The generalized trapezoidal model in financial data analysis

Longitudinal studies are widely used in various fields, such as public health, clinic trials and financial data analysis. A major challenge for longitudinal studies is the repeated measurements from each subject, which cause time dependent correlations within subjects. Generalized Estimating Equations (GEE) can deal with correlated outcomes for longitudinal data through marginal effect. Our proposed model will be based on GEE, with a semi-parametric approach, to provide a flexible structure for regression models: coefficients for parametric covariates will be estimated and nuisance covariates will be fitted in kernel smoothers for the non-parametric part. The profile kernel estimator and the seemingly unrelated kernel estimator (SUR) will be used to obtain consistent and efficient semi-parametric estimators. We provide simulation results for estimating semi-parametric models with one or multiple non-parametric terms. Financial market data is a major component of data analysis; thus, we focus on the financial market in the application part. Credit card loan data will be used with the payment information for each customer across six months to investigate whether gender, income, age, or other factors will influence payment status significantly. Furthermore, we propose model comparisons to evaluate whether different models should be fitted for different subgroups of consumers, such as male and female.

The opening up of the market for equity market data raises the question of whether data will be sufficiently consolidated and of high enough quality post-MiFID, or whether it will become too fragmented, thereby hindering price transparency and the implementation of best execution policies. This paper outlines the market for financial market data, the provisions of MiFID, and the implementing measures regarding financial data and data consolidation. It also looks at the approaches taken by the Committee of European Securities Regulators (CESR), the UK's Financial Services Authority (FSA) and the US authorities. It concludes that markets should be capable of adapting, that additional licensing requirements, such as those proposed by the FSA, are, in fact, premature and act as a barrier to the single market, and that neither would a US-style monopoly consolidator be needed in this case.

This study aims to explore the influence of decision-making, financial data analysis, and human resource training on company performance, as well as the role of artificial intelligence (AI) moderation in these relationships. Using the path analysis method with AMOS software, data was collected from 100 employees in a Telecommunications company in Indonesia. The results show that decision-making and analysis of financial data have a positive and significant influence on company performance, while human resource training does not show a significant influence. Additionally, AI serves as a significant moderator in the relationship between decision-making and company performance, but not in the relationship between financial data analysis and performance. These findings emphasize the importance of good decision-making and financial data analysis in improving organizational performance, and demonstrate the potential of AI in amplifying the positive impact of decision-making. This study has limitations in sample size and context, which need to be considered for further research.

This book provides an introduction to R programming and a summary of financial mathematics. It is not always easy for graduate students to grasp an overview of the theory of finance in an abstract form. For newcomers to the finance industry, it is not always obvious how to apply the abstract theory to the real financial data they encounter. Introducing finance theory alongside numerical applications makes it easier to grasp the subject. Popular programming languages like C++, which are used in many financial applications are meant for general-purpose requirements. They are good for implementing large-scale distributed systems for simultaneously valuing many financial contracts, but they are not as suitable for small-scale ad-hoc analysis or exploration of financial data. The R programming language overcomes this problem. R can be used for numerical applications including statistical analysis, time series analysis, numerical methods for pricing financial contracts, etc. This book provides an overview of financial mathematics with numerous examples numerically illustrated using the R programming language.

Modern critical care medicine is based on flow of information. This information requires significant amount of interpretation to become clinically valuable. Despite the wealth of data in the modern Intensive Care Units (ICUs), intensivists often rely on fragmentary information. In an attempt to improve data trending utilization, application of financial analysis (FA) methods to vital sign data samples was examined. Two randomly chosen vital sign datasets of patients who spent at least 30 days in the ICU were retrospectively reviewed. Hourly vital sign data were retrieved and recorded for each patient. Variables tracked included systolic blood pressure for patient #1 and heart rate for patient #2. These variables were then entered into specialized FA software and subjected to computer-based processing. Trends in the recorded data were examined using (1) the stochastic oscillator (SO), (2) the moving average (MAV) convergence-divergence tool, (3) price envelope analysis and (4) MAV analysis. Both blood pressure and heart rate analyses demonstrated that vital sign data could be successfully trended using FA techniques. Not only was the vital sign data easy to read and interpret when formatted in financial-like fashion, but some trends that were not apparent on gross inspection of the numeric data were clearly demonstrated on FA. Much like with financial patterns, trends noted within vital sign data appeared to be more significant when more than one indicator identified them, utilizing the concept of a confirmatory variable. Vital sign data, much like financial data, were subject to trend reversals. Such reversals in vital sign data appeared to follow rules similar to those followed by financial vehicles and markets. This report demonstrates that vital sign data can be subjected to the same manipulations as financial market data. Furthermore, FA tools appear to provide the interpreter of the data with means to define, confirm, and possibly predict trends and trend reversals.The following core competencies are addressed in this article: Medical knowledge, Practice-based learning and improvement, Systems-based practice.Republished with permission from: Stawicki SP. Application of financial analysis techniques to vital sign data – A novel method of trend interpretation in the Intensive Care Unit. OPUS 12 Scientist 2007;1(1):14-16.

The purpose of this research is to evaluate tax compliance based on the behavior of taxpayers' financial and non-financial data. This research is expected to be useful as material for evaluating the level of tax compliance of taxpayers. This study uses a case study method (single case) and uses a mixed method. Descriptive analysis is used to analyze data from the research object in the form of a single case unit analysis. The results of this study indicate the perceived behavior control of taxpayers that shows low tax compliance behavior. The combination of analysis of financial and non-financial data from taxpayers produces a useful evaluation in determining the level of tax compliance. Based on this, the low achievement of tax revenue realization and high tax audit performance is directly influenced by the low tax compliance behavior of taxpayers.
 Keywords: Tax Compliance; Behavior; Financial Data; Non-Financial Data.

During the last years, the interested scientific community has witnessed the emergence of a whole range of novel architectures and improvement proposals for existing ones. At the same time, Network Service Providers are constantly looking for original business models since the profit margins for classic communication services like telephone calls and internet flat-rate contracts is sinking continuously. The present paper will introduce an innovative network architecture, extending Software Defined Networking (SDN) and Content Centric Networking (CCN) concepts, targeting special traffic categories such as those generated by financial applications and stock market data providers. Although new, our design is perfectly aware of existing network constraints related to already deployed hardware and software components and the resulting compatibility implications. As a consequence, our goal is to provide technological advancements that will harmoniously integrate with existing infrastructure.

The first hitting time of a boundary or threshold by the sample path of a stochastic process is the central concept of threshold regression models for survival data analysis. Regression functions for the process and threshold parameters in these models are multivariate combinations of explanatory variates. The stochastic process under investigation may be a univariate stochastic process or a multivariate stochastic process. The stochastic processes of interest to us in this report are those that possess stationary independent increments (i.e., Lévy processes) as well as the Esscher property. The Esscher transform is a transformation of probability density functions that has applications in actuarial science, financial engineering, and other fields. Lévy processes with this property are often encountered in practical applications. Frequently, these applications also involve a ‘cure rate’ fraction because some individuals are susceptible to failure and others not. Cure rates may arise endogenously from the model alone or exogenously from mixing of distinct statistical populations in the data set. We show, using both theoretical analysis and case demonstrations, that model estimates derived from typical survival data may not be able to distinguish between individuals in the cure rate fraction who are not susceptible to failure and those who may be susceptible to failure but escape the fate by chance. The ambiguity is aggravated by right censoring of survival times and by minor misspecifications of the model. Slightly incorrect specifications for regression functions or for the stochastic process can lead to problems with model identification and estimation. In this situation, additional guidance for estimating the fraction of non-susceptibles must come from subject matter expertise or from data types other than survival times, censored or otherwise. The identifiability issue is confronted directly in threshold regression but is also present when applying other kinds of models commonly used for survival data analysis. Other methods, however, usually do not provide a framework for recognizing or dealing with the issue and so the issue is often unintentionally ignored. The theoretical foundations of this work are set out, which presents new and somewhat surprising results for the first hitting time distributions of Lévy processes that have the Esscher property.

Decision support systems use statistical and computational techniques. These statistical and computational techniques are used to predict the outcomes when it is applied on financial data. This chapter discusses the time series forecasting and predictive analytics within the financial domain. This work will cover the ARIMA method and deep learning methods. These methods will facilitate forecasting future through the analysis of financial time series data. The outcomes generated by these methods will help the financial managers and other professionals in the financial domain to improve their results. This work investigates predictive analytic techniques in the finance domain and computational algorithms to forecast the future trends.

It is known that when the random variables being compared have a uniform probability measure, as is the case when using financial market data, the computation of efficient sets for first- and second-degree dominance can be greatly simplified if majorization conditions are used instead of the dominance definitions. This paper presents an equally efficient technique for third-degree dominance.

Although text-mining, sentiment analysis, and other forms of analysis have been carried out on financial investment applications, a significant amount of associated research is ad hoc searching for meaningful patterns. Other research in finance develops theory using limited data sets. These efforts are at two extremes. To bridge the gap between financial data analytics and finance domain theory, this research analyzes a specific conceptual model, the Business Intelligence Model (BIM), to identify constructs and concepts that could be beneficial for matching data analytics to domain theory. Doing so, provides a first step towards understanding how to effectively generate and validate domain theories that significantly benefit from data analytics.

The word ‘actuary’ derives from the Latin word ‘actuarius’, who was the business manager of the Senate of Ancient Rome. It was first applied to a mathematician of an insurance company in 1775 in the Equitable Life Insurance Society (of London, UK). By the middle of the nineteenth century, actuaries were active in life insurance, friendly societies, and pension schemes. As time has gone on, actuaries have also grown in importance in relation to general insurance, investment, health care, social security, and also in other financial applications such as banking, corporate finance, and financial engineering. Over the times, there have been several attempts to give a concise definition of the term actuary . No such attempted definition has succeeded in becoming universally accepted. As a starting point, reference is made to the International Actuarial Association's description of what actuaries are: Actuaries are multi‐skilled strategic thinkers, trained in the theory and application of mathematics, statistics, economics, probability and finance and what they do: Using sophisticated analytical techniques, actuaries confidently make financial sense of the short term as well as the distant future by identifying, projecting and managing a spectrum of contingent and financial risks. This essay will adopt a descriptive approach to what actuaries are and what they do, specifically by considering the actuarial community from the following angles: Actuarial science – the foundation upon which actuarial practice rests. Actuarial practice. Some characteristics of the actuarial profession.

The article explores data mining methods that allow us to get helpful information from the data. The possibility of using these methods in practice in the financial sector was considered. Since financial activity is closely related to our social life, the use of data mining methods plays an essential role in the analysis and forecasting of the financial market in the modern era of big data. However, due to differences in the experience of researchers in different disciplines, it is not easy to use data mining methods when analyzing financial data. Therefore, creating a methodological basis for the practical application of data mining methods in the analysis of financial data is an urgent issue. The purpose of this article is to create a methodological basis for using data mining methods for efficient trading. When processing product data, a priori methods and visualization methods were used, and their implementation in practice was described. As a result, scenarios of computer applications were created as a sample of the practical implementation of the algorithms of these methods. Building a quantitative trading strategy requires first statistical analysis of the information in the market and then testing the quantitative model on the collected data. This study developed a quantitative trading system based on data mining methods. The primary development tool used is the Jupyter web platform, and three cores have been developed: quantitative data selection, strategy testing on data, time series analysis, and visualization. The developed system supports modules for making simple trading decisions.

Regulators require financial institutions to estimate counterparty default risks from liquid CDS quotes for the valuation and risk management of OTC derivatives. However, the vast majority of counterparties do not have liquid CDS quotes and need proxy CDS rates. Existing methods cannot account for counterparty-specific default risks; we propose to construct proxy CDS rates by associating to illiquid counterparty liquid CDS Proxy based on Machine Learning Techniques. After testing 156 classifiers from 8 most popular classifier families, we found that some classifiers achieve highly satisfactory accuracy rates. Furthermore, we have rank-ordered the performances and investigated performance variations amongst and within the 8 classifier families. This paper is, to the best of our knowledge, the first systematic study of CDS Proxy construction by Machine Learning techniques, and the first systematic classifier comparison study based entirely on financial market data. Its findings both confirm and contrast existing classifier performance literature. Given the typically highly correlated nature of financial data, we investigated the impact of correlation on classifier performance. The techniques used in this paper should be of interest for financial institutions seeking a CDS Proxy method, and can serve for proxy construction for other financial variables. Some directions for future research are indicated. Thanks for the feedbacks from participants at the conferences/seminars; please feel free to check out some of the updated presentation slides: 1. Departmental Seminar of Statistics at London School of Economics, London, Mar. 2017; 2. New Methods, Perspectives and Approaches Seminar at Bank of England, London, Oct. 2017; 3. Machine Learning and AI in Quantitative Finance 2017 Conference, London, Nov. 2017; 4. Machine Learning and AI in Quantitative Finance 2018 Conference, London, Mar. 2018; 5. Call for Paper 2018 Winner of Risk's Quant Summit Europe Conference, London, Mar 2018; 6. Global Finance Conference Jun. 2018, Paris; 7. 10th World Congress of Bachelier Finance Society Jul. 2018, Dublin.

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