We generalize the classic Shiller cyclically adjusted price-earnings ratio (CAPE) used for prediction of future total returns of the American stock market. We split total returns into three components: earnings growth, dividend yield, and valuation change. The first two components are fundamental, the third is speculative. We treat earnings growth as exogenous. Combining the other two components gives us a new valuation measure, which fits autoregression of order 1 with Gaussian innovations, centered at 4.6%. Therefore, long-term total returns equals long-term earnings growth plus 4.6%. We compare this new valuation measure with CAPE. We confirm the classic rule: a retiree should invest in stocks and withdraw 4% of initial wealth after adjusting for inflation, this will preserve wealth and establish retirement income.

Bounded rationality is an important consideration stemming from the fact that agents often have limits on their processing abilities, making the assumption of perfect rationality inapplicable to many real tasks. We propose an information-theoretic approach to the inference of agent decisions under Smithian competition. The model explicitly captures the boundedness of agents (limited in their information-processing capacity) as the cost of information acquisition for expanding their prior beliefs. The expansion is measured as the Kullblack-Leibler divergence between posterior decisions and prior beliefs. When information acquisition is free, the homo economicus agent is recovered, while in cases when information acquisition becomes costly, agents instead revert to their prior beliefs. The maximum entropy principle is used to infer least-biased decisions based upon the notion of Smithian competition formalised within the Quantal Response Statistical Equilibrium framework. The incorporation of prior beliefs into such a framework allowed us to systematically explore the effects of prior beliefs on decision-making in the presence of market feedback, as well as importantly adding a temporal interpretation to the framework. We verified the proposed model using Australian housing market data, showing how the incorporation of prior knowledge alters the resulting agent decisions. Specifically, it allowed for the separation of past beliefs and utility maximisation behaviour of the agent as well as the analysis into the evolution of agent beliefs.

Modelling joint dynamics of liquid vanilla options is crucial for arbitrage-free pricing of illiquid derivatives and managing risks of option trade books. This paper develops a nonparametric model for the European options book respecting underlying financial constraints and while being practically implementable. We derive a state space for prices which are free from static (or model-independent) arbitrage and study the inference problem where a model is learnt from discrete time series data of stock and option prices. We use neural networks as function approximators for the drift and diffusion of the modelled SDE system, and impose constraints on the neural nets such that no-arbitrage conditions are preserved. In particular, we give methods to calibrate \textit{neural SDE} models which are guaranteed to satisfy a set of linear inequalities. We validate our approach with numerical experiments using data generated from a Heston stochastic local volatility model.

Background: A major question in Covid-19 research is whether democracies handled the Covid-19 pandemic crisis better or worse than authoritarian countries. However, it is important to consider the issues of democracy versus authoritarianism, and state fragility, when examining official Covid-19 death counts in research, because these factors can influence the accurate reporting of pandemic deaths by governments. In contrast, excess deaths are less prone to variability in differences in definitions of Covid-19 deaths and testing capacities across countries. Here we use excess pandemic deaths to explore potential relationships between political systems and public health outcomes.

Methods: We address these issues by comparing the official government Covid-19 death counts in a well-established John Hopkins database to the generally more reliable excess mortality measure of Covid-19 deaths, taken from the recently released World Mortality Dataset. We put the comparison in the context of the political and fragile state dimensions.

Findings: We find (1) significant potential underreporting of Covid-19 deaths by authoritarian governments and governments with high state fragility and (2) substantial geographic variation among countries and regions with regard to standard democracy indices. Additionally, we find that more authoritarian governments are (weakly) associated with more excess deaths during the pandemic than democratic governments.

Interpretations: The inhibition and censorship of information flows, inherent to authoritarian states, likely results in major inaccuracies in pandemic statistics that confound global public health analyses. Thus, both excess pandemic deaths and official Covid-19 death counts should be examined in studies using death as an outcome variable.

This paper presents a framework of imitating the price behavior of the underlying stock for reinforcement learning option price. We use accessible features of the equities pricing data to construct a non-deterministic Markov decision process for modeling stock price behavior driven by principal investor's decision making. However, low signal-to-noise ratio and instability that appear immanent in equity markets pose challenges to determine the state transition (price change) after executing an action (principal investor's decision) as well as decide an action based on current state (spot price). In order to conquer these challenges, we resort to a Bayesian deep neural network for computing the predictive distribution of the state transition led by an action. Additionally, instead of exploring a state-action relationship to formulate a policy, we seek for an episode based visible-hidden state-action relationship to probabilistically imitate principal investor's successive decision making. Our algorithm then maps imitative principal investor's decisions to simulated stock price paths by a Bayesian deep neural network. Eventually the optimal option price is reinforcement learned through maximizing the cumulative risk-adjusted return of a dynamically hedged portfolio over simulated price paths of the underlying.

Four radical ideas are presented. First, that the rationale for cancellation of principal can be modified in modern banking. Second, that non-cancellation of loan principal upon payment may cure an old problem of maintenance of positive equity in the non-governmental sector. Third, that crediting this money to local/state government, and fourth crediting to at-risk loans that create new utility value, creates an additional virtuous monetary circuit that ties finances of government directly to commercial activity.

Taking these steps can cure a problem I have identified with modern monetary theory, which is that breaking the monetary circuit of taxation in the minds of politicians will free them from centuries of restraint, optimizing their opportunities for implementing tyranny. It maintains and strengthens the current circuit, creating a new, more direct monetary circuit that in some respects combats inequality.

Both within the United States and worldwide, the city of Detroit has become synonymous with economic decline, depopulation, and crime. Is Detroit's situation unique, or can similar neighborhoods be found elsewhere? This study examines Census block group data, as well as local crime statistics for 2014, for a set of five Midwestern cities. Roughly three percent of Chicago's and Milwaukee's block groups--all of which are in majority nonwhite areas--exceed Detroit's median values for certain crimes, vacancies, and a poverty measure. This figure rises to 11 percent for St. Louis, while Minneapolis has only a single "Detroit-like" block group. Detroit's selected areas are more likely to be similar to the entire city itself, both spatially and statistically, while these types of neighborhoods for highly concentrated "pockets" of poverty elsewhere. Development programs that are targeted in one city, therefore, must take these differences into account and should be targeted to appropriate neighborhoods.

The energy trade is an important pillar of each country's development, making up for the imbalance in the production and consumption of fossil fuels. Geopolitical risks affect the energy trade of various countries to a certain extent, but the causes of geopolitical risks are complex, and energy trade also involves many aspects, so the impact of geopolitics on energy trade is also complex. Based on the monthly data from 2000 to 2020 of 17 emerging economies, this paper employs the fixed-effect model and the regression-discontinuity (RD) model to verify the negative impact of geopolitics on energy trade first and then analyze the mechanism and heterogeneity of the impact. The following conclusions are drawn: First, geopolitics has a significant negative impact on the import and export of the energy trade, and the inhibition on the export is greater than that on the import. Second, the impact mechanism of geopolitics on the energy trade is reflected in the lagging effect and mediating effect on the imports and exports; that is, the negative impact of geopolitics on energy trade continued to be significant 10 months later. Coal and crude oil prices, as mediating variables, decreased to reduce the imports and exports, whereas natural gas prices showed an increase. Third, the impact of geopolitics on energy trade is heterogeneous in terms of national attribute characteristics and geo-event types.

The difficulty of balance between environment and energy consumption makes countries and enterprises face a dilemma, and improving energy efficiency has become one of the ways to solve this dilemma. Based on data of 158 countries from 1980 to 2018, the dynamic TFP of different countries is calculated by means of the Super-SBM-GML model. The TFP is decomposed into indexes of EC (Technical Efficiency Change), TC (Technological Change) and EC has been extended to PEC (Pure Efficiency Change) and SEC (Scale Efficiency Change). Then the fixed effect model and fixed effect panel quantile model are used to analyze the moderating effect and exogenous effect of energy efficiency on PM2.5 concentration on the basis of verifying that energy efficiency can reduce PM2.5 concentration. We conclude, first, the global energy efficiency has been continuously improved during the sample period, and both of technological progress and technical efficiency have been improved. Second, the impact of energy efficiency on PM2.5 is heterogeneous which is reflected in the various elements of energy efficiency decomposition. The increase of energy efficiency can inhibit PM2.5 concentration and the inhibition effect mainly comes from TC and PEC but SEC promotes PM2.5 emission. Third, energy investment plays a moderating role in the environmental protection effect of energy efficiency. Fourth, the impact of energy efficiency on PM2.5 concentration is heterogeneous in terms of national attribute, which is embodied in the differences of national development, science & technology development level, new energy utilization ratio and the role of international energy trade.

Today, almost all banks have adopted ICT as a means of enhancing their banking service quality. These banks provide ICT based electronic service which is also called electronic banking, internet banking or online banking etc to their customers. Despite the increasing adoption of electronic banking and it relevance towards end users satisfaction, few investigations has been conducted on factors that enhanced end users satisfaction perception. In this research, an empirical analysis has been conducted on factors that influence electronic banking user's satisfaction perception and the relationship between these factors and the customer's satisfaction. The study will help bank industries in improving the level of their customer's satisfaction and increase the bond between a bank and its customer.

This study evaluates the effect of collection policy on portfolio quality of microfinance banks in Adamawa State, Nigeria. Real data were collected from 51 credit officers, then a multi-stage sampling method was used to select a sample of 21 respondents from the population (i.e., 51 credit officers). In addition, we used regression analysis and descriptive statistics to analyze the data collected and to also test our proposed hypothesis. Based on the evaluation performed, the results showed that collection policy has a higher effect on portfolio quality. Hence, the study showed that microfinance banks should adhere to strict or stiff debt collection policy as strictness in collection policy help the banks to recover their loans, thereby improving the portfolio quality of the bank.

We present an alternative approach to the forecasting of motor vehicle collision rates. We adopt an oft-used tool in mathematical finance, the Heston Stochastic Volatility model, to forecast the short-term and long-term evolution of motor vehicle collision rates. We incorporate a number of extensions to the Heston model to make it fit for modelling motor vehicle collision rates. We incorporate the temporally-unstable and non-deterministic nature of collision rate fluctuations, and introduce a parameter to account for periods of accelerated safety. We also adjust estimates to account for the seasonality of collision patterns. Using these parameters, we perform a short-term forecast of collision rates and explore a number of plausible scenarios using long-term forecasts. The short-term forecast shows a close affinity with realised rates (over 95% accuracy), and outperforms forecasting models currently used in road safety research (Vasicek, SARIMA, SARIMA-GARCH). The long-term scenarios suggest that modest targets to reduce collision rates (1.83% annually) and targets to reduce the fluctuations of month-to-month collision rates (by half) could have significant benefits for road safety. The median forecast in this scenario suggests a 50% fall in collision rates, with 75% of simulations suggesting that an effective change in collision rates is observed before 2044. The main benefit the model provides is eschewing the necessity for setting unreasonable safety targets that are often missed. Instead, the model presents the effects that modest and achievable targets can have on road safety over the long run, while incorporating random variability. Examining the parameters that underlie expected collision rates will aid policymakers in determining the effectiveness of implemented policies.

We present the method of complementary ensemble empirical mode decomposition (CEEMD) and Hilbert-Huang transform (HHT) for analyzing nonstationary financial time series. This noise-assisted approach decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the associated instantaneous energy-frequency spectrum to illustrate the properties of various timescales embedded in the original time series. Using HHT, we generate a collection of new features and integrate them into machine learning models, such as regression tree ensemble, support vector machine (SVM), and long short-term memory (LSTM) neural network. Using empirical financial data, we compare several HHT-enhanced machine learning models in terms of forecasting performance.

We propose a method for conducting asymptotically valid inference for treatment effects in a multi-valued treatment framework where the number of units in the treatment arms can be small and do not grow with the sample size. We accomplish this by casting the model as a semi-/non-parametric conditional quantile model and using known finite sample results about the law of the indicator function that defines the conditional quantile. Our framework allows for structural functions that are non-additively separable, with flexible functional forms and heteroskedasticy in the residuals, and it also encompasses commonly used designs like difference in difference. We study the finite sample behavior of our test in a Monte Carlo study and we also apply our results to assessing the effect of weather events on GDP growth.

Starting solely with a set of possible prices for a traded asset $S$ (in infinite discrete time) expressed in units of a numeraire, we explain how to construct a Daniell type of integral representing prices of integrable functions depending on the asset. Such functions include the values of simple dynamic portfolios obtained by trading with $S$ and the numeraire. The space of elementary integrable functions, i.e. the said portfolio values, is not a vector lattice. It then follows that the integral is not classical, i.e. it is not associated to a measure. The essential ingredient in constructing the integral is a weak version of the no-arbitrage condition but here expressed in terms of properties of the trajectory space. We also discuss the continuity conditions imposed by Leinert (Archiv der Mathematik, 1982) and K\"onig (Mathematische Annalen, 1982) in the abstract theory of non-lattice integration from a financial point of view and establish some connections between these continuity conditions and the existence of martingale measures

This paper studies the infinite horizon optimal consumption with a path-dependent reference under the exponential utility. The performance is measured by the difference between the non-negative consumption rate and a fraction of the historical consumption maximum. The consumption running maximum process is chosen as an auxiliary state process that renders the value function two dimensional. The Hamilton-Jacobi-Bellman (HJB) equation can be heuristically expressed in a piecewise manner across different regions to take into account all constraints. By employing the dual transform and smooth-fit principle, some thresholds of the wealth variable are derived such that the classical solution to the HJB equation and the feedback optimal investment and consumption strategies can be obtained in the closed form in each region. The complete proof of the verification theorem is provided and numerical examples are presented to illustrate some financial implications.

We consider indifference pricing of contingent claims consisting of payment flows in a discrete time model with proportional transaction costs and under exponential disutility. This setting covers utility maximisation as a special case. A dual representation is obtained for the associated disutility minimisation problem, together with a dynamic procedure for solving it. This leads to efficient and convergent numerical procedures for indifference pricing, optimal trading strategies and shadow prices that apply to a wide range of payoffs, a large range of time steps and all magnitudes of transaction costs.

Involving residential actors in the energy transition is crucial for its success. Local energy generation, consumption and trading are identified as desirable forms of involvement, especially in energy communities. The potentials for energy communities in the residential building stock are high but are largely untapped in multi-family buildings. In many countries, rapidly evolving legal frameworks aim at overcoming related barriers, e.g. ownership structures, principal-agent problems and system complexity. But academic literature is scarce regarding the techno-economic and environmental implications of such complex frameworks. This paper develops a mixed-integer linear program (MILP) optimisation model for assessing the implementation of multi-energy systems in an energy community in multi-family buildings with a special distinction between investor and user. The model is applied to the German Tenant Electricity Law. Based on hourly demands from appliances, heating and electric vehicles, the optimal energy system layout and dispatch are determined. The results contain a rich set of performance indicators that demonstrate how the legal framework affects the technologies' interdependencies and economic viability of multi-energy system energy communities. Certain economic technology combinations may fail to support national emissions mitigation goals and lead to lock-ins in Europe's largest residential building stock. The subsidies do not lead to the utilisation of a battery storage. Despite this, self-sufficiency ratios of more than 90% are observable for systems with combined heat and power plants and heat pumps. Public CO2 mitigation costs range between 147.5-272.8 EUR/tCO2. Finally, the results show the strong influence of the heat demand on the system layout.

COVID-19 pandemic has sharply projected the globally persistent multi-dimensional fundamental challenges in securing general socio-economic wellbeing of the society. The problems intensify with increasing population densities and also vary with several socio-economic-geo-cultural activity parameters. These problems directly highlight the urgent need for accomplishing the interdependent United Nations Sustainable Development Goals (SDGs) to ensure that in future we do not enter into vicious loops of contracting newer zoonotic viruses and need not search for their vaccines while incurring socio-economic havoc. Behavioural changes in human activities/responses are indispensable for achieving the interdependent SDGs. Using root cause analysis approach, we have developed a yearly assessment framework for viably analysing and identifying requisite region-specific downstream/upstream socio-economic policies to reach the SDGs. The framework makes use of an infographic bar chart representation based on the normalised values of 20 human activity/impact parameters classified under three categories as - negative, limiting and positive. With a holistic view encompassing the SDGs, we illustrate through this framework the impact and urgent need of region-specific human behavioural reforms. This framework enables the foresight about policies regarding their potential in bringing down the negative parameter values to the desired zero level for accomplishing the SDGs through planetary health.

It has been claimed that COVID-19 public stimulus packages are sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 degree C target of the Paris Agreement. We argue that this view is short-sighted, overly reliant on public investment and misrepresents the grand challenges that climate change mitigation entails.

We investigate the links between various no-arbitrage conditions and the existence of pricing functionals in general markets, and prove the Fundamental Theorem of Asset Pricing therein. No-arbitrage conditions, either in this abstract setting or in the case of a market consisting of European Call options, give rise to duality properties of infinite-dimensional sub- and super-hedging problems. With a view towards applications, we show how duality is preserved when reducing these problems over finite-dimensional bases. We finally perform a rigorous perturbation analysis of those linear programming problems, and highlight numerically the influence of smile extrapolation on the bounds of exotic options.

In this article, we present an approach which allows to take into account the effect of extreme values in the modeling of financial asset returns and in the valorisation of associeted options. Specifically, the marginal distribution of assets returns is modeled by a mixture of two gaussiens distributions. Moreover, we model the joint dependence structure of the returns using an extremal copula which is suitable for our financial data. Applications are made on the Atos and Dassault Systems actions of the CAC40 index. Monte-Carlo method is used to compute the values of some equity options: the call on maximum, the call on minimum, the digital option and the spreads option with the basket (Atos, Dassault systems).

Monte Carlo integration using quantum computers has been widely investigated, including applications to concrete problems. It is known that quantum algorithms based on quantum amplitude estimation (QAE) can compute an integral with a smaller number of iterative calls of the quantum circuit which calculates the integrand, than classical methods call the integrand subroutine. However, the issues about the iterative operations in the integrand circuit have not been discussed so much. That is, in the high-dimensional integration, many random numbers are used for calculation of the integrand and in some cases similar calculations are repeated to obtain one sample value of the integrand. In this paper, we point out that we can reduce the number of such repeated operations by a combination of the nested QAE and the use of pseudorandom numbers (PRNs), if the integrand has the separable form with respect to contributions from distinct random numbers. The use of PRNs, which the authors originally proposed in the context of the quantum algorithm for Monte Carlo, is the key factor also in this paper, since it enables parallel computation of the separable terms in the integrand. Furthermore, we pick up one use case of this method in finance, the credit portfolio risk measurement, and estimate to what extent the complexity is reduced.

Spaces of convex and concave functions appear naturally in theory and applications. For example, convex regression and log-concave density estimation are important topics in nonparametric statistics. In stochastic portfolio theory, concave functions on the unit simplex measure the concentration of capital, and their gradient maps define novel investment strategies. The gradient maps may also be regarded as optimal transport maps on the simplex. In this paper we construct and study probability measures supported on spaces of concave functions. These measures may serve as prior distributions in Bayesian statistics and Cover's universal portfolio, and induce distribution-valued random variables via optimal transport. The random concave functions are constructed on the unit simplex by taking a suitably scaled (mollified, or soft) minimum of random hyperplanes. Depending on the regime of the parameters, we show that as the number of hyperplanes tends to infinity there are several possible limiting behaviors. In particular, there is a transition from a deterministic almost sure limit to a non-trivial limiting distribution that can be characterized using convex duality and Poisson point processes.

This study examined the problems of personal income tax on revenue generation in Gombe state. The methodology used in data collection is survey, which utilized both primary and secondary types of data. Purposive sampling technique was adopted in selecting a sample of 150 respondents from both employees of state board of internal revenue service and tax payers in the state. The chi square statistics test was used in testing the hypotheses. The study found that tax avoidance/evasion and complete absences of information technology are serious problems affecting revenue generation in the state. It recommends that government should device strict measures in dealing and punishing individuals engage in tax avoidance and evasion. It should also employ the use of information technology as it is the only way problems experience in personal income tax collection can be reduced drastically.

In this work, we modify the Affine Wealth Model of wealth distributions to examine the effects of nonconstant redistribution on the very wealthy. Previous studies of this model, restricted to flat redistribution schemes, have demonstrated the presence of a phase transition to a partially wealth-condensed state, or "partial oligarchy", at the critical value of an order parameter. These studies have also indicated the presence of an exponential tail in wealth distribution precisely at criticality. Away from criticality, the tail was observed to be Gaussian. In this work, we generalize the flat redistribution within the Affine Wealth Model to allow for an essentially arbitrary redistribution policy. We show that the exponential tail observed near criticality in prior work is in fact a special case of a much broader class of critical, slower-than-Gaussian decays that depend sensitively on the corresponding asymptotic behavior of the progressive redistribution model used. We thereby demonstrate that the functional form of the tail of the wealth distribution of a near-critical society is not universal in nature, but rather is entirely determined by the specifics of public policy decisions. This is significant because most major economies today are observed to be near-critical.

We employ an ARDL bounds testing approach to cointegration and Unrestricted Error Correction Models (UECMs) to estimate the relationship between income and CO2 emissions per capita in 21 Latin American Countries (LACs) over 1960-2017. Using time series we estimate six different specifications of the model to take into account the independent effect on CO2 emissions per capita of different factors considered as drivers of different dynamics of CO2 emissions along the development path. This approach allows to address two concerns. First, the estimation of the model controlling for different variables serves to assess if the EKC hypothesis is supported by evidence in any of the LACs considered and to evaluate if this evidence is robust to different model specifications. Second, the inclusion of control variables accounting for the effect on CO2 emissions is directed at increasing our understanding of CO2 emissions drivers in different countries. The EKC hypothesis effectively describes the long term income-emissions relationship only in a minority of LACs and, in many cases, the effect on CO2 emissions of different factors depends on the individual country experience and on the type and quantity of environmental policies adopted. Overall, these results call for increased environmental action in the region.

Recently, financial institutes have been dealing with an increase in financial crimes. In this context, financial services firms started to improve their vigilance and use new technologies and approaches to identify and predict financial fraud and crime possibilities. This task is challenging as institutions need to upgrade their data and analytics capabilities to enable new technologies such as Artificial Intelligence (AI) to predict and detect financial crimes. In this paper, we put a step towards AI-enabled financial crime detection in general and money laundering detection in particular to address this challenge. We study and analyse the recent works done in financial crime detection and present a novel model to detect money laundering cases with minimum human intervention needs.