Financial Analysis Model Research Articles

Firm-Level Investor Sentiment and Risk: Insight from Behavioral Finance

The aim of this study to analyses the investor sentiment, attentions and types of risk which are incurred after specific event like scam etc. This study used monthly dataset of prices from 2006 to 2022 due to availability of data. The event approach is used for data analysis with different data segregation like affected or unaffected datasets of firms. Firm-specific sentiment and attention increased slightly after the scam, indicating market interest and better firm perceptions. Political, noise trader, arbitrage, sovereign, and realised volatility increased during events. The perception of instability and uncertainty suggests scams increase market fears and risk aversion while attracting attention. A detailed comparison between scammed and unaffected firms showed stark differences. Financial scams lowered market confidence and firm stability, causing negative sentiment, lower attention, and higher risks across multiple dimensions. The differential impact suggests that scams' reputational and operational damage can cripple a firm's finances and investor perceptions. The study affects financial economics and market behaviour theory. The study shows how firm-specific sentiment and attention affect risk measures, explaining market dynamics' psychological and behavioural underpinnings. Financial anomalies like scams affect market outcomes due to investor sentiment and attention. Sentiment can force through risk perceptions, so integrated financial analysis models must account for behavioural and psychological factors. Financial scandals affect firm-specific and market-wide variables, according to event studies. With the insights, investors, regulators, and corporate managers can improve market and regulatory practices. This study examines how financial scams affect firm-specific and market-wide variables using theory and practice during and after event.

An Enhanced Hybrid Model for financial market and economic analysis: a case study of the Nasdaq Index

An Enhanced Hybrid Model for financial market and economic analysis: a case study of the Nasdaq Index

Dry Port Financial Feasibility Analysis Model

The sea transportation system that is interconnected and provides value for loading and unloading services at the main port of Tanjung Priuk is already over capacity. The establishment of a dry port in Tangerang Regency is expected to facilitate the flow of export and import goods, streamline logistics costs, and solve the dwelling time problem that occurs. The plan to establish the Tangerang Dry Port requires an analysis of the financial feasibility of the investment. The method used to solve this problem is using the Internal Rate of Return (IRR), Net Present Value (NPV), and Payback Period. This feasibility analysis calculation was carried out during pre-construction and after construction so that these calculations can be used as reference material in every decision-making, especially those related to the finances of Tangerang Dry Port. The feasibility analysis calculation uses 3 (three) scenarios, namely for optimistic conditions the IRR value is 28.8%, for moderate conditions the IRR value is 13.6%, and for pessimistic conditions the IRR value is 7.2%. For optimistic conditions the NPV value is 1,716.7 billion, for moderate conditions the NPV value is 697 billion, for optimistic conditions the NPV value is 255 billion. For optimistic conditions the payback period is 6.87 years, for moderate conditions the payback period is 9.26 years, and for pessimistic conditions the payback period is 11.2 years. From the results above, optimistic, and moderate conditions are the most feasible scenarios for developing the Tangerang Dry Port.

Integrating Merkle Trees with Transformer Networks for Secure Financial Computation

In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of the Transformer model. A series of experiments on key tasks, such as financial behavior detection and stock price prediction, were conducted to validate the effectiveness of the model. The results demonstrate that the Merkle-Transformer significantly outperforms existing deep learning models (such as RoBERTa and BERT) across performance metrics, including precision, recall, accuracy, and F1 score. In particular, in the task of stock price prediction, the performance is notable, with nearly all evaluation metrics scoring above 0.9. Moreover, the performance of the model across various hardware platforms, as well as the security performance of the proposed method, were investigated. The Merkle-Transformer exhibits exceptional performance and robust data security even in resource-constrained environments across diverse hardware configurations. This research offers a new perspective, underscoring the importance of considering data security in financial data processing and confirming the superiority of integrating data verification mechanisms in deep learning models for handling financial data. The core contribution of this work is the first proposition and empirical demonstration of a financial data analysis model that fuses data integrity verification with efficient data processing, providing a novel solution for the fintech domain. It is believed that the widespread adoption and application of the Merkle-Transformer model will greatly advance innovation in the financial industry and lay a solid foundation for future research on secure financial data processing.

Analisis Model Grover, Altman, Springate dan Zmijewski Dalam Memprediksi Financial Distress Pada Perusahaan Sektor Transportasi, Logistik, Real Estate, dan Property

This final assignment aims to determine the financial condition of companies in the logistics transportation, real estate and property sectors listed on the Indonesia Stock Exchange for the 2018-2022 period, using a purposive sampling method with a total sample of 36 companies (180 data per company year). The type of data used is secondary data obtained from the company's financial reports. This research uses 4 financial distress analysis models, namely the Grover, Altman, Springate, and Zmijewski models. The results of this research show that the Grover and Altman model produces the same pattern, where the transportation and logistics sector is predicted to be in a distressed condition, while the real estate and property sector is predicted to be in a non-distressed condition. The Springate model produces a different pattern where the transportation logistics, real estate and property sectors are more dominantly predicted to be in distress. Meanwhile, the Zmijewski model for companies in the transportation logistics, real estate and property sectors is predicted to be in a non-distress condition. Overall, the transportation and logistics sector experienced distress of 67% and 34% for the real estate and property sectors.

Application of Financial Big Data Analysis Method Based on Collaborative Filtering Algorithm in Supply Chain Enterprises

At present, the financial situation of China’s supply chain finance is still relatively unstable, and there are still some problems between supply chain enterprises and banks such as asymmetric information, insufficient model innovation and high operational risks. Based on this, this paper proposes and constructs a risk control model of financial big data analysis based on collaborative filtering algorithm. The purpose of this study is to realize the resource integration of supply chain enterprises and optimize the logistics chain, financial chain and information chain through the analysis of financial big data based on collaborative filtering algorithm, provide quality services for supply chain enterprises and good support for solving the financing problems of small and medium-sized enterprises. In order to verify the feasibility of the model, an experimental analysis is carried out. The experimental results show that this model has good scalability and operability, and the algorithm itself also has good scalability. The results of empirical analysis further verify that the design method in this paper has a good recommendation effect in terms of matching degree and user satisfaction. Compared with other risk control models, it is more practical and feasible. This research has certain practical significance for the financial management of supply chain enterprises.

A Financial Analysis for Tesla Based on the Multiple Valuation

After introducing the current state of the industry in which Tesla operates and the current situation and challenges faced by the Tesla company itself in the industry, this paper will interpret and analyze Tesla's success path and business model based on various aspects such as financial performance, competitors' situation, industry trends and local policies. In addition, the paper includes the profitability, activity, liquidity, solvency, and business valuation of Tesla's operating model. After analyzing Tesla's dilemmas and solutions in relation to its production model and competitors, it can be concluded that Tesla's future growth potential is significant until there is any unpredicted change in regional policy. The purpose of this paper is to explore the business model analysis done by combining basic data, financial report analysis, financial analysis model, and business analysis model. An attempt is made to extrapolate the results of a fair, complete, and thorough business model analysis in conjunction with the reality of the situation.

Evaluation of Spatial Spillover Effect of Multidimensional Hybrid Financial Risk Contagion Based on the DAI Spatial Econometric Model

The process of global integration has accelerated, and the international financial market has become increasingly closely linked. The financial risks that come with them are becoming more complex and difficult to guard against. Multimedia modeling in Health Cloud biometric authentication and data management systems can be applied to the analysis of financial markets. Most of the current financial risk analysis models are based on a single time, and the models are relatively simple and cannot adapt to the current complex multidimensional mixed financial risk environment. Therefore, this paper aimed to analyze the spatial spillover effect of financial risk contagion based on the directed asymmetric (DAI) spatial econometric model. This paper proposed to transfer entropy information weight information and introduce the GARCH (generalized auto-regressive conditional heteroskedasticity) model to improve the traditional econometric model. By constructing a DAI measurement model, the spatial contagion of multidimensional mixed financial risks was analyzed, and on this basis, a generalized multidimensional economic space was established to analyze spatial spillover effects and analyze the specific path of spatial spillover effects. The model results in this paper showed that the degree of correlation between the stock and bond market varied greatly between countries. Among them, the change coefficient W s ⟶ b r s D of the event period was judged to have a large degree of negative change in the United Kingdom, Germany, and France in the European Union, which were −0.9885, −0.9876, and −0.9748, respectively. This showed that the model in this paper had a good and reliable ability to cope with the current multidimensional mixed complex financial risk environment and could be used as a reference for financial risk-related research. At the same time, it also proved that multimedia modeling in health cloud biometric authentication and data management system could provide a role in financial risk contagion analysis.

Integrating Economic Theory, Domain Knowledge, and Social Knowledge into Hybrid Sentiment Models for Predicting Crude Oil Markets

For several decades, sentiment analysis has been considered a key indicator for assessing market mood and predicting future price changes. Accurately predicting commodity markets requires an understanding of fundamental market dynamics such as the interplay between supply and demand, which are not considered in standard affective models. This paper introduces two domain-specific affective models, CrudeBERT and CrudeBERT+, that adapt sentiment analysis to the crude oil market by incorporating economic theory with common knowledge of the mentioned entities and social knowledge extracted from Google Trends. To evaluate the predictive capabilities of these models, comprehensive experiments were conducted using dynamic time warping to identify the model that best approximates WTI crude oil futures price movements. The evaluation included news headlines and crude oil prices between January 2012 and April 2021. The results show that CrudeBERT+ outperformed RavenPack, BERT, FinBERT, and early CrudeBERT models during the 9-year evaluation period and within most of the individual years that were analyzed. The success of the introduced domain-specific affective models demonstrates the potential of integrating economic theory with sentiment analysis and external knowledge sources to improve the predictive power of financial sentiment analysis models. The experiments also confirm that CrudeBERT+ has the potential to provide valuable insights for decision-making in the crude oil market.

Portfolio Construction in Terms of APPL Based on Fama and French Five-factor Model

In the contemporary world, research has been working towards creating a model that can better explain the relationship between financial investment and risk. Iterative financial models such as CAPM, Fama French three factor model (FF3F) are strengthening the explanatory power of the extra investment risk of portfolio investments. Nevertheless, due to the global pandemic, the variability of the explanatory power of financial analysis models in the stock market has become more fickle. The objective of this paper is to analyze the portfolio investment data of Apple, one of the world's largest high technology companies, from 2017 to 2022 using the Fama French five factor model (FF5F) to determine whether the model still has strong explanatory power on the stock market during the pandemic. According to the analysis, the FF5F model coefficients still robust in explaining Apple's stock markets over five-year global pandemic period. It seems as if the epidemic and similar force majeure factors do not affect the explanatory efficiency of the model, but it is unknown that whether the actual impact is not captured by the FF5F model. The results of this study based on the FF5F model shed lights for reducing portfolio investment risks.

Financial Implications of Covid-19 Pandemic. An Empirical DuPont Analysis on Economic Value-Added Reported by European Developed Countries

The recent COVID-19 pandemic has raised various concerns related to firms’ financial vulnerabilities and resilience. The best way to achieve financial resilience is to identify drivers of sustainable economic development, mainly resumed to strategies designed aiming for shareholders’ value creation. The way firms’ resources are allocated indicate the premises for reaching the potential of value creation. This study provides a general picture of firms’ capacity to create value add. For this purpose, was performed a factorial analysis, considering a DuPont model. The analysis is performed on a sample of firms originating from well-developed European economies, considering a four-based DuPont factorial model analysis. For a better understanding of the contribution of each financial ratio of the DuPont financial analysis model, a CHAID decision tree was estimated. The results show generalized effects on the calculated economic valueadded indicator, no matter the country firms considered in the analysis are originated from, or the industry the operate in. However, in 2020, most of the firms have reported significantly better results in terms of economic value-added, so, an essential part of the discussion is related to the impact of savings made on the cost of the economic capital.

Judging the True Health of Finance Institutions Based on Risk Behavior and Operation Performance

As the core of the financial system, financial institutions are playing a significant role in financial stability in the process of development; traditional analysis mainly discusses the institution’s revenue of assets. However, the current financial stability system pays more attention to financial institution risk behavior and operational efficiency; to solve the previous two issues, we propose the two-stage model. Firstly, we measure the dynamic financial institution’s risk behavior coefficient based on the volatility and return principle for different institutions. Secondly, according to the cross-efficiency principle, different financial institution operation efficiencies that assimilate risk behavior will be obtained, and the institution risk behavior valve is also given. Finally, we analyze the 31 banks listed in China to verify the validity and applicability of the two-stage model; the model has made a certain theoretical contribution to the financial institution analysis model, especially when we consider the risk behavior and multiple indexes. Therefore, the two-stage model that we built can help investors make a portfolio in banking enterprises; it also can help financial institutions evaluate their risk behavior for making an optimal decision and help government agencies to supervise banks based on their risk behavior.

Construction of Cross-Border e-Commerce Financial Risk Analysis System Based on Support Vector Machine

The rapid changes in the economic situation and the complex market environment make the cross-border e-commerce industry, as an important part of the market economy, face many challenges and risks in the development process. The particularity of its transaction and the imperfect tax mechanism have virtually increased the financial management risk. The research constructs the financial risk analysis model with the help of the support vector machine (SVM) and fuzzy theory. Through algorithm test and empirical research, it is found that the average accuracy of the optimized SVM on the selected data set is more than 90%, and after parameter optimization, the change of model fitness tends to be stable with the increase of iteration times, which greatly improves the search ability of sample data, and the accuracy of financial data classification with high risk is 46.8%. In the empirical research, the model established by fuzzy SVM can effectively eliminate the irrelevant index data, and the prediction accuracy of investment risk and operation risk has reached 80% or more. In the prediction of financing risk and tax risk, its accuracy has been improved by 12.4% compared to that before use.

Analysis of Regional Financial Risk in Guangdong Province Based on the DCN Deep Learning Model.

In the free flow of financial factors oriented to capital, returns will be accompanied by the concentration and diffusion of financial resources to form regional financial spatial differences, which is an objective phenomenon of regional financial practice. Localized regional financial risks may appear in the process of regional financial practice in each region. To address the abovementioned problems, we propose a model for regional financial risk analysis based on the DCN deep learning model. The main contents are as follows: elaborating the financial risk transmission mechanism involving intra- and interregional financial risks, sorting out the relationship between sectors as clues; the designing process of regional financial risk index as well as the measurement method, and the regional financial risk index for typical regions is measured and found to be at peak in 2017 with a risk index of 0.58; and the construction of an early warning model based on the value of the regional financial risk index and the expansion of the RNN network applied to the construction of the regional financial risk early warning system. Based on the construction of the RNN network application risk early warning system, the three types of risks, payment risk, loan loss risk, and market risk with the percentages of 49.62%, 26.82%, and 23.56%, respectively, are derived, and the focus is on their supervision and management in the follow-up work.

Internal Control Model of Enterprise Financial Management Based on Market Economy Environment

In order to better solve the problems existing in the internal environment, risk assessment, and internal supervision of enterprise financial management, this paper proposes a method of enterprise financial analysis and control model based on artificial neural network algorithm. The results show that, through the internal control model and management optimization of financial management, a large number of calculations and subjective affairs in the process of financial work can be avoided to the greatest extent. For enterprises, a large number of human, material, and financial resources can be saved for data collection, which is more conducive to improving the enterprise financial management system, improving the quality of financial information, providing technical support for enterprise decision-making, and further reducing the risk in the process of enterprise operation.

A semantic and syntactic enhanced neural model for financial sentiment analysis

This paper studies the methodology of inferring bullish or bearish sentiments in the financial domain. The task aims to predict a real value to represent the sentiment intensity concerning a target (company or stock symbol) in a text. Previous researches have proved the validity of using deep neural networks to automatically learn semantic and syntactic information for sentiment prediction. Despite the promising performance, these approaches implicitly obtain the target-sentiment representation by a sentence-level vector, lacking explicitly modeling the semantic relatedness between a target and its context. In this paper, we tackle the task by a novel semantic and syntactic enhanced neural model (SSENM), which incorporates dependency graph and context words to guide a target representation. In particular, we devise a self-attentive mechanism to capture semantic contextual information and an edge-enhanced graph convolutional network (E-GCN) to aggregate node-to-node features. In addition, the existing FSA is limited in size, which is prone to the overfitting problem for modern neural models. We further develop a Manifold Mixup strategy to generate pseudo data in training. We perform extensive experiments on two public benchmarks, SemEval2017task5 and FiQA challenges. Results show that our model outperforms the state-of-the-art model by 2% wcs scores on SemEval2017task5 and 3% R2 scores on FiQA, respectively. Finally, we present detailed analysis to indicate the effectiveness of each proposed component.

A surrogate model of offshore wind farm annual energy production to support financial evaluation

The preliminary financial evaluation of wind farm profitability requires fast analysis of energy production and costs while having very little specific information around the project. Early in the design process, the selection of specific wind turbines and the layout design may not yet be defined. Techno-economic and financial analysis models have been developed to use input from a small set of high-level project characteristics to estimate major cost elements and energy production for a wind farm to support quick analysis of levelized cost of energy (LCoE), or other financial metrics. Such models are typically based on prior project data and/or very simple analytical models. However, as capabilities for financial analysis of wind farms advance, so does the desire to improve the accuracy of the physical and cost modelling of the system. In this work, we develop a surrogate model of Annual Energy Production (AEP) for offshore wind farms for financial analysis applications in the early stages of development. The surrogate is developed from an parameterized engineering model and covers a large potential wind farm design space addressing different technological and site conditions. The surrogate model uncovers the underlying structure in the model in terms of input-output relationships and achieves a coefficient of determination of 0.994. The method used to develop the surrogate model can be adapted for additional dimensions of inputs as needed.

Model of Financial Analysis for Investments

Model of Financial Analysis for Investments

Analisis Rasio Keuangan Untuk Memprediksi Kondisi Financial Distress (Studi Empiris Pada Perusahaan Property dan Real Estate yang Terdaftar Di BEI Tahun 2018-2020)

<p><em>This study aims to compare which financial distress analysis model is the best and provide evidence of whether financial ratios have an effect on predicting financial distress conditions in companies. The population in this study are property and real estate companies listed on the Indonesia Stock Exchange from 2018 to 2020. The sampling method used is purposive sampling. The sample used in this study was 35 companies with an observation period of 3 years with sampling criteria. The type of data used is secondary data. The analytical method used in this research is logistic regression analysis with statistical techniques and descriptive analysis. The results of this study indicate that (1) Return On Equity (ROE) has a significant effect in predicting financial distress conditions; (2) Current Ratio (CR), Debt to Asset Ratio (DAR), Debt to Equity Ratio (DER), Current Liabilities to Total Asset Ratio (CLAR), and Return On Assets (ROA) have no significant effect in predicting financial distress. ; (3) Grover Method is the most significant method to be used in predicting financial distress conditions in property and real estate companies listed on the Indonesia Stock Exchange in 2018-2020.</em></p>

Financial Literation Analysis, Marketing Strategies, and Institutional Models of Coffee Farming Creation

Financial Literation Analysis, Marketing Strategies, and Institutional Models of Coffee Farming Creation