In the framework of evidence theory, data fusion combines the confidence functions of multiple different information sources to obtain a combined confidence function. Stock price prediction is the focus of economics. Stock price forecasts can provide reference data. The Dempster combination rule is a classic method of fusing different information. By using the Dempster combination rule and confidence function based on the entire time series fused at each time point and future time points, and the preliminary forecast value obtained through the time relationship, the accurate forecast value can be restored. This article will introduce the prediction method of evidence theory. This method has good running performance, can make a rapid response on a large amount of stock price data, and has far-reaching significance.

This paper develops a new model of business cycles. The model is economical in that it is solved with an aggregate demand-aggregate supply diagram, and the effects of shocks and policies are obtained by comparative statics. The model builds on two unconventional assumptions. First, producers and consumers meet through a matching function. Thus, the model features unemployment, which fluctuates in response to aggregate demand and supply shocks. Second, wealth enters the utility function, so the model allows for permanent zero-lower-bound episodes. In the model, the optimal monetary policy is to set the interest rate at the level that eliminates the unemployment gap. This optimal interest rate is computed from the prevailing unemployment gap and monetary multiplier (the effect of the nominal interest rate on the unemployment rate). If the unemployment gap is exceedingly large, monetary policy cannot eliminate it before reaching the zero lower bound, but a wealth tax can.

We consider a model of a simple financial system consisting of a leveraged investor that invests in a risky asset and manages risk by using Value-at-Risk (VaR). The VaR is estimated by using past data via an adaptive expectation scheme. We show that the leverage dynamics can be described by a dynamical system of slow-fast type associated with a unimodal map on [0,1] with an additive heteroscedastic noise whose variance is related to the portfolio rebalancing frequency to target leverage. In absence of noise the model is purely deterministic and the parameter space splits in two regions: (i) a region with a globally attracting fixed point or a 2-cycle; (ii) a dynamical core region, where the map could exhibit chaotic behavior. Whenever the model is randomly perturbed, we prove the existence of a unique stationary density with bounded variation, the stochastic stability of the process and the almost certain existence and continuity of the Lyapunov exponent for the stationary measure. We then use deep neural networks to estimate map parameters from a short time series. Using this method, we estimate the model in a large dataset of US commercial banks over the period 2001-2014. We find that the parameters of a substantial fraction of banks lie in the dynamical core, and their leverage time series are consistent with a chaotic behavior. We also present evidence that the time series of the leverage of large banks tend to exhibit chaoticity more frequently than those of small banks.

By interpreting exporters' dynamics as a complex learning process, this paper constitutes the first attempt to investigate the effectiveness of different Machine Learning (ML) techniques in predicting firms' trade status. We focus on the probability of Colombian firms surviving in the export market under two different scenarios: a COVID-19 setting and a non-COVID-19 counterfactual situation. By comparing the resulting predictions, we estimate the individual treatment effect of the COVID-19 shock on firms' outcomes. Finally, we use recursive partitioning methods to identify subgroups with differential treatment effects. We find that, besides the temporal dimension, the main factors predicting treatment heterogeneity are interactions between firm size and industry.

In this note an assessment of the condition \(K_w/K=S_w/S\) is made to interpret its meaning to the Passineti's theory of distribution\cite{pasinetti1962rate}. This condition leads the theory to enforce the result \(s_w\rightarrow0\) as \(P_w\rightarrow 0\), which is the Pasinetti's description about behavior of the workers. We find that the Pasinetti's claim, of long run worker's propensity to save as not influencing the distribution of income between profits and the wage can not be generalized. This claim is found to be valid only when \(W>>P_w\) or \(P_w=0\) with \(W\ne0\). In practice, the Pasinetti's condition imposes a restriction on the actual savings by one of the agents to a lower level compared to its full saving capacity. An implied relationship between the propensities to save by workers and capitalists shows that the Passineti's condition can be practiced only through a contract for a constant value of \(R=s_w/s_c\), to be agreed upon between the workers and the capitalists. It is showed that the Passineti's condition can not be described as a dynamic equilibrium of economic growth. Implementation of this condition (a) may lead to accumulation of unsaved income, (b) reduces growth of capital, (c)is not practicable and (d) is not warranted. We have also presented simple mathematical steps for the derivation of the Pasinetti's final equation compared to those presented in \cite{pasinetti1962rate}

Demand-pull and technology-push are linked to an empirical two-layer network-based on coupled cross-industrial input-output (IO) and patent citation links among 155 4-digit (NAICS) US-industries in 1976-2006 to study the evolution of industry hierarchies and link formation. Both layers co-evolve, but differently: The patent network became denser and increasingly skewed, while market hierarchies are balanced and sluggish in change. Industries became more similar by patent citations, but less by IO linkages. Having similar R&D capabilities as other big industries is positively related to innovation and growth, but relying on the same market inputs is unfavorable but may incite industries to explore other technological pathways. A tentative interpretation is the non-rivalry of intangible knowledge. This may strengthen existing R&D trajectories. Growth in the market is constrained by competition and market pressure may trigger a re-direction in both layers. This work is limited by its reliance on endogenously evolving classifications.

As environmental concerns mostly drive the electrification of our economy and the corresponding increase in demand for battery storage systems, information about the potential environmental impacts of the different battery systems is required. However, this kind of information is scarce for emerging post-lithium systems such as the magnesium-sulfur (MgS) battery. Therefore, we use life cycle assessment following a cradle-to-gate perspective to quantify the cumulative energy demand and potential environmental impacts per Wh of the storage capacity of a hypothetical MgS battery (46 Wh/kg). Furthermore, we also estimate global warming potential (0.33 kg CO2 eq/Wh) , fossil depletion potential (0.09 kg oil eq / Wh), ozone depletion potential (2.5E-08 kg CFC-11/Wh) and metal depletion potential (0.044 kg Fe eq/Wh), associated with the MgS battery production. The battery is modelled based on an existing prototype MgS pouch cell and hypothetically optimised according to the current state of the art in lithium-ion batteries (LIB), exploring future improvement potentials. It turns out that the initial (non-optimised) prototype cell cannot compete with current LIB in terms of energy density or environmental performance, mainly due to the high share of non-active components, decreasing its performance substantially. Therefore, if the assumed evolutions of the MgS cell composition are achieved to overcome current design hurdles and reach a comparable lifespan, efficiency, cost and safety levels to that of existing LIB; then the MgS battery has significant potential to outperform both existing LIB, and lithium-sulfur batteries.

Financial markets are difficult to predict due to its complex systems dynamics. Although there have been some recent studies that use machine learning techniques for financial markets prediction, they do not offer satisfactory performance on financial returns. We propose a novel one-dimensional convolutional neural networks (CNN) model to predict financial market movement. The customized one-dimensional convolutional layers scan financial trading data through time, while different types of data, such as prices and volume, share parameters (kernels) with each other. Our model automatically extracts features instead of using traditional technical indicators and thus can avoid biases caused by selection of technical indicators and pre-defined coefficients in technical indicators. We evaluate the performance of our prediction model with strictly backtesting on historical trading data of six futures from January 2010 to October 2017. The experiment results show that our CNN model can effectively extract more generalized and informative features than traditional technical indicators, and achieves more robust and profitable financial performance than previous machine learning approaches.

Progress to-date towards the UN Agenda 2030 has fallen short of expectations. We undertake a model-based global integrated assessment to project future progress by 2030, 2050, and 2100 and to characterise the transformations needed to deliver the global Sustainable Development Goals and an increasingly ambitious 21st century sustainability agenda. Our results quantify the scale and pace of transformations required through eight key entry points: increasing education access, powering sustainable economic development, controlling global population growth, lowering energy intensity across sectors, decarbonising energy systems, promoting healthy food diets, limiting agricultural land expansion, and reducing global emissions intensity. Our findings indicate many actions that appear to make a limited contribution to initial progress are in fact vital for accelerating change towards sustainable development later in the century.

In this paper we explore ways of numerically computing recursive dynamic monetary risk measures and utility functions. Computationally, this problem suffers from the curse of dimensionality and nested simulations are unfeasible if there are more than two time steps. The approach considered in this paper is to use a Least Squares Monte Carlo (LSM) algorithm to tackle this problem, a method which has been primarily considered for valuing American derivatives, or more general stopping time problems, as these also give rise to backward recursions with corresponding challenges in terms of numerical computation. We give some overarching consistency results for the LSM algorithm in a general setting as well as explore numerically its performance for recursive Cost-of-Capital valuation, a special case of a dynamic monetary utility function.

A novel forecasting combination and weighted quantile based tail risk forecasting framework is proposed, aiming to reduce the impact of modelling uncertainty in financial tail risk forecasting. The proposed approach is based on a two-step estimation procedure. The first step involves the combination of Value-at-Risk (VaR) forecasts at a grid of different quantile levels. A range of parametric and semi-parametric models is selected as the model universe which is incorporated in the forecasting combination procedure. The quantile forecasting combination weights are estimated by optimizing the quantile loss. In the second step, the Expected Shortfall (ES) is computed as a weighted average of combined quantiles. The quantiles weighting structure used to generate the ES forecast is determined by minimizing a strictly consistent joint VaR and ES loss function of the Fissler-Ziegel class. The proposed framework is applied to six stock market indices and its forecasting performance is compared to each individual model in the model universe and a simple average approach. The forecasting results based on a number of evaluations support the proposed framework.

A Systemic Optimal Risk Transfer Equilibrium (SORTE) was introduced in: "Systemic optimal risk transfer equilibrium", Mathematics and Financial Economics (2021), for the analysis of the equilibrium among financial institutions or in insurance-reinsurance markets. A SORTE conjugates the classical B\"{u}hlmann's notion of a Risk Exchange Equilibrium with a capital allocation principle based on systemic expected utility optimization. In this paper we extend such a notion to the case when the value function to be optimized is multivariate in a general sense, and it is not simply given by the sum of univariate utility functions. This takes into account the fact that preferences of single agents might depend on the actions of other participants in the game. Technically, the extension of SORTE to the new setup requires developing a theory for multivariate utility functions and selecting at the same time a suitable framework for the duality theory. Conceptually, this more general framework allows us to introduce and study a Nash Equilibrium property of the optimizer. We prove existence, uniqueness, and the Nash Equilibrium property of the newly defined Multivariate Systemic Optimal Risk Transfer Equilibrium.

We provide a new investigation of the relationship between oil and stock prices in the context of the outbreak of the new coronavirus crisis. Specifically, we assess to what extent the uncertainty induced by COVID-19 affects the interaction between oil and the United States (US) stock markets. To this end, we use a wavelet approach and daily data from February 18, 2020 to August 15, 2020. We identify the lead-lag relationship between oil and stock prices, and the intensity of this relationship at different frequency cycles and moments in time. Our unique findings show that co-movements between oil and stock prices manifest at 3-5-day cycle and are stronger in the first part of March and the second part of April 2020, when oil prices are leading stock prices. The partial wavelet coherence analysis, controlling for the effect of COVID-19 and US economic policy-induced uncertainty, reveals that the coronavirus crisis amplifies the shock propagation between oil and stock prices.

Carbon emission right allowance is a double-edged sword, one edge is to reduce emission as its original design intention, another edge has in practice slain many less developed coal-consuming enterprises, especially for those in thermal power industry. Partially governed on the hilt in hands of the authority, body of this sword is the prices of carbon emission right. How should the thermal power plants dance on the blade motivates this research. Considering the impact of price fluctuations of carbon emission right allowance, we investigate the operation of Chinese thermal power plant by modeling the decision-making with optimal stopping problem, which is established on the stochastic environment with carbon emission allowance price process simulated by geometric Brownian motion. Under the overall goal of maximizing the ultimate profitability, the optimal stopping indicates the timing of suspend or halt of production, hence the optimal stopping boundary curve implies the edge of life and death with regard to this enterprise. Applying this methodology, real cases of failure and survival of several Chinese representative thermal power plants were analyzed to explore the industry ecotope, which leads to the findings that: 1) The survival environment of existed thermal power plants becomes severer when facing more pressure from the newborn carbon-finance market. 2) Boundaries of survival environment is mainly drawn by the technical improvements for rising the utilization rate of carbon emission. Based on the same optimal stopping model, outlook of this industry is drawn with a demarcation surface defining the vivosphere of thermal power plants with different levels of profitability. This finding provides benchmarks for those enterprises struggling for survival and policy makers scheming better supervision and necessary intervene.

During the onset of the COVID-19 pandemic, conflicting incentives caused most shareholders to adverse corporate social responsibility (CSR) -- measured by firms' charitable donations -- since it would further burden firms' already strained finances. Those shareholders that favored donations, large individual investors, did so to bolster their own images as they are typically synonymous with the donating firms. Image gains do not pass through to institutional shareholders, who instead preferred to donate themselves rather than having the firms they invested in donate. Taken together, our results cast doubts on large corporations' willingness to demand costly CSR measures across firms in their portfolios.

Online dating emerged as a key platform for human mating. Previous research focused on socio-demographic characteristics to explain human mating in online dating environments, neglecting the commonly recognized relevance of sport. This research investigates the effect of sport activity on human mating by exploiting a unique data set from an online dating platform. Thereby, we leverage recent advances in the causal machine learning literature to estimate the causal effect of sport frequency on the contact chances. We find that for male users, doing sport on a weekly basis increases the probability to receive a first message from a woman by 50%, relatively to not doing sport at all. For female users, we do not find evidence for such an effect. In addition, for male users the effect increases with higher income.

The trade off between risks and returns gives rise to multi-criteria optimisation problems that are well understood in finance, efficient frontiers being the tool to navigate their set of optimal solutions. Motivated by the recent advances in the use of deep neural networks in the context of hedging vanilla options when markets have frictions, we introduce the Efficient Hedging Frontier (EHF) by enriching the pipeline with a filtering step that allows to trade off costs and risks. This way, a trader's risk preference is matched with an expected hedging cost on the frontier, and the corresponding hedging strategy can be computed with a deep neural network.

We further develop our framework to improve the EHF and find better hedging strategies. By adding a random forest classifier to the pipeline to forecast market movements, we show how the frontier shifts towards lower costs and reduced risks, which indicates that the overall hedging performances have improved. In addition, by designing a new recurrent neural network, we also find strategies on the frontier where hedging costs are even lower.

Ten years ago we presented a modified version of Okun law for the biggest developed economies and reported its excellent predictive power. In this study, we revisit the original models using the estimates of real GDP per capita and unemployment rate between 2010 and 2019. The initial results show that the change in unemployment rate can be accurately predicted by variations in the rate of real economic growth. There is a discrete version of the model which is represented by a piece wise linear dependence of the annual increment in unemployment rate on the annual rate of change in real GDP per capita. The lengths of the country-dependent time segments are defined by breaks in the GDP measurement units associated with definitional revisions to the nominal GDP and GDP deflator (dGDP). The difference between the CPI and dGDP indices since the beginning of measurements reveals the years of such breaks. Statistically, the link between the studied variables in the revised models is characterized by the coefficient of determination in the range from R2=0.866 (Australia) to R2=0.977 (France). The residual errors can be likely associated with the measurement errors, e.g. the estimates of real GDP per capita from various sources differ by tens of percent. The obtained results confirm the original finding on the absence of structural unemployment in the studied developed countries.

Knowledge of the spatial organisation of economic activity within a city is key to policy concerns. However, in developing cities with high levels of informality, this information is often unavailable. Recent progress in machine learning together with the availability of street imagery offers an affordable and easily automated solution. Here we propose an algorithm that can detect what we call 'visible firms' using street view imagery. Using Medell\'in, Colombia as a case study, we illustrate how this approach can be used to uncover previously unseen economic activity. Applying spatial analysis to our dataset we detect a polycentric structure with five distinct clusters located in both the established centre and peripheral areas. Comparing the density of visible and registered firms, we find that informal activity concentrates in poor but densely populated areas. Our findings highlight the large gap between what is captured in official data and the reality on the ground.

Vaccination may be the solution to the pandemic-induced health crisis, but the allocation of vaccines is a complex task in which economic and social considerations can be important. The central problem is to use the limited number of vaccines in a country to reduce the risk of infection and mitigate economic uncertainty at the same time. In this paper, we propose a simple economic model for vaccine allocation across two types of workers: white-collars can work from home; while blue-collars must work on site. These worker types are complementary to each other, thus a negative shock to the supply of either one decreases the demand for the other that leads to unemployment. Using parameters of blue and white-collar labor supply, their infection risks, productivity losses at home office during lock-down, and available vaccines, we express the optimal share of vaccines allocated to blue-collars. The model points to the dominance of blue-collar vaccination, especially during waves when their relative infection risks increase and when the number of available vaccines is limited. Taking labor supply data from 28 European countries, we quantify blue-collar vaccine allocation that minimizes unemployment across levels of blue- and white-collar infection risks. The model favours blue-collar vaccination identically across European countries in case of vaccine scarcity. As more vaccines become available, economies that host large-shares of employees in home-office shall increasingly immunize them in case blue-collar infection risks can be kept down. Our results highlight that vaccination plans should include workers and rank them by type of occupation. We propose that prioritizing blue-collar workers during infection waves and early vaccination can also favour economy besides helping the most vulnerable who can transmit more infection.

We live in an age of consumption with an ever-increasing demand of already scarce resources and equally fast growing problems of waste generation and climate change. To tackle these difficult issues, we must learn from mother nature. Just like waste does not exist in nature, we must strive to create circular ecosystems where waste is minimized and energy is conserved. This paper focuses on how public procurement can help us transition to a more circular economy, while navigating international trade laws that govern it.