Research articles for the 2021-01-11

A $C^{0,1}$-functional It\^o's formula and its applications in mathematical finance
Bruno Bouchard,Grégoire Loeper,Xiaolu Tan

Using Dupire's notion of vertical derivative, we provide a functional (path-dependent) extension of the It\^o's formula of Gozzi and Russo (2006) that applies to C^{0,1}-functions of continuous weak Dirichlet processes. It is motivated and illustrated by its applications to the hedging or superhedging problems of path-dependent options in mathematical finance, in particular in the case of model uncertainty

A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules
Mehran Taghian,Ahmad Asadi,Reza Safabakhsh

A wide variety of deep reinforcement learning (DRL) models have recently been proposed to learn profitable investment strategies. The rules learned by these models outperform the previous strategies specially in high frequency trading environments. However, it is shown that the quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by these models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved very effective in other popular tasks like neural machine translation and video captioning in which the models face a similar problem. The encoder-decoder framework extracts highly informative features from a long sequence of prices along with learning how to generate outputs based on the extracted features. In this paper, a novel end-to-end model based on the neural encoder-decoder framework combined with DRL is proposed to learn single instrument trading strategies from a long sequence of raw prices of the instrument. The proposed model consists of an encoder which is a neural structure responsible for learning informative features from the input sequence, and a decoder which is a DRL model responsible for learning profitable strategies based on the features extracted by the encoder. The parameters of the encoder and the decoder structures are learned jointly, which enables the encoder to extract features fitted to the task of the decoder DRL. In addition, the effects of different structures for the encoder and various forms of the input sequences on the performance of the learned strategies are investigated. Experimental results showed that the proposed model outperforms other state-of-the-art models in highly dynamic environments.

AdaVol: An Adaptive Recursive Volatility Prediction Method
Nicklas Werge,Olivier Wintenberger

Quasi-Maximum Likelihood (QML) procedures are theoretically appealing and widely used for statistical inference. While there are extensive references on QML estimation in batch settings, it has attracted little attention in streaming settings until recently. An investigation of the convergence properties of the QML procedure in a general conditionally heteroscedastic time series model is conducted, and the classical batch optimization routines extended to the framework of streaming and large-scale problems. An adaptive recursive estimation routine for GARCH models named AdaVol is presented. The AdaVol procedure relies on stochastic approximations combined with the technique of Variance Targeting Estimation (VTE). This recursive method has computationally efficient properties, while VTE alleviates some convergence difficulties encountered by the usual QML estimation due to a lack of convexity. Empirical results demonstrate a favorable trade-off between AdaVol's stability and the ability to adapt to time-varying estimates for real-life data.

Analyzing the response to TV serials retelecast during COVID19 lockdown in India
Sandeep Ranjan

TV serials are a popular source of entertainment. The ongoing COVID19 lockdown has a high probability of degrading the publics mental health. The Government of India started the retelecast of yesteryears popular TV serials on public broadcaster Doordarshan from 28th March 2020 to 31st July 2020. Tweets corresponding to the Doordarshan hashtag were mined to create a dataset. The experiment aims to analyze the publics response to the retelecast of TV serials by calculating the sentiment score of the tweet dataset. Datasets mean sentiment score of 0.65 and high share 64.58% of positive tweets signifies the acceptance of Doordarshans retelecast decision. The sentiment analysis result also reflects the positive state of mind of the public.

Are Managers Susceptible to the Ostrich Effect?
Bernard, Darren,Cade, Nicole L.,Connors, Elizabeth
Using data from an information provider in the cannabis industry, we observe that managers of retail dispensaries appear to suffer from the “ostrich effect”â€"the selective acquisition of news based on an expectation of the likely hedonic response (e.g., avoiding bad news to avoid psychological discomfort). Managers are more likely to acquire store and product performance information as its expected valence (i.e., its “goodness” versus “badness”) increases and revisit this information more as its actual valence increases. These relations are attenuated when managers can more easily attribute the performance to external factors, suggesting managers intuitively acquire good news they can take credit for and avoid bad news they must internalize. Managers’ information acquisition decisions also appear to have real effectsâ€"future product stock-outs are greater when managers avoid the information. Our results suggest that hedonic effects of information influence key information acquisition choices of managers.

Asset Prices When Investors Ignore Discount Rate Dynamics
Wang, Renxuan
Commonly used valuation methods assume the discount rate will remain constant, yet studies have demonstrated that discount rates are dynamic. In this paper, I propose and test a “Constant Discount Rate” hypothesis: some investors ignore the volatility of discount rates when forming return expectations. In theory, such a heuristic should cause investors to develop positive biases in their expectations of returns; and their biases should be stronger for stocks with higher potential cash flow growth and/or uncertainty, which leads them to buy more of these stocks. At the same time, risk-averse arbitrageurs know these stocks are more sensitive to aggregate discount rate shocks, so mispricing persists. To test this hypothesis, I measure mispricing at the firm level and find that overvalued stocks exhibit large and long-lasting negative abnormal returns, even among stocks in the S&P 500. A tradable mispricing factor explains the CAPM alphas of 12 leading anomalies (9 out of the 11 in Stambaugh and Yuan (2017)) including investment, profitability, beta, idiosyncratic volatility and cash flow duration. The empirical relationship between cross-section of fundamental characteristics and investors' subjective beliefs are also consistent with the hypothesis.

Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noise
Chirag Nagpal,Robert E. Tillman,Prashant Reddy,Manuela Veloso

We consider the problem of aggregating predictions or measurements from a set of human forecasters, models, sensors or other instruments which may be subject to bias or miscalibration and random heteroscedastic noise. We propose a Bayesian consensus estimator that adjusts for miscalibration and noise and show that this estimator is unbiased and asymptotically more efficient than naive alternatives. We further propose a Hierarchical Bayesian Model that leverages our proposed estimator and apply it to two real world forecasting challenges that require consensus estimates from error prone individual estimates: forecasting influenza like illness (ILI) weekly percentages and forecasting annual earnings of public companies. We demonstrate that our approach is effective at mitigating bias and error and results in more accurate forecasts than existing consensus models.

Challenging Practical Features of Bitcoin by the Main Altcoins
Andrew Spurr,Marcel Ausloos

We study the fundamental differences that separate: Litecoin; Bitcoin Gold; Bitcoin Cash; Ethereum; and Zcash from Bitcoin, and draw analysis to how these features are appreciated by the market, to ultimately make an inference as to how future successful cryptocurrencies may behave. We use Google Trend data, as well as price, volume and market capitalization data sourced from to support this analysis. We find that Litecoin's shorter block times offer benefits in commerce, but drawbacks in the mining process through orphaned blocks. Zcash holds a niche use for anonymous transactions, benefitting areas of the world lacking in economic freedom. Bitcoin Cash suffers from centralization in the mining process, while the greater decentralization of Bitcoin Gold has generally left it to stagnate. Ether's greater functionality offers the greatest threat to Bitcoin's dominance in the market. A coin that incorporates several of these features can be technically better than Bitcoin, but the first-to-marketadvantage of Bitcoin should keep its dominant position in the market.

Closed form optimal exercise boundary of the American put option
Yerkin Kitapbayev

We present three models of stock price with time-dependent interest rate, dividend yield, and volatility, respectively, that allow for explicit forms of the optimal exercise boundary of the finite maturity American put option. The optimal exercise boundary satisfies the nonlinear integral equation of Volterra type. We choose time-dependent parameters of the model so that the integral equation for the exercise boundary can be solved in the closed form. We also define the contracts of put type with time-dependent strike price that support the explicit optimal exercise boundary.

Comparison of the effects of investor attention using search volume data before and after mobile device popularization
Jonghyeon Min

In this study, we will study investor attention measurement using the Search Volume Index in the recent market. Since 2009, the popularity of mobile devices and the spread of the Internet have made the speed of information delivery faster and the investment information retrieval data for obtaining investment information has increased dramatically. In these circumstances, investor attention measurement using search volume data can be measured more accurately and faster than before mobile device popularization. To confirm this, we will compare the effect of measuring investor attention using search volume data before and after mobile device popularization. In addition, it is confirmed that the measured investor attention is that of retail traders, not institutional traders or professional traders, and the relationship between investor attention and short-term price pressure theory. Using SVI data provided by Google Trends, we will experiment with Russell 3000 stocks and IPO stocks and compare the results. In addition, the results of investigating the investor's interest using the search volume data from various angles through experiments such as the comparison of the results based on the inclusion of the noise ticker group, the comparison of the limitations of the existing investor attention measurement method, and the comparison of explanatory variables with existing IPO related studies. We would like to verify its practicality and significance.

Containment efficiency and control strategies for the Corona pandemic costs
Claudius Gros,Roser Valenti,Lukas Schneider,Kilian Valenti,Daniel Gros

The rapid spread of the Coronavirus (COVID-19) confronts policy makers with the problem of measuring the effectiveness of containment strategies, balancing public health considerations with the economic costs of social distancing measures. We introduce a modified epidemic model that we name the controlled-SIR model, in which the disease reproduction rate evolves dynamically in response to political and societal reactions. An analytic solution is presented. The model reproduces official COVID-19 cases counts of a large number of regions and countries that surpassed the first peak of the outbreak. A single unbiased feedback parameter is extracted from field data and used to formulate an index that measures the efficiency of containment strategies (the CEI index). CEI values for a range of countries are given. For two variants of the controlled-SIR model, detailed estimates of the total medical and socio-economic costs are evaluated over the entire course of the epidemic. Costs comprise medical care cost, the economic cost of social distancing, as well as the economic value of lives saved. Under plausible parameters, strict measures fare better than a hands-off policy. Strategies based on current case numbers lead to substantially higher total costs than strategies based on the overall history of the epidemic.

Credit Cards and the Floating Rate Channel of Monetary Policy
Grodzicki, Daniel
I quantify the impact of Federal Funds Rate (FFR) movements on consumers' welfare via the floating, or variable, rate on their credit cards. I first newly document that 96% of card rates adjust to the FFR within 3 months of a change in the latter. Exploiting these rate changes, I construct a model of card use and estimate it using a large national database of U.S. card accounts. The model estimates imply that a hypothetical 25 bp rise in the FFR lowers annual consumers' surplus by 0.24% of personal consumption expenditures ($33.4 billion), and disproportionately more so in lower income areas.

Deep Learning-Based Exchange Rate Prediction during the COVID-19
Akhtaruzzaman, Md,Hasan Moon, Mahmudul,Hammami, Helmi,Abedin, Mohammad Zoynul
We propose two ensemble deep learning approaches: Bagging Ridge regression (BR) and Bi-LSTM Bagging Ridge (Bi-LSTM BR) to predict the exchange rates of 21 currencies against USD during COVID-19 and non-COVID-19 periods. We also apply machine learning algorithms, such as Decision Tree (DT), Support Vector Regression (SVR), Random Forest Regression (RFR), and deep learning algorithms, such as Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (Bi-LSTM) for the prediction. Our proposed ensemble deep learning approaches perform well to obtain accuracy in forecasting exchange rates. However, the performance of algorithms varies during COVID-19 and non-COVID-19 periods across currencies. Our study is beneficial for foreign exchange traers in forecast performance and potential trading profitability.

Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies
Sophia Gu

With the recent advancement in Deep Reinforcement Learning in the gaming industry, we are curious if the same technology would work as well for common quantitative financial problems. In this paper, we will investigate if an off-the-shelf library developed by OpenAI can be easily adapted to mean reversion strategy. Moreover, we will design and test to see if we can get better performance by narrowing the function space that the agent needs to search for.We achieve this through augmenting the reward function by a carefully picked penalty term.

Deep reinforcement learning for portfolio management based on the empirical study of chinese stock market
Gang Huang,Xiaohua Zhou,Qingyang Song

The objective of this paper is to verify that current cutting-edge artificial intelligence technology, deep reinforcement learning, can be applied to portfolio management. We improve on the existing Deep Reinforcement Learning Portfolio model and make many innovations. Unlike many previous studies on discrete trading signals in portfolio management, we make the agent to short in a continuous action space, design an arbitrage mechanism based on Arbitrage Pricing Theory,and redesign the activation function for acquiring action vectors, in addition, we redesign neural networks for reinforcement learning with reference to deep neural networks that process image data. In experiments, we use our model in several randomly selected portfolios which include CSI300 that represents the market's rate of return and the randomly selected constituents of CSI500. The experimental results show that no matter what stocks we select in our portfolios, we can almost get a higher return than the market itself. That is to say, we can defeat market by using deep reinforcement learning.

Double-Robust Identification for Causal Panel Data Models
Dmitry Arkhangelsky,Guido W. Imbens

We study identification and estimation of causal effects in settings with panel data. Traditionally researchers follow model-based identification strategies relying on assumptions governing the relation between the potential outcomes and the unobserved confounders. We focus on a novel, complementary, approach to identification where assumptions are made about the relation between the treatment assignment and the unobserved confounders. We introduce different sets of assumptions that follow the two paths to identification, and develop a double robust approach. We propose estimation methods that build on these identification strategies.

Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19
Nick James

This paper uses new and recently introduced methodologies to study the similarity in the dynamics and behaviours of cryptocurrencies and equities surrounding the COVID-19 pandemic. We study two collections; 45 cryptocurrencies and 72 equities, both independently and in conjunction. First, we examine the evolution of cryptocurrency and equity market dynamics, with a particular focus on their change during the COVID-19 pandemic. We demonstrate markedly more similar dynamics during times of crisis. Next, we apply recently introduced methods to contrast trajectories, erratic behaviours, and extreme values among the two multivariate time series. Finally, we introduce a new framework for determining the persistence of market anomalies over time. Surprisingly, we find that although cryptocurrencies exhibit stronger collective dynamics and correlation in all market conditions, equities behave more similarly in their trajectories, extremes, and show greater persistence in anomalies over time.

Early-life Income Shocks and Old-Age Cause-Specific Mortality
Hamid NoghaniBehambari,Farzaneh Noghani,Nahid Tavassoli

This paper investigates the causal relationship between income shocks during the first years of life and adulthood mortality due to specific causes of death. Using all death records in the United States during 1968-2004 for individuals who were born in the first half of the 20th century, we document a sizable and statistically significant association between income shocks early in life, proxied by GDP per capita fluctuations, and old age cause-specific mortality. Conditional on individual characteristics and controlling for a broad array of current and early-life conditions, we find that a 1 percent decrease in the aggregate business cycle in the year of birth is associated with 2.2, 2.3, 3.1, 3.7, 0.9, and 2.1 percent increase in the likelihood of mortality in old ages due to malignant neoplasms, Diabetes Mellitus, cardiovascular diseases, Influenza, chronic respiratory diseases, and all other diseases, respectively.

Enhancing Stock Market Anomalies with Machine Learning
Azevedo, Vitor,Hoegner, Christopher
We examine the predictability of 299 capital market anomalies enhanced by 30 machine learning approaches and over 250 models in a dataset with more than 500 million firm-month-anomaly observations. We find significant monthly (out-of-sample) returns of around 1.8-2.0%, and over 80% of the models yield returns equal or larger than our linearly constructed baseline factor. The risk-adjusted returns are significant across alternative asset pricing models, considering transaction costs with round-trip costs of up to 2% and including only anomalies after publication. Our results indicate that non-linear models can reveal market inefficiencies (mispricing) that are hard to conciliate with risk-based explanations.

Explicit solution simulation method for the 3/2 model
Iro René Kouarfate,Michael A. Kouritzin,Anne MacKay

An explicit weak solution for the 3/2 stochastic volatility model is obtained and used to develop a simulation algorithm for option pricing purposes. The 3/2 model is a non-affine stochastic volatility model whose variance process is the inverse of a CIR process. This property is exploited here to obtain an explicit weak solution, similarly to Kouritzin (2018). A simulation algorithm based on this solution is proposed and tested via numerical examples. The performance of the resulting pricing algorithm is comparable to that of other popular simulation algorithms.

Exploring the association between R&D expenditure and the job quality in the European Union
Fernando Almeida,Nelson Amoedo

Investment in research and development is a key factor in increasing countries' competitiveness. However, its impact can potentially be broader and include other socially relevant elements like job quality. In effect, the quantity of generated jobs is an incomplete indicator since it does not allow to conclude on the quality of the job generated. In this sense, this paper intends to explore the relevance of R&D investments for the job quality in the European Union between 2009 and 2018. For this purpose, we investigate the effects of R&D expenditures made by the business sector, government, and higher education sector on three dimensions of job quality. Three research methods are employed, i.e. univariate linear analysis, multiple linear analysis, and cluster analysis. The findings only confirm the association between R&D expenditure and the number of hours worked, such that the European Union countries with the highest R&D expenses are those with the lowest average weekly working hours.

Functional False Discovery Rate in Mutual Fund Performance
Ma, Tren,Kyriakou, Ioannis,Sermpinis, Georgios
We introduce a novel multiple hypothesis testing framework for selecting outperforming mutual funds with control of luck called the functional False Discovery Rate "plus". We show that our method, which incorporates informative covariates to control for the false discovery rate, gains considerable power over the False Discovery Rate "plus" of Barras, Scaillet and Wermers. We experiment the method with five covariates that commonly affect the mutual funds' performance by constructing portfolios that generate positive alphas and consistently beat portfolios based on sorting on covariates or the False Discovery Rate "plus". Our results confirm the informative power of the five covariates and we demonstrate, for the first time in the literature, their economic value in mutual funds' selection after controlling of luck. Finally, by applying a set of linear combinations and shrinkage regressions, we achieve superior trading performance.

Going Green by Putting a Price on Pollution: Firm-level Evidence from the EU
De Jonghe, Olivier,Mulier, Klaas,Schepens, Glenn
This paper shows that, when the price of emission allowances is sufficiently high, emission trading schemes improve the emission efficiency of highly polluting firms. The efficiency gain comes from a relative decrease in emissions rather than a relative increase in operating revenue. Part of the improvement is realized via the acquisition of green firms. The size of the improvement depends on the initial allocation of free emission allowances: highly polluting firms receiving more emission allowances for free, such as firms on the carbon leakage list, have a weaker incentive to become more efficient. For identification, we exploit the tightening in EU ETS regulation in 2017, which led to a steep price increase of emission allowances and made the ETS regulation more binding for polluting firms.

Lazy Momentum with Growth-Trend timing: Resilient Asset Allocation (RAA)
Keller, Wouter J.
Resilient Asset Allocation (RAA) is a more aggressive version of our Lethargic Asset Allocation (LAA) strategy. It combines a more robust “All Weather” portfolio with lazy growth-trend (GT) timing, canary crash-protection and breadth momentum. GT timing goes risk-off only when both the US unemployment (UE) and the US capital markets are bearish. To arrive at RAA, we adapt LAA in three steps. First, the (risky, near-static) portfolio is changed to an even more robust and more diversified “all-weather” portfolio, now with five (instead of four) equal weighted assets and with only bonds as risk-off assets (“cash”). Second, the “canary” technology from our DAA paper is used for determining the market trend with a faster filter. Third, we change the unemployment trend filter to a slower one, where we simply compare the recent unemployment level with that of one year ago. As a result, RAA is more aggressive and more robust than LAA, while at the same time nearly as “lazy” with respect to trading and turnover (on average one trading month per year).

Mean-Variance Investment and Risk Control Strategies -- A Time-Consistent Approach via A Forward Auxiliary Process
Yang Shen,Bin Zou

We consider an optimal investment and risk control problem for an insurer under the mean-variance (MV) criterion. By introducing a deterministic auxiliary process defined forward in time, we formulate an alternative time-consistent problem related to the original MV problem, and obtain the optimal strategy and the value function to the new problem in closed-form. We compare our formulation and optimal strategy to those under the precommitment and game-theoretic framework. Numerical studies show that, when the financial market is negatively correlated with the risk process, optimal investment may involve short selling the risky asset and, if that happens, a less risk averse insurer short sells more risky asset.

Mechanistic Framework of Global Value Chains
Sourish Dutta

Indeed, the global production (as a system of creating values) is eventually forming like a gigantic and complex network/web of value chains that explains the transitional structures of global trade and development of the global economy. It's truly a new wave of globalisation, and we term it as the global value chains (GVCs), creating the nexus among firms, workers and consumers around the globe. The emergence of this new scenario asks: how an economy's firms, producers and workers connect in the global economy. And how are they capturing the gains out of it in terms of different dimensions of economic development? This GVC approach is very crucial for understanding the organisation of the global industries and firms. It requires the statics and dynamics of diverse players involved in this complex global production network. Its broad notion deals with different global issues (including regional value chains also) from the top down to the bottom up, founding a scope for policy analysis (Gereffi & Fernandez-Stark 2011). But it is true that, as Feenstra (1998) points out, any single computational framework is not sufficient to quantification this whole range of economic activities. We should adopt an integrative framework for accurate projection of this dynamic multidimensional phenomenon.

Nutritional deficiency and infants health outcomes
Hossein Shahri

Previous studies show that prenatal shocks to embryos could have adverse impacts on health endowment at birth. Using the universe of birth data and a difference-in-difference-in-difference strategy, I find that exposure to Ramadan during prenatal development has negative birth outcomes. Exposure to a full month of fasting is associated with 96 grams lower birth-weight. These results are robust across specifications and do not appear to be driven by mothers selective fertility.

On the RND under Heston's stochastic volatility model
Ben Boukai

We consider Heston's (1993) stochastic volatility model for valuation of European options to which (semi) closed form solutions are available and are given in terms of characteristic functions. We prove that the class of scale-parameter distributions with mean being the forward spot price satisfies Heston's solution. Thus, we show that any member of this class could be used for the direct risk-neutral valuation of the option price under Heston's SV model. In fact, we also show that any RND with mean being the forward spot price that satisfies Hestons' option valuation solution, must be a member of a scale-family of distributions in that mean. As particular examples, we show that one-parameter versions of the {\it Log-Normal, Inverse-Gaussian, Gamma, Weibull} and the {\it Inverse-Weibull} distributions are all members of this class and thus provide explicit risk-neutral densities (RND) for Heston's pricing model. We demonstrate, via exact calculations and Monte-Carlo simulations, the applicability and suitability of these explicit RNDs using already published Index data with a calibrated Heston model (S\&P500, Bakshi, Cao and Chen (1997), and ODAX, Mr\'azek and Posp\'i\v{s}il (2017)), as well as current option market data (AMD).

Political Sentiment and Syndicated Loan Borrowing Costs of Multinational Enterprises
Karavitis, Panagiotis,Kazakis, Pantelis
International business literature widely recognizes that political forces play a crucial role in modern corporations. Yet, rare are the studies of how foreign operations mitigate the detrimental effect that firm-level political exposure has on the cost of lending. We study such channels in a sample of U.S. corporations with foreign subsidiaries in 69 countries. We proxy firm-level political exposure via political sentiment. We show that firms with lower political sentiment (i.e., higher political exposure) have a higher cost of lending. We document that multinational enterprises with a presence in many countries, and those having an extended network of foreign subsidiaries can lower the harmful effects of increased political uncertainty. This outcome also holds in the presence of foreign economies of scale, and when multinational corporations have foreign subsidiaries in countries with higher political polarization.

Pricing the COVID-19 Vaccine: A Mathematical Approach
Susan Martonosi,Banafsheh Behzad,Kayla Cummings

According to the World Health Organization, development of the COVID-19 vaccine is occurring in record time. Administration of the vaccine has started the same year as the declaration of the COVID-19 pandemic. The United Nations emphasized the importance of providing COVID-19 vaccines as "a global public good", which is accessible and affordable world-wide. Pricing the COVID-19 vaccines is a controversial topic. We use optimization and game theoretic approaches to model the COVID-19 U.S. vaccine market as a duopoly with two manufacturers Pfizer-BioNTech and Moderna. The results suggest that even in the context of very high production and distribution costs, the government can negotiate prices with the manufacturers to keep public sector prices as low as possible while meeting demand and ensuring each manufacturer earns a target profit. Furthermore, these prices are consistent with those currently predicted in the media.

Quantum Technology for Economists
Isaiah Hull,Or Sattath,Eleni Diamanti,Göran Wendin

Research on quantum technology spans multiple disciplines: physics, computer science, engineering, and mathematics. The objective of this manuscript is to provide an accessible introduction to this emerging field for economists that is centered around quantum computing and quantum money. We proceed in three steps. First, we discuss basic concepts in quantum computing and quantum communication, assuming knowledge of linear algebra and statistics, but not of computer science or physics. This covers fundamental topics, such as qubits, superposition, entanglement, quantum circuits, oracles, and the no-cloning theorem. Second, we provide an overview of quantum money, an early invention of the quantum communication literature that has recently been partially implemented in an experimental setting. One form of quantum money offers the privacy and anonymity of physical cash, the option to transact without the involvement of a third party, and the efficiency and convenience of a debit card payment. Such features cannot be achieved in combination with any other form of money. Finally, we review all existing quantum speedups that have been identified for algorithms used to solve and estimate economic models. This includes function approximation, linear systems analysis, Monte Carlo simulation, matrix inversion, principal component analysis, linear regression, interpolation, numerical differentiation, and true random number generation. We also discuss the difficulty of achieving quantum speedups and comment on common misconceptions about what is achievable with quantum computing.

Quantum credit loans
Ardenghi Juan Sebastian

Quantum models based on the mathematics of quantum mechanics (QM) have been developed in cognitive sciences, game theory and econophysics. In this work a generalization of credit loans is introduced by using the vector space formalism of QM. Operators for the debt, amortization, interest and periodic installments are defined and its mean values in an arbitrary orthonormal basis of the vectorial space give the corresponding values at each period of the loan. Endowing the vector space of dimension M, where M is the loan duration, with a SO(M) symmetry, it is possible to rotate the eigenbasis to obtain better schedule periodic payments for the borrower, by using the rotation angles of the SO(M) transformation. Given that a rotation preserves the length of the vectors, the total amortization, debt and periodic installments are not changed. For a general description of the formalism introduced, the loan operator relations are given in terms of a generalized Heisenberg algebra, where finite dimensional representations are considered and commutative operators are defined for the specific loan types. The results obtained are an improvement of the usual financial instrument of credit because introduce several degrees of freedom through the rotation angles, which allows to select superposition states of the corresponding commutative operators that enables the borrower to tune the periodic installments in order to obtain better benefits without changing what the lender earns.

Recent Developments in Real Estate Investment Trusts in Spain
Vaquero, Victor G.,Roibas, Irene
Real estate investment trusts in Spain (SOCIMIs by their Spanish abbreviation) are instruments for investing in real estate assets which were regulated for the first time in Spain in 2009. They have grown rapidly in recent years to reach a relative size, approximated by their stock market capitalisation in terms of GDP, which is above the average for this type of companies in the euro area as a whole. In Spain this sector is highly concentrated, since a few, large vehicles exist alongside a sizeable group of small companies. SOCIMIs listed in regulated markets and those listed in alternative markets are notably different in terms of their size, balance sheet composition and ownership structure. The low exposure of Spanish SOCIMIs to the residential real estate segment, although it has risen in recent years, is worth noting, as is the high proportion of their capital owned by non-resident investors.

Relative Arbitrage Opportunities in $N$ Investors and Mean-Field Regimes
Tomoyuki Ichiba,Tianjiao Yang

The relative arbitrage portfolio, formulated in Stochastic Portfolio Theory (SPT), outperforms a benchmark portfolio over a given time-horizon with probability one. This paper analyzes the market behavior and optimal investment strategies to attain relative arbitrage both in the $N$ investors and mean field regimes under some market conditions. An investor competes with a benchmark of market and peer investors, expecting to outperform the benchmark and minimizing the initial capital.

With market price of risk processes depending on the market portfolio and investors, we develop a systematic way to solve multi-agent optimization problem within SPT's framework. The objective can be characterized by the smallest nonnegative continuous solution of a Cauchy problem. By a modification in the structure of the extended mean field game with common noise and its notion of the uniqueness of Nash equilibrium, we show a unique equilibrium in $N$-player games and mean field games with mild conditions on the equity market.

Research on the Competitive Consequences of Common Ownership: A Methodological Critique
Azar, José,Schmalz, Martin C.,Tecu, Isabel
This note argues that the evidence presented in several critiques of Azar, Schmalz, and Tecu’s “airlines” paper does often not back the conclusion these studies draw. Specifically, widely circulated studies claiming that there are no anticompetitive effects of common ownership, or that there is no evidence of it, either do not attempt to refute AST’s findings of anticompetitive effects in the U.S. airlines industry or in fact confirm the evidence by AST and even dispel valid concerns about AST’s methodology. Focusing on Kennedy, O’Brien, Song, and Waehrer (KOSW), we note that their panel regressions using market-share-free indices of common ownership concentration confirm the positive correlation between common ownership concentration and price, which AST showed with a measure containing potentially endogenous market shares. We then examine the alternative empirical methods KOSW propose: (i) their conclusion that estimates from a structural model show no evidence of anticompetitive effects is based on an estimation that discards 90% of the available data and therefore, at best, is only valid for that subsample; (ii) their structural model makes no economic sense because it produces a negative effect of route distance on marginal cost; and (iii) they construct an alternative version of the widely used BlackRock-BGI instrument that is arguably invalid. Even absent these methodological concerns, KOSW’s structural estimates are so noisy that they do not in fact reject the hypothesis that common ownership concentration has a positive effect on prices. A more recent structural paper by Park and Seo has shown these concerns to be well-founded: using a different and larger subsample of AST’s data and more standard estimation methods compared to KOSW, they estimate a positive effect of common ownership on prices, as well as a positive effect of route distance on cost. A lesson for future research â€" and readers of the literature -- is to critically evaluate the conclusions drawn by studies in this field, including those that advertise themselves as providing evidence against the existence of anticompetitive effects of common ownership.

Risk-taking in Impact Investing: The Role of Gender and Experience
Alemany, Luisa,Scarlata, Mariarosa,Zacharakis, Andrew
Relying on gender role congruity theory, this paper investigates the relationship between the gender of the top management team of venture philanthropy (VP) firms and their risk-taking orientation. Our research also assesses if and how experience moderates this relationship. Using a combination of survey data to capture the VP firm’s risk orientation, and biographical data to identify managers’ gender and experience, we find that only gender affects the risk-taking orientation in these firms. Yet, this is in an opposite direction than what theorized, whereby teams with a higher proportion of women have a higher risk-taking profile. This suggests the existence of a gender bind dilemma in VP.

Shareholder Monitoring and Discretionary Disclosure
Nagar, Venky,Schoenfeld, Jordan
Theories of delegated monitoring predict that when public disclosure is costly, monitoring by a large investor leads management to supply more private information to that investor, and less public disclosure to other similarly aligned investors who free-ride off the monitor. We test this prediction in the setting where large shareholders contractually bind management to share private information. We find that after the execution of such contracts, firms improve their performance and reduce their public disclosures. The large shareholders in our setting do not trade on their private information, so information asymmetry among trading shareholders, as proxied for by bid-ask spreads, does not change after the disclosure reductions. Overall, our evidence supports the disclosure prediction of delegated monitoring theories, and is inconsistent with performance, expropriation, and trading-based theories of disclosure.

Subjective Return Expectations
Wang, Renxuan
How do people form return expectations? Existing studies find overwhelming evidence of people extrapolating from past returns, but remain silent on why people extrapolate. I first document that return expectations can in fact be contrarian: sell-side analysts hold strongly volatile and contrarian return expectations and are more contrarian as they become more experienced. Besides, sell-side analysts' aggregate expectations are positively related to those of buy-side analysts but negatively related to those of CFOs, retail investors and price to fundamental ratios. Second, I propose an expectation formation model to explain why people hold heterogeneous expectation dynamics, and show the model also compatible with evidence on return predictability. In the model, different forecasters acknowledge the imperfections of return predictors and minimize their own subjective forecast errors. Since not all parameters in the objective predictive system are identifiable, different forecasters rationally learn from past returns, which contain information about the discount rate; they agree to disagree because of their different prior beliefs about a) what fundamental news means for future returns; b) whether discount rates or future fundamentals are more important for asset prices. Model estimation results reveal that buy-side and sell-side analysts believe positive fundamental news leads to lower future returns, while CFOs, retail investors believe the opposite. However, different forecasters agree on future fundamental growth as the dominant force driving asset prices.

Team Production Theory Across the Waves
Cheffins, Brian R.,Williams, Richard
Team production theory, which Margaret Blair developed in tandem with Lynn Stout, has had a major impact on corporate law scholarship. The team production model, however, has been applied sparingly outside the United States. This paper, given as part of a symposium honoring Margaret Blair’s scholarship, serves as a partial corrective by drawing on team production theory to assess corporate arrangements in the United Kingdom. Even though Blair and Stout are dismissive of “shareholder primacy” and the U.K. is thought of as a “shareholder-friendly” jurisdiction, deploying team production theory sheds light on key corporate law topics such as directors’ duties and the allocation of managerial authority. In particular, the case study offered here shows that board centrality â€" a key element of team production thinking -- features prominently in U.K. corporate governance despite Britain’s shareholder-oriented legal framework. The case study also draws attention to the heretofore neglected role that private ordering can play in the development of team production-friendly governance arrangements.

The 'COVID' Crash of the 2020 U.S. Stock Market
Min Shu,Ruiqiang Song,Wei Zhu

We employed the log-periodic power law singularity (LPPLS) methodology to systematically investigate the 2020 stock market crash in the U.S. equities sectors with different levels of total market capitalizations through four major U.S. stock market indexes, including the Wilshire 5000 Total Market index, the S&P 500 index, the S&P MidCap 400 index, and the Russell 2000 index, representing the stocks overall, the large capitalization stocks, the middle capitalization stocks and the small capitalization stocks, respectively. During the 2020 U.S. stock market crash, all four indexes lost more than a third of their values within five weeks, while both the middle capitalization stocks and the small capitalization stocks have suffered much greater losses than the large capitalization stocks and stocks overall. Our results indicate that the price trajectories of these four stock market indexes prior to the 2020 stock market crash have clearly featured the obvious LPPLS bubble pattern and were indeed in a positive bubble regime. Contrary to the popular belief that the COVID-19 led to the 2020 stock market crash, the 2020 U.S. stock market crash was endogenous, stemming from the increasingly systemic instability of the stock market itself. We also performed the complementary post-mortem analysis of the 2020 U.S. stock market crash. Our analyses indicate that the 2020 U.S. stock market crash originated from a bubble which began to form as early as September 2018; and the bubbles in stocks with different levels of total market capitalizations have significantly different starting time profiles. This study not only sheds new light on the making of the 2020 U.S. stock market crash but also creates a novel pipeline for future real-time crash detection and mechanism dissection of any financial market and/or economic index.

The Averaging Principle for Non-autonomous Slow-fast Stochastic Differential Equations and an Application to a Local Stochastic Volatility Model
Filippo de Feo

In this work we study the averaging principle for non-autonomous slow-fast systems of stochastic differential equations. In particular in the first part we prove the averaging principle assuming the sublinearity, the Lipschitzianity and the Holder's continuity in time of the coefficients, an ergodic hypothesis and an $\mathcal{L}^2$-bound of the fast component. In this setting we prove the weak convergence of the slow component to the solution of the averaged equation. Moreover we provide a suitable dissipativity condition under which the ergodic hypothesis and the $\mathcal{L}^2$-bound of the fast component, which are implicit conditions, are satisfied.

In the second part we propose a financial application of this result: we apply the theory developed to a slow-fast local stochastic volatility model. First we prove the weak convergence of the model to a local volatility one. Then under a risk neutral measure we show that the prices of the derivatives, possibly path-dependent, converge to the ones calculated using the limit model.

The Case Against the Universal Basic Income
Le Dong Hai Nguyen

With the cost of implementation shrinking and the robot-to-workers ratio skyrocketing, the effects of automation on our economy and society are more palpable than ever. Over half of our jobs could be fully executed by machines over the next decade or two, with severe impacts concentrated disproportionately on manufacturing-focused developing countries. In response to the threat of mass displacement of labor due to automation, economists, politicians, and even the business community have come to see Universal Basic Income (UBI) as the panacea. This paper argued against a UBI by addressing its implementation costs and inefficiency in mitigating the impact of automation through quantitative evidence as well as results of failed UBI-comparable programs across the world. The author made a case for the continuation of existing means-tested welfare systems and further investment in education and training schemes for unskilled and low-skilled labor as a more sustainable and effective solution to the automation-induced large-scale displacement of workers.

The ESG - Innovation Disconnect: Evidence from Green Patenting
Cohen, Lauren,Gurun, Umit G.,Nguyen, Quoc
No firm or sector of the global economy is untouched by innovation. In equilibrium, innovators will flock to (and innovation will occur where) the returns to innovative capital are the highest. In this paper, we document a strong empirical pattern in green patent production. Specifically, we find that oil, gas, and energy-producing firms - firms with lower Environmental, Social, and Governance (ESG) scores, and who are often explicitly excluded from ESG funds’ investment universe â€" are key innovators in the United States’ green patent landscape. These energy producers produce more, and significantly higher quality, green innovation. Our findings raise important questions as to whether the current exclusions of many ESG-focused policies â€" along with the increasing incidence of explicit divestiture campaigns - are optimal, or whether reward-based incentives would lead to more efficient innovative outcomes.

The Effect of Mandatory Information Disclosure on Financial Constraints
Cabezon, Felipe
This paper studies the effects of the mandatory implementation of a more informative disclosure regime on firms’ financial constraints and investment policies. I run a difference-in-difference analysis and find that firms moving from a voluntary use of the regime to a mandatory use increase debt issuance and investment in tangible assets, and reduce the level of discussion about difficulties in obtaining debt financing. At the same time, they report higher difficulties obtaining external finance through equity. These findings support the hypothesis that mandatory disclosure provides a commitment device to future disclosure but shuts down the signaling value of voluntary disclosure.

The Impact of the Eurozone Crisis and Its Regulation on the Decrease of European Bank Mergers
Nissioti, Evangelia
The paper shall begin by covering the main incentives of banks to engage in Mergers and Acquisitions. It will shortly present the current European Bank Union situation, with its goals and future agenda. Following that, the legal framework of the supervision of Bank M&As will be covered. Having analyzed the incentives of banks to merge, the past five years of the European Banking Union history and the current trends of the bank mergers, two relevant questions arise. Firstly, it is not clear which shall be considered the appropriate monitoring authority of a European Bank Merger from an efficiency point of view. Secondly, it is crucial to understand the underlying reasons of the failure of the European bank mergers and their potential relation to the regulatory strategies of the monitoring authorities. In order to approach the first research question, it is important to compare the duties, tasks, competencies and toolboxes of both the European Commission and the European Central Bank when it comes to assessing a domestic or cross-border bank merger. For the second question, we shall present an attempt of two banking institutions in Greece to merge and the reasons why the agreement fail through in the end. Other stories of failed or non-executed European bank mergers will be shortly mentioned. As a consequence, we will attempt to infer the general reasons why European bank mergers fail by remaining few and apprehend if this phenomenon it to be attributed to the inefficiency of the Merger Monitoring Authorities.

The Quality of Finance Matters: Bank Efficiency, Stock Market Volatility, and Post-Pandemic Recovery
Dissanayake, Ruchith,Wu, Yanhui
Financial development is an essential catalyst of economic growth. A hitherto unexplored finding is the role of financial institutions efficiency in reducing uncertainty â€" measured using stock market volatility â€" during an economic crisis. The differences in the exogenous component of banking efficiency â€" component defined by legal origins and creditor protections â€" explain the heterogeneity in uncertainty across countries during the Covid-19 crisis. Countries with regulation that restricts banks from conducting insurance activities and increases barriers to initially capitalize are associated with higher uncertainty. In addition, we document that countries with efficient banks are associated with superior post-pandemic growth based on economic forecasts.

The ‘New Normal’ During Normal Times â€" Liquidity Regulation and Conventional Monetary Policy
Kroon, Sînziana,Bonner, Clemens,van Lelyveld, Iman,Wrampelmeyer, Jan
We analyze the impact of a requirement similar to the Basel III Liquidity Coverage Ratio (LCR) on conventional monetary policy implementation. Combining unique data sets of Dutch banks from 2002 to 2005, we find that the introduction of the LCR impacts banks’ behaviour in open market operations. After the introduction of the LCR, banks bid for higher volumes and pay higher interest rates for central bank funds. In line with theory, banks reduce their reliance on overnight and short term unsecured funding. We do not observe a worsening of collateral quality pledged in open market operations. Thus, to correctly anticipate an open market operation’s effect on interest rates, monetary policy requires central banks to consider not only the size of the operation, but also how it impacts banks’ liquidity management and compliance with the LCR.

Tiered Intermediation in Business Groups and Targeted SME Supports
Shi, Yu,Townsend, Robert M.,Zhu, Wu
Using business registry data from China, we show that internal capital markets in business groups can play the role of financial intermediary and propagate corporate shareholders’ credit supply shocks to their subsidiaries. An average of 16.7% local bank credit growth where corporate shareholders are located would increase subsidiaries investment by 1% of their tangible fixed asset value, which accounts for 71% (7%) of the median (average) investment rate among these firms. We argue that equity exchanges is one channel through which corporate shareholders transmit bank credit supply shocks to the subsidiaries and provide evidence to support the channel.

Time-to-Build and Capacity Expansion
Jeon, Haejun
We study a firm's optimal investment timing and capacity decisions in the presence of uncertain time-to-build. Because of the time-to-build, the firm can expand its capacity before or after the initial project is completed and the lags of the follow-up investment can be shorter than those of the initial project due to learning by doing. We derive the optimal investment strategies in each scenario and examine the impact of time-to-build on the investment dynamics. We show that both the initial and the follow-up investment can be made earlier in the presence of time-to-build than they would in the absence of the lags, especially in a volatile market. This is in contrast to the case of a single investment, whose timing is always delayed by the time-to-build. Furthermore, the capacity of the follow-up project can dominate that of the initial one in the presence of time-to-build, whereas the latter always dominates the former in the absence of the lags. The capacity choice of each project, however, is non-monotone with respect to the size of the lags. We can endogenize the degree of learning by doing based on the proportion of capacity in each stage of the investment. Endogenous learning by doing is found to be non-monotone with respect to the size of the initial lags because the learning incurs costs of more investment at the earlier stage.

Upswing in Industrial Activity and Infant Mortality during Late 19th Century US
Nahid Tavassoli,Hamid Noghanibehambari,Farzaneh Noghani,Mostafa Toranji

This paper aims to assess the effects of industrial pollution on infant mortality between the years 1850-1940 using full count decennial censuses. In this period, US economy experienced a tremendous rise in industrial activity with significant variation among different counties in absorbing manufacturing industries. Since manufacturing industries are shown to be the main source of pollution, we use the share of employment at the county level in this industry to proxy for space-time variation in industrial pollution. Since male embryos are more vulnerable to external stressors like pollution during prenatal development, they will face higher likelihood of fetal death. Therefore, we proxy infant mortality with different measures of gender ratio. We show that the upswing in industrial pollution during late nineteenth century and early twentieth century has led to an increase in infant mortality. The results are consistent and robust across different scenarios, measures for our proxies, and aggregation levels. We find that infants and more specifically male infants had paid the price of pollution during upswing in industrial growth at the dawn of the 20th century. Contemporary datasets are used to verify the validity of the proxies. Some policy implications are discussed.

Using the Econometric Models for Identification of Risk Factors for Albanian SMEs (Case study: SMEs of Gjirokastra region)
Lorenc Kociu,Kledian Kodra

Using the econometric models, this paper addresses the ability of Albanian Small and Medium-sized Enterprises (SMEs) to identify the risks they face. To write this paper, we studied SMEs operating in the Gjirokastra region. First, qualitative data gathered through a questionnaire was used. Next, the 5-level Likert scale was used to measure it. Finally, the data was processed through statistical software SPSS version 21, using the binary logistic regression model, which reveals the probability of occurrence of an event when all independent variables are included. Logistic regression is an integral part of a category of statistical models, which are called General Linear Models. Logistic regression is used to analyze problems in which one or more independent variables interfere, which influences the dichotomous dependent variable. In such cases, the latter is seen as the random variable and is dependent on them. To evaluate whether Albanian SMEs can identify risks, we analyzed the factors that SMEs perceive as directly affecting the risks they face. At the end of the paper, we conclude that Albanian SMEs can identify risk

Visualizing the Financial Impact of Presidential Tweets on Stock Markets
Ujwal Kandi,Sasikanth Gujjula,Venkatesh Buddha,V S Bhagavan

As more and more data being created every day, all of it can help take better decisions with data analysis. It is not different from data generated in financial markets. Here we examine the process of how the global economy is affected by the market sentiment influenced by the micro-blogging data (tweets) of American President Donald Trump. The news feed is gathered from The Guardian and Bloomberg from the period between December 2016 and October 2019, which are used to further identify the potential tweets that influenced the markets as measured by changes in equity indices.