Field Of Financial Analysis Research Articles

CharGPT: Pioneering the Future of Financial Analysis and Informed Decisions

In recent years, the field of financial analysis has undergone a trans-formative shift with the advent of advanced intelligence technologies. One such technology , charGPT based on the GPT-3.5 architecture, has emerged as a powerful tool for extracting insights from financial data and aiding decision making processes. This paper explore the capabilities of CharGPT in the realm of financial analysis, demonstrating its potential to provide meaningful insights , generate accurate forecasts , and assist in strategic decision-making

High‐Accurate Numerical Schemes for Black–Scholes Models with Sensitivity Analysis

The significance of both the linear and nonlinear Black‐Scholes partial differential equation model is huge in the field of financial analysis. In most cases, the exact solution to such a nonlinear problem is very hard to obtain, and in some cases, it is impossible to get an exact solution to such models. In this study, both the linear and the nonlinear Black‐Scholes models are investigated. This research mainly focuses on the numerical approximations of the Black‐Scholes (BS) model with sensitivity analysis of the parameters. It is to note that most applied researchers use finite difference and finite element‐based schemes to approximate the BS model. Thus, an urge for a high accurate numerical scheme that needs fewer grids/nodes is huge. In this study, we aim to approximate and analyze the models using two such higher‐order schemes. To be specific, the Chebyshev spectral method and the differential quadrature method are employed to approximate the BS models to see the efficiency of such highly accurate schemes for the option pricing model. First, we approximate the model using the mentioned methods. Then, we move on to use the numerical results to analyze different aspects of stock market through sensitivity analysis. All the numerical schemes have been illustrated through some graphics and relevant discussions. We finish the study with some concluding remarks.

Payment system as a basic element of the digital economy: institutional-behavioral aspects

Methods. The methodological basis of the research is the scientific works of domestic and foreign scientists and leading experts in the field of financial analysis and modeling of economic processes, as well as statistical and financial reporting data that are publicly available. To construct an integral indicator of the financial security of an enterprise, a fuzzy conclusion is used. Results. It is shown that for assessing the level of the financial security indicator of an enterprise, the fuzzy set method is the most effective in comparison with linear models, since it takes into account the uncertainty of the internal and external environment of the enterprise as much as possible. The fuzzy expert system actually allows you to use a significant number of input parameters, both quantitative and qualitative. The specific choice of input parameters should be justified, on the one hand, by their weight, and on the other hand, by the complexity of collecting data necessary to assess the state of financial security of the enterprise. To implement the proposed fuzzy-logical approach to determining the integral indicator of financial security of an enterprise (Financial Security (FS)), Financial coefficients are used that characterize solvency (Current Ratio (CR)), financial independence (Equity Ratio (ER)), profitability of total assets of the enterprise (Return on Assets (ROA)) and affect the target indicator. Novelty. The general structure of the fuzzy logic expert system constructor is presented as a data transformation within the system itself. For a fuzzy-logical approach, in order to determine the integral indicator of financial security of an enterprise Y123 (Financial Security (FS)), three input indicators are selected that affect the target indicator, namely: the current liquidity ratio X1(CR), the autonomy ratio X2 (ER) and the return on total assets ratio X3(ROA). Practical value. Indicators that make it possible to assess the financial security of enterprises to the greatest extent are proposed, and practical recommendations are given for the ranges of their optimal values for constructing an integral indicator of the financial security of enterprises using a fuzzy-logical approach.

FUZZY-LOGICAL EXPERT SYSTEM FOR ASSESSING THE FINANCIAL SECURITY OF ENTERPRISES

The purpose of this study is to build a fuzzy expert system for assessing the financial component of the economic security of telecommunications enterprises. The methodological basis of the research is founded on scientific works of domestic and foreign scientists and leading experts in the field of financial analysis and modeling of economic processes, as well as statistical and financial reporting data that are publicly available. To construct an integral indicator of the financial security of an enterprise, a fuzzy conclusion is used. Three financial indicators are used as input variables. The first indicator X1 is the Current Ratio (CR). The second indicator X2 is Equity Ratio (ER). The third indicator is Return on Assets (ROA). The output variable is defined as an indicator of the financial security of an enterprise Y123 (FS). Both the input variables and the output variable are converted to fuzziness by constructing membership functions. The type and parameters of the affiliation function are justified, and the bell-shaped affiliation function is chosen to describe the uncertainty of values that fall under the normal distribution. The quantity of fuzzy sets at every input is considered as z=3 and the quantity of input variables is considered as ω=3. To achieve completeness of the model, the quantity of logic rules is considered as r=33=9. To calculate a degree of market concentration, Mamdani fuzzy conclusion is applied. Defuzzification is engaged to calculate the value of the output variable Y123(FS) for an indicator that determines the degree of financial security of an enterprise and, as a result, the degree of its economic security. To assess the level of the financial security indicator of an enterprise, a fuzzy expert system is constructed. The fuzzy expert system allows you to use various indicators thanks to the fuzzy logic methodology, which takes into account the fuzziness of input variables and output variables as much as possible. For the three telecommunications companies whose core business is wireline communication, ratios are calculated based on financial reports. Financial coefficients are used to determine the integral indicator of financial security of enterprises. This indicator can be characterized by both numerical values and linguistic terms.

Methodological Approaches to Computer Modelling Possibilities in Financial Analysis

The current research intention’ is to present some methodological approaches regarding the possibilities of computerizing the field of financial analysis. Therefore, at first, the characteristics, necessity and objectives of the financial analysis are clearly presented. Secondly, the classical models of application development are compared within a table, pointing out the advantaged versus the disadvantages of each model. Also, the agile models are included for the purpose of deciding which model is the best suited for the analyzed filed. Thirdly, the specific languages for enterprise modelling, namely UML language and ontology are presented and specific ideas are formulated. The article states, at the end, which methodology, model and modelling language is the best suited for computerizing the field of financial analysis and also presents a conceptual computer modelling for financial analysis.

Stock picking via nonsymmetrically pruned binary decision trees

Stock picking is the field of financial analysis that is of particular interest for many professional investors and researchers. In this study stock picking is implemented via binary classification trees. Optimal tree size is believed to be the crucial factor in forecasting performance of the trees. While there exists a standard method of tree pruning, which is based on the cost-complexity approach and used in the majority of studies employing decision trees, this paper introduces a novel methodology of tree pruning called Best Node Strategy (BNS). An important property of BNS is proven that provides an easy way to implement the search of the optimal tree size in practice. BNS is compared with the traditional pruning approach by composing two recursive portfolios out of XETRA DAX stocks. Performance forecasts for each of the stocks are provided by constructed decision trees. It is shown that BNS clearly outperforms the traditional approach according to the backtesting results.

Stock Picking via Nonsymmetrically Pruned Binary Decision Trees

Stock picking is the field of financial analysis that is of particular interest for many professional investors and researchers. In this study stock picking is implemented via binary classification trees. Optimal tree size is believed to be the crucial factor in forecasting performance of the trees. While there exists a standard method of tree pruning, which is based on the cost-complexity tradeoff and used in the majority of studies employing binary decision trees, this paper introduces a novel methodology of nonsymmetric tree pruning called Best Node Strategy (BNS). An important property of BNS is proven that provides an easy way to implement the search of the optimal tree size in practice. BNS is compared with the traditional pruning approach by composing two recursive portfolios out of XETRA DAX stocks. Performance forecasts for each of the stocks are provided by constructed decision trees. It is shown that BNS clearly outperforms the traditional approach according to the backtesting results and the Diebold-Mariano test for statistical significance of the performance difference between two forecasting methods.

Study on the Non-Random and Chaotic Behavior of Chinese Equities Market

After the stock market crash of October 19, 1987, interest in nonlinear dynamics and chaotic dynamics have increased in the field of financial analysis. The extent that the daily return data from the Shanghai Stock Exchange Index and the Shenzhen Stock Exchange Index exhibit non-random, nonlinear and chaotic characteristics are investigated by employing various tests from chaos theory. The Hurst exponent in R/S analysis rejects the hypothesis that the index return series are random, independent and identically distributed. The BDS test provides evidence for nonlinearity. The estimated correlation dimensions provide evidence for deterministic chaotic behaviors.

Expert systems in finance—a cross-section of the field

Abstract This paper surveys a number of well-known expert systems in the domain of finance. The idea is to illustrate the span of typical expert systems in finance and to provide an insight into the approaches and techniques they employ. The systems considered in the paper are all working systems, and come from different fields of finance. Specifically, the systems considered are FINEVA (from the field of financial analysis), PORT-MAN (banking management), INVEX (investment advisory) and FAME (financial marketing). The paper also briefly presents DEVEX, an expert system for currency exchange advising in international business transactions. Financial institutions in less-developed and undeveloped countries that deal with currency exchange often face some specific problems that do not exist in well-developed regions of the world. DEVEX helps bank employees to cope with such problems. Since business transactions between developed countries and Third World ones represent an important part of the world's financial affairs, the problems covered in DEVEX go beyond local financial institutions.

Knowledge acquisition and representation for expert systems in the field of financial analysis

Knowledge acquisition and representation has been characterised as the major bottleneck in the development of expert systems (Barr & Geigenbaum, 1982), especially in problem domains of high complexity. Financial analysis is one of the most complicated practical problems, where the expert systems technology is highly applicable, mainly because of its symbolic reasoning and its explanation capabilities. The aim of this paper is to present a complete methodology for knowledge acquisition and representation for expert systems development in the field of financial analysis. This methodology has been implemented in the development of the FINEVA multicriteria knowledge-based decision support system for the assessment of corporate performance and viability. The application of this methodology in the development of the FINEVA system is presented.