Search

We study combinatorial auctions with interdependent valuations, where each agent i has a private signal si that captures her private information and the valuation function of every agent depends on the entire signal profile, s=(s1,...,sn). The literature in economics shows that the interdependent model gives rise to strong impossibility results and identifies assumptions under which optimal solutions can be attained. The computer science literature provides approximation results for simple single-parameter settings (mostly single-item auctions or matroid feasibility constraints). Both bodies of literature focus largely on valuations satisfying a technical condition termed single crossing (or variants thereof). We consider the class of submodular over signals (SOS) valuations (without imposing any single crossing-type assumption) and provide the first welfare approximation guarantees for multidimensional combinatorial auctions achieved by universally ex post incentive-compatible, individually rational mechanisms. Our main results are (i) four approximation for any single-parameter downward-closed setting with single-dimensional signals and SOS valuations; (ii) four approximation for any combinatorial auction with multidimensional signals and separable-SOS valuations; and (iii) (k + 3) and (2 log(k) + 4) approximation for any combinatorial auction with single-dimensional signals, with k-sized signal space, for SOS and strong-SOS valuations, respectively. All of our results extend to a parameterized version of SOS, d-approximate SOS, while losing a factor that depends on d. Funding: A. Eden was partially supported by NSF Award IIS-2007887, the European Research Council (ERC) under the European Union's Seventh Framework Programme [FP7/2007-2013]/ERC Grant Agreement 337122, by the Israel Science Foundation [Grant 317/17], and by an Amazon research award. M. Feldman received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program [Grant Agreement 866132], by the Israel Science Foundation [Grant 317/17], by an Amazon research award, and by the NSF-BSF [Grant 2020788]. The work of K. Goldner was supported partially by NSF awards DMS-1903037 and CNS-2228610 and a Shibulal Family Career Development Professorship. A. R. Karlin was supported by the NSF-CCF [Grant 1813135]. [ABSTRACT FROM AUTHOR]

The technical characteristics of government audits make the purchase of social audit services different from the purchase of other public services. Carrying out performance evaluations of outsourced social audit services is important for ensuring effective social audit service outsourcing and for completing comprehensive high-quality audits. On the basis of constructing a conceptual framework for performance evaluations of social audit service purchases under the background of comprehensive audit coverage, the objectives, principles, processes, models, and steps of performance evaluations are clarified. From the perspective of performance evaluation theory, a performance evaluation model and an index system are constructed. On this basis, the evaluation framework is applied to performance evaluations of audit institutions that purchase social audit services. Although outsourcing social audit services solves the problem of coverage quantity relatively economically, there is still room for improvement in terms of efficiency and effectiveness. Audit institutions can continuously improve the performance of outsourced social audit services by building and continuously optimizing the pool of institutions and social audit personnel, constructing a real-time management system for outsourced social audit services, and improving the management incentive system for and performance of outsourced social audit services.

This study investigated the relationship between political uncertainty and corporate philanthropy in emerging markets, using China as the research setting. The growth of companies in China depends largely on various national policies which are also the main drivers on Chinese economic development. Those polices are actually executed by local leaders with a certain degree of autonomy. Each leader has a personal style of policy implementation in his/her jurisdictions. Once the local leader shifts, enterprises in the region may face the risk of policy implementation changes. Leadership transitions in Chinese cities were chosen to represent political uncertainty. After a change in local government leadership, more philanthropic activities and higher charitable spending were identified for listed companies in the region. The trend was exceptionally prominent for companies with higher regulatory risks. In that regard, companies in the private sector or owned by the local government, from pollution-intensive industries, or with a considerable size were riskier. In addition, the study identified the potential of charitable giving in lessening administrative punishments for the donating companies. The research expanded the literature on political uncertainty and corporate philanthropy by illustrating the effectiveness of the former as a response to the latter.

In this paper, multiple linear regression model is used to predict and test the effect of audit policy changes in the capital markets. In 2016, China issued a new reporting guideline which included the addition of key audit matters (KAMs) as the core focus. It was required that all A+H-share companies should implement these in their reporting in 2017, as well as all domestic listed companies in 2018. In order to study the impact of the KAMs standard on the audit quality of Chinese commercial banks, we analysed the KAMs as disclosed by commercial banks as well as any further change in audit quality at these banks before and after implementation of the new auditing reporting standards. The results show that the disclosure of KAMs significantly promotes the improvement of loan impairment provisions in commercial banks, thus inhibiting potential earnings manipulation by management and improving the quality of financial information. The level of loan impairment provision plays an intermediary role in the improvement of bank audit quality in the new reporting standards.

Given the expanding high-level opening to the outside world, there is a potential correlation between consumers’ behavioral patterns on cross-border e-commerce platforms and economic policies. This paper proposes a model for extracting consumer behavioral willingness features based on a convolutional neural network with sentence vector improvements, which optimizes the effect of cluster analysis on consumer behavioral willingness. The extraction model is utilized to extract consumer behavioral willingness features, and the outcomes demonstrate that the proportion of consumption and recommendation willingness is 47.65% and 52.05%, respectively, with a positive clustering effect. The regression results show that consumers’ behavioral willingness to cross-border e-commerce platforms increases with their preference for free trade and mixed trade policies, with an impact coefficient of 0.095 and 0.096. The degree of free trade simultaneously influences consumers’ willingness to recommend, with an impact coefficient of 0.154. However, the import substitution policy has an impact coefficient of −0.078, indicating that consumers are less likely to recommend cross-border e-commerce platforms to others.

A convenient and efficient rural road network helps to develop the rural economy and promote national rural revitalization. This paper systematically analyzes the role of rural road infrastructure construction on economic growth from a theoretical perspective. Simultaneously, it employs a computer algorithm based on vector autoregression to construct an econometric model. Based on this theoretical analysis empirically examines the relationship between rural infrastructure construction and economic development. It collects data from a city’s rural area from 2000 to 2020 as a research sample and conducts a series of analyses, including VAR model estimation, impulse response, and variance decomposition. The results show that rural road infrastructure construction has a significant impact on economic growth, with coefficients of −0.288 and 0.356 for lags 1 and 2. The construction of rural road infrastructure is responsible for more than 90% of the variation in economic growth, whereas the opposite is about 10%. Therefore, rural road infrastructure development does not have a significant effect on economic growth in the short term but has a significant effect in the medium and long term.

The process of globalization is deepening, and the international division of labor has further evolved from the traditional pattern into the production mode of global value chains. This paper calculates the degree of participation in the global value chain of China’s manufacturing industry and analyzes the index of comparative explicit advantage of China’s manufacturing industry in each category, i.e., the NRCA index. The decomposition method of trade flows of intermediate goods is used to interpret the value added of trade based on the international input-output table. In accordance with OECD industrial classification, subdivide the manufacturing industries and evaluate the overall export competitiveness of China’s manufacturing industry. Propose the new explicit comparative advantage index (RCA_VA index) and carry out the overall measurement and the measurement of each technology level industry separately. From the point of view of the RCA_VA index of China’s manufacturing industry as a whole 1.29 < RCA_VA

The article borrows game theory to analyze the development dilemma of new rural cooperative economic organizations. A rural cooperative economic organization that is based on game theory is proposed by describing the new rural cooperative economic organization and game theory. According to the subject of interest, the basic assumptions of the model are determined, and the game theory model for rural cooperative economic organizations is constructed by combining the basic assumptions and relevant variables, and the evolutionary stabilization strategy solution process is given at the same time. The development status of new rural cooperative economic organizations in the context of information technology is evaluated using the model mentioned above. When E2 = 0.18, the probability of both parties choosing {cooperation, cooperative} strategy will increase to 82.3%. And E2 = 0.82 will reduce the probability of forming complete cooperation to 71.5% through the introduction of cost and benefit distribution, supervision and punishment mechanisms to achieve a win-win situation for the community of interest, but there is still the probability of the two sides cooperating to choose the optimal strategy will become smaller, revealing that the current development of the rural cooperative economic organization has problems and shortcomings. In order to improve the development of cooperative economic organizations in rural areas, relevant strategies are proposed.

Intangible cultural heritage has important historical, cultural, and spiritual values. This paper selects the intangible cultural heritage of Yunnan Painting, the national architectural decoration of Dali, as the case study object. We use web crawler technology to extract the consumer’s comment text data, establishing the Yunnan Painting consumer comment text dataset. Secondly, we extract high-frequency words from the dataset using text mining technology, TF-IDF keyword extraction, and LDA themes, and cluster them using the K-means method. Finally, we evaluate the economic potential of Yunnan Painting in terms of consumers’ cognitive abilities and the emotional appeal of its products, based on the extracted results. The study reveals that the dressmaking, painting, and tourism industries are the main economic activities of Yunnan color painting, with the tourism industry requiring further strengthening. According to the text mining of high-frequency words, the frequency of “product” in Yunnan painting was the highest at 296 times, followed by “culture,” “technology,” “cultural creativity,” and “theme.” The analysis of consumers’ emotional text features reveals that most consumers have positive emotions towards Yunnan-colored paintings, while very few have negative emotions. The economic optimization strategy that is proposed is based on the results and provides a reference value for the economic development of other intangible cultural heritage.

In the face of the rising heat of the blockchain concept, many enterprises compete to be the first to develop. However, the excessive enthusiasm of enterprises will inevitably lead to a large amount of waste of resources, therefore, it is particularly important to study the impact of the application of blockchain on the enterprise economy and finance. This paper first puts forward relevant assumptions on the behavioral characteristics of the participating subjects in the supplier order financing system, calculates the expected return function based on the return matrix and the initial ratio between the supplier, the company and the financial institution, and derives the replicated dynamic equation through further projection, according to which the evolutionary stabilization strategy is analyzed. Numerical simulation experiments are used to analyze how blockchain technology, receivable period, and guarantee rate affect the three-party gaming system. The concept of blockchain+supply chain finance model is proposed, and the method of empirical analysis is utilized to study the performance impact it has on enterprises. The thresholds of blockchain technology cost, accounts receivable period, and guarantee rate are [170,000 yuan, 190,000 yuan], [1.9a,2.0a], and [0.12,0.14], respectively. The operating revenue growth rate of the FT enterprise shows an increase in 2019 and 2020, which is 15.25% and 23.54%, respectively, and both of them are beyond the industry average in the same period, while the operating profit growth rate grows steadily and reached 102.54% in 2022, which can quickly respond to market changes and challenges.

Artificial intelligence, blockchain, and other intelligent technologies serve as the primary technical support for the financial industry, enabling it to achieve digital transformation and enhance the quality of development. This paper utilizes the banking industry as a representative of the financial sector, showcasing the benefits of applying artificial intelligence and blockchain technology to the digital transformation of commercial banks through case studies. This approach indirectly underscores the importance of intelligent technology for the robust growth of digital finance. Taking “profitability” and “risk management level” of commercial banks as specific indicators, the relationship between the application of intelligent technology and the profitability and risk management level of banks is explored through multiple linear regression, thus reflecting the role of intelligent technology on the economy and the development of high quality. This paper further analyzes the relationship between banks’ application of intelligent technology and profitability and risk management levels through multiple linear regression. This paper also analyzes the influencing factors of banks’ intelligence levels to provide a reference for achieving high-quality development in digital finance and the economy. According to the regression results, the quadratic term of the intelligence index is 0.00282, which indicates that banks have effectively improved their profitability in the later stages of applying intelligent technology. Before and after the introduction of control variables, the coefficients of the degree of intelligence are 0.41997 and 0.47969, respectively, indicating that the application of intelligent technology is conducive to banks’ ability to improve risk management. It has also been found that factors such as fintech innovation ability, financial regulation, and asset size have a positive impact on the application of intelligent technology in commercial banks.

In the context of the era of economic green transformation, enterprises face greater litigation risks, and the opening of the asset market has an important impact on the internal decision-making and control of enterprises under green ecological development. In this paper, the enterprises listed in Shanghai and Shenzhen are selected as research samples, and the explanatory variables, interpretive variables, mediating variables, and control variables are selected for the study. The double difference model is used to construct an empirical model between the variables to explore the relationship between corporate litigation risk and asset opening under green ecological development. The study shows that the correlation coefficients between corporate asset openness (x) and corporate litigation risk dummy variables and numerical variables under green ecological development policy are -0.078 and -0.088, respectively, which are significantly negatively correlated at the 1% level. The robustness test validates this empirical result, indicating a strong correlation between the two. It has also been found that corporate asset openness has a greater negative effect on the litigation risk of heavy-polluting enterprises and state-owned enterprises. The study of enterprise litigation risk and asset opening is rethought in this paper, and it provides a reference basis for the effective management of enterprises under green ecological development.

In this paper, an impact degree indicator system with several indicators is established, and the stability, estimation, lag selection and forecasting of the VAR model are considered in the model construction, and the MVAR model structure and estimation methods are also studied. Chongqing city is selected as the object for the actual case, and the influence degree of international logistics factors on international trade is explored, and the influence of transportation costs on real trade is studied in depth from two aspects: the spatial and temporal dimensions and the influence of high logistics costs on the trade of CIS countries. The study results show that: high logistics costs lead to the concentration of international trade in Central Asian countries up to 0.4 to 0.5. More than half of the goods the six CIS countries sell to the EU are concentrated on one or two specific items. The impact of international logistics costs is so significant that it cannot be ignored in international trade.

This paper uses a clustering algorithm to extract and classify credit rating features of tobacco retailers and evaluates whether the classification results are reasonable by combining clustering evaluation indexes. The distance between samples is calculated using the similarity measure. The natural domain method density and peak clustering method are used to analyze the distribution of sample points in the data set. Combining the cluster analysis creates the tobacco retail credit rating evaluation index. The results show that cluster analysis can effectively extract credit rating features from tobacco retailers. When the number of features is 25, the model has the best classification effect, with a classification accuracy rate of 91.1%, a recall rate of 91.5%, and an F1 value of 91.3%. The classification of tobacco retailers’ credit ratings can be improved effectively by the research in this paper.

The interaction between financial assistance and enterprise development is examined in this paper by constructing a coupled coordination degree model. The weights of the secondary indicators in the financial assistance and enterprise development system are calculated using the entropy weighting method. By calculating the coupling degree between financial assistance and enterprise development, we can judge the strength of the coupling effect between them and the time interval. The financial system and enterprise development evaluation indexes were constructed to explore the factors affecting financial assistance for enterprise development. Under certain conditions, financial support and enterprise development can positively affect each other, as shown in the results. The development support weights in the financial support system are 0.1979 and 0.2231, and the total weight is 0.421. The enterprise development system has a technical weight of 0.5806 for technological innovation.

In this paper, we first study data-based e-commerce operation from the big data perspective and elaborate on data-based e-commerce operation from four aspects: professional terminology, operation core, theoretical operation basis, and operation method. Secondly, in the process of e-commerce data-based operation sales prediction, data pre-processing and feature selection are required, and the quality of data and features directly determines the accuracy of the model and based on deep learning, the structure of convolutional and XG fusion prediction model is proposed. Then, three-dimensional data frames are constructed based on four-dimensional data facets, which leads to more accurate model predictiveness, and the prediction model and e-commerce financial and operational risks are studied and analyzed. The results show that the convolutional and XG fusion forecasting model has a greater efficiency enhancement function than the traditional data computation model in the practical application of the forecasting model, proving the model’s stability in the long-term forecasting process. By predicting risk in e-commerce operations in time and improving it in real-time, this study can help the enterprise develop more competitively.

This paper analyzes the main methods of stochastic optimization algorithms to construct a stochastic optimization model. The focus is on the calculation method for risk minimization, combined with the SGD algorithm to guarantee the speed of sublinear convergence. The mean variance of the risk evaluation model is determined by constructing the objective function and constraints, and the investor’s risk is minimized based on calculating the minimum variance of the model. The asset portfolio risk evaluation model can accurately describe the risk of different industries, as demonstrated by the results. According to the correlation coefficient reality, the correlation between industry indices is relatively strong, where the correlation coefficient between raw materials and the optional consumption industry is 0.865, and the correlation coefficient between the optional consumption industry and the financial industry is 0.697.

This paper establishes a dataset through big data mining algorithms, obtains data objects in the subspace according to data outlier characteristics, and derives the subspace outlier probability formula. This paper establishes a dataset through big data mining algorithms, obtains data objects in the subspace according to data outlier characteristics, and derives the subspace outlier probability formula. The data distribution characteristics are analyzed using entropy detection, and the multi-information entropy data are extracted for clustering detection by inputting the number of database samples for differential evolution. The adaptive search method is used for feature extraction of big data information flow, and text data with similar characteristics are divided into uniform fuzzy clustering centers to mine the optimal clustering indicators. Four categories of fashion photography styles were obtained by clustering fashion advertisement photos through data mining. Big data technology positively impacted the influence of consumers' consumption concepts, as their social self-concept increased from 4 to 10 points.

This paper uses a local path fusion method of medium to simulate the angular deviation between the end direction of the trajectory and the target direction according to a specific evaluation function. The media fusion algorithm is guided to achieve global optimality of the path by fusing global path planning information and avoiding local dynamic obstacles. The smart tourism and tourism management systems are fused to balance the intensity loss constraint weights and perform Gaussian filtering to derive the tourism management situation. By decomposing the highest level of tourism information and normalizing the pixel intensity values and tourism characteristic information, it was found that the smart tourism penetration rate increased by 3.6%, the total tourism revenue increased by 88.87% over the previous year, and the working variance of tourism project effectiveness was 62.430.

In this paper, based on the matrix model to count the number of elements and judge the symmetry of matrix elements, the nearest neighbor space in high-dimensional space is converted into a symmetry matrix. The boundary extraction is discretized and iterated repeatedly until the fitness satisfies the final condition. The genetic algorithm finds the optimal solution, which is transformed into the fitness function, and the system reliability constraints are processed to solve the cost control problem. According to the results, the cost control cost of the construction project is reduced by 10% based on the matrix model. It shows that the proposed model can promptly solve the existing project cost over-budget situation, which makes the enterprise obtain more economic benefits and enhance the market’s competitiveness.

To provide construction units with comprehensive and accurate information to assist in decision-making, this paper utilizes econometric models to use the Internet for management monitoring and engineering construction schedule scheduling. The parametric linearity constraint test method is set up, production function examples are established for engineering tests, and statistics are approximated to obey degrees of freedom and cardinal distribution. To obtain the inverse of the estimated information matrix, the least squares method is employed to maximize the engineering construction expression. The engineering economic management error value is found to be the lowest value of 0.84, the maximum value of elasticity coefficient is 0.379, and the minimum value of covariance is 0.185. To fully improve the risk prediction capability of engineering construction, econometric modeling techniques must be integrated.

Using a gradient descent algorithm, a study on the linkage between strategic human resource management and organizational effectiveness. A logarithmic function with a base of 10 is used to represent the useful value of the fused elements of the association and simplify the expression formula for the usefulness of management. The usefulness of the management approach in achieving organizational effectiveness is measured using the information gain approach. The square of the difference between the predicted and true values is used as the loss function value, and the gradient descent algorithm is combined with the gradient descent algorithm to divide the effective joint data and calculate the solution gradient for all samples. Evaluation indexes such as accuracy and recall test the joint degree effective prediction model. The results show that the usefulness of the joint degree effective prediction model is classified into three degree levels: average [0,0.2), useful [0.2,0.4), and 40% or more as very useful. The team’s organizational commitment results in 50% of employees working hard, and only 5% are unmotivated to work.

The magnitude and type structure of local government debt are first analyzed in this work. Then, using the “4E” evaluation theory, a local government debt performance assessment index system is built. Second, an entropy-TOPSIS technique is used to calculate the risk index of local government debt performance, and a spatial econometric model is used to examine the geographical spillover impact of local government debt performance across different areas. Finally, the heterogeneity of local government debt management was evaluated using the Malmquist index. The findings demonstrate that the hypothesized national effect is often superior to the sub-regional effects, with a debt ratio regression result of -0.065. Despite the overall local government debt coefficient being -0.032, the direct impact coefficient for the eastern area is -0.025.

Based on the construction objectives of the enterprise financial sharing management platform, this paper focuses on the design concept and implementation process of each system module based on the overall system design under the framework of the financial sharing platform. The BP neural network algorithm is used to evaluate the financial information risk, the particle swarm optimization algorithm is used to optimize the database, the big data visualization technology is used for digitizing the financial information, and the metadata sharing technology is used to transfer the financial sharing data, to complete the financial information sharing. Through testing the platform, it is concluded that the real-time packet loss rate of financial sharing data is kept below 20%, and the average response time of multi-user clicks is within 2S. Therefore, the sharing function, the function of each module, and the performance of the platform in this paper can meet the needs of enterprises.

In this paper, we propose a method for monitoring the construction quality and progress of assembled buildings based on point cloud model feature extraction to achieve accurate identification and rapid detection of construction process information because of the existing problems in precast beam construction data collection and processing, combined with 3D laser scanning technology. Soil excavation and filling calculations are performed to determine the site design elevation before the main structure construction starts, the prefabricated components are simulated to be assembled and viewed for assembly errors during the construction process, and the verticality and flatness of the prefabricated beams are inspected after the construction is completed in combination with least squares. The construction quality problems and delays in construction progress were also corrected and adjusted to ensure a smooth construction process. After the main bridge was constructed, it was measured to have a damping ratio of around 0.011, which means it can dissipate external energy input.

This paper first considers population aging as a breakthrough, analyzes the demand for long-term care due to population aging, and classifies the models and characteristics of long-term care. Secondly, the combinatorial optimization algorithm and its classical cases are analyzed, and a care resource allocation system is built based on the solution set performance metrics and multi-objective combinatorial optimization algorithm for an aging population society. In city A, a research experiment examines the elderly population and the allocation of nursing care resources. The results show that the Gini coefficient of the population distribution of nursing institutions is 0.027 and the Gini coefficient of the population distribution of the number of institutional beds is 0.296, both of which are less than 0.3, thus indicating that the overall structure of the current social nursing resource allocation is relatively fair.

This paper uses multivariate statistical analysis to explore the ordinary least squares regression algorithm, principal component regression algorithm, and partial least squares regression algorithm. Then, an index system for entrepreneurial management performance assessment was constructed with public innovation space, resource collocation and entrepreneurial performance indicators, and an entrepreneurial management performance mediation model was established with this. Finally, the quartiles of entrepreneurial performance's principal components were derived by studying the relationship between entrepreneurial performance assessment and each input. The results show that the first principal component Z1 of entrepreneurial performance is significant at all quartiles, while the second principal component Z2 is significant only at the 0.6 quartiles, and the constant terms are significant at the 0.6, 0.7, and 0.8 quartiles. Multivariate statistical analysis is employed in this paper to provide a reference for entrepreneurship management and to assess entrepreneurial performance more accurately.

This paper first analyzes the definition and characteristic attributes of virtual currency transactions, secondly constructs the legal risk indicator evaluation system, then pre-processes the evaluation data and assigns the indicators, and then constructs the legal risk evaluation model using TOPSIS-grey correlation. Finally, the quantitative data analysis was carried out by analyzing the indicator weights and evaluating indicators. The results show that the overall score of the trading platform is 73.86, with the evaluation scores of the first-level indicators being 24.56, 11.35, 16.23, 15.42, and 6.30, respectively. This study fully illustrates that the use of TOPSIS-grey correlation analysis can provide data support and a decision-making basis for legal risk assessment and prevention and new directions for reforming and developing the legal risks of virtual currency trading.

This paper presents a method to process the dataset of departing employees. The clustering categories of separated employees are accurately determined by fuzzy c-mean clustering and improved clustering FCM algorithm, and new samples of separated employees are generated using SMOTE algorithm to reduce noisy data. The kernel function trick of SVM is used to achieve clustering oversampling, improving classification accuracy. For imbalanced data, this paper uses a new integrated learning algorithm for constructing evaluations, PIBoost, combined with a full-sample cost-aware weight algorithm to improve the generalization ability of the SVM classifier, which has better classification results for various data sets. In the model performance comparison, K-AFCM-SMOTE-SVM has the highest accuracy with a value of 0.89. In the ten-fold cross-validation accuracy comparison, the K-AFCM-SMOTE-SVM model has a better overall performance index than the other two, with an average cross-validation accuracy of 0.932.

This paper first analyzes the international financial systemic risk transmission mechanism in the big data environment, constructs an international financial systemic risk index system, and measures the index based on principal component analysis. Then, the existing international financial and macroeconomic literature is sorted out, a macroeconomic resilience index system is constructed, and the index is measured using the entropy value method. Finally, MS-VAR and TVP-VAR models are constructed based on the VAR model to analyze the non-linear effects of international financial systemic financial risks on macroeconomic impacts. The results show that a shock of 1 unit of CFSI causes a positive response of MR of about 0.23 in the long run, and CFSI all produce a negative shock to MI until this negative shock reaches a maximum of -0.023 in the 5th period.

In this research, we intended to employ the Pearson correlation and a multiscale generalized Shannon-based entropy to trace the transition and type of inherent mutual information as well as correlation structures simultaneously. An optimal value for scale is found to prevent over smoothing, which leads to the removal of useful information. The lowest Singular Value Decomposition Multiscale Generalized Cumulative Residual Entropy (SVDMWGCRE), or SVD Entropy (SVDE), is obtained for periodic–chaotic series, generated by logistic map; hence, the different dynamic, correlation structures, and intrinsic mutual information have been characterized correctly. It is found out that the mutual information between emerging markets entails higher sensitivity, and moreover emerging markets have demonstrated the highest uncertainty among investigated markets. Additionally, the fractional order has synergistic effects on the enhancement of sensitivity with the multiscale feature. According to the logistic map and financial time series results, it can be inferred that the logistic map can be utilized as a financial time series. Further investigations can be performed in other fields through this financial simulation. The temporal evolutions of financial markets are also investigated. Although the results demonstrated higher noisy information for emerging markets, it was illustrated that emerging markets are getting more efficient over time. Additionally, the temporal investigations have demonstrated long-term lag and synchronous phases between developed and emerging markets. We also focused on the COVID-19 pandemic and compared the reactions of developing and emerging markets. It is ascertained that emerging markets have demonstrated higher uncertainty and overreaction to this pandemic. [ABSTRACT FROM AUTHOR]

This paper uses accounting concepts—particularly the concept of Return on Investment (ROI)—to reveal the quantitative value of scientific research pertaining to a major US cyberinfrastructure project (XSEDE—the eXtreme Science and Engineering Discovery Environment). XSEDE provides operational and support services for advanced information technology systems, cloud systems, and supercomputers supporting non-classified US research, with an average budget for XSEDE of US$20M+ per year over the period studied (2014–2021). To assess the financial effectiveness of these services, we calculated a proxy for ROI, and converted quantitative measures of XSEDE service delivery into financial values using costs for service from the US marketplace. We calculated two estimates of ROI: a Conservative Estimate, functioning as a lower bound and using publicly available data for a lower valuation of XSEDE services; and a Best Available Estimate, functioning as a more accurate estimate, but using some unpublished valuation data. Using the largest dataset assembled for analysis of ROI for a cyberinfrastructure project, we found a Conservative Estimate of ROI of 1.87, and a Best Available Estimate of ROI of 3.24. Through accounting methods, we show that XSEDE services offer excellent value to the US government, that the services offered uniquely by XSEDE (that is, not otherwise available for purchase) were the most valuable to the facilitation of US research activities, and that accounting-based concepts hold great value for understanding the mechanisms of scientific research generally. [ABSTRACT FROM AUTHOR]

This paper considers a capital accumulation game where the installation costs of investments are lowered by the firm's own capital stock because of learning and by the competitor's capital stock because of spillover effects. To properly understand the impact of the two capital stocks, we consider six information structures which differ in whether a firm takes into account that their competitor's strategy depends only on time or on one or both capital stocks. We find that if firms are aware that their own capital stock makes investments of the competitor more efficient, a firm would invest less. Due to this effect, information only pays off if it is taken into account by both competitors, because otherwise the less informed and therefore less cautious player invests more and has a higher capital stock and revenues in the long run. [ABSTRACT FROM AUTHOR]

We prove an anticipative sufficient stochastic minimum principle in a jump process setup with initially enlarged filtrations. We apply the result to several portfolio selection problems like mean and minimal variance hedging under enlarged filtrations. We also investigate utility maximizing portfolio selection under future information. Contrarily to classical optimization methods like dynamic programing, our stochastic minimum principle likewise applies to non-Markovian setups. On the mathematical side, we are concerned with jump processes, forward and backward stochastic differential equations and forward integrals. [ABSTRACT FROM AUTHOR]

We extend the arithmetic multi-factor electricity spot price model proposed by Benth et al. (Appl Math Finance 14(2):153–169, 2007) by adding stochastic mean-level processes to their model and by taking additional information on the future behavior of these mean-level processes into account. The available anticipative information is modeled by an initially enlarged filtration in our paper. We further derive pricing formulas for electricity forwards under future information and investigate the associated information premium. [ABSTRACT FROM AUTHOR]

In the context of a general semimartingale model, we aim at determining how much an investor is willing to pay to learn additional information that allows achieving arbitrage. If such a value exists, we call it the value of informational arbitrage. We are interested in the case where the information yields arbitrage opportunities but not unbounded profits with bounded risk. As in Amendinger et al. (Finance Stoch. 7:29–46, 2003), we rely on an indifference valuation approach and study optimal consumption–investment problems under initial information and arbitrage. We establish some new results on models with additional information and characterise when the value of informational arbitrage is universal. [ABSTRACT FROM AUTHOR]

This paper quantifies the interplay between the no-arbitrage notion of no unbounded profit with bounded risk (NUPBR) and additional progressive information generated by a random time. This study complements the one of Aksamit et al. (Finance Stoch. 21:1103-1139, 2017) in which the authors have studied similar topics for the model stopped at the random time, while here we deal with the question of what happens after the random time. Given that the existing literature proves that NUPBR is always violated after honest times that avoid stopping times in a continuous filtration, we propose here a new class of honest times for which NUPBR can be preserved for some models. For these honest times, we obtain two principal results. The first result characterizes the pairs of initial market and honest time for which the resulting model preserves NUPBR, while the second result characterizes honest times that do not affect NUPBR of any quasi-left-continuous model (i.e., in which the asset price process has no predictable jump times). Furthermore, we construct explicitly local martingale deflators for a large class of models. [ABSTRACT FROM AUTHOR]

We study in detail and explicitly solve the version of Kyle's model introduced in a specific case in Back and Baruch (Econometrica 72:433-465, 2004), where the trading horizon is given by an exponentially distributed random time. The first part of the paper is devoted to the analysis of time-homogeneous equilibria using tools from the theory of one-dimensional diffusions. It turns out that such an equilibrium is only possible if the final payoff is Bernoulli distributed as in Back and Baruch (Econometrica 72:433-465, 2004). We show in the second part that the signal the market makers use in the general case is a time-changed version of the one they would have used had the final payoff had a Bernoulli distribution. In both cases, we characterise explicitly the equilibrium price process and the optimal strategy of the informed trader. In contrast to the original Kyle model, it is found that the reciprocal of the market's depth, i.e., Kyle's lambda, is a uniformly integrable supermartingale. While Kyle's lambda is a potential, i.e., converges to 0, for the Bernoulli-distributed final payoff, its limit in general is different from 0. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a set-valued cubic functional equation and a set-valued quartic functional equation and prove the Hyers-Ulam stability of the set-valued cubic functional equation and the set-valued quartic functional equation by using the fixed point method. [ABSTRACT FROM AUTHOR]

Optimal strategies are known for the finite and infinite horizon discrete-time search with constant unit cost and without recall. These strategies were obtained in the theory of optimal stopping, based on the martingale convergence theorem and other tools from probability theory. We present here an elementary approach to these problems, relying only on routine calculation of expected values. In the finite horizon case, the solution utilizes a simple form of backward induction, in conjunction with a nonlinear dynamical system, to compute the parameters of the optimal strategy. An elementary proof is also given that a simple threshold search is optimal among all strategies with finite expected total cost. [ABSTRACT FROM PUBLISHER]

This paper studies the impact, on no-arbitrage conditions, of stopping the price process at an arbitrary random time. As price processes, we consider the class of quasi-left-continuous semimartingales, i.e., semimartingales that do not jump at predictable stopping times. We focus on the condition of no unbounded profit with bounded risk (called NUPBR), also known in the literature as no arbitrage of the first kind. The first principal result describes all the pairs of quasi-left-continuous market models and random times for which the resulting stopped model fulfils NUPBR. Furthermore, for a subclass of quasi-left-continuous local martingales, we construct explicitly martingale deflators, i.e., strictly positive local martingales whose product with the price process stopped at a random time is a local martingale. The second principal result characterises the random times that preserve NUPBR under stopping for any quasi-left-continuous model. The analysis carried out in the paper is based on new stochastic developments in the theory of progressive enlargements of filtrations. [ABSTRACT FROM AUTHOR]

In this paper, we introduce set-valued quadratic functional equations and prove the Hyers-Ulam stability of the set-valued quadratic functional equations by using the fixed point method. [ABSTRACT FROM AUTHOR]

There are many examples in the literature of non-cooperative games in which players prefer not to have additional information in order to improve their payoff. We present a general quadratic game in which, if one of the players improves his payoff upon obtaining more information, the other player’s payoff worsens in such a way that there is a net social loss due to having more information. How can we ensure this does not occur? The results of this paper are (1) the mathematical expression of the (social) value of information in a quadratic non-cooperative game, and (2) the conditions that ensure the social value of information is non-negative. [ABSTRACT FROM AUTHOR]

For the linear Hotelling model with firms located at the boundaries of the segment line, we study the price competition in a scenario of incomplete information in the production costs of both firms. We introduce the bounded uncertain costs (BUC) condition in the production costs and we prove that there is a local optimum price strategy if and only if the BUC condition holds. We compute explicitly the local optimum price strategy and we prove that it does not depend upon the distributions of the production costs of the firms, except on their first moments. We prove that the ex-post profit of a firm is smaller than its ex-ante profit if and only if the production cost of the other firm is greater than its expected cost. [ABSTRACT FROM AUTHOR]