Research articles for the 2021-05-03

134 Audit Committee Effectiveness and Company Performance: Evidence from Egypt
Elhawary, Engy
SSRN
The purpose of this paper is to investigate the impact of audit committee characteristics (size, independence, experience, gender diversity, and frequency of meetings) on the company‘s financial performance (ROA and ROE) in Egypt. In 2016, the Egyptian Stock Exchange announced a new listing requirement for the audit committee members‘ characteristics to enhance its effectiveness. Data are gathered from the board of directors (BOD) and annual reports of the EGX 30 index non-financial listed companies in Egypt for the period of 2016â€"2018. Data is analyzed by using panel data cross-section data analysis and correlation analysis. The findings reveal that the audit committee size has a significant relationship with ROA only and committee members‘ experience is significantly related with ROE only. The other characteristics (independence, meetings, and gender diversity) have no impact on ROA and ROE. Such findings contribute to the literature by providing new understandings regarding the audit committee as a key component of corporate governance and its impact on financial performance. It could also guide and improve the boards‘ selection of the audit committee members and gives Egyptian regulators a better understanding of the impact of their latest listing requirements on protecting the shareholders‘ interests and increasing their confidence through having transparent financial statements.

A Post-COVID Recovery is Unlikely to Resemble the Roaring 20s; The Years 1919 and 1999 Serve as More Insightful Comparisons
Higgins, Mark
SSRN
As of May 2, 2021, the end of the COVID-19 pandemic appeared to be approaching. As such, there is now increased speculation with regard to the nature of a post-COVID economic recovery. Several financial writers have referenced the Roaring 20s as a comparable period that may provide useful lessons. However, a deep review of the financial events during this period suggests that a repeat of the Roaring 20s is unlikely. Instead, this paper presents a different potential post-COVID economic scenario by highlighting similarities between our current circumstances and the years 1919 and 1999. Building on the lessons from these two years, the core thesis is that a post-COVID recovery may combine a consumer spending rebound that is reminiscent of 1919 with speculation in the stock market that is reminiscent of 1999. The paper concludes by encouraging caution, noting that neither the spending rebound in 1919 nor the speculative stock market bubble of 1999 lasted long. It is conceivable, therefore, that after the initial, post-pandemic euphoria wanes, a similarly painful economic contraction and market correction could follow. Should this sequence of events occur, the post-COVID recovery would not resemble the Roaring 20s, rendering this comparison of little value.

A Practitioners' Guide to Leverage Through Downturns
Ruben, Joshua
SSRN
There is extensive research addressing questions of optimal capital structure. Studies typically focus on estimating the appropriate level of debt and the factors associated with financial leverage. This paper addresses a slightly different question: how does a company's choice of leverage affect its performance through an economic downturn and ensuing recovery?This is precisely the question many companies were asking in 2019 when this paper was written, over a decade after the last recession. Corporate managers are increasingly trying to incorporate the possibility of a downturn in capital structure decisions today.The author summarizes the findings of his analysis of the relative performance of three large‐cap portfolios constructed on the basis of leverage: high, medium, and low. It focuses on measuring both financial (share price return) and fundamental (growth and margins) performance.During the period 2006‐2012, which aims to encompass a full downturn and recovery cycle, higher leverage had the expected effect of magnifying share price movements in both bull and bear markets. Nevertheless, these swings in share prices appear to have significantly exaggerated what amounted to negligible differences in the fundamental operating metrics that are most commonly associated with valuation.The author's policy prescription: Despite the temptation to reduce leverage to reduce potential share price declines, management teams should not prioritize debt reduction over investment in anticipation of economic downturns. Instead, articulate a clear leverage policy that supports an overall growth strategy and stick with it. The risk of short‐term share price declines pales in comparison to the risk of prolonged subpar growth from underinvestment.

A note on a PDE approach to option pricing under xVA
Falko Baustian,Martin Fencl,Jan Pospíšil,Vladimír Švígler
arXiv

In this paper we study partial differential equations (PDEs) that can be used to model value adjustments. Different value adjustments denoted generally as xVA are nowadays added to the risk-free financial derivative values and the PDE approach allows their easy incorporation. The aim of this paper is to show how to solve the PDE analytically in the Black-Scholes setting to get new semi-closed formulas that we compare to the widely used Monte-Carlo simulations and to the numerical solutions of the PDE. Particular example of collateral taken as the values from the past will be of interest.



Active peer effects in residential photovoltaic adoption: evidence on impact drivers among potential and current adopters in Germany
Fabian Scheller,Sören Graupner,James Edwards,Jann Weinand,Thomas Bruckner
arXiv

While the importance of peer influences has been demonstrated in several studies, little is known about the underlying mechanisms of active peer effects in residential photovoltaic (PV) diffusion. Empirical evidence indicates that the impacts of inter-subjective exchanges are dependent on the subjective mutual evaluation of the interlocutors. This paper aims to quantify, how subjective evaluations of peers affect peer effects across different stages of PV adoption decision-making. The findings of a survey among potential and current adopters in Germany(N=1,165)confirm two hypotheses. First, peer effects play a role in residential PV adoption: the number of peer adopters in the decision-maker's social circle has a positive effect on the decision-maker's belief that their social network supports PV adoption; their ascription of credibility on PV-related topics to their peers; and their interest in actively seeking information from their peers in all decision-making stages. Second, there is a correlation between the perceived positive attributes of a given peer and the reported influence of said peer within the decision-making process, suggesting that decision-makers' subjective evaluations of peers play an important role in active peer effects. Decision-makers are significantly more likely to engage in and be influenced by interactions with peers who they perceive as competent, trustworthy, and likeable. In contrast, attributes such as physical closeness and availability have a less significant effect. From a policymaking perspective, this study suggests that the density and quality of peer connections empower potential adopters. Accordingly, peer consultation and community-led outreach initiatives should be promoted to accelerate residential PV adoption.



Analysis of the Influence of the Price of Raw Oil and Natural Gas on the Prices of Indices and Shares of the Turkish Stock Exchange
Akbulaev, Nurkhodzha,Aliyeva, Basti,Rzayeva, Shehla
SSRN
This article is a review on the impact of prices and their dependence on the cost of oil and natural gas on the world stock markets. The main studies and results achieved in the field of the impact of prices on both the stock index and industrial stocks and the dependence on the level of oil prices are presented. The paper presents an econometric study on the choice of offers on the securities market that allows us to identify the main specifics of changes in prices for the stock index and industrial shares in the daily period from 13. 05. 2012 to 01. 12. 2019. The article uses methods for estimating the impact of the price of natural gas and WTI crude oil using the Gretl statistical program, taking into account the selection of the main correlation features of the price matrix. Of the 13 proposed research models, only one model showed its statistical insignificance. A paired linear model of the CocaCola share price dependence and its dependence on NGFO prices was presented and analyzed in detail. Based on the results of econometric modeling, linear regression models were constructed for the dependence of stock prices on the NGFO and WTISPOT prices. The Gretl environment allows you to evaluate the situation in the econometric environment and make a forecast based on the obtained models of the dependence of stock prices and make appropriate conclusions

Asset Tokenization: A blockchain Solution to Financing Infrastructure in Emerging Markets and Developing Economies
Tian, Yifeng,Adriaens, Peter,Minchin, R. Edward,Chang, Charles,Lu, Zheng,Qi, Chaoying
SSRN
Infrastructure is essential to alleviate poverty and generate long-term growth in emerging markets and developing counties (EMDEs). Nevertheless, financing of infrastructure in EMDEs is faced with pressure on increasing government deficits, issues of transparency, and high financing cost, as well as the lack of performance tracking under the current financial system. This paper explores the potential of tokenization to improve the efficiency of public finance and to mobilize broader private sources to bridge the widening infrastructure gap. Tokenization would elevate the private sector’s confidence and enthusiasm by improving infrastructure asset liquidity, opening access to small-scale projects, and enlarging the group of investors to participate in EMDE infrastructure development, as indicated in this research. From the EMDE governments’ perspective, administrative and financial efficiencies can be improved through automated auditing, enhanced project monitoring, and lower financing costs. Four case studies are presented to illustrate the asset tokenization process and define benefits associated with the emerging technology in the context of public finance and private finance. Regulatory and technical risks at present are identified. Implications for EMDE policymakers and international organizations, such as multilateral development banks, to initiate coordinated efforts to facilitate the widespread adoption of infrastructure asset tokenization in EMDEs are elaborated in the research. Once the potential risks and barriers for broader applications of tokenization are carefully examined and mitigated, the technology offers great potential to contribute to economic development and quality of life in EMDEs.

Cost Efficiency of Applying Trade Finance for Agricultural Supply Chains
Laktionova, Aleksandra,Dobrovolskyi, Oleksandr,Karpova, Tatyana,Zahariev, Andrey
SSRN
Agricultural enterprises are active participants of the trade finance market, especially in terms of the acquisition of plant protection products. Financial agents offer to take advantage of credit or provide services for promissory bill. Choosing the best proposals is connected with clearly understanding of interaction scheme between the participants, the cost services and other parameters of transaction. The aim of the research â€" to construct patterns interaction of the entities and explore the cost effectiveness calculation of alternative transaction. The schemes of interaction between agricultural producer, distributor and guarantor bank, using promissory and exchange bill, is investigated. It is proved that a promissory bill provides additional income, exchange bill increases the number of customers by granting a deferment. The financial scheme for producers without direct banking borrowing is proposed.

Credibility in Second-Price Auctions: An Experimental Test
Ahrash Dianat,Mikhail Freer
arXiv

We provide the first direct test of how the credibility of an auction format affects bidding behavior and final outcomes. To do so, we conduct a series of laboratory experiments where the role of the seller is played by a human subject who receives the revenue from the auction and who (depending on the treatment) has agency to determine the outcome of the auction. We find that a large majority of bids in the non-credible version of the second-price auction lie between the theoretical benchmarks of the first-price auction and the credible second-price auction. While sellers in the non-credible second-price auction often break the rules of the auction and overcharge the winning bidder, they typically do not maximize revenue. We provide a behavioral explanation for our results based on incorrect beliefs (on the part of bidders) and aversion to rule-breaking (on the part of sellers), which is confirmed by revealed preference tests.



Crop Insurance in India: A Review of Pradhan Mantri Fasal Bima Yojana (PMFBY)
Tiwari, Rajesh,Chand, Khem,Anjum, Bimal
SSRN
Farmers in India have been the victim of systemic neglect and live a marginalized life. Crop failure due to natural calamities and unfavourable climatic conditions puts farmers in a challenging situation leading to extreme hopelessness and suicides. This article provides an overview of the crop insurance scheme, Pradhan Mantri Fasal Bima Yojana (PMFBY) launched in India by Mr. Narendra Modi in 2016. PMFBY has poor state support, unviable subsidy model, delayed claim settlement and skewed benefit pattern. A technology-enabled demand-driven approach is recommended. Crop insurance should be delinked from political affiliation. Velocity, variety and verifiability in PMFBY will make crop insurance scheme work better for farmers than insurers, administrators and politicians.

Decision Making under Uncertainty: An Experimental Study in Market Settings
Federico Echenique,Taisuke Imai,Kota Saito
arXiv

We implement nonparametric revealed-preference tests of subjective expected utility theory and its generalizations. We find that a majority of subjects' choices are consistent with the maximization of some utility function. They respond to price changes in the direction subjective expected utility theory predicts, but not to a degree that makes them consistent with the theory. Maxmin expected utility a dds no explanatory power. The degree of deviations from the theory is uncorrelated with demographic characteristics. Our findings are essentially the same in laboratory data with a student population and in a panel survey with a general sample of the U.S. population.



Detecting bid-rigging coalitions in different countries and auction formats
David Imhof,Hannes Wallimann
arXiv

We propose an original application of screening methods using machine learning to detect collusive groups of firms in procurement auctions. As a methodical innovation, we calculate coalition-based screens by forming coalitions of bidders in tenders to flag bid-rigging cartels. Using Swiss, Japanese and Italian procurement data, we investigate the effectiveness of our method in different countries and auction settings, in our cases first-price sealed-bid and mean-price sealed-bid auctions. We correctly classify 90\% of the collusive and competitive coalitions when applying four machine learning algorithms: lasso, support vector machine, random forest, and super learner ensemble method. Finally, we find that coalition-based screens for the variance and the uniformity of bids are in all the cases the most important predictors according the random forest.



Determinants of Islamic Banking Profitability: Empirical Evidence from Palestine
Abugamea, Gaber
RePEC
The objective of this study is to examine the impact of bank-specific and major macroeconomic factors on the profitability of the biggest two Islamic banks in Palestine over the time period 1997-2018. It employs Pooled Regression analysis to investigate the effect of bank's asset size, capital, loans, liabilities, operating cost, economic growth and inflation on key bank profitability indicators; return on assets (ROA) and return on equity (ROE), respectively. The main findings show that size and capital have positive impact on ROE. Loans are positively correlated with both ROA and ROE. Liabilities are negatively related to ROA and operating cost has negative impact on both ROA and ROE. Moreover, Islamic banks not benefited significantly from both the inflationary environment and economic growth.

Distributionally robust portfolio maximisation and marginal utility pricing in discrete time
Jan Obloj,Johannes Wiesel
arXiv

We consider the optimal investment and marginal utility pricing problem of a risk averse agent and quantify their exposure to a small amount of model uncertainty. Specifically, we compute explicitly the first-order sensitivity of their value function, optimal investment policy and marginal option prices to model uncertainty. The latter is understood as replacing a baseline model $\mathbb{P}$ with an adverse choice from a small Wasserstein ball around $\mathbb{P}$ in the space of probability measures. Our sensitivities are thus fully non-parametric. We show that the results entangle the baseline model specification and the agent's risk attitudes. The sensitivities can behave in a non-monotone way as a function of the baseline model's Sharpe's ratio, the relative weighting of assets in an agent's portfolio can change and marginal prices can increase when an agent faces model uncertainty.



Do Hedge and Merger Arbitrage Funds Actually Hedge? A Time-Varying Volatility Spillover Approach
Papathanasiou, Spyros,Vasiliou, Dimitrios,Magoutas, Anastasios,Koutsokostas, Drosos
SSRN
We examine the interaction between funds implementing hedge and merger arbitrage strategies and a set of traditional assets comprising equities, bonds, gold, crude oil, currency, commodities and real estate, by applying a time-varying spillover approach for the period 1/1/2010-7/31/2020. Results indicate that the funds absorb the fewest shocks from equities, crude oil, gold and currency compared to commodities, bonds and real estate. Furthermore, we test the effective hedging ability of these funds by estimating hedge ratios and optimal portfolio weights. Taking a short position in the volatility of the funds provides impeccable hedging effectiveness for all asset classes, except currency.

Domain Specific Concept Drift Detectors for Predicting Financial Time Series
Filippo Neri
arXiv

Concept drift detectors allow learning systems to maintain good accuracy on non-stationary data streams. Financial time series are an instance of non-stationary data streams whose concept drifts (market phases) are so important to affect investment decisions worldwide. This paper studies how concept drift detectors behave when applied to financial time series. General results are: a) concept drift detectors usually improve the runtime over continuous learning, b) their computational cost is usually a fraction of the learning and prediction steps of even basic learners, c) it is important to study concept drift detectors in combination with the learning systems they will operate with, and d) concept drift detectors can be directly applied to the time series of raw financial data and not only to the model's accuracy one. Moreover, the study introduces three simple concept drift detectors, tailored to financial time series, and shows that two of them can be at least as effective as the most sophisticated ones from the state of the art when applied to financial time series.



Duality for optimal consumption with randomly terminating income
Ashley Davey,Michael Monoyios,Harry Zheng
arXiv

We establish a rigorous duality theory, under No Unbounded Profit with Bounded Risk, for an infinite horizon problem of optimal consumption in the presence of an income stream that can terminate randomly at an exponentially distributed time, independent of the asset prices. We thus close a duality gap encountered by Vellekoop and Davis in a version of this problem in a Black-Scholes market. Many of the classical tenets of duality theory hold, with the notable exception that marginal utility at zero initial wealth is finite. We use as dual variables a class of supermartingale deflators such that deflated wealth plus cumulative deflated consumption in excess of income is a supermartingale. We show that the space of discounted local martingale deflators is dense in our dual domain, so that the dual problem can also be expressed as an infimum over the discounted local martingale deflators. We characterise the optimal wealth process, showing that optimal deflated wealth is a potential decaying to zero, while deflated wealth plus cumulative deflated consumption over income is a uniformly integrable martingale at the optimum. We apply the analysis to the Vellekoop and Davis example and give a numerical solution.



Improving Financial Reporting with Cognitive Automation
Kumar, Jm
SSRN
Improving Financial Reporting with Cognitive Automation - A banking perspective in the context of reg reporting.

Increasing Financial Literacy through Simulations: The Case of the CFA Society Italy Fund Management Challenge
Martelli, Duccio,Dal Santo, Andreas
SSRN
The need to develop household personal finance literacy is an increasingly important issue in many countries, especially in the wake of the latest financial crisis. The literature broadly demonstrates that most individuals do not have an adequate level of financial literacy. A number of initiatives aimed at different groups (usually defined by gender, age, work status and income) have been developed. The present paper is part of a strand of literature that focuses on the financial literacy of university students, and in particular, the potential benefits of their participation in investment simulations, in terms of improved skills and knowledge. It analyzes an innovative online portfolio management competition for graduate students, the Fund Management Challenge, which is promoted by the CFA Society Italy. The results demonstrate how this Challenge, and investment simulations in general, set with specific rules to mitigate opportunistic behaviors, can help to improve participants’ financial literacy levels. In addition to this, the use of quality indicators encourages students to learn and helps mentors and educators to better allocate resources to those in need of assistance. The study represents an original analysis of the Challenge. If further analysis supports this preliminary evidence, the Challenge could become a reference point for future investment simulations targeting university students.

Integrating Hydrogen in Single-Price Electricity Systems: The Effects of Spatial Economic Signals
Frederik vom Scheidt,Jingyi Qu,Philipp Staudt,Dharik S. Mallapragada,Christof Weinhardt
arXiv

Hydrogen can contribute substantially to the reduction of carbon emissions in industry and transportation. However, the production of hydrogen through electrolysis creates interdependencies between hydrogen supply chains and electricity systems. Therefore, as governments worldwide are planning considerable financial subsidies and new regulation to promote hydrogen infrastructure investments in the next years, energy policy research is needed to guide such policies with holistic analyses. In this study, we link a electrolytic hydrogen supply chain model with an electricity system dispatch model. We use this methodology for a cross-sectoral case study of Germany in 2030. We find that hydrogen infrastructure investments and their effects on the electricity system are strongly influenced by electricity prices. Given current uniform zonal prices, hydrogen production increases congestion costs in the electricity grid by 11%. In contrast, passing spatially resolved electricity price signals leads to electrolyzers being placed at low-cost grid nodes and further away from consumption centers. This causes lower end-use costs for hydrogen. Moreover, congestion management costs decrease substantially, by 24% compared to the benchmark case without hydrogen. These savings could be transferred into according subsidies for hydrogen production. Thus, our study demonstrates the benefits of differentiating subsidies for hydrogen production based on spatial criteria.



Learning Bermudans
Riccardo Aiolfi,Nicola Moreni,Marco Bianchetti,Marco Scaringi,Filippo Fogliani
arXiv

American and Bermudan-type financial instruments are often priced with specific Monte Carlo techniques whose efficiency critically depends on the effective dimensionality of the problem and the available computational power. In our work we focus on Bermudan Swaptions, well-known interest rate derivatives embedded in callable debt instruments or traded in the OTC market for hedging or speculation purposes, and we adopt an original pricing approach based on Supervised Learning (SL) algorithms. In particular, we link the price of a Bermudan Swaption to its natural hedges, i.e. the underlying European Swaptions, and other sound financial quantities through SL non-parametric regressions. We test different algorithms, from linear models to decision tree-based models and Artificial Neural Networks (ANN), analyzing their predictive performances. All the SL algorithms result to be reliable and fast, allowing to overcome the computational bottleneck of standard Monte Carlo simulations; the best performing algorithms for our problem result to be Ridge, ANN and Gradient Boosted Regression Tree. Moreover, using feature importance techniques, we are able to rank the most important driving factors of a Bermudan Swaption price, confirming that the value of the maximum underlying European Swaption is the prevailing feature.



Log-modulated rough stochastic volatility models
Christian Bayer,Fabian Andsem Harang,Paolo Pigato
arXiv

We propose a new class of rough stochastic volatility models obtained by modulating the power-law kernel defining the fractional Brownian motion (fBm) by a logarithmic term, such that the kernel retains square integrability even in the limit case of vanishing Hurst index $H$. The so-obtained log-modulated fractional Brownian motion (log-fBm) is a continuous Gaussian process even for $H = 0$. As a consequence, the resulting super-rough stochastic volatility models can be analysed over the whole range $0 \le H < 1/2$ without the need of further normalization. We obtain skew asymptotics of the form $\log(1/T)^{-p} T^{H-1/2}$ as $T\to 0$, $H \ge 0$, so no flattening of the skew occurs as $H \to 0$.



MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price
Qiutong Guo,Shun Lei,Qing Ye,Zhiyang Fang
arXiv

Bitcoin, one of the major cryptocurrencies, presents great opportunities and challenges with its tremendous potential returns accompanying high risks. The high volatility of Bitcoin and the complex factors affecting them make the study of effective price forecasting methods of great practical importance to financial investors and researchers worldwide. In this paper, we propose a novel approach called MRC-LSTM, which combines a Multi-scale Residual Convolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to implement Bitcoin closing price prediction. Specifically, the Multi-scale residual module is based on one-dimensional convolution, which is not only capable of adaptive detecting features of different time scales in multivariate time series, but also enables the fusion of these features. LSTM has the ability to learn long-term dependencies in series, which is widely used in financial time series forecasting. By mixing these two methods, the model is able to obtain highly expressive features and efficiently learn trends and interactions of multivariate time series. In the study, the impact of external factors such as macroeconomic variables and investor attention on the Bitcoin price is considered in addition to the trading information of the Bitcoin market. We performed experiments to predict the daily closing price of Bitcoin (USD), and the experimental results show that MRC-LSTM significantly outperforms a variety of other network structures. Furthermore, we conduct additional experiments on two other cryptocurrencies, Ethereum and Litecoin, to further confirm the effectiveness of the MRC-LSTM in short-term forecasting for multivariate time series of cryptocurrencies.



Merton Investment Problems in Finance and Insurance for the Hawkes-based Models
Anatoliy Swishchuk
arXiv

We show how to solve Merton optimal investment stochastic control problem for Hawkes-based models in finance and insurance, i.e., for a wealth portfolio X(t) consisting of a bond and a stock price described by general compound Hawkes process (GCHP), and for a capital R(t) of an insurance company with the amount of claims described by the risk model based on GCHP. The novelty of the results consists of the new Hawkes-based models and in the new optimal investment results in finance and insurance for those models.



Multivariate tempered stable additive subordination for ?nancial models
Patrizia Semeraro
arXiv

We study a class of multivariate tempered stable distributions and introduce the associated class of tempered stable Sato subordinators. These Sato subordinators are used to build additive inhomogeneous processes by subordination of a multiparameter Brownian motion. The resulting process is additive and time inhomogeneous. Furthermore, these processes are associated with the distribution at unit time of a class of L\'evy process with good fit properties on fifinancial data. The main feature of the Sato subordinated Brownian motion is that it has time dependent correlation, whereas the L\'evy counterpart does not. We provide a numerical illustration of the correlation dynamics.



Mutual Fund Advisory Fees: From Gartenberg to Jones
Brown, Stewart L.
SSRN
The paper surveys three important mutual fund advisory fee cases that defined the 36(b) litigation landscape between Gartenberg v. Merrill Lynch and Jones v. Harris.

Neo-humanism and COVID-19: Opportunities for a socially and environmentally sustainable world
Francesco Sarracino,Kelsey J. O'Connor
arXiv

A series of crises, culminating with COVID-19, shows that going Beyond GDP is urgently necessary. Social and environmental degradation are consequences of emphasizing GDP as a measure of progress. This degradation created the conditions for the COVID-19 pandemic and limited the efficacy of counter-measures. Additionally, rich countries did not fare the pandemic much better than poor ones. COVID-19 thrived on inequalities and a lack of cooperation. In this article we leverage on defensive growth models to explain the complex relationships between these factors, and we put forward the idea of neo-humanism, a cultural movement grounded on evidence from quality-of-life studies. The movement proposes a new culture leading towards a socially and environmentally sustainable future. Specifically, neo-humanism suggests that prioritizing well-being by, for instance, promoting social relations, would benefit the environment, enable collective action to address public issues, which in turn positively affects productivity and health, among other behavioral outcomes, and thereby instills a virtuous cycle. Arguably, such a society would have been better endowed to cope with COVID-19, and possibly even prevented the pandemic. Neo-humanism proposes a world in which the well-being of people comes before the well-being of markets, in which promoting cooperation and social relations represents the starting point for better lives, and a peaceful and respectful coexistence with other species on Earth.



Observational Learning with Ordered States
Navin Kartik,SangMok Lee,Daniel Rappoport
arXiv

When does society eventually learn the truth, or underlying state, via sequential observational learning? This paper develops the interplay of preferences satisfying single-crossing differences (SCD) and a new informational condition, {directionally unbounded beliefs} (DUB). SCD preferences and DUB information are a jointly minimal pair of sufficient conditions for learning. When there are more than two states, DUB is weaker than unbounded beliefs, which characterizes learning for all preferences (Smith and Sorensen, 2000). Unlike unbounded beliefs, DUB is compatible with the monotone likelihood ratio property, and satisfied, for example, by normal information.



Optimal stopping with signatures
Christian Bayer,Paul Hager,Sebastian Riedel,John Schoenmakers
arXiv

We propose a new method for solving optimal stopping problems (such as American option pricing in finance) under minimal assumptions on the underlying stochastic process $X$.

We consider classic and randomized stopping times represented by linear and non-linear functionals of the rough path signature $\mathbb{X}^{<\infty}$ associated to $X$, and prove that maximizing over these classes of signature stopping times, in fact, solves the original optimal stopping problem. Using the algebraic properties of the signature, we can then recast the problem as a (deterministic) optimization problem depending only on the (truncated) expected signature $\mathbb{E}\left[ \mathbb{X}^{\le N}_{0,T} \right]$. By applying a deep neural network approach to approximate the non-linear signature functionals, we can efficiently solve the optimal stopping problem numerically.

The only assumption on the process $X$ is that it is a continuous (geometric) random rough path. Hence, the theory encompasses processes such as fractional Brownian motion, which fail to be either semi-martingales or Markov processes, and can be used, in particular, for American-type option pricing in fractional models, e.g. on financial or electricity markets.



Order flow and price formation
Fabrizio Lillo
arXiv

I present an overview of some recent advancements on the empirical analysis and theoretical modeling of the process of price formation in financial markets as the result of the arrival of orders in a limit order book exchange. After discussing critically the possible modeling approaches and the observed stylized facts of order flow, I consider in detail market impact and transaction cost of trades executed incrementally over an extended period of time, by comparing model predictions and recent extensive empirical results. I also discuss how the simultaneous presence of many algorithmic trading executions affects the quality and cost of trading.



Post-Brexit power of European Union from the world trade network analysis
Justin Loye,Katia Jaffrès-Runser,Dima Shepelyansky
arXiv

We develop the Google matrix analysis of the multiproduct world trade network obtained from the UN COMTRADE database in recent years. The comparison is done between this new approach and the usual Import-Export description of this world trade network. The Google matrix analysis takes into account the multiplicity of trade transactions thus highlighting in a better way the world influence of specific countries and products. It shows that after Brexit, the European Union of 27 countries has the leading position in the world trade network ranking, being ahead of USA and China. Our approach determines also a sensitivity of trade country balance to specific products showing the dominant role of machinery and mineral fuels in multiproduct exchanges. It also underlines the growing influence of Asian countries.



Probability Premium and Attitude Towards Probability
Louis R. Eeckhoudt,Roger J. A. Laeven
arXiv

Employing a generalized definition of Pratt (1964) and Arrow's (1965, 1971) probability premium, we introduce a new concept of attitude towards probability. We illustrate in a problem of risk sharing that whether attitude towards probability is a first-order or second-order phenomenon has important economic applications. By developing a local approximation to the probability premium, we show that the canonical rank-dependent utility model usually exhibits attitude towards probability of first order, whereas under the dual theory with smooth probability weighting functions attitude towards probability is a second-order trait.



Revisit the Use of Asset Turnover and Profit Margin in Forecasting Operating Profitability: Further Evidence
Nguyen, James
SSRN
This paper extends the work of prior search in studying the incremental information captured by the DuPont components, changes in asset turnover (∆ATO) and changes in operating profit margin (∆PM), in forecasting the changes in return on net operating assets (∆RNOA) one year ahead. This study extends the research by utilizing updated data from COMPUSTAT firm database till the end of 2020. It first starts withe baseline model then advance further by incorporating the direction of the change in PM and possible firm’s earning management. Once again, the model confirms significant relationship between DuPont components of current RNOA and future change in RNOA.

Role of Private Sector Banks for Financial Inclusion
Anjum, Bimal,Tiwari, Rajesh
SSRN
The article explores the geographical distribution of private sector banks in India and its impact on financial inclusion. The article evaluates the correlation of number of private bank branches with economic freedom and ratio of development expenditure of states to gross state domestic product. At end March 2010, 50.6 million no frills account were opened by the banking system. The banks have a challenge to keep these accounts operational. Banks were advised to provide small overdraft in these accounts, and up to March 2010 banks provided overdraft of Rs. 27.54 crore. No frills account provides the opportunity for a common man to open bank account. These accounts have no pre condition and low minimum balance maintenance. RBI initiated scheme of no frills account in 2005 to improve financial inclusion.

Selection and Behavioral Responses of Health Insurance Subsidies in the Long Run: Evidence from a Field Experiment in Ghana
Patrick Opoku Asuming,Hyuncheol Bryant Kim,Armand Sim
arXiv

We conduct a randomized experiment that varies one-time health insurance subsidy amounts (partial and full) in Ghana to study the impacts of subsidies on insurance enrollment and health care utilization. We find that both partial and full subsidies promote insurance enrollment in the long run, even after the subsidies expired. Although the long run enrollment rate and selective enrollment do not differ by subsidy level, long run health care utilization increased only for the partial subsidy group. We show that this can plausibly be explained by stronger learning-through-experience behavior in the partial than in the full subsidy group.



The Black Market for Beijing License Plates
Øystein Daljord,Guillaume Pouliot,Junji Xiao,Mandy Hu
arXiv

Black markets can reduce the effects of distortionary regulations by reallocating scarce resources toward consumers who value them most. The illegal nature of black markets, however, creates transaction costs that reduce the gains from trade. We take a partial identification approach to infer gains from trade and transaction costs in the black market for Beijing car license plates, which emerged following their recent rationing. We find that at least 11% of emitted license plates are illegally traded. The estimated transaction costs suggest severe market frictions: between 61% and 82% of the realized gains from trade are lost to transaction costs.



The Politics of Personalized News Aggregation
Lin Hu,Anqi Li,Ilya Segal
arXiv

We study how personalized news aggregation for rational inattentive voters (NARI) affects policy polarization and public opinion. In a two-candidate electoral competition game, an attention-maximizing infomediary aggregates information about candidates' valence into news. Voters decide whether to consume news, trading off the expected gain from improved expressive voting against the attention cost. NARI generates policy polarization even if candidates are office-motivated. Personalized news serves extreme voters with skewed signals and makes them the disciplining entities of policy polarization. Analysis of disciplining voters' identities and policy latitudes yields insights into the political effects of recent regulatory proposals to tame tech giants.



Why Are Rational Expectations Violated in Social Interactions?
Mohsen Foroughifar
arXiv

Individuals often interact with each other through observation -- they observe the choices of other people who possess private information. In such social interactions, it is typically assumed that decision makers have rational expectations, therefore they can infer what other decision makers know via observation of their choices. In this study, I assess the validity of the rational expectations assumption in a social interaction experiment. I use a simple and transparent experimental setting to show that decision makers often fail to exhibit rational expectations in social interactions and this behavior is independent of commonly documented errors in statistical reasoning: subjects exhibit a higher level of irrationality in the presence than in the absence of social interaction, even when they receive informationally equivalent signals across the two conditions. A series of treatments aimed at identifying mechanisms suggests that the behavior of other people are often "ambiguous" to a decision maker who observes their choices. So, the decision maker behaves as if she has limited ability to infer the relationship between what other people choose and what they know.