Stock markets can become inefficient due to calendar anomalies known as day-of-the-week effect. Calendar anomalies are well-known in financial literature, but the phenomena remain to be explored in econophysics. In this paper we use multifractal analysis to evaluate if the temporal dynamics of market returns also exhibits calendar anomalies such as day-of-the-week effects. We apply the multifractal detrended fluctuation analysis (MF-DFA) to daily returns of market indices around the world for each day of the week. Our results indicate that individual days of the week are characterized by distinct multifractal properties. Monday returns tend to exhibit more persistent behavior and richer multifractal structures than other day-resolved returns. Shuffling the series reveals that multifractality arises both from a broad probability density function and from long-term correlations. From the time-dependent multifractal analysis we find that multifractal spectra for Monday returns are much wider than for other days during periods of financial crises. The presence of day-of-the-week effects in multifractal dynamics of market returns motivates further research on calendar anomalies from an econophysics perspective.

Financial speculators often seek to increase their potential gains with leverage. Debt is a popular form of leverage, and with over 39.88B USD of total value locked (TVL), the Decentralized Finance (DeFi) lending markets are thriving. Debts, however, entail the risks of liquidation, the process of selling the debt collateral at a discount to liquidators. Nevertheless, few quantitative insights are known about the existing liquidation mechanisms.

In this paper, to the best of our knowledge, we are the first to study the breadth of the borrowing and lending markets of the Ethereum DeFi ecosystem. We focus on Aave, Compound, MakerDAO, and dYdX, which collectively represent over 85% of the lending market on Ethereum. Given extensive liquidation data measurements and insights, we systematize the prevalent liquidation mechanisms and are the first to provide a methodology to compare them objectively. We find that the existing liquidation designs well incentivize liquidators but sell excessive amounts of discounted collateral at the borrowers' expenses. We measure various risks that liquidation participants are exposed to and quantify the instabilities of existing lending protocols. Moreover, we propose an optimal strategy that allows liquidators to increase their liquidation profit, which may aggravate the loss of borrowers.

Strategies adopted globally to mitigate the threat of COVID-19 have primarily involved lockdown measures with substantial economic and social costs with varying degrees of success. Morbidity patterns of COVID-19 variants have a strong association with age, while restrictive lockdown measures have association with negative mental health outcomes in some age groups. Reduced economic prospects may also afflict some age cohorts more than others. Motivated by this, we propose a model to describe COVID-19 community spread incorporating the role of age-specific social interactions. Through a flexible parameterisation of an age-structured deterministic Susceptible Exposed Infectious Removed (SEIR) model, we provide a means for characterising different forms of lockdown which may impact specific age groups differently. Social interactions are represented through age group to age group contact matrices, which can be trained using available data and are thus locally adapted. This framework is easy to interpret and suitable for describing counterfactual scenarios, which could assist policy makers with regard to minimising morbidity balanced with the costs of prospective suppression strategies. Our work originates from an Irish context and we use disease monitoring data from February 29th 2020 to January 31st 2021 gathered by Irish governmental agencies. We demonstrate how Irish lockdown scenarios can be constructed using the proposed model formulation and show results of retrospective fitting to incidence rates and forward planning with relevant ``what if/instead of'' lockdown counterfactuals with uncertainty quantification. Our formulation is agnostic to a specific locale, in that lockdown strategies in other regions can be straightforwardly encoded using this model. The methods we describe are made publicly available online through an accessible and easy to use web interface.

Conventional energy production based on fossil fuels causes emissions which contribute to global warming. Accurate energy system models are required for a cost-optimal transition to a zero-emission energy system, an endeavor that requires an accurate modeling of cost reductions due to technological learning effects. In this review, we summarize common methodologies for modeling technological learning and associated cost reductions. The focus is on learning effects in hydrogen production technologies due to their importance in a low-carbon energy system, as well as the application of endogenous learning in energy system models. Finally, we present an overview of the learning rates of relevant low-carbon technologies required to model future energy systems.

The study investigates the relationship between bank profitability and a comprehensive list of bank specific, industry specific and macroeconomic variables using unique panel data from 23 Bangladeshi banks with large market shares from 2005 to 2019 employing the Pooled Ordinary Least Square (POLS) Method for regression estimation. The random Effect model has been used to check for robustness. Three variables, namely, Return on Asset (ROA), Return on Equity (ROE), and Net Interest Margin (NIM), have been used as profitability proxies. Non-interest income, capital ratio, and GDP growth have been found to have a significant relationship with ROA. In addition to non-interest income, market share, bank size, and real exchange rates are significant explaining variables if profitability is measured as NIM. The only significant determinant of profitability measured by ROE is market share. The primary contribution of this study to the existing knowledge base is an extensive empirical analysis by covering the entire gamut of independent variables (bank specific, industry related, and macroeconomic) to explain the profitability of the banks in Bangladesh. It also covers an extensive and recent data set. Banking sector stakeholders may find great value from the outputs of this paper. Regulators and policymakers may find this useful in undertaking analyses in setting policy rates, banking industry stability, and impact assessment of critical policy measures before and after the enactment, etc. Investors and the bank management are to use the findings of this paper in analyzing the real drivers of profitability of the banks they are contemplating to invest and managing on a daily basis.

We interpret attitudes towards science and pseudosciences as cultural traits that diffuse in society through communication efforts exerted by agents. We present a tractable model that allows us to study the interaction among the diffusion of an epidemic, vaccination choices, and the dynamics of cultural traits. We apply it to study the impact of homophily between pro-vaxxers and anti-vaxxers on the total number of cases (the cumulative infection). We show that, during the outbreak of a disease, homophily has the direct effect of decreasing the speed of recovery. Hence, it may increase the number of cases and make the disease endemic. The dynamics of the shares of the two cultural traits in the population is crucial in determining the sign of the total effect on the cumulative infection: more homophily is beneficial if agents are not too flexible in changing their cultural trait, is detrimental otherwise.

Standard, PCA-based factor analysis suffers from a number of well known problems due to the random nature of pairwise correlations of asset returns. We analyse an alternative based on ICA, where factors are identified based on their non-Gaussianity, instead of their variance. Generalizations of portfolio construction to the ICA framework leads to two semi-optimal portfolio construction methods: a fat-tailed portfolio, which maximises return per unit of non-Gaussianity, and the hybrid portfolio, which asymptotically reduces variance and non-Gaussianity in parallel. For fat-tailed portfolios, the portfolio weights scale like performance to the power of $1/3$, as opposed to linear scaling of Kelly portfolios; such portfolio construction significantly reduces portfolio concentration, and the winner-takes-all problem inherent in Kelly portfolios. For hybrid portfolios, the variance is diversified at the same rate as Kelly PCA-based portfolios, but excess kurtosis is diversified much faster than in Kelly, at the rate of $n^{-2}$ compared to Kelly portfolios' $n^{-1}$ for increasing number of components $n$.

This review paper identifies the core evidence of research on employee engagement , considering a stern challenge facing the financial sector nowadays. The study highlights the noteworthy knowledge gaps that will support human resource management practitioners to embed in the research towards sectoral context. Pertinent articles were selected through key search points and excerpt-related literature. The key search points covered the topic related to different terms of engagement for example "employee engagement" OR "work engagement" OR "job engagement" OR "organization engagement" OR "staff engagement" OR "personnel engagement" which were steered in diverse context particularly financial sector. Through critically reviewing the literature for the last 11 years i.e., 2009-2019, we discovered 91 empirical studies in financial sector. From these studies, we found the overall concept of engagement and its different determinants (e.g., organizational factors, individual factors, job factors) as well as its various outcomes (e.g., employee outcomes, organizational outcomes). We also formulated a conceptual model to expand the body of knowledge in the area of employee engagement for a better understanding of its predictors and outcomes. Besides, limitations of the study and future recommendations are also contemplated.

In this paper we propose a multivariate quantile regression framework to forecast Value at Risk (VaR) and Expected Shortfall (ES) of multiple financial assets simultaneously, extending Taylor (2019). We generalize the Multivariate Asymmetric Laplace (MAL) joint quantile regression of Petrella and Raponi (2019) to a time-varying setting, which allows us to specify a dynamic process for the evolution of both VaR and ES of each asset. The proposed methodology accounts for the dependence structure among asset returns. By exploiting the properties of the MAL distribution, we then propose a new portfolio optimization method that minimizes the portfolio risk and controls for well-known characteristics of financial data. We evaluate the advantages of the proposed approach on both simulated and real data, using weekly returns on three major stock market indices. We show that our method outperforms other existing models and provides more accurate risk measure forecasts compared to univariate ones.

Modelling in finance is a challenging task: the data often has complex statistical properties and its inner workings are largely unknown. Deep learning algorithms are making progress in the field of data-driven modelling, but the lack of sufficient data to train these models is currently holding back several new applications. Generative Adversarial Networks (GANs) are a neural network architecture family that has achieved good results in image generation and is being successfully applied to generate time series and other types of financial data. The purpose of this study is to present an overview of how these GANs work, their capabilities and limitations in the current state of research with financial data, and present some practical applications in the industry. As a proof of concept, three known GAN architectures were tested on financial time series, and the generated data was evaluated on its statistical properties, yielding solid results. Finally, it was shown that GANs have made considerable progress in their finance applications and can be a solid additional tool for data scientists in this field.

We introduce a prototype agent-based model of the macroeconomy, with budgetary constraints at its core. The model is related to a class of constraint satisfaction problems (CSPs), which has been thoroughly investigated in computer science. The CSP paradigm allows us to propose an alternative price-setting mechanism: given agents' preferences and budgets, what set of prices satisfies the maximum number of agents? Such an approach permits the coupling of production and output within the economy to the allowed level of debt in a simplified framework. Within our model, we identify three different regimes upon varying the amount of debt that each agent can accumulate before defaulting. In presence of a very loose constraint on debt, endogenous crises leading to waves of synchronized bankruptcies are present. In the opposite regime of very tight debt constraining, the bankruptcy rate is extremely high and the economy remains structure-less. In an intermediate regime, the economy is stable with very low bankruptcy rate and no aggregate-level crises. This third regime displays a rich phenomenology:the system spontaneously and dynamically self-organizes in a set of cheap and expensive goods (i.e. some kind of "speciation"), with switches triggered by random fluctuations and feedback loops. Our analysis confirms the central role that debt levels play in the stability of the economy. More generally, our model shows that constraints at the individual scale can generate highly complex patterns at the aggregate level.

Financial portfolio management is one of the most applicable problems in reinforcement learning (RL) owing to its sequential decision-making nature. Existing RL-based approaches, while inspiring, often lack scalability, reusability, or profundity of intake information to accommodate the ever-changing capital markets. In this paper, we propose MSPM, a modularized and scalable, multi-agent RL-based system for financial portfolio management. MSPM involves two asynchronously updated units: an Evolving Agent Module (EAM) and Strategic Agent Module (SAM). A self-sustained EAM produces signal-comprised information for a specific asset using heterogeneous data inputs, and each EAM employs its reusability to have connections to multiple SAMs. An SAM is responsible for asset reallocation in a portfolio using profound information from the connected EAMs. With the elaborate architecture and the multi-step condensation of volatile market information, MSPM aims to provide a customizable, stable, and dedicated solution to portfolio management, unlike existing approaches. We also tackle the data-shortage issue of newly-listed stocks by transfer learning, and validate the indispensability of EAM with four different portfolios. Experiments on 8-year U.S. stock market data prove the effectiveness of MSPM in profit accumulation, by its outperformance over existing benchmarks.

We study mean field portfolio games in incomplete markets with random market parameters, where each player is concerned with not only her own wealth but also the relative performance to her competitors. We use the martingale optimality principle approach to characterize the unique Nash equilibrium in terms of a mean field FBSDE with quadratic growth, which is solvable under a weak interaction assumption. Motivated by the weak interaction assumption, we establish an asymptotic expansion result in powers of the competition parameter. When the market parameters do not depend on the Brownian paths, we get the Nash equilibrium in closed form. Moreover, when all the market parameters become time-independent, we revisit the games in [21] and our analysis shows that nonconstant equilibria do not exist in $L^\infty$, and the constant equilibrium obtained in [21] is unique in $L^\infty$, not only in the space of constant equilibria.

Stock prediction, with the purpose of forecasting the future price trends of stocks, is crucial for maximizing profits from stock investments. While great research efforts have been devoted to exploiting deep neural networks for improved stock prediction, two major issues still exist in recent studies. First, the capture of long-range dependencies in time series is not sufficiently addressed. Second, the chaotic property of financial time series fundamentally lowers prediction performance. In this study, we propose a novel framework to address both issues regarding stock prediction. Specifically, in terms of transforming time series into complex networks, we convert market price series into graphs. Then, structural information, referring to associations among temporal points and the node weights, is extracted from the mapped graphs to resolve the problems regarding long-range dependencies and the chaotic property. We take graph embeddings to represent the associations among temporal points as the prediction model inputs. Node weights are used as a priori knowledge to enhance the learning of temporal attention. The effectiveness of our proposed framework is validated using real-world stock data, and our approach obtains the best performance among several state-of-the-art benchmarks. Moreover, in the conducted trading simulations, our framework further obtains the highest cumulative profits. Our results supplement the existing applications of complex network methods in the financial realm and provide insightful implications for investment applications regarding decision support in financial markets.

We present new numerical schemes for pricing perpetual Bermudan and American options as well as $\alpha$-quantile options. This includes a new direct calculation of the optimal exercise barrier for early-exercise options. Our approach is based on the Spitzer identities for general L\'evy processes and on the Wiener-Hopf method. Our direct calculation of the price of $\alpha$-quantile options combines for the first time the Dassios-Port-Wendel identity and the Spitzer identities for the extrema of processes. Our results show that the new pricing methods provide excellent error convergence with respect to computational time when implemented with a range of L\'evy processes.

Nested stochastic modeling has been on the rise in many fields of the financial industry. Such modeling arises whenever certain components of a stochastic model are stochastically determined by other models. There are at least two main areas of applications, including (1) portfolio risk management in the banking sector and (2) principle-based reserving and capital requirements in the insurance sector. As financial instrument values often change with economic fundamentals, the risk management of a portfolio (outer loop) often requires the assessment of financial positions subject to changes in risk factors in the immediate future. The valuation of financial position (inner loop) is based on projections of cashflows and risk factors into the distant future. The nesting of such stochastic modeling can be computationally challenging.

Most of existing techniques to speed up nested simulations are based on curve fitting. The main idea is to establish a functional relationship between inner loop estimator and risk factors by running a limited set of economic scenarios, and, instead of running inner loop simulations, inner loop estimations are made by feeding other scenarios into the fitted curve. This paper presents a non-conventional approach based on the concept of sample recycling. Its essence is to run inner loop estimation for a small set of outer loop scenarios and to find inner loop estimates under other outer loop scenarios by recycling those known inner loop paths. This new approach can be much more efficient when traditional techniques are difficult to implement in practice.

According to common understanding, in free completion of a private product, market and price, the two main factors in the competition that leads to economic efficiency, always exist together. This paper, however, points out the phenomenon that in some free competitions the two factors are separated hence causing inefficiency. For one type, the market exists whereas the price is absent, i.e. free, for a product. An example of this type is the job application market where the problem of over-application commonly exists, costing recruiters much time in finding desired candidates from massive applicants, resulting in inefficiency. To solve the problem, this paper proposes a solution that the recruiters charge submission fees to the applications to make the competition complete with both factors, hence enhancing the efficiency. For the other type, the price exists whereas the market is absent for a product. An example of this type is the real estate agent market, where the price of the agents exists but the market, i.e. the facility allowing the sellers' information to be efficiently discovered, is largely absent, also causing inefficiency. In summary, the contribution of this paper consists of two aspects: one is the discovery of the possible separation of the two factors in free competitions; the other is, thanks to the discovery, a solution to the over-application problem in the job market.