Financial Analysis Model Research Articles

Financial sentiment analysis model utilizing knowledge-base and domain-specific representation

Financial sentiment analysis model utilizing knowledge-base and domain-specific representation

Research on Intellectual Dichotomiser 3 Decision Tree Algorithm Model for Financial Analysis of Colleges and Universities

The rapid development of college information construction has promoted the processing and analysis of a large number of data in the college system. Decision tree algorithm is often used in the field of financial data analysis, but it has a bias in the selection of attributes. Aiming at the defects of the decision tree algorithm in attribute selection, ID3 algorithm in the decision tree algorithm is selected for weighted improvement, and it is optimized based on Synthetic Minority Oversampling Technique (SMOTE) algorithm and Bagging algorithm to balance the positive and negative data of its training samples, thus obtaining the DSB-ID3 financial analysis model. Using this model to analyze the financial data of a university, its G value and F value are both about 0.78, the recognition accuracy rate for normal samples is 0.7345, and the total recognition accuracy rate is 0.7893, which are the highest among the four models. Compared with other models, model designed in this study has significantly improved classification performance, and its distribution is the most centralized, showing superior stability. The experimental results show that the classification effect of model designed in this study is the best, and it shows superior accuracy and stability in the analysis of financial data. Its superior classification performance shows the potential of decision tree algorithm optimization and the feasibility and necessity of improving it. From the experimental data, it can be seen that the service life and parameters of the model designed in this study are obviously better than those commonly used in the financial analysis industry of colleges and universities. It can be seen from the overall analysis that this model provides a practical reference for the application of decision tree optimization in college financial analysis, and greatly improves the accuracy of financial system data analysis.

The Role of the External Audit in Assessing Continuity of Companies under the Financial Crisis: An Applied Study in the Iraqi Banks Listed in the Iraq Stock Exchange for the Period 2016-2019

In the first decade of the third millennium, shareholding companies faced several crises, with four major crises affecting financial markets, beginning with the collapses of a group of large companies following the Enron Energy company and similar companies in various countries, followed by the financial crisis and the collapse of global oil prices, which quickly ended until the sta As a result, the purpose of this study is to shed light on the role of external audit in assessing bank continuity, as Iraqi banks are among the first institutions and companies to move toward the application of international standards as well as being committed to accounting principles and assumptions, and they face a wide range of risks, the most dangerous of which is financial instability. Furthermore, the Kida model has been adopted in assessing the stability of banks and their ability to meet their obligations and perform their duties through a set of financial ratios that distinguish successful banks from those whose ability is suspect and vulnerable to financial failure, as well as trying to identify the nature of their activities at an early stage. However, using financial ratios, Kida's model was applied to a sample of three Iraqi banks, and the research concluded that the external auditor should use financial analysis models to know and evaluate the banks' continuity in terms of anticipating financial collapse.

GIS-Based Financial Analysis Model for Integrated Maintenance and Rehabilitation of Underground Pipe Networks

AbstractThe monitoring and determination of the suitable time for performing the maintenance works of municipal infrastructures consisting of water, sewer, drainage, and road networks have been mat...

Techno-economic feasibility analysis of a eucalyptus-based power plant using woodchips

Forest biomass plays a critical role in developing countries, with high potential to contribute to the energy needs as a cheaper and carbon-friendly alternative. In Brazil, eucalyptus plantations have gained attention in recent years as a promising source for the power sector. Aiming to assess this potential, this work analyzed the techno-economic feasibility of a eucalyptus woodchip-based power plant. A typical 10 MW power plant was modeled using empirical parameters based on the Steam Rankine Cycle. Woodchip samples were collected and energy properties, fuel demand, eucalyptus area required and unit variable cost were determined. In economic analysis, NEWAVE (Long and Medium Term Interconnected Hydrothermal Systems Operation Planning Model) and ANAFIN (Financial Analysis Model for Electrical Projects), both developed by CEPEL (Brazilian Electrical Research Center) were used for the first time to analyze forest biomass projects taking into account fuel availability and hydrological risks. Three energy trading scenarios were evaluated: (1) full commercialization in Brazilian regulated market, (2) full in free market and (3) partial in both markets. Results showed that 4,200 ha of eucalyptus plantations would be needed to ensure sustainable woodchip supply. Moisture content, basic density and woodchip price played an essential role in determining future costs. All scenarios of energy trading were economic feasible, and scenario (3) mitigated energy spot price risk and might be considered the effective risk-return strategy. The simulated strategies may improve decision makers visibility over the risk-return relation of a eucalyptus power plant. This methodology may be used to evaluate new forest biomass energy projects in potential areas. This study will help encourage bioenergy and power diversification in developing countries.

A Framework for Analysis and Prediction of Operational Risk Stress

A model for financial stress testing and stability analysis is presented. Given operational risk loss data within a time window, short-term projections are made using Loess fits to sequences of lognormal parameters. The projections can be scaled by a sequence of risk factors, derived from economic data in response to international regulatory requirements. Historic and projected loss data are combined using a lengthy nonlinear algorithm to calculate a capital reserve for the upcoming year. The model is embedded in a general framework, in which arrays of risk factors can be swapped in and out to assess their effect on the projected losses. Risk factor scaling is varied to assess the resilience and stability of financial institutions to economic shock. Symbolic analysis of projected losses shows that they are well-conditioned with respect to risk factors. Specific reference is made to the effect of the 2020 COVID-19 pandemic. For a 1-year projection, the framework indicates a requirement for an increase in regulatory capital of approximately 3% for mild stress, 8% for moderate stress, and 32% for extreme stress. The proposed framework is significant because it is the first formal methodology to link financial risk with economic factors in an objective way without recourse to correlations.

The Role of Strategic Cost Management in Marketing Decisions: A Case Evidence of Brand Acquisition Assessment

While the role of strategic cost management (SCM) is heralded to pervade through all the links in a firm’s value-chain (Anderson, 2006; Shank & Govindarajan, 2004) the specific role of SCM in relation to a firm’s marketing function has thus far been limited to a few areas such as product pricing and analysing customer profitability through activity-based cost allocations (Datar & Rajan, 2018; Foster & Gupta, 1994; Van Raaij, 2005). This paper presents a case study to show how SCM concepts can be extended to evaluate brand acquisition by a large organisation. Using shareholder value analysis for pricing the brand and combining with financial statement analysis and strategic positioning models such as Porter’s five forces and SWOT, this paper conducts an assessment in order to offer recommendations for a multi-billion peso investment of a large Philippine-based processed foods company seeking to acquire a new brand.

APPLICATION OF FRACTAL DIMENSION OF FRACTIONAL BROWNIAN MOTION TO SUPPLY CHAIN FINANCING AND OPERATIONAL COMPREHENSIVE DECISION-MAKING

For a long time, the mismatch between material flow and capital flow in the supply chain operation management practice is very prominent, which has led to the inefficiency of supply chain operation and hindered the exertion of supply chain’s advantages. In the field of supply chain management theory research, the information completeness in capital market has long been a hypothetical premise, which has led to the separation of corporate financing and operational decision-making research. In addition to production, inventory, procurement, pricing and other strategies in supply chain operations, payment options and credit incentives are also important decisions for both parties, especially for products with long production and sales cycles; different payment methods directly affect corporate capital flows and the enterprise’s long-term development. On the basis of summarizing and analyzing previous works, this paper analyzed the research status and significance of supply chain financing and operational comprehensive decision-making, expounded the development background, current situation, and future challenges of the fractal dimension of fractional Brownian motion; elaborated the principles and methods of the scaling properties of fractional Brownian motion and the phase space reconstruction of time series, established a financial management analysis model based on the fractal dimension of fractional Brownian motion, performed the analysis of the agglomeration degree, time series and multi-fractal characteristics of supply chain financing, explored the coupling relationship between the comprehensive operational decision-making and Brownian motion’s scaling properties. The final empirical analysis showed that when own funds are sufficient, the production should be carried out with the goal of maximizing profits, and full consideration should be given customer channel stickiness, relative costs of offline and online channel products, and product profitability; the proposed analysis model can achieve the optimal order quantity in supply chain, and reach risk and benefit sharing among financial institutions, retailers, and suppliers by setting the financing interest rate, wholesale price, repurchase price and other parameters, thereby improving supply chain performance. This study results of this paper provided a reference for further researches on the application of fractal dimension of the fractional Brownian motion to the supply chain financing and operational comprehensive decision-making.

Construction and Application of a Financial Big Data Analysis Model Based on Machine Learning

Construction and Application of a Financial Big Data Analysis Model Based on Machine Learning

Financial risk analysis of real estate bubble based on machine learning and factor analysis model

The regional real estate price bubble regulation policy is an external factor for the real estate industry. The effect of real estate regulation is difficult to determine, which is a typical problem of uncertain system analysis and forecasting, and the gray Bayesian network forecasting model is to solve the forecasting problem of economic system subject to external regulation. Based on machine learning and factor analysis models, this paper constructs a real estate bubble financial risk analysis model based on machine learning and factor analysis models. Moreover, starting from the real estate price bubble, which is a hot and difficult issue of the social economy, this paper discusses the causes of the formation of real estate price bubbles and the mechanism of the formation of real estate price bubbles, looks for the importance of policy regulation of real estate price bubbles, and clarifies the functional game model of policy regulation of real estate price bubbles. In addition, this paper uses examples to study the model constructed in this paper. The results show that the model constructed in this paper has a certain effect.

Conducting Business with God: Is God Blessing Islamic Banks with Better Financial Performance than Conventional Banks?

This study aims to compare the financial performance between Islamic and conventional banks listed at Kuwait stock exchange over the period 2011-2018 using the modified DuPont model of financial analysis which is based on the analysis of return on equity (ROE). Unlike previous studies where researchers compared the performance on a bank-to-bank basis, this study examines the aggregate ratios of Islamic banks and compare it to aggregate ratios of conventional banks. The study also adds volatility into the model since consistency in returns indicated a more stable sector. Results obtained from this study showed that conventional banks in Kuwait had a better mean performance during the study period in terms of both return on assets (ROA) and return in equity (ROE), Islamic banks also showed a higher deviation in these two ratios resulting in a lower Sharpe ratio. While the results showed no statistically significant mean difference between Islamic and conventional banks in terms of return on assets (ROA), the results also showed a statistically significant difference in mean return on equity (ROE) between the two sub-sectors. On the other hand, Islamic banks showed an impressive improvement in their ratios during the last three years of the study period which impose a real threat to conventional banks in the future.

Computer Model for Financial, Environmental and Risk Analysis of a Wind–Diesel Hybrid System with Compressed Air Energy Storage

Remote and isolated communities in Canada experience gaps in access to stable energy sources and must rely on diesel generators for heat and electricity. However, the cost and environmental impact resulting from the use of fossil fuels, especially in local energy production, heating, industrial processes and transportation are compelling reasons to support the development and deployment of renewable energy hybrid systems. This paper presents a computer model for economic analysis and risk assessment of a wind–diesel hybrid system with compressed air energy storage. The proposed model is developed from the point of view of the project investor and it includes technical, financial, risk and environmental analysis. Robustness is evaluated through sensitivity analysis. The model has been validated by comparing the results of a wind–diesel case study against those obtained using HOMER (National Renewable Energy Laboratory, Golden, CO, United States) and RETScreen (Natural Resources Canada, Government of Canada, Canada) software. The impact on economic performance of adding energy storage system in a wind–diesel hybrid system has been discussed. The obtained results demonstrate the feasibility of such hybrid system as a suitable power generator in terms of high net present value and internal rate of return, low cost of energy, as well as low risk assessment. In addition, the environmental impact is positive since less fuel is used.

Prediction of Developments in the Textile and Clothing Industry in Slovakia by Selected Indicators of Financial Analysis

The textile and clothing industry in Europe can be considered as a not profitable sector . The goal of the contribution is an evaluation of selected indicators of financial analysis, credit score and bankruptcy models as well as strategic analysis in selected companies of the textile and clothing industry in Slovakia. The next goal is an outline of development possibilities of the sector in the future. During the research we used data from the five most important companies doing business in the textile and clothing industry in Slovakia. The data obtained were processed by the bonity and Altman index, providing the possibility to determine possible future development in the industry. The results show a decrease in the number of textile and clothing companies in Slovakia. Such results can be used for the setting of scenarios of development, which show that the Slovakian textile, clothing and leather industry should multiply its effort to maintain its position on the international markets.

A lifecycle financial analysis model for offshore wind farms

Simulation and modelling allow a range of offshore wind farm stakeholders to test and improve a project's viability in a cost-effective and safe manner. This paper presents a model developed to conduct detailed financial analysis of an offshore wind farm. It extends the current state of the art by employing stochastic time-series simulation modules performing in-depth analysis of the technologies, strategies and procedures applied during the installation, operation and maintenance, and decommissioning phases of a wind farm lifecycle. The model was designed for versatility and can consider both fixed and floating technologies, a wide variety of strategies, and any site specified by the user. Results include energy production, costs and the duration of activities at each stage. These populate financial spreadsheets, which calculate key performance indicators including the Levelised Cost of Energy. The model has been successfully validated against real-life case-studies where possible; published data; and uses sensitivity analysis to ensure the model is working as expected. Through a case-study, the paper demonstrates how 1) the model enables the identification of key cost and time drivers, facilitating scenario optimisation; 2) the stochastic nature of the model considers the impact of uncertain variables on results such as weather conditions and wind turbine failure rates; 3) the model can be used to assess different business models and financing structures. This comprehensive range of abilities means that the model is suited to a variety of end-users and meets the demands of a growing industry, striving to achieve further cost-reductions across a range of site conditions, technologies and markets.

Procena finansijskog (ne)uspeha srednjih preduzeća u Republici Srbiji

The aim of this research is to evaluate financial (non)success of medium-sized enterprises in the Republic Serbia (RS). The assessment has been carried out in sectors C - Manufacturing industry, G - Wholesale and retail trade; motor repair vehicles and motorcycles (Trade), then sector A - Agriculture, forestry and fisheries (Agriculture), F - Construction, and at the level of all medium-sized enterprises in RS. The sectors were selected according to the number of registered medium-sized enterprises. The subject of investigation are key financial performance indicators gathered from summary balances of observed sectors in 2018. year. Data were obtained from Statistical Office of the Republic of Slovenia (SBS). The aim of the research was realized by applying the methods of financial analysis and Altman Z-score model. The purpose of the research is identifying the likelihood of bankruptcy in the above sectors and at the level of all medium-sized enterprises in RS.

Research on financial data analysis based on data mining algorithm

SummaryAt present, computer technology and Internet technology have been deeply integrated with the development of the Internet. This paper constructs a financial time series analysis and prediction model under the background of Internet e‐commerce development. As IT technology, data mining must be simple and fast. Therefore, starting from the model and based on the algorithm, this paper establishes a feasible serial data mining technology and theoretical system. Based on this, this paper deeply studies the data repair technology and interpolation technology such as EM algorithm for the actual more missing data, and the result is very satisfactory. Finally, this paper further studies the forward search algorithm, which is based on clustering to pretreat can complete the calculation process in a shorter time, reduce the possibility of errors, and improve efficiency.

Uma Abordagem para Análise do Risco de Crédito Utilizando o Modelo Fleuriet

Objective: To construct a model that can assess the credit risk in Brazilian publicly-traded companies, using indicators from Fleuriet’s model of financial analysis. Method: Methodologically, the research was defined as quantitative, with a descriptive design. The financial statements were collected from Economatica and the website BM&FBOVESPA. The sample consisted of 121 companies, being 70 solvent and 51 insolvent, from different sectors. Results: For the financial structure, working capital and working capital requirement indicators, the companies seek to achieve a constant growth model, expanding or gaining markets, in view of the continuing need for additional working capital over time. The results found for the liquidity thermometer demonstrate the importance of the financial accounts called treasury account to calculate the company’s short-term corporate liquidity and solvency. Finally, financial indebtedness as a structural index contributed significantly to the model. Contributions: This study can contribute to the Brazilian literature by evidencing that some of the indicators in Fleuriet’s model are significant to assess the credit risk in Brazilian publicly traded companies.

Research on intelligence analysis technology of financial industry data based on genetic algorithm

Based on the improved genetic algorithm and the BP neural network, the financial data analysis model is built on the basis of the data research problem in the financial industry, especially the stock market economy. Genetic algorithm (GA) is a bionic algorithm that simulates the evolutionary mechanism of “natural selection and survival of the fittest.” Through a suitable fitness function, it enables the high-quality individuals to inherit the next generation with a larger probability. Using this method to select variables, we can optimize the variables that affect the stock price and effectively solve the selection problem of the input layer variables of the neural network. On the basis of genetic algorithm, a new GA-BP model based on genetic algorithm and neural network is proposed, and it is applied to the prediction and analysis of securities price or trend in the securities intelligence analysis system. By selecting the Shanghai and Shenzhen 300 index data, the model based on genetic algorithm is used to carry out the experiment, and the experimental results show that the method can effectively reduce the number of variables while ensuring the accuracy of the prediction.

Evaluasi Tawaran Kerjasama Kemitraan dengan Go-Mart dari Sudut Pandang Konsumen dan Usaha Retail di Kota Bandung

This study aimed to describe response of public and retail stores in the Bandung city about cooperation (partnership) conducted by Go-Mart which is a business unit of PT. Go-Jek Indonesia. The research method used was case study (about Go-Mart), which interview and survey was conducted on retail store and people in Bandung. The data analysis technique used was describe the survey result (diagram) and NMAx. The calculation result (Nmax) is used as a consideration for feasibility cooperation. In addition, the willingness of society to make purchases using Go-Jek and willingness retail stores to join with Go-Mart be other factors that are discussed in this study. This study also conducted an analysis of the feasibility of cooperate between retail store and PT. Go-Jek Indonesia (Go-Mart). This study is limited to objectives, activities, and cash flows of the partnership were probably carried out by retail business with Go-Mart. Development model of financial planning analysis is done in order to help retail businesses to decide to join the Go-Mart or not. In addition, this design also helps Go-Mart to determine the exact costs to be imposed on its partners.

KAJIAN ANALISIS USAHATANI PENGGUNAAN PUPUK ORGANIK NON KOMERSIAL TERHADAP HASIL DAN PENDAPATAN PETANI PADI

Policy of providing chemical fertilizers at low prices through the subsidies continue to increase every year, causing inefficient use of fertilizers by farmers and in turn lower the productivity of the land. The study was conducted in the province of West Java and Central Java in 2012, aims to (1) evaluate the costs and revenues of non commercial organic fertilizer and (2) analyzing farm income and the factors that affect rice production. Total farmer respondents interviewed 60 farmers with 30 farmers who earn program Organic Fertilizer Processing Unit (UPPO) and 30 farmers who do not follow the program UPPO. To determine the effect of the use of organic fertilizers used in rice cultivation model of linear production function CobbDouglas and financial analysis or benefit cost ratio (B/C). The results showed good organic fertilizer production enterprises in the form of solid or liquid organic fertilizer is quite profitable. Farmers earn net income of Rp. 83-112 per kg of organic fertilizer in 6 weeks. The amount of chemical fertilizers used on semi-organic rice farming is reduced by approximately 50% of the amount of chemical fertilizers used in nonorganic farming. The use of labor in semi-rice organic farming is higher than nonorganic farming, especially at the stage of cultivation and weeding. R/C ratio of semiorganic rice farming is greater than non-organic rice which indicates that the semiorganic rice farming more profitable financially.