explain variation in the persistence of a companyâ€™s revenues and earnings. Using GPS location data from customersâ€™ mobile devices that encompasses nearly 2.5 billion visits to over 1 million retail locations belonging to 288 U.S. companies, we find that revenues and earnings are more persistent when customers are more loyal. Specifically, revenues and earnings are more persistent when customers (a) have more regular shopping patterns, (b) are repeat rather than one-time customers, (c) shop during the week rather than on weekends, and (d) spend more time in the store. However, despite the higher persistence of revenues and earnings when customer loyalty is higher, revenue and earnings response coefficients are not higher, which suggests that investors do not immediately and fully incorporate the implications of customer loyalty into prices. We also show that analystsâ€™ forecasts do not fully account for customer loyalty, leading to predictable forecast errors, particularly when companies do not provide guidance. Our results illustrate the value of customer data to firms, investors, and analysts in understanding the conditions under which revenues and earnings are sustainable.

Deep ReLU Network Expression Rates for Option Prices in high-dimensional, exponential L\'evy models
Lukas Gonon,Christoph Schwab
arXiv

We study the expression rates of deep neural networks (DNNs for short) for option prices written on baskets of $d$ risky assets, whose log-returns are modelled by a multivariate L\'evy process with general correlation structure of jumps. We establish sufficient conditions on the characteristic triplet of the L\'evy process $X$ that ensure $\varepsilon$ error of DNN expressed option prices with DNNs of size that grows polynomially with respect to $\mathcal{O}(\varepsilon^{-1})$, and with constants implied in $\mathcal{O}(\cdot)$ which grow polynomially with respect $d$, thereby overcoming the curse of dimensionality and justifying the use of DNNs in financial modelling of large baskets in markets with jumps.

In addition, we exploit parabolic smoothing of Kolmogorov partial integrodifferential equations for certain multivariate L\'evy processes to present alternative architectures of ReLU DNNs that provide $\varepsilon$ expression error in DNN size $\mathcal{O}(|\log(\varepsilon)|^a)$ with exponent $a \sim d$, however, with constants implied in $\mathcal{O}(\cdot)$ growing exponentially with respect to $d$. Under stronger, dimension-uniform non-degeneracy conditions on the L\'evy symbol, we obtain algebraic expression rates of option prices in exponential L\'evy models which are free from the curse of dimensionality. In this case the ReLU DNN expression rates of prices depend on certain sparsity conditions on the characteristic L\'evy triplet. We indicate several consequences and possible extensions of the present results.

Deposit Insurance, Moral Hazard and Bank Risk
Karas, Alexei,Pyle, William,Schoors, Koen J. L.
SSRN
Using evidence from Russia, we explore the effect of the introduction of deposit insurance on bank risk. Drawing on variation in the ratio of firm deposits to total household and firm deposits before the announcement of deposit insurance, so as to capture the magnitude of the decrease in market discipline after the introduction of deposit insurance, we demonstrate that larger declines in market discipline generate larger increases in traditional measures of risk. These results hold in a difference-in-difference setting in which private domestic banks serve as the treatment group and state and foreign-owned banks, whose deposit insurance regime does not change, serve as a control group.

Dissecting Currency Momentum
Zhang, Shaojun
SSRN
This paper shows that the cross-sectional and time series momentum in currencies, which cannot be explained by carry and dollar factors, summarize the autocorrelation of these factors. These momentum strategies long currency factors following positive factor returns and short them following losses. Carry and dollar factors are strongly autocorrelated and only earn significantly positive excess returns following positive factor returns. In contrast, idiosyncratic currency returns contain little momentum. Consequently, factor momentum not only outperforms the cross-sectional and time series momentum but also explains them. Limits to arbitrage and time-varying risk premium help explain carry and dollar momentum, respectively.

Dynamic Resource Allocation with Hidden Volatility
Feng, Felix Zhiyu,Westerfield, Mark M.
SSRN
We study a firm's internal resource allocation using a dynamic principal-agent model with endogenous cash flow volatility. The principal supplies the agent with resources for productive use, but the agent has private control over both project volatility and resource intensity and may misallocate resources to obtain private benefits. The optimal contract can yield either overly risky or overly prudent project selection. It can be implemented with a constant pricing schedule (i.e., a static, decentralized, linear mechanism), giving the agent control over the resource quantities, project risk, and agent's equity share. The implementation rationalizes the use of hurdle rates above a firm's cost of capital and transfer prices above marginal cost, while showing that hurdle rates or transfer prices may not vary with the agentâ€™s risk choice.

Effects of Inflation Accounting on Organizational Decisions and Financial Performance in South African Retail Stores
Olarewaju, Odunayo,Mbambo , Mzwandile Atkins,Ngiba, Brian
SSRN
The inflation accounting technique allows a business to show or have a sensible picture of their gains due to present cost coordinates with present revenues. Thus, the effects of inflation accounting on organizational decisions and financial performance of Kwa-Zulu Natal retail stores were evaluated in this study. The study used a quantitative research method. A total of 161 completed questionnaires were received from respondents in the selected 20 listed stores in Kwa-Zulu Natal. Thus, the Exploratory Factor Analysis and linear regressions were employed in this study. The empirical study reveals how inflation accounting significantly impacts organizational decisions and financial performance of the retail business with such coefficients (F (1, 159) = 49.269, p < .0005; F (1, 159) = 28.959, p < .0005). The findings of this study highlighted positive relationships between the variables that were used. Thus, the study recommends that retail stores always consider inflation changes and apply inflation accounting techniques to make adjustments to produce more accurate results in their financial statements. Heated discussions now surround the basis of financial performance measurement via historical cost accounting. This influences their decision making and financial performance positively.

Executive Mobility in the United States, 1920 to 2011
Graham, John R.,Kim, Dawoon,Kim, Hyunseob
SSRN
We examine the evolution of executive mobility from 1920-2011. In the decades leading up to 1999, CEO and CFO mobility increased. Starting in the early-2000s, in contrast, executive mobility declined sharply. We develop a new measure of aggregate executive mobility that nests both turnover and across-firm mobility. We use this measure to document that the benefits of reallocating executives, labor market size, compensation, and general managerial skills help explain executive mobility trends. We also show that following an increase in mobility, firms shift CEO pay towards option grants, consistent with a retention effect of option pay for executives.

Exposing the Revolving Door in Executive Branch Agencies
Emery, Logan P.,Faccio, Mara
SSRN
We develop the first comprehensive mapping of the revolving door phenomenon in the U.S. by examining the work experience in executive branch agencies of 1,910,150 individuals covering top corporate positions in 373,011 unique firms. We document that the phenomenon is prevalent, with one out of every 15 firms, and one out of every three publicly traded firms, having at least one top employee with prior work experience in U.S. executive branch agencies. On average, former regulators are hired in response to or concomitant with increases in regulation as well as concomitant with more aggressive regulator behavior in the form of a higher incidence of fines. Firms headquartered in more corrupt states, firms seemingly more corruption-prone, and established violators receive benefits in the form of a reduction in the incidence of fines after hiring former regulators from fine-imposing agencies. In contrast, we do not observe other firms receiving benefits on average.

Facial Attractiveness and CEO Compensation: Evidence from the Banking Industry
Ahmed, Shaker,Ranta, Mikko,VÃ¤hÃ¤maa, Sami
SSRN
This paper examines the effect of facial attractiveness on the compensation of bank Chief Executive Officers (CEOs). Consistent with the so-called beauty premium hypothesis, we document that good looks pay off for bank CEOs. Specifically, by utilizing machine learning to assess the facial appearance of the CEOs of large U.S. banks, we find that CEO facial attractiveness is positively associated with the annual total compensation and pay-performance sensitivity while being largely unrelated to the annual base salary. The total compensation of above-average looking bank CEOs is almost 17 percent higher than the compensation of CEOs with below-average looks after controlling for various CEO-specific and bank-specific attributes that are known to affect executive compensation. Overall, our empirical findings provide strong evidence for the existence of a beauty premium in the executive labor market.

Finding Value in NFL Draft Picks
Foltice, Bryan,Markus, Justin
SSRN
This paper analyzes the salaries and performance of the 636 NFL draft picks in the first two round from 2006-2015 during their rookie contracts. We employ a simple metric, average cost per AV, which calculates a playerâ€™s salary divided their performance, Approximate Value (AV). Here, we find that 2nd round draft picks are a significantly better value than 1st round picks over the sample period and the cost per AV decreases by approximately $8,000 with each increasing pick. Additionally, we find that the five teams choosing the best value draft picks post a significantly larger winning percentage over the sample period than the five teams with the worst valued draft picks. Finally, we compare the differences of the NFLâ€™s 2001 Collective Bargaining Agreement (CBA) and find that the CBA successfully reduced the cost of AV, but not statistically significantly so. Forecasting Realized Stock-Market Volatility: Do Industry Returns Have Predictive Value? Demirer, Riza,Gupta, Rangan,Pierdzioch, Christian SSRN Yes, they do. Utilizing a machine-learning technique known as random forests to compute forecasts of realized (good and bad) stock market volatility, we show that incorporating the information in lagged industry returns can help improve out-of sample forecasts of aggregate stock market volatility. While the predictive contribution of industry level returns is not constant over time, industrials and materials play a dominant predictive role during the aftermath of the 2008 global financial crisis, highlighting the informational value of real economic activity on stock market volatility dynamics. Finally, we show that incorporating lagged industry returns in aggregate level volatility forecasts benefits forecasters who are particularly concerned about under-predicting market volatility, yielding greater economic benefits for forecasters as the degree of risk aversion increases. Heterogeneity in Retail Investors: Evidence from Comprehensive Account-Level Trading and Holdings Data Jones, Charles M.,Shi, Donghui,Zhang, Xiaoyan,Zhang, Xinran SSRN Retail investors are heterogeneous, with vast differences in wealth, skills and demographics. Using comprehensive proprietary account-level data on trading and holdings from the Shanghai Stock Exchange from 2016 to 2019, we separate tens of millions of retail investors into five groups by their account sizes as well as other demographic variables, and we examine their trading behavior and return performance in Chinese equities. Retail investors with account sizes less than three million CNY follow momentum trading strategies, and the prices of stocks they buy experience negative returns next day, while the ones they sell experience positive returns. In contrast, retail investors with larger account balances follow contrarian strategies, and they buy and sell stocks in directions consistent with future price movements. In addition, retail investors with smaller account sizes fail to process public news and they incur losses from trading, while retail investors with larger account sizes incorporate public news in their trading and experience trading gains. These patterns are stronger for young male retail investors. Investors Embrace Gender Diversity, Not Female CEOs: The Role of Gender in Startup Fundraising Christopher Cassion,Yuhang Qian,Constant Bossou,Margareta Ackerman arXiv The allocation of venture capital is one of the primary factors determining who takes products to market, which startups succeed or fail, and as such who gets to participate in the shaping of our collective economy. While gender diversity contributes to startup success, most funding is allocated to male-only entrepreneurial teams. In the wake of COVID-19, 2020 is seeing a notable decline in funding to female and mixed-gender teams, giving raise to an urgent need to study and correct the longstanding gender bias in startup funding allocation. We conduct an in-depth data analysis of over 48,000 companies on Crunchbase, comparing funding allocation based on the gender composition of founding teams. Detailed findings across diverse industries and geographies are presented. Further, we construct machine learning models to predict whether startups will reach an equity round, revealing the surprising finding that the CEO's gender is the primary determining factor for attaining funding. Policy implications for this pressing issue are discussed. L\'evy-Ito Models in Finance George Bouzianis,Lane P. Hughston,Sebastian Jaimungal,Leandro Sánchez-Betancourt arXiv We present an overview of the broad class of financial models in which the prices of assets are L\'evy-Ito processes driven by an$n$-dimensional Brownian motion and an independent Poisson random measure. The Poisson random measure is associated with an$n\$-dimensional L\'evy process. Each model consists of a pricing kernel, a money market account, and one or more risky assets. We show how the excess rate of return above the interest rate can be calculated for risky assets in such models, thus showing the relationship between risk and return when asset prices have jumps. The framework is applied to a variety of asset classes, allowing one to construct new models as well as interesting generalizations of familiar models.

Market Price of Risk Estimation: Does Distribution Matter?
Theodossiou, Panayiotis,Savva, Christos S.
SSRN
This study re-examines the risk-return relation using a contemporaneous asset pricing model under various probability distribution functions that account for skewness and kurtosis effects in the data. Once these effects are taken into account a positive risk premium is established, suggesting that a failure to account for the effects of higher moments on the risk-return trade-off is the main reason for the mixed results documented in the literature. The best estimates are given by the skew generalized t distribution.

Moment-Matching Approximations for Stochastic Sums in Non-Gaussian Ornsteinâ€"Uhlenbeck Models
Brignone, Riccardo,Kyriakou, Ioannis,Fusai, Gianluca
SSRN
In this paper, we recall actuarial and financial applications of sums of dependent random variables that follow a non-Gaussian mean-reverting process and contemplate distribution approximations. Our work complements previous related studies restricted to lognormal random variables; we revisit previous approximations and suggest new ones. We then derive moment-based distribution approximations for random sums attuned to Asian option pricing and computationof risk measures of random annuities. Various numerical experiments highlight the speed-accuracy benefits of the proposed methods.

Product Sales Incentive Spillovers to the Lending Market
Jansen, Mark,Nguyen, Hieu,Pierce, Lamar,Snyder, Jason
SSRN
We examine how deadline-based convex incentives in physical product markets can affect the credit markets that finance these products. Auto dealerships respond to monthly sales targets in manufacturer incentive programs by shifting borrowers from used to new cars at the end of the month. End-of-month loans default more often, particularly among financially constrained buyers of new cars. At month-end, dealerships sway financially unsophisticated buyers to buy new cars instead of more reliable models that are available as used vehicles. We find no evidence that lenders or dealerships are hurt by this increased default risk from manufacturers' incentives.

Quantum Computing for Finance: State of the Art and Future Prospects
Daniel J. Egger,Claudio Gambella,Jakub Marecek,Scott McFaddin,Martin Mevissen,Rudy Raymond,Andrea Simonetto,Stefan Woerner,Elena Yndurain
arXiv

This article outlines our point of view regarding the applicability, state-of-the-art, and potential of quantum computing for problems in finance. We provide an introduction to quantum computing as well as a survey on problem classes in finance that are computationally challenging classically and for which quantum computing algorithms are promising. In the main part, we describe in detail quantum algorithms for specific applications arising in financial services, such as those involving simulation, optimization, and machine learning problems. In addition, we include demonstrations of quantum algorithms on IBM Quantum back-ends and discuss the potential benefits of quantum algorithms for problems in financial services. We conclude with a summary of technical challenges and future prospects.

Risk Return Analysis of Equity Stocks: A Study of Selected Indian IT Companies
Bantwa, Ashok,Ansari, Faizan Ulhaqq
SSRN
The present study is an attempt to diagnose the risk return profile of equity stocks of selected Indian IT companies listed on IT Index of NSE. The risk return profile of selected IT companies has been examined on various parameters including the absolute return, abnormal return, required rate of return as per CAPM model, volatility of return, systematic risk and risk adjusted return. Authors found that Tata Elxsi, Infibeam Avenues and NIIT technologies have offered highest rate of return. The returns of Infibeam avenues, Tata Elxsi and Mindtree are highly volatile. The risk adjusted return is highest in case of Mindtree and Tech Mahindra. Stocks prices of Infosys, HCL technologies and Tech Mahindra are highly sensitive to market movement indicating the highest degree of systematic risk. Except HCL Technologies, Wipro and Oracle Fin Serv, remaining seven IT companies have offered higher rate of return than the minimum required rate of return.

Scenario Forecast of Cross-border Electric Interconnection towards Renewables in South America
Wenhao Wang,Jing Meng,Duan Chen,Wei Cong
arXiv

Cross-border Electric Interconnection towards renewables is a promising solution for electric sector under the UN 2030 sustainable development goals which is widely promoted in emerging economies. This paper comprehensively investigates state of art in renewable resources and cross-border electric interconnection in South America. Based on the raw data collected from typical countries, a long-term scenario forecast methodology is applied to estimate key indicators of electric sector in target years, comparing the prospects of active promoting cross-border Interconnections Towards Renewables (ITR) scenario with Business as Usual (BAU) scenario in South America region. Key indicators including peak load, installed capacity, investment, and generation cost are forecasted and comparative analyzed by year 2035 and 2050. The comparative data analysis shows that by promoting cross-border interconnection towards renewables in South America, renewable resources can be highly utilized for energy supply, energy matrix can be optimized balanced, economics can be obviously driven and generation cost can be greatly reduced.

Share Repurchases, Undervaluation, and Corporate Social Responsibility
Bobenhausen, Nils,Knetsch, Andreas,Salzmann, Astrid Juliane
SSRN
Share repurchases have experienced growing popularity in recent years. The wealth transfer between shareholders associated with share repurchases has however been widely neglected in the literature, yet. Since managers are free to time repurchases so that ongoing shareholders profit at the expense of selling shareholders or vice versa, we investigate how the wealth transfer from share repurchases relates to a firmâ€™s priorities regarding its stakeholder orientation. Based on the idea that firms with higher corporate social responsibility (CSR) performance are less shareholder oriented, we posit that their managers are less inclined to take ad-vantage of the wealth transfer from selling to ongoing shareholders, which occurs if the firm is undervalued. Consistent with this notion, our results show that firms with higher CSR performance announce their repurchases in periods of ceteris paribus lower undervaluation. This result also shows that managers are aware of the exploitation of selling shareholders occurring with most buybacks.

The Aggregate-Demand Doom Loop: Precautionary Motives and the Welfare Costs of Sovereign Risk
RoldÃ¡n, Francisco
SSRN
Sovereign debt crises coincide with deep recessions. I propose a model of sovereign debt that rationalizes large contractions in economic activity via an aggregate-demand amplification mechanism. The mechanism also sheds new light on the response of consumption to sovereign risk, which I document in the context of the Eurozone crisis. By explicitly separating the decisions of households and the government, I examine the interaction between sovereign risk and precautionary savings. When a default is likely, households anticipate its negative consequences and cut consumption for self-insurance reasons. Such shortages in aggregate spending worsen economic conditions through nominal wage rigidities and boost default incentives, restarting the vicious cycle. I calibrate the model to Spain in the 2000s and find that about half of the output contraction is caused by default risk. More generally, sovereign risk exacerbates volatility in consumption over time and across agents, creating large and unequal welfare costs even if default does not materialize.

Trading Volume Shares and Market Quality in a Zero Commission World
Jain, Pankaj K.,Mishra, Suchi,O'Donoghue, Shawn,Zhao, Le
SSRN