Research articles for the 2021-06-15

$N$-player and Mean-field Games in It\^{o}-diffusion Markets with Competitive or Homophilous Interaction
Ruimeng Hu,Thaleia Zariphopoulou
arXiv

In It\^{o}-diffusion environments, we introduce and analyze $N$-player and common-noise mean-field games in the context of optimal portfolio choice in a common market. The players invest in a finite horizon and also interact, driven either by competition or homophily. We study an incomplete market model in which the players have constant individual risk tolerance coefficients (CARA utilities). We also consider the general case of random individual risk tolerances and analyze the related games in a complete market setting. This randomness makes the problem substantially more complex as it leads to ($N$ or a continuum of) auxiliary ''individual'' It\^{o}-diffusion markets. For all cases, we derive explicit or closed-form solutions for the equilibrium stochastic processes, the optimal state processes, and the values of the games.



A Closed-form Pricing Solution for Options on Assets with Pricing Errors
Piccotti, Louis R.
SSRN
This paper examines the effects that pricing errors in the underlying asset have on options prices, their Greeks, and their implied risk neutral densities. Pricing errors can be viewed as a random proportional transaction cost. When pricing errors are information-unrelated, options prices are unambiguously higher than the Black-Scholes case and increasing in the pricing error variance.Hedging volatility is higher and the optimal exercise price for American put options is decreased. The option implied risk-neutral density and option Greeks are materially affected, which leads to suboptimal risk management and hedging when pricing errors are not accounted for.

A Keynesian Approach to Modeling the Long-Term Interest Rate
Akram, Tanweer
SSRN
There are several widely used benchmark models of the long-term interest rate in quantitative finance. However, these models have yet to incorporate Keynes’s valuable insights about interest rate dynamics. The Keynesian approach to interest rate dynamics can be readily incorporated in the benchmark models of the long-term interest rate. This paper modifies several benchmark interest rate models. In these modified models the long-term interest rate is related to the short-term interest rate and a Wiener process. The Keynesian approach to interest rate dynamics can be useful in addressing theoretical and policy issues.

A Lawyer's Guide To Empirical Corporate Governance
Eldar, Ofer
SSRN
Empirical studies in corporate law have proliferated in the last two decades, and have had a major impact on legal practice and policy relating to corporate governance. As a result, lawyers increasingly engage with these studies in order to understand their implications for legal practice and policy. This is often challenging because most lawyers are not trained in econometrics and statistical methods, and thus, there is a risk that they misinterpret these studies and their implications. These studies, while critical for evaluating different corporate governance regimes, suffer from well-known weaknesses. The purposes of this guide are to provide an overview of the key empirical strategies for evaluating the relationship between governance and shareholder value, and to demonstrate to lawyers how they can engage with their weaknesses. The main theme of the guide is that largely all empirical strategies are based on three key intuitive comparisons: (1) comparing firms with and without the governance provisions, (2) evaluating the value of firms before and after adoptions or removals of governance provisions, and (3) evaluating what happens to shareholder value after a legal change. By identifying these basic comparisons and the roles they play in the key methodologies for evaluating shareholder value, lawyers will be better positioned to assess empirical studies and evaluate their implications for legal policy.

Action Learning as a Human Resource Development Resource to Realize Collective Leadership
Raelin, Joseph A.
SSRN
Action learning has been used as a potential resource of human resource development for adopting collective leadership. The word potential is noteworthy because it is not necessarily the conventional purpose of action learning when adopted as a human resource development tool. It is primarily used as a form of work-based learning. However, its components can be aptly constituted for the former purpose; in fact, action learning may be one of the best HRD methods to prepare participants for collective leadership in the organization.

An Analysis of the Interrelationship Between Various Economic Indicators and Their Overall Effect on the U.S Economy
Kasongo, Jessica
SSRN
The United States economy is the largest economy in terms of monetary value and the second largest in terms of purchasing power parity (PPP). As a result, various economic indicators help the United States reflect on the previous, current, and future conditions of the economy and help the country remain a successful economy and improve economic conditions. Various reputable resources provide credible information on the numerous economic indicators. The main resources used for the purpose of this paper include Automatic Data Processing National Employment report and Salaries and Wages Growth, World Trade Organization, The Conference Board, Bureau of Labor Statistics, Congressional Budget Office, National Federation of Independent Business, United States Census, Bureau of Economic Analysis, and The Secrets of Economic Indicators. These resources have been utilized to review various economic indicators and examine recent past, present, and short-term forecasted conditions of the United States economy. According to the U.S Bureau of Economic Analysis, the US economy experienced its strongest growth rate in the recent quarter in the three months up to June of 2016 (“Trading Economies”, 2016). The health of the economy can be reflected using all of the economic indicators that have been analyzed in this paper. When there are changes in GDP, employment, personal income and spending, existing home sales, small business, international trade in goods and services, interest rates, and import and export prices, conclusions can be drawn about the overall behavior of the economy. This research has revealed how indicators can be related to each other to predict the status of the United States economy. The paper will assist policy makers, academia and people engaged in macro economic planning to determine the importance of positive economic indicators in spurring growth of the U.S economy.

Attention Discrimination under Time Constraints: Evidence from Retail Lending
Huang, Bo,Li, Jiacui,Lin, Tse-Chun,Tai, Mingzhu,Zhou, Yiyuan
SSRN
Using proprietary loan screening data, we document that loan officers engage in “attention discrimination”: they exert less effort reviewing ex-ante disadvantage applicants, leading to higher rejection rates than otherwise justified by those applicants’ credit quality. Attention discrimination increases with the officers’ time constraints induced by quasi-random workload variations. When officer workload rises from the bottom to the top decile, they devote 70% less time to disadvantaged applicants, and the approval rate for those applicants declines by three-fifths. Our results indicate that attention constraints magnify discrimination, which provides policy implications about how to reduce discrimination in practice.

CeFi vs. DeFi -- Comparing Centralized to Decentralized Finance
Kaihua Qin,Liyi Zhou,Yaroslav Afonin,Ludovico Lazzaretti,Arthur Gervais
arXiv

To non-experts, the traditional Centralized Finance (CeFi) ecosystem may seem obscure, because users are typically not aware of the underlying rules or agreements of financial assets and products. Decentralized Finance (DeFi), however, is making its debut as an ecosystem claiming to offer transparency and control, which are partially attributable to the underlying integrity-protected blockchain, as well as currently higher financial asset yields than CeFi. Yet, the boundaries between CeFi and DeFi may not be always so clear cut.

In this work, we systematically analyze the differences between CeFi and DeFi, covering legal, economic, security, privacy and market manipulation. We provide a structured methodology to differentiate between a CeFi and a DeFi service. Our findings show that certain DeFi assets (such as USDC or USDT stablecoins) do not necessarily classify as DeFi assets, and may endanger the economic security of intertwined DeFi protocols. We conclude this work with the exploration of possible synergies between CeFi and DeFi.



Chasing the Fame: Investing in Brand Equity
Feng, Wei,Reich, Robert W.,Sheng, Shirley
SSRN
This paper examines the relationship between a firm’s brand equity and its investment value. “Brand equity is defined as the incremental cash flows which accrue to branded products over unbranded products. The estimation technique extracts the value of brand equity from the value of the firm's other assets” (Simon & Sullivan, 1993). This study illustrates how stocks with higher growth of brand equity provide stronger investment value. Conversely, stocks with deterioratingbrand equity generally feature lower return potential. An empirical portfolio-strategy is used to demonstrate the assumptions and predicts how to capitalize upon return potential from such a relationship.

Deep Reinforcement Learning for Finance and the Efficient Market Hypothesis
Odermatt, Leander,Beqiraj, Jetmir,Osterrieder, Joerg
SSRN
Is there an informational gain by training a Deep Reinforcement Learning agent for automated stock trading using other time series than the one to be traded? In this work, we implement a DRL algorithm in a solid framework within a model-free and actor-critic approach and learn it with 21 global Multi Assets to predict and trade on the S&P 500. The Efficient Market Hypothesis sets out that it is impossible to gather more information from the broader input. We demand to learn a DRL agent on this index with and without the additional information of these several Multi Assets to determine if the agent could capture invisible dependencies to end up with an informational gain and a better performance.The aim of this work is not to tune the hyperparameters of a DRL agent; several papers already exist on this subject. Nevertheless, we use a proven setup as model architecture. We take a Multi Layer Perceptron (short: MLP) as the neural network architecture with two hidden layers and 64 neurons each layer. The activation function used is the hyperbolic tangent. Further, Proximal Policy Optimization (short: PPO) is used as the policy for simple implementation and enabling a continuous state space. To deal with uncertainties of neural nets, we learn 100 agents for each scenario and compared both results. Neither the Sharpe ratios nor the cumulative returns are better in the more complex approach with the additional information of the Multi Assets, and even the single approach performed marginally better. However, we demonstrate that the complexly learned agent delivers less scattering over the 100 simulations in terms of the risk-adjusted returns, so there is an informational gain due to Multi Assets. A DRL agent learned with additional information delivers more robust results compared to the taken risk. We deliver valuable results for the further development of Deep Reinforcement Learning and provide a unique and resourceful approach.

Did the COVID-19 Shock Impair the Stock Performance of Companies with Older CEOs?
Ferri, Giovanni,Lagravinese, Raffaele,Resce, Giuliano
SSRN
Since its lethality increases exponentially with age, the early 2020 COVID-19 shock unexpectedly raised the risk of corporate disruption at companies led by older CEOs. While normally unprepared successions might be beneficial by replacing entrenched CEOs, this systemic shock projected a possible crowding of older CEOs’ successions, with disruption costs dominating changeover benefits. Within this natural experiment, we find that stock returns and volatility worsened at S&P 500 listed companies with older CEOs when the COVID-19 lethal risk emerged. Our results resist various robustness checks. This advises companies to adopt contingency strategies of top managers’ replacement against possibly recurring pandemics.

Do Family Successions (Really) Reduce Firm Performance? Evidence from Large US Firms
Jagannathan, Murali,Myers, Brett W.,Niu, Xu
SSRN
This paper examines whether founder family successions lower performance in large US firms. We find no significant decreases in either accounting performance or firm value, and instead find evidence that both are improved. Consistent with this, we present evidence that family successions are associated with higher levels of employee trust, and that employee's human capital is not as easily transferable at family firms. Consistent with the literature, we find that the decision to appoint a founder family member as a successor CEO is not random, and occurs in low-growth, yet profitable firms. To mitigate these endogeneity problems, we first propensity match founder family firms with non-founder firms and examine the change in performance around founder successions relative to the matched firms. We also use two instruments of founder family successions in our analysis.

Do Share Repurchases Facilitate Movement Towards Target Capital Structure? International Evidence
Wang, Zigan,Yin, Qie Ellie,Yu, Luping
SSRN
We use a new international setting to test and strengthen the identification of “target leverage hypothesis” in the payout policy literature. We conduct a quasi-natural experiment induced by staggered share repurchase legalization in 17 economies and analyze its influences on leverage dynamics. Under-leveraged firms before legalization are more likely to buy back shares immediately after the law change, controlling for other repurchasing motives. Post-legalization repurchases also facilitate firms’ movement towards target leverage, especially for under-leveraged firms. This facilitating effect is stronger under less repurchasing restriction, higher dividend tax penalty, and lower financial constraint.

Does Proxy Advice Allow Funds to Cast Informed Votes?
Matsusaka, John G.,Shu, Chong
SSRN
This paper estimates to what extent proxy advice allows funds to vote as if they were informed. A fund’s vote is classified as “informed“ if the fund accessed the company’s proxy statement from the SEC’s Edgar website prior to voting. A fund’s proxy advisor, if any, is identified from the format of its Form N-PX filing. Our main finding, for the period 2004-2017, is that proxy advice did not result in funds voting as if they were informed â€" more often than not it pushed them in the opposite direction â€" and this distorting effect was particularly noticeable for ISS. The finding is robust to several strategies designed to control for endogeneity of acquiring information and seeking proxy advice, including fixed effects and instrumental variables. We also show that advice distorted votes toward policies favored by socially responsible investment (SRI) funds, and provide suggestive evidence consistent with the idea that proxy advisors slanted their recommendations toward the preferences of SRI funds because of pressure from activists.

Economic Impact of the Move to Strategic Reporting in the U.K.
Wang, Ruizhe,Wai Fong, Chua,Simnett, Roger,Zhou, Shan
SSRN
The demand for enhanced corporate reporting has surged amidst debates as to its benefits. In 2013, the United Kingdom (U.K.) mandated Strategic Reporting (SR) for corporations, replacing Enhanced Business Reporting (EBR) (a form of enhanced Management Discussion and Analysis (MD&A)). We identify factors associated with high-quality SR, studying its economic impact. We also examine the economic impact of changes from EBR to SR. Using proprietary data from PwC U.K., we find corporate governance, financial resources, external monitoring mechanisms and CSR performance are key drivers for high-quality SR. Regarding economic impact, we find a positive association between the disclosure quality of Strategic Reports and capital market benefits, including higher liquidity, lower cost of capital, and more accurate, less dispersed analysts’ forecasts. The effects of higher liquidity and lower cost of capital are more pronounced in the post- compared to pre-SR period, suggesting SR enables more effective capital market communication than EBR.

Elections and Good Governance in Nigeria's Fourth Republic (1999-2010)
Oravee, Aule,Bello, Dr. Matthew F.,Mohammed, Idris Danjuma
SSRN
The electoral process is an ideal and integral part of the democratic process, whether in developed or developing nation. A mal-functioning electoral system inadvertently produces maladministration or governance. In most developing countries, crisis of governance is usually the major problem because of the manner people ascend into political power. The paper, through the use of systems analytical framework therefore, discusses the negative effect of electoral vices on good governance in Nigeria’s Fourth Republic. Using secondary sources of information, the paper accentuates gender imbalance and political inequality among political actors in Nigeria as being responsible for poor electoral process which has resulted to poor governance. The study recommends among others that for good governance to thrive in the nation, the marginalization and under-representation of women in governance should be discouraged entirely. They (women) should be encouraged and supported so that they can also contribute maximally to good governance in the nation. In fact, there is need for a realistic implementation of the United Nation Convention on Elimination of all forms of Discrimination against Women (CEDAW) as well as Nigerian laws to engender fairness and equity in governance and public spheres in the nation. Politicians should desist from conducting politics as a warfare and do-or-die affair as these make citizens who are supposed to benefit from good governance scapegoats of the democratic process. Elections should be conducted in a free and fair basis, upholding tenets of the rule of law such that the Nigerian citizenry is given fair opportunity to choose their representatives and also to contribute to the policy making process.

Estimating Welfare Effects in a Nonparametric Choice Model: The Case of School Vouchers
Vishal Kamat,Samuel Norris
arXiv

We develop new robust discrete choice tools to learn about the average willingness to pay and average cost of a school voucher in a program that randomly allocates vouchers. We consider a nonparametric, nonseparable choice model that places no restrictions on the functional form of utilities or the distribution of unobserved heterogeneity. We exploit the insight that the welfare parameters in this model can be expressed as functions of the demand for the different schools. However, while the random allocation of the voucher reveals the value of demand at two prices, the parameters generally depend on its values beyond these prices. We show how to sharply characterize what we can learn when demand is specified to be entirely nonparametric or to be parameterized in a flexible manner, both of which imply that the parameters are not necessarily point identified. We use our tools to analyze the welfare effects of voucher provision in the DC Opportunity Scholarship Program, a school voucher program in Washington, DC. We find that the provision of both the status-quo voucher and a wide range of counterfactual vouchers of different amounts have positive benefits net of costs. In comparison, traditional logit models produce estimates towards the lower end of our bounds, and hence may understate the benefits. We also find that the positive results can be explained by the popularity of low-tuition schools in the program; removing them from the program can result in a negative net benefit.



Factoring as a Determinant of Capital Structure for Large Firms: Theoretical and Empirical Analysis
Bilgin, Rumeysa,Dinc, Yusuf
SSRN
Firms engage in factoring as an external financing option. Factoring is generally considered as a costly option. However, firms may prefer factoring financing when they reach a certain level of indebtedness that increasing it may negatively affect their firm value. Up to now, far too little attention paid on the role of factoring on the capital structure decisions. This paper is the first attempt to provide a theoretical framework and empirical evidence on the role of factoring as a determinant of capital structure. A Fractional Regression Model is estimated using a sample of 261 publicly listed firms in Turkey for the 2012e2017 period. The empirical evidence presented in this paper implies that factoring does not effect on the initial decision of leveraging, whereas it is a determinant of capital structure for leveraged firms. Another significant finding is the existence of the relationship between increasing factoring and increased leverage.

Federal Reserve Intervention and Systemic Risk during Financial Crises
Sedunov, John
SSRN
I examine the relation between Federal Reserve emergency actions and aggregate U.S. systemic risk during the Global Financial Crisis (GFC) and the COVID-19 crisis. I divide these actions in to three categories: lender of last resort (LLR), liquidity provision, and open market operations (OMO). Evidence suggests that during the GFC, liquidity provision and OMO was related to reduced systemic risk, while evidence on LLR actions is mixed. Further, I find that Federal Reserve actions were related to increased stability in other G8 financial systems during the GFC, and that after the GFC, facilities that remained operational were no longer related to aggregate systemic risk. I do not find a relation between Federal Reserve actions and systemic risk during the COVID-19 crisis. Together, these findings can inform actions and policy decisions in future financial crises.

Financial Literacy, Naive Diversification, and Security Selection
Hanson, Thomas A.,Kalthoff, Jenna
SSRN
Low levels of financial literacy have been linked to costly errors in investing behavior. This paper examines the relationship between financial literacy and the two financial tasks of asset allocation and security selection in an online survey of college students. Results suggest that financial literacy can slightly attenuate the naïve diversification bias and improve security selection decisions. The results support educational efforts to increase financial literacy to improve retirement savings and financial decisions.

Gittins' theorem under uncertainty
Samuel N. Cohen,Tanut Treetanthiploet
arXiv

We study dynamic allocation problems for discrete time multi-armed bandits under uncertainty, based on the the theory of nonlinear expectations. We show that, under strong independence of the bandits and with some relaxation in the definition of optimality, a Gittins allocation index gives optimal choices. This involves studying the interaction of our uncertainty with controls which determine the filtration. We also run a simple numerical example which illustrates the interaction between the willingness to explore and uncertainty aversion of the agent when making decisions.



How Do Institutional Factors and Venture Capital Strategies Affect the Performance of VC-backed Companies? Evidence from China
Song, Tianyi,Kutsuna, Kenji
SSRN
This study investigates how the performance of venture capital-backed companies is influenced by institutional factors and venture capital (VC) strategies in China. We conducted empirical analysis using national-level data and data obtained from companies listed on China’s Growth Enterprise Market (GEM). The findings show that the influence of institutional factors on the economic performance of VC-backed companies is mixed, with different VC investment strategies producing different results. Some VC strategies can counteract the negative effects of institutional factors, promote positive institutional effects, or turn negative institutional effects into positive ones. Conversely, other VC strategies may aggravate the negative effects of institutional factors or turn positive effects into negative ones.

Informed Trading Intensity
Bogousslavsky, Vincent,Fos, Vyacheslav,Muravyev, Dmitriy
SSRN
We train a state-of-the-art machine-learning method (ML) on a class of informed trades to develop a new measure of informed trading, the Informed Trading Intensity ("ITI"). Though the measure is trained on a particular class of informed trades, it predicts various informational events, including stock price reactions to earnings surprises, M&A announcements, and unscheduled news releases. The measure also increases on days with opportunistic insider trades and large changes in short interest. Returns on days with high ITI reverse less than returns on other days. In the cross-section, higher ITI is associated with higher returns next month. Our main insight is that learning from data on informed trades can generate an effective measure of informed trading.

International Trade and Safety Measures: An In-depth Assessment of the Sanitary and Phytosanitary (SPS) Agreement and its Implications on Trade
Kasongo, Jessica
SSRN
The SPS Agreement is an agreement on the application of Sanitary and Phytosanitary Measures established by the World Trade Organization in 1995. Annex A of the WTO SPS agreement defines an SPS measure as any measure applied to protect animal or plant health or health within the Member's territory from risks arising from the entry establishment or spread of pests' diseases, disease-carrying organisms, or disease-causing organisms. It also protects human or animal life within the Member's territory from risk arising from additives, contaminants, toxins, disease-causing organisms, food, beverages, and feedstuffs. It also protects human lives within the Member's territory from risks arising from diseases carried by animals, plants, and products thereof or from the entry establishment or spread of pests. Lastly, it prevents or limits other damage within the Member's territory from the entry establishment or spread of pests. The WTO SPS agreement sets rules governing the sanitary and phytosanitary aspects of international trade. It is the regulatory framework aimed to make sure that phytosanitary measures relevant to trade are consistent within international standards, justified by scientific principles and evidence, harmonized internationally to the extent possible, transparent only as restrictive as absolutely necessary to meet the appropriate level of protection required, non-discriminatory, appropriate to the conditions in the importing and exporting countries. What is good about the SPS agreement is its consistency. Every member country is treated the same way, and its transparency, in which the trading countries must have the proofs of how the country conducting the analysis got to the result so that there is no place for discrimination. According to the standards imposed in the SPS agreement that they have signed with the WTO, countries must keep prejudices from happening: If a country says "no" to another country's product, it has to have a sound scientific reason. Otherwise, the WTO's court may intervene because the goal is to promote fair trade and make it easy for all the countries to trade.

Learning from Prospectuses
Abis, Simona,Buffa, Andrea M,Javadekar, Apoorva,Lines, Anton
SSRN
We study qualitative information disclosure by mutual funds when investors learn from these disclosures in addition to past performance. We show theoretically that fund managers with specialized strategies optimally choose to disclose detailed strategy descriptions, while managers with standardized strategies provide generic descriptions. Generic descriptions lead to errors in benchmarking by investors and thus higher volatility in capital flows. While all fund managers dislike such volatility, those with above-average factor exposures also benefit from benchmarking errors as investors incorrectly ascribe factor returns to managerial skill. The model generates a number of predictions that we are able to test empirically using a comprehensive dataset of fund prospectuses. Consistent with the model’s predictions, funds with standardized strategies include more boilerplate in their descriptions, grow larger and have lower flow-performance sensitivity, despite having greater flow volatility.

Machine learning in U.S. Bank Merger Prediction: A Text-Based Approach
Katsafados, Apostolos G.,Leledakis, George N.,Pyrgiotakis, Emmanouil G.,Androutsopoulos, Ion,Fergadiotis, Emmanouel
SSRN
This paper investigates the role of textual information in a U.S. bank merger prediction task. Our intuition behind this approach is that text could reduce bank opacity and allow us to understand better the strategic options of banking firms. We retrieve textual information from bank annual reports using a sample of 9,207 U.S. bank-year observations during the period 1994-2016. To predict bidders and targets, we use textual information along with financial variables as inputs to several machine learning models. Our key findings suggest that: (1) when textual information is used as a single type of input, the predictive accuracy of our models is similar, or even better, compared to the models using only financial variables as inputs, and (2) when we jointly use textual information and financial variables as inputs, the predictive accuracy of our models is substantially improved compared to models using a single type of input. Therefore, our findings highlight the importance of textual information in a bank merger prediction task.

Mandatory Gender Pay Gap Disclosure in the UK: Did Inequity Fall and Do these Disclosures Affect Firm Value?
Raghunandan, Aneesh,Rajgopal, Shivaram
SSRN
We study the 2017 UK rule requiring firms that employ more than 250 workers to publicly disclose gender pay gap data. We focus on two questions of interest to regulators and socially conscious investors: (i) does gender pay equity improve after mandatory disclosure? and (ii) is gender pay equity useful to investors in predicting future operating performance, stock returns, and Tobin’s Q? We find a 0.41% reduction in the gender pay gap after the implementation of gender pay gap reporting only in entities with between 250 and 499 employees but no change for entities with 500+ employees. Only 9% of these entities belong to publicly listed firms. Further analysis suggests a modest one-time levelling up in the gender pay gap, in anticipation of the rule, in the two-year period between when the rule was announced and when it went into effect (but no subsequent improvement). We also find a link between Employment Tribunal settlements â€" most commonly resulting from unfair dismissal cases brought by employees â€" and lower gender pay gaps, suggesting a potential unintended adverse consequence of the regulation. There is no robust association between gender pay gaps and ROA (return on assets), EBITDA, operating margins, and stock returns. We conclude that (i) the impact of the rule thus far is modest at best, and at worst may have had unintended consequences for existing employees; and (ii) the evidence linking gender pay inequity to firm value or operating performance is scant.

Model-free bounds for multi-asset options using option-implied information and their exact computation
Ariel Neufeld,Antonis Papapantoleon,Qikun Xiang
arXiv

We consider derivatives written on multiple underlyings in a one-period financial market, and we are interested in the computation of model-free upper and lower bounds for their arbitrage-free prices. We work in a completely realistic setting, in that we only assume the knowledge of traded prices for other single- and multi-asset derivatives, and even allow for the presence of bid-ask spread in these prices. We provide a fundamental theorem of asset pricing for this market model, as well as a superhedging duality result, that allows to transform the abstract maximization problem over probability measures into a more tractable minimization problem over vectors, subject to certain constraints. Then, we recast this problem into a linear semi-infinite optimization problem, and provide two algorithms for its solution. These algorithms provide upper and lower bounds for the prices that are $\varepsilon$-optimal, as well as a characterization of the optimal pricing measures. These algorithms are efficient and allow the computation of bounds in high-dimensional scenarios (e.g. when $d=60$). Moreover, these algorithms can be used to detect arbitrage opportunities and identify the corresponding arbitrage strategies. Numerical experiments using both synthetic and real market data showcase the efficiency of these algorithms, while they also allow to understand the reduction of model risk by including additional information, in the form of known derivative prices.



Of interest? Estimating the average interest rate on debt across firms and over time
Submitter, Motu,Fabling, Richard
SSRN
We use tax data from the Longitudinal Business Database to estimate the firm-level averageinterest rate on liabilities. The mean of this measure has similar time series properties to officialstatistics on the business borrowing rate, while also enabling detailed disaggregation acrossdifferent firm types. We document significant variation in interest rate across firms in differentindustries, and across firms with different apparent borrowing risk. Finally, we compare firmsself-reported views on whether they are finance-constrained to an estimated firm-specificinterest rate premium, showing that: finance-constrained firms have higher interest rate premiathan unconstrained firms; and that at least part of this difference in premia is explained by firm leveldifferences in risk between constrained and unconstrained firms.

Oil price volatility and firmprofitability: an empirical analysisof Shariah-compliant andnon-Shariah-compliant firms
Bugshan, Abdullah Salem,Bakry, Walid,Li, Yongqing
SSRN
Purposeâ€"This study examines the impact of oil price volatility on firm profitability. As Shariah-compliant firms operate under restrictions, the study also explores whether oil price volatility affects Shariah-compliant firms differently from their non-Shariah-compliant counterparts.Design/methodology/approachâ€"The study sample includes all non-financial firms listed on Gulf Cooperation Council stock exchanges from 2005 to 2019. In evaluating the oil price volatilityâ€"profitability relationship, static (panel fixed effects) and dynamic (system generalised method of moments) models were used.Findingsâ€"Oil price volatility significantly depresses firm profitability. In addition, Shariah-compliant firms are more significantly affected by oil price volatility than their non-Shariah-compliant peers. The results suggest that high oil price volatility exposes Shariah-compliant firms to higher bankruptcy risk than non-Shariah-compliant firms and that positive and negative oil price shocks have asymmetric effects on firm performance.Research limitations/implicationsâ€"The findings of the paper call for more economic diversification by supporting non-oil sectors in the region and raise the need for more development of Islam-compliant products that compete with traditional instruments to help Shariah-compliant firms cope with uncertainty. Moreover,managers need to prepare quick alert and response procedures to reduce the negative impacts of oil price volatility on profitability.Originality/valueâ€"To the best of the authors’ knowledge, this study is the first to explore the relationship between oil price volatility and profitability of non-financial firms. Further, the study extends prior Islamic corporate finance literature by enhancing the understanding of how Islamic corporate decisions affect firm performance during instability.

On existence of private unemployment insurance with advance information on future job losses
Denderski, Piotr
SSRN
We study the existence of a profitable unemployment insurance market in a dynamic economy with adverse selection rooting in information on future job losses. The new feature of the model is that the insurer and workers interact repeatedly. Repeated interactions make it possible to threaten workers with exclusion from future insurance benefits after a default on insurance premia. With exclusion, not only the insurance against the fundamental risk, but also against future bad news about job losses matters. In contrast to conventional wisdom, we find that private unemployment insurance in the US can be profitable for a relatively short exclusion length of one year. To stimulate the emergence of a private unemployment insurance market, policy makers can facilitate the creation of a registry that archives past defaults on insurance premia.

Parimutuel Betting Markets: Racetracks and Lotteries Revisited
Ziemba, William T.
SSRN
This paper discusses the state of the art in research in racetrack and lottery investment markets. Market efficiency and the pricing of various wagers is studied along with new developments since the Thaler and Ziemba (1988) review. The weak form inefficient market approach using stochastic programming optimization models changed racetrack betting from handicapping to a financial market allowing professional syndicates to operate as successful hedge funds. The topics discussed include the role of arbitrage and risk arbitrage, syndicates, betting exchange rebates, behavioral biases, portfolio insurance and fundamental and mispricing information in racetrack and lottery markets as well as other sports betting markets. I co-authored the Beat the Racetrack books which provided a weak form winning system for place and show bets and analyzed racing as a financial market and Efficiency of Racetrack Betting Markets, the "bible" for Hong Kong racing betting syndicates. These made me known to practitioners and consultants and I have consulted for several racing syndicates

Performance of Mutual Funds Management of Bangladesh-Evidence from Close End Mutual Funds in Dhaka Stock Exchange Limited (DSE)
Nur Alam, Quazi,Ahmed, Siraj Uddin
SSRN
The mutual fund market in Bangladesh is still small though the concept of the mutual fund was introduced in the year of 1980. Mutual fund sector is lucrative at all over the world where it has much impact on GDP and total market capitalization but Bangladesh is lagging behind in this sector and unable to make proper use of this sector. Open end mutual fund performance is quite good rather than close end mutual fund in Bangladesh. Weekly NAV data of 10 close end mutual fund has been taken in this paper. Not all the funds are doing bad, still few funds‟ performance are extraordinary. Management's inefficiency, lack of trust of investors and poor governance by regulator are the main reasons behind the poor performance of mutual fund sector in Bangladesh. Moreover, the recent unusual upsurge in the stock markets, the redemption of the matured funds, unstable margin rule, unskilled investors, asymmetric information, declaration of re-investment units as dividend have made the mutual fund industry much tougher than ever before. To bring back the confidence of the investors in the mutual fund sector this paper is bringing some recommendations to reduce the stressors up to a certain level.

Preferences for deferred annuities in the Japanese retirement market
Kitamura, Tomoki,NAKASHIMA, Kunio
SSRN
PurposeDeferred annuities, which offer longevity insurance with relatively low premiums, are a potential payout option in defined contribution (DC) pension plans in Japan. This study aims to measure individual preferences for these annuities.Design/methodology/approachThis study conducts stated choice experiments using an original internet survey. This methodology provides a decision-making scenario similar to that faced by individuals when making real retirement saving decisions. Subjective valuations of deferred, immediate and term annuities are compared.FindingsThis study finds that male individuals have an insignificant preference for deferred annuities â€" the benefits of which begin at an advanced age. On average, deferred annuities are considered a gamble, betting against life and individuals who are married and have higher financial assets tend to value them less.Originality/valueWhile previous studies, based on theory and simulations, have found that deferred annuities should be included in individual retirement assets, this study examines annuity preferences from the demand side (i.e. DC plan participants) â€"an approach that has not been addressed in the literature.

Quantum Machine Learning (Presentation Slides)
Kondratyev, Alexei
SSRN
The presentation slides cover two promising machine learning applications of parameterised quantum circuits: Quantum Neural Network (discriminative QML model) and Quantum Circuit Born Machine (generative QML model).

Quantum Portfolio Optimization with Investment Bands and Target Volatility
Samuel Palmer,Serkan Sahin,Rodrigo Hernandez,Samuel Mugel,Roman Orus
arXiv

In this paper we show how to implement in a simple way some complex real-life constraints on the portfolio optimization problem, so that it becomes amenable to quantum optimization algorithms. Specifically, first we explain how to obtain the best investment portfolio with a given target risk. This is important in order to produce portfolios with different risk profiles, as typically offered by financial institutions. Second, we show how to implement individual investment bands, i.e., minimum and maximum possible investments for each asset. This is also important in order to impose diversification and avoid corner solutions. Quite remarkably, we show how to build the constrained cost function as a quadratic binary optimization (QUBO) problem, this being the natural input of quantum annealers. The validity of our implementation is proven by finding the efficient frontier, using D-Wave Hybrid and its Advantage quantum processor, on static portfolios taking assets from the S&P500. We use three different subsets of this index. First, the S&P100 which consists of 100 of the largest companies of the S&P500; second, the 200 best-performing companies of the S&P500; and third, the full S&P500 itself. Our results show how practical daily constraints found in quantitative finance can be implemented in a simple way in current NISQ quantum processors, with real data, and under realistic market conditions. In combination with clustering algorithms, our methods would allow to replicate the behaviour of more complex indexes, such as Nasdaq Composite or others, in turn being particularly useful to build and replicate Exchange Traded Funds (ETF).



Re-examining the Philosophical Underpinnings of the Melting Pot vs. Multiculturalism in the Current Immigration Debate in the United States
Daniel Woldeab,Robert Yawson,Irina Woldeab
arXiv

Immigration to the United States is certainly not a new phenomenon, and it is therefore natural for immigration, culture and identity to be given due attention by the public and policy makers. However, current discussion of immigration, legal and illegal, and the philosophical underpinnings is lost in translation, not necessarily on ideological lines, but on political orientation. In this paper we reexamine the philosophical underpinnings of the melting pot versus multiculturalism as antecedents and precedents of current immigration debate and how the core issues are lost in translation. We take a brief look at immigrants and the economy to situate the current immigration debate. We then discuss the two philosophical approaches to immigration and how the understanding of the philosophical foundations can help streamline the current immigration debate.



Re: Docket No. CFPB-2021-0004, Request for Information and Comment on Financial Institutions’ Use of Artificial Intelligence, including Machine Learning, Questions 17, 8, 10, and 12
Willis, Lauren E.
SSRN
These Comments, submitted in response to the Consumer Financial Protection Bureau's Request for Information: Financial Institutions' Use of Artificial Intelligence, including Machine Learning, explain:(1) how and why financial institutions' use of Artificial Intelligence/Machine Learning (AI/ML) systems to produce and micro-target digital marketing and online sales processes will lead to deception, unfair treatment, and abuse of consumers, (2) why current approaches to financial institution supervision and enforcement can be stymied by these uses of AI/ML systems, (3) the risks to financial institutions of relying on AI/ML systems produced by third parties to design and micro-target digital marketing and online sales processes, (4) the risk that financial institutions' AI/ML digital marketing and online sales systems will target protected classes of consumers for deception, unfair treatment, and abuse, and (5) some suggestions for addressing these problems.

Real Interest Rate Behaviour and Monetary Policy in Myanmar:Based on Financial Libralisation Hypothesis
Han, Si Thu
SSRN
McKinnon’s complementarity hypothesis articulates that money and physicalcapital complements each other instead of substituting under a repressed financialsector. In spite of various economic and financial reforms in Myanmar, thefinancial sector of Myanmar is lagging behind international standards. Thus, thisstudy is designed to test McKinnon’s complementarity hypothesis in case ofMyanmar. Time series studies has been analyzed for said purpose. This study notonly determines unit root problem but also tests unit root problem in presence ofstructural break. Long run relationship is testified through bounds test in presenceof structural break. Long run and short run estimates are determined in order tocheck McKinnon’s complementarity hypothesis in static and dynamic setting asbounds testing approach distinguish feature is to provide static long run estimatesalong with dynamic short run estimates. Moreover, this study applies innovativeaccounting approach to determine strength of casual relationship betweenvariables of the study. Results of the study confirms McKinnon’scomplementarity hypothesis in long run and no evidence is found in case of shortrun. Thus, it can be deduced that investment is restricted due to availability offinance rather than cost of capital in Myanmar.

Real Interest Rate Behaviour and Monetary Policy in Myanmar:Based on Financial Libralisation Hypothesis
Han, Si Thu
SSRN
McKinnon’s complementarity hypothesis articulates that money and physicalcapital complements each other instead of substituting under a repressed financialsector. In spite of various economic and financial reforms in Myanmar, thefinancial sector of Myanmar is lagging behind international standards. Thus, thisstudy is designed to test McKinnon’s complementarity hypothesis in case ofMyanmar. Time series studies has been analyzed for said purpose. This study notonly determines unit root problem but also tests unit root problem in presence ofstructural break. Long run relationship is testified through bounds test in presenceof structural break. Long run and short run estimates are determined in order tocheck McKinnon’s complementarity hypothesis in static and dynamic setting asbounds testing approach distinguish feature is to provide static long run estimatesalong with dynamic short run estimates. Moreover, this study applies innovativeaccounting approach to determine strength of casual relationship betweenvariables of the study. Results of the study confirms McKinnon’scomplementarity hypothesis in long run and no evidence is found in case of shortrun. Thus, it can be deduced that investment is restricted due to availability offinance rather than cost of capital in Myanmar.

Reinsurance of multiple risks with generic dependence structures
Manuel Guerra,Alexandra B. Moura
arXiv

We consider the optimal reinsurance problem from the point of view of a direct insurer owning several dependent risks, assuming a maximal expected utility criterion and independent negotiation of reinsurance for each risk. Without any particular hypothesis on the dependency structure, we show that optimal treaties exist in a class of independent randomized contracts. We derive optimality conditions and show that under mild assumptions the optimal contracts are of classical (non-randomized) type. A specific for mof the optimality conditions applies in that case. We present a numerical scheme to solve the optimality conditions.



Relative Importance of Country and Firm-Specific Determinants of Capital Structure: A Multilevel Approach
Bilgin, Rumeysa
SSRN
This paper evaluates the relative importance of country and firm-specific determinants of capital structure using a multilevel modelling approach. Annual data for 18,201 public and non-financial firms from 66 countries are analysed for the period 2000â€"2016. Variance decomposition analysis is employed in order to assess the relative importance of country and firm levels. Additionally, random intercept and random coefficient models are used to analyse direct and indirect effects of capital structure determinants. Our results showed that country and firm levels explain approximately 10% and 60% of the total variability in capital structures, respectively. This shows that managers assign a higher importance to the firm-level factors when making capital structure decisions. Also country-level variables affect leverage choices to a lower extent.

Reservoir optimization and Machine Learning methods
Xavier Warin
arXiv

After showing the efficiency of feedforward networks to estimate control in high dimension in the global optimization of some storages problems, we develop a modification of an algorithm based on some dynamic programming principle. We show that classical feedforward networks are not effective to estimate Bellman values for reservoir problems and we propose some neural networks giving far better results. At last, we develop a new algorithm mixing LP resolution and conditional cuts calculated by neural networks to solve some stochastic linear problems.



Security Tokens
Momtaz, Paul P.
SSRN
This chapter synthesizes the economics, law, and technology of security tokens and Security Token Offerings (STOs). Security tokens are blockchain-based investment contracts that are subject to securities law. Interoperability, fractional ownership, market liquidity, and rapid settlement are among the main reasons why security tokens are a primary catalyst for the digitization of finance. We also empirically compare STOs with Initial Exchange Offerings (IEOs) and Initial Coin Offerings (ICOs). STOs take longer and raise more funding, however, controlling for other factors, the valuation of STOs and IEOs is lower than that in utility-token ICOs. These findings suggest an interesting avenue for future research. Moreover, both the law and the technology of security tokens needs to address important challenges related to the competent jurisdiction in multinational activities as well as blockchain interoperability, scalability, and natural resource degradation, respectively.

Singular Exotic Perturbation
Monciaud, Florian,Reghai, Adil
SSRN
The Local Stochastic Volatility model is the main model used to take into account the correct pricing and hedging with the volatility dynamic.We introduce a new methodology that combines Singular perturbation analysis and exotic greek computation. We obtain asymptotic formulae for the LSV impact which work extremely well. Tests are performed on the mostly traded Autocalls in the equity derivatives business.

The Alphas of Beta and Idiosyncratic Volatility
Poon, Percy,Yao, Tong,Zhang, Andrew (Jianzhong)
SSRN
We find that the relation between the idiosyncratic volatility (IVOL) anomaly and the beta anomaly is quite different at long horizons than at short horizons. IVOL has a significantly negative relation with subsequent stock returns at the short horizon of up to six months and beta does not predict stock returns at any horizon. However, both IVOL and beta significantly negatively predict stock alphas over horizons from a few months to beyond one year. At short horizons, neither anomaly can fully explain the other. At long horizons of beyond six months, the IVOL-alpha relation is explained by the beta-alpha relation. A measure of idiosyncratic volatility over a long window, popularly used by the investments industry to construct low-volatility portfolios, behaves similarly to beta in predicting returns and alphas at various horizons, and its predictive power is mostly explained by beta. Overall, IVOL and beta each has unique short-term information, but at long horizons the two anomalies appear to be the same. Our findings help reconcile a perceptional gap between academic studies and the investment industry on low volatility investing, and enrich the debate about the relation between the two low-risk anomalies.

The Economics of Crypto Funds
Momtaz, Paul P.
SSRN
We compile a unique dataset combining token offerings data with insitutional investment data, as well as proprietary performance data of crypto funds. Crypto funds are a new intermediary in entrepreneurial finance markets that employ sophisticated investment strategies typically only seen in public equity markets thanks to the liquidity of cryptocurrency markets. We find that token offerings receive higher valuations in the presence of crypto funds and post-offering institutional investments are also characterized by a jump in the cryptocurrency price. Consistent with the asset management literature, we find that crypto funds underperform the market (interestingly even before fees). We also examine how these patterns vary in the cross-section of crypto fund types and startup characteristics.

The Effect of Client Appraisal on the Efficiency of Micro Finance Bank
Esther Yusuf Enoch,Usman Abubakar Arabo,Abubakar Mahmud Digil
arXiv

One of the major problems confronting financial institutions most especially microfinance institutions is the increasing incidence of loan defaults and consequence loan losses which manifested in their financial performance with huge uncollectible loans and advances. This study assessed the effects of credit management on financial performance on microfinance institutions in Adamawa State, Nigeria. Specifically, we examine the effect of client appraisal on the efficiency of microfinance banks in Adamawa State. The methodology employed in this study is the survey method in which both primary and secondary sources were used in the collection of data. A multi-stage sampling method was adopted in selecting a sample of 21 respondents from a total population of 52 credit officers. Questionnaires were used in the due collection of data from the respondents. Descriptive statistics (simple percentage) and inferential statistics (regression analysis) were used to analyze the data collected and in testing the hypotheses. The study showed that client appraisal has a positive effect on efficiency and productivity.



The Effect of Self-Control and Financial Literacy on Impulse Borrowing: Experimental Evidence
Grohmann, Antonia,Hamdan, Jana
SSRN
This paper examines the effect of reduced self-control on impulsive borrowing in a laboratory experiment. We manipulate self-control using an ego depletion task and show that it is effective. Following the ego depletion task, participants can anonymously buy hot drinks on credit. We find no significant average effects, but find that treated individuals that have low financial literacy are more likely to borrow impulsively. We complement our experimental analysis with survey evidence that suggests that people with low self-control have more problems with the repayment of consumer debt. This relationship is, in line with the experimental results, weaker for individuals with high financial literacy.

The Effect of Tax Authority Enforcement on Earnings Informativeness
Zhao, Le
SSRN
This paper examines the impact of tax authority monitoring and enforcement on earnings informativeness. Using a staggered difference-in-differences design, I exploit the introduction of a new tax administration information system as a proxy for increased tax enforcement. The results imply that the informativeness of earnings improves with an increase in tax authority enforcement. Furthermore, I find that these results are concentrated in firms that are tax noncompliant, firms that are profitable and firms that have more severe income diversion and downward earnings manipulation. Additional tests show that tax expenses are more informative when tax enforcement increases. Overall, this study suggests that tax authority oversight engenders a positive effect on earnings informativeness by reducing the noise in earnings signals.

The Real Effects of Distressed Bank Mergers
Dinger, Valeriya,Schmidt, Christian,Theissen, Erik
SSRN
In this paper we employ a novel identification scheme to show the causal effect of negative shocks to banks on the real economy. The identification is based on exploiting distressed mergers of German savings banks. We show that these mergers represent exogenous shocks to the (initially non-distressed) acquiring bank. We find that in the years after a distressed merger: (i) the performance of acquiring savings banks deteriorates; (ii) the shock is transmitted to firms in the acquirer’s region which cut back their investments and reduce employment and (iii) the overall macroeconomic dynamics in the region of the acquirer deteriorates, leading to reductions in investment and employment growth. To support a causal interpretation of our results we also perform several tests that confirm that local economic dynamics is affected by the shock to the acquiring bank and not by real economic contagion.

The Relationship between Foreign Direct Investment and Economic Growth: A Case of Turkey
Orhan Gokmen
arXiv

This paper examines the relationship between net FDI inflows and real GDP for Turkey from 1970 to 2019. Although conventional economic growth theories and most empirical research suggest that there is a bi-directional positive effect between these macro variables, the results indicate that there is a uni-directional significant short-run positive effect of real GDP on net FDI inflows to Turkey by employing the Vector Error Correction Model, Granger Causality, Impulse Response Functions and Variance Decomposition. Also, there is no long-run effect has been found. The findings recommend Turkish authorities optimally benefit from the potential positive effect of net incoming FDI on the real GDP by allocating it for the productive sectoral establishments while effectively maintaining the country's real economic growth to attract further FDI inflows.



The Use of a Blue Ocean Marketing Strategy For Market Capture in the U.S: A Case Study of the Blujade Company
Kasongo, Jessica,Begolli, Anjeza,Haider, Danish,Zhang, Jinfeng
SSRN
Blujade is a new company which is still in the process of capturing a market share in the United States, for this reason, we have recommended a blue ocean strategy for them. For Blujade, our recommended business model is multilevel marketing. There are several reasons for recommending this business model, which are included in our analysis above. Blujade is a young company that wants to penetrate the US market, to stand out from its competitors we have come up with a blue ocean strategy, which would allow Blujade capture market share in the US, without going head-on with large competitors, such as Etsy and Pandora. To implement the brand ambassadorship model, Blujade will incur a one-time expenditure of $10,000 for creating an App and upgrading the website with computer vision. People will link their Instagram account to upload pictures to the APP or the website. The computer vision system will be automated at the server site. When a customer buys a product, she will be sent a link from the server site to become a brand ambassador if she wishes so. Since the link is going to be sent from the server site, it will be coded with a unique ID. So when she clicks on the link to sign up for the app to become a brand ambassador, her sales will be tracked through her unique ID. Lastly, since the multilevel marketing model works as a matrix system, meaning; people at the top level will make a commission based on what they sell, and a part of their compensation will also include what or how much the people at the bottom or the next tier sell, for this reason, the server site will also keep a tab of the links that brand ambassadors are sending to recruit other brand ambassadors. As per the survey conducted by our team, the recommended sales model will have an estimated sales conversion ratio of 20 percent. However, for the pilot program we assumed a sales conversion ratio of 3 percent. The biggest issue that we believe Blujade will have to tackle is to ensure that enough people join the App or become a brand ambassador. To address this problem, we recommend using the services of fashion influencers on Instagram to increase Blujade’s Instagram following. Also, we recommend hiring a business development officer here in Atlanta, this person will be responsible for reaching out to the 60,000-female population of university system of Georgia via events and student organizations. We believe this approach will translate into more people joining the recommended brand ambassadorship model. Based on a sales conversion ratio of 3%, our analysis concludes Blujade will be in a capacity to break even in an year by selling 3,600 units. This will translate into a net revenue of $180,000.

The impact of uncertainty on investment: Empirical challenges and a new estimator
Li, Delong,Sun, Yiguo
SSRN
This article re-examines the impact of uncertainty reflected in a firms stock-return volatilities on investment. In contrast to earlier empirical work, we simultaneously address nonlinearity, endogeneity, and mismeasurement issues in our study through a novel nonparametric approach. Our results indicate that the relation between investment and uncertainty is not only decreasing but also strongly concave. This nonlinear relation provides previously undocumented empirical evidence supporting the existing theoretical literature. We further show that untreated measurement error in Tobins q can introduce a substantial bias to the estimation, but the bias due to the endogeneity of uncertainty is small.

The link between Bitcoin and Google Trends attention
Aslanidis, Nektarios,Bariviera, Aurelio F.,G. López, Ã"scar
SSRN
This paper shows that Bitcoin is not correlated to a general uncertainty index as measured by the Google Trends data of Castelnuovo and Tran (2017). Instead, Bitcoin is linked to a Google Trends attention measure specific for the cryptocurrency market. First, we find a bidirectional relationship between Google Trends attention and Bitcoin returns up to six days. Second, information flows from Bitcoin volatility to Google Trends attention seem to be larger than information flows in the other direction. These relations hold across different sub-periods and different compositions of the proposed Google Trends Cryptocurrency index.

Two-Stage Electricity Markets with Renewable Energy Integration: Market Mechanisms and Equilibrium Analysis
Nathan Dahlin,Rahul Jain
arXiv

We consider a two-stage market mechanism for trading electricity including renewable generation as an alternative to the widely used multi-settlement market structure. The two-stage market structure allows for recourse decisions by the market operator, which are not possible in today's markets. We allow for different conventional generation cost curves in the forward and the real-time stages. We have considered costs of demand response programs and black outs, and adopt a DC power flow model to account for network constraints. Our first result is to show existence (by construction) of a sequential competitive equilibrium (SCEq) in such a two-stage market. We argue social welfare properties of such an SCEq, and then design a market mechanism that achieves social welfare maximization when the market participants are non-strategic. We also show that under either a congestion-free or a monopoly-free condition, an efficient Nash equilibrium exists.



Young people between education and the labour market during the COVID-19 pandemic in Italy
Davide Fiaschi,Cristina Tealdi
arXiv

We analyse the distribution and the flows between different types of employment (self-employment, temporary, and permanent), unemployment, education, and other types of inactivity, with particular focus on the duration of the school-to-work transition (STWT). The aim is to assess the impact of the COVID-19 pandemic in Italy on the careers of individuals aged 15-34. We find that the pandemic worsened an already concerning situation of higher unemployment and inactivity rates and significantly longer STWT duration compared to other EU countries, particularly for females and residents in the South of Italy. In the midst of the pandemic, individuals aged 20-29 were less in (permanent and temporary) employment and more in the NLFET (Neither in the Labour Force nor in Education or Training) state, particularly females and non Italian citizens. We also provide evidence of an increased propensity to return to schooling, but most importantly of a substantial prolongation of the STWT duration towards permanent employment, mostly for males and non Italian citizens. Our contribution lies in providing a rigorous estimation and analysis of the impact of COVID-19 on the carriers of young individuals in Italy, which has not yet been explored in the literature.



[enter Paper Title] Political Risks, Excess and Carry Trade Returns in Global Markets
Blenman, Lloyd P.
SSRN
Abstract Double-sorting 34 currencies from developed, and emerging economies into different portfolios based on the level of macro risk and political risk, we provide novel evidence that local determinants of sovereign risk, including local currency volatility, are priced in the FX markets. Local political risk in particular seems to have become an important carry trade risk factor in the post-2007 financial crisis era. This is the first research to explain carry trade excess returns with local sovereign risk factors as opposed to global sovereign risk factors as a whole. We find that currency volatility risk is also important as conjectured. High-macro risk portfolios in the face of high political risk lead to higher excess returns. Our results are robust across country categories and currency regimes, with stronger results for emerging economies.