Financial Analysis Techniques Research Articles

Research and Empirical Evidence of Machine Learning based Financial Statement Analysis Methods

This study presents a novel approach called FinAnalytix which merging machine learning’s prowess in pattern recognition with financial statement analysis. This integrated algorithm combines deep neural networks and recurrent neural networks for predictive accuracy in stock return analysis, alongside logistic regression and random forest models for robust fraud detection in financial statements. The empirical evidence demonstrates FinAnalytix effectiveness in identifying abnormal financial patterns and predicting market reactions to earnings announcements. The study utilizes extensive data from listed companies, ensuring a comprehensive and practical application. FinAnalytix represents a significant advancement in the field, providing a dual approach to financial analysis for enhancing investment strategies through accurate stock return forecasts and bolstering financial integrity by detecting fraudulent activities. The simulation of the study based on the financial data of 100 sample listed companies. This research not only bridges the gap between traditional financial analysis and modern machine learning techniques but also offers a powerful tool for investors and regulatory bodies in navigating the complex financial landscape.

Strategies for the Development of Tilapia Fish Floss Processing Businesses: A Case Study in Cijambe Village, Cijambe Sub-district, Subang District

Aims: This study aims to analyze the strategic development of UMKM Ibu AAS's tilapia fish floss processing business, evaluating its internal and external factors to determine appropriate growth strategies. Study Design: The research follows a case study design focusing on a single business unit, UMKM Ibu AAS, to understand its operational, financial, and strategic aspects. Place and Duration of Study: The study was conducted at UMKM Ibu AAS, Cijambe Village, Subang District, over a period of six months from January to June 2021. Methodology: This study employed qualitative and quantitative methods, including SWOT analysis, financial analysis, and observational techniques. The financial analysis covered short-term and long-term aspects, while the SWOT analysis helped identify the strengths, weaknesses, opportunities, and threats of the business. Primary data were collected from business records, interviews with key personnel, and direct observations. Results: The study revealed that UMKM Ibu AAS, despite having solid operational strengths and a strategic location, faces challenges in capital limitation and high production costs. The business shows potential for growth, indicated by a strong market demand and advantageous relationships with consumers. Financial analysis suggest profitability with an R/C ratio of 2.027, indicating efficient revenue generation against costs. The SWOT analysis positions the business in Quadrant I, suitable for aggressive growth strategies focusing on exploiting strengths and opportunities. Conclusion: UMKM Ibu AAS has a promising future in the tilapia fish floss industry, contingent on strategic capital investment and efficient management. The study recommends adopting aggressive growth strategies, enhancing production technology, expanding market reach, and establishing stronger partnerships to capitalize on the available opportunities.

Evaluating Predictive Financial Analysis Techniques, Impacts, and Challenges in Nigerian Banking Institutions

This study examined the techniques, impacts, and challenges of predictive financial analysis in Nigeria Banking Institutions. The Nigerian Banking Institutions is the life wire of the nation's economy. They play a vital and massive role in the nation's economic development and financial system. The role of these institutions remains a catalyst for a nation's growth and development. Despite the importance of predictive financial analysis in improving financial performance, accurately forecasting financial outcomes, mitigating the impact of financial crises, and making informed decisions, many Nigerian banks still need help to adopt and use this technique effectively. As a result, this study aims to examine the predictive financial analysis, evaluate the techniques, impact, and challenges, and recommend possible solutions that can help address the challenges faced by the banking sector. Methodology: The study adopted a descriptive survey research design. A simple random sampling technique was used to select 7 respondents representing five banks (Access Bank, Fidelity Bank, First Bank, Wema Bank, and Zenith Bank). A self-designed online questionnaire (SDOQ) was used for data collection. The data collected were analyzed using descriptive statistics (percentage). Results: Banks: 2 of the respondents, representing 28.6%, work at Access Bank; 1 of the respondents, representing 14.3%, works at Fidelity Bank; 2 of the respondents representing 28.6%, works at First Bank,1 respondent representing 14.3%, works at Wema Bank and the remaining 1 representing 14.3% works at Zenith bank. Role of Respondents: AHOP, Compliance Officer, Data Administrator, Data Analyst/Analytics, Relationship Manager and Team Lead. Analysis techniques: 3 banks representing 42.9% rely on the time series analysis technique, 2 banks representing 28.6% rely on the Regression analysis technique and 2 banks representing 28.6% rely on the machine learning algorithms technique. Satisfaction Level: 3 respondents representing 42.9% are very satisfied with current predictive analytics capabilities, while 4 respondents representing 57.1% are moderately satisfied with current predictive analytics capabilities. Discussion: The study's findings revealed that the primary challenges faced by banks in implementing predictive financial analysis techniques include data quality issues, regulatory constraints, lack of expertise, and integration with existing systems. Similarly, most banks rely on time series analysis to forecast future financial trends. Conclusion and Recommendations: Based on the findings in this study, it was concluded that the primary challenges faced by banks in implementing predictive financial analysis techniques include data quality issues, regulatory constraints, lack of expertise, integration with existing systems, and others. Similarly, most banks rely on the time series analysis technique to forecast future financial trends. The findings in this study also revealed that respondents perceive the impact of technological advancements to improve accuracy significantly. It was therefore recommended that a robust data management system practice be established to ensure the accuracy and consistency of data. Also, beyond time series analysis, banks should explore other predictive analytics techniques to enhance forecasting accuracy.

The Effect of Legal - Ethical And Psychological Factors In Reducing Accounting Manipulations: A Field Study

The study aimed to elucidate the extent of accountants' adherence to legal and ethical factors, understand the psychological state of the auditor, explore the impact of regulations governing the auditing profession, and delineate the effect of psychological and ethical factors on the auditor's behavior. The study also sought to gather opinions from auditors, academics, and educators at the University of Baghdad regarding legal, ethical, and psychological factors within the profession. The study found significant correlations between legal, ethical, and psychological factors, and accounting manipulations. There was a statistically significant relationship between legal factors and accounting manipulations, with a correlation coefficient of (0.383) at a significance level of less than (0.01). Similarly, a significant relationship was found between ethical factors and accounting manipulations, with a correlation coefficient of (0.416) at a significance level of less than (0.01). Moreover, a significant relationship was established between psychological factors and accounting manipulations, with a correlation coefficient of (0.659) at a significance level of less than (0.01). The study recommends providing objective solutions to contribute to identifying the reasons for committing manipulation within organizations. It emphasizes the need to implement financial statement analysis techniques as a means of detecting manipulation and underscores the importance of legal actions (civil and criminal) related to filing claims in court, and preserving and protecting evidence. Additionally, auditors should act impartially, separating personal interests from their supervisory role.

Using Financial Statements Analysis as a Tool on Identifying the Solvency of SMES And Its Relation with Forensic Accounting; Evidence from Albania

Objective: The study aims to determine the impact of e-learning on learning outcomes in the Jordanian Objective: Accounting professionals who are directly responsible for the preparation of the reports must use the principles and techniques of Forensic Accounting which help them to investigate legitimate financial activity and conduct due diligence reviews to properly assess the activity of economic entities. This paper identifies the importance of financial statement analysis tools and techniques in this process, being identified as Fraud Indicators of financial statements indicators. Theoretical Framework: Business development plays a vital role in economic development, so the effective and efficient management of activities carried out by businesses is to a great importance. But their identification in financial report is also object of fraud activities, a phenomen, which is turning into a headline of the day, especially for developing countries and also in Albania. Method: In the study are been taken into consideration the financial statement of 70 SMEs that carry out commercial activity and that through financial statements reports it was studied how “red flags” can be identified in financial reporting indicators to further investigate whether fraud conditions are met or not. The paper tests the relationship between two important indicators for every economic entity, which are liquidity and profitability as the most studied indicators. Results and Discussion: Data analysis showed that there was a significant statistical relationship between liquidity and performance indicators, which serve as a starting point for going into further use of technical forensic accounting techniques by professionals. Originality/Value: This study contributes to the literature with an empirical study on techniques and principles of Forensic Accounting and Financial Statements Analysis. Solvency analysis helps us to identify those transactions for which accounting professionals and not only should focus to further deepen the investigations based on the techniques and principles of forensic accounting. Based on the findings of this study as well as those of other researchers, we recommend to professionals that the use of financial statement analysis instruments in the investigation process is a key factor.

The Discussion of Convertible Bond Valuation

With the development of financial analysis techniques, computer technology, and artificial intelligence, the valuation methods for convertible bonds in the financial markets are constantly evolving and developing. This article discusses various approaches to valuing convertible bonds, including the Black-Scholes model, the Binomial model, the Monte Carlo model, and artificial intelligence techniques like machine learning. The article provides a detailed overview of each valuation method and analyzes the strengths and weaknesses of each approach.

Актуальные вопросы развития методики анализа финансового состояния по данным бухгалтерской отчётности

The article is devoted to the development and improvement of methods for analyzing the financial condition of organizations. As a result of reviewing and researching the content of existing methods for analyzing the financial condition of organizations, difficulties of use for users were identified, consisting of a large or small number of stages, sections, oversaturation of coefficients, lack of a common conceptual apparatus for all, incompleteness and obsolescence of recommended values of coefficients characterizing financial state, in low demand and the possibility of using the methodology for analyzing financial condition equally for all organizations. Based on the assessment and generalization of current analysis techniques, an optimal structural and logical diagram of the financial condition analysis technique has been developed, highlighting specific areas of analysis in a certain sequence of their implementation. In order to optimize the number of coefficients, a set of calculation indicators has been proposed and justified, which will allow a faster and more comprehensive assessment of the financial condition of organizations, which will increase the interest of users in the use of improved methods, as well as in the use of analysis results to improve the financial condition and increase the efficiency of management of the organization.

CONCENTRATION OF INVESTMENT CAPITAL IN MERGERS AND ACQUISITIONS: PREREQUISIGTES FOR A TECHNOLOGICAL BREAKTHROUGH

Purpose. The purpose of the article is to substantiate the prerequisites for systemic economic shifts based on the analysis of the structure of capital investments and mergers and acquisitions. Methodology of research. General scientific and special methods were used in the course of the study, in particular, analysis, synthesis, and generalization were applied while working with statistical data on the mergers and acquisitions market, and the monographic method was used when studying literary sources on issues related to the research topic. Financial analysis techniques (specifically, ratio and horizontal anal-ysis) were used in assessing the structure of capital investments in various sectors of the economy over the past twelve years (up to 2022). Additionally, the analysis of structural changes and shifts, as well as meth-ods of comparison and graphical analysis, were employed to study the dynamics of structural shifts in the economy under the influence of investment capital movement. Findings. The study of the preconditions for structural economic transformations was conducted through the assessment of the dynamics of investment capital concentration in economic sectors and the analysis of the dynamics of the number and value of mergers and acquisitions in pre-war and wartime peri-ods, as well as a review of the motives behind these transactions. The preconditions for structural changes in the economy, which are being laid at this stage through the investment decisions of investors and the political decisions of government representatives, were analyzed. The conducted research indicates the need to maintain a high share of industrial capital concentration alongside the decline in the share of indus-try in GDP due to the substantiated necessity for significant modernization of certain sectors. Originality. The study for the first time highlighted the role of investment capital by analysing the dominance of progressive industries with high added value in the structure of mergers and acquisitions, which can become the beginning of the structural transformation of the Ukrainian economy with the concen-tration of investment capital in high-income sectors. The use of analysis of the structure of capital invest-ments by economic sectors has further developed as a tool for determining the investment attractiveness of individual sectors and the dynamics of changes in the investment preferences of entities. Practical value. The results of the study can be used by government bodies in the development and refinement of the country's investment strategy, the formulation of the principles of the state's investment policy, and the adjustment of existing plans for post-war recovery; by private companies and investors (both local and foreign) for developing plans for the analysis and investment in Ukrainian economic assets, adjusting their expectations and priorities; by members of the scientific community who are interested in further development of this research topic; and by experts in related disciplines to expand their understand-ing of the issue, which may be relevant to their area of expertise, among others. Key words: investments, capital investments, capital concentration, capital market, mergers and ac-quisitions.

Case study of Midea Group’s merger with Little Swan

Faced with fierce market competition and changing markets, many companies choose mergers and acquisitions to expand business scale or achieve new business directions, expand into new areas, achieve business diversification, improve competitiveness, and maintain original business conditions and field. In recent years, other companies that need to merge can refer to the analysis results and make correct merger and acquisition decisions by analyzing the performance of the merged company. This article introduces the merger and acquisition of Swan on the American Stock Exchange, and uses financial performance analysis and case analysis techniques to analyze the financial performance of the Midea Group's merger and acquisition. It is hoped that the analysis results will have certain reference value and relevance to the merged company.

Financial Distress During the COVID-19 Pandemic: Altman and Springate Model Prediction

Background: The retail trade industry has experienced a sharp decline in economic growth during the COVID-19 pandemic. Regulation of social distancing, restrictions on retail shop operating hours, and disruption of online shopping behavior contribute to worsen the financial performance of retail trade companies. </p> <p> Purpose: The purpose of this research is to analyze the prediction of financial distress of retail trade companies. This research chose to use the Altman and Springate Model since both of them are the most accurate and conservative models to predict. </p> <p> Research Methodology: The financial distress analysis technique used is Altman and Springate Model. The calculation of the Altman and Springate Score is based on an equation formula for data in the period before and during the COVID-19 pandemic. The Altman’s Score are grouped in the Safe Zone, Grey Area, and Financial Distress; while the Springate’s Score are grouped in the Safe Zone and Financial Distress categories. </p> <p> Findings: Altman Model Analysis stated that most of the companies experienced a decreased scores, the company’s financial performance during COVID-19 pandemic was predicted financial distress and belong to the Grey Zone category than before COVID-19 pandemic. Springate Model Analysis states that all companies have decreased scores, and the company’s financial performance during COVID-19 pandemic is predicted financial distress than before COVID-19 pandemic. Springate Model revealed a more conservative indicator, but both models stated there were still exist several companies in the Safe Zone category to continue their business. </p> <p> Research limitations: The limitations of this study are only using Altman and Springate Model. The object of this research is the retail trade industry only, then future research may be able to expand the analyzed industry in the new normal conditions. </p> <p> Originality/value: The originality of this research is focused on the retail trade companies as the most affected industry by COVID-19 pandemic. In addition, this research analyzes financial distress based on Altman Z-Score and Springate Model as the most powerful model to predict.

The hidden vulnerabilities behind financial sustainability: a case study of a sugarcane farm business in Pemalang City, Central Java, Indonesia

This study assesses the financial viability of a 4-hectare sugarcane farming enterprise operated by Perhutani, an Indonesian State-Owned Enterprise. The farm is situated in the geographical limits of Pemalang City, which is located in the Central Java Province of Indonesia. This study employs a financial feasibility analysis approach that involves the computation of key financial indicators such as Net Present Value (NPV), Internal Rate of Return (IRR), Pay Back Period (PBP), Benefit Cost Ratio (BCR), and the application of sensitivity analysis. Overall, the assessment of the viability of a 4-hectare sugarcane farming enterprise indicates that it is financially feasible and has the potential to operate as a sustainable business. This conclusion is drawn from the positive numerical values obtained through various financial analysis techniques. Nevertheless, it is evident from the conducted sensitivity analysis that sugarcane farming is a highly susceptible business to fluctuations in prevailing conditions. When molasses production experienced reductions of 10% and 15%, most financial feasibility analysis estimates yielded negative values. The issue of production vulnerability extending beyond economic feasibility has been rendered inconspicuous by many reasons, including climate change.

Investor sentiment and the NFT hype index: to buy or not to buy?

PurposeThe primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.Design/methodology/approachThe research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.FindingsThe built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.Practical implicationsThe NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.Originality/valueThe authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.

The Application of Machine Learning for Creating a Typology of Universities' Financial Models

This study presents an application of machine learning for creating a typology of Russian universities’ financial models. Large-scale national initiatives aimed at enhancing human potential and academic excellence, such as Project 5-100, university-industry consortia, world class research center programs as well as the Priority-2030 program, require relevant financial and management accounting tools enabling appropriate analyses of universities’ contribution to national scientific policy implementation. However, when conventional financial analysis and audit techniques are adopted from the corporate sector, they may prove to be irrelevant for assessing the societal impacts of universities. Existing impact study methods, such as those applied in the Russell Group universities’ impact assessment, are expensive and time consuming, so promising machine learning techniques and existing open data from government information systems were used in this study to assess universities’ financial models.

PENGARUH EARNING PER SHARE, PROFITABILITAS, DAN KEBIJAKAN DIVIDEN TERHADAP HARGA SAHAM PADA PERUSAHAAN INDEKS LQ45 PERIODE 2018-2021

One of the factors which influence the movement of stock prices is the company's fundamentals. This company's fundamental analysis can use information from financial reports. One of the financial statement analysis techniques is to use financial ratios. The purposes of this study are to determine the effect of earnings per share, profitability, and dividend policy on stock prices in LQ45 index companies for the 2018-2021 period. This study uses a quantitative approach to the type of associative research. The sampling technique in this study used a purposive sampling method and obtained a sample of 11 companies in each period. The data used is secondary data obtained from the Indonesia Stock Exchange website, namely www.idx.co.id, which is in the form of data on company financial reports listed on the LQ45 Index for the 2018-2021 period. The data analysis method in this study used panel data regression analysis in testing the data in the research to be carried out using the Eviews 10 software program. The results of this study indicate that 1) Partial earnings per share has a positive and significant effect on stock prices, 2) Partial profitability has a positive and significant effect on stock prices, 3) Partial dividend policy has no significant effect on stock prices, 4) Simultaneously, earnings per share, profitability, and dividend policy have a significant effect on stock prices in LQ45 Index companies for the 2018-2021 period.

ОСОБЕННОСТИ ФИНАНСОВОГО АНАЛИЗА БАЛАНСА ДЛЯ ВНУТРЕННИХ ПОЛЬЗОВАТЕЛЕЙ ОТЧЁТНОСТИ

Over the past year, many enterprises with foreign capital have announced the termina-tion of their activities in the Russian market, but at the same time only less than 9% of them have finally left Russia. Many small and medium-sized enterprises with foreign participation, including Finnish participation, still operate in Russia, since Finland has been one of the main strategic and invest-ment partners for many years. In connection with the presence of such enterprises, diffi-culties arise in the preparation and analysis of financial (accounting) statements for its internal users (Finnish founders and manag-ers). This article provides a financial analysis technique that can give the internal user of reporting (foreign owner and head of the or-ganization) a complete picture of the organi-zation's performance based on the organiza-tion's balance sheet data.

HEALTH LEVEL ANALYSIS OF PT. BANK BJB USING THE CAMEL METHOD

AbstractThis study aims to assess the financial soundness level of Regional Development Bank of West Java and Banten (PT. Bank BJB) during the 2012-2021 period using the CAMEL method. The CAMEL method is used to assess the financial aspects of a financial institution, namely Capital, Asset Quality, Management, Earnings, and Liquidity. This study uses a quantitative approach with a descriptive method, which is carried out by analyzing data from the financial report sources of PT.Bank BJB during the 2012-2021 period. The data used in this study are secondary and are analyzed using various financial analysis techniques. The study results indicate that the financial soundness of Regional Development Bank of West Java and Banten (PT. Bank BJB) during the 2012-2021 period can be stated to be very healthy. This can be the basis for managers to predict the bank's financial condition in the future and take appropriate policies to improve the bank's financial performance. This research has an essential meaning because the level of the financial health of a financial institution can reflect how the bank implements its economic policies.Keywords: Assets, Capital, Earnings, Liquidity, Management

FORECASTING MODELS FOR SECTOR-WISE STOCK PRICE INDICES: COLOMBO STOCK EXCHANGE

The stock markets of a country play a vital role in its economy. Stock market indices are vital fragments of information for investors. It is very important to develop models that reflect the pattern of the stock price movements for different sectors since it becomes very significant to investors and policymakers. Therefore, the aim of this research study was to develop models to forecast different sector indices in Colombo Stock Exchange and to compare sector-wise models. The investigation was performed using secondary data for a sample of ten listed sectors in the Colombo Stock Exchange (CSE) for the thirty-four years from 2nd January 1985 to 31st March 2019. Secondary data were collected by using the data library maintained by Colombo Stock Exchange. Financial time series data analysis techniques were used to analyze the collected data. It was applied the ARCH family models in this research study, which included the Autoregressive conditional heteroscedasticity model (ARCH), Generalized Autoregressive conditional heteroscedasticity model (GARCH), Threshold Autoregressive conditional heteroscedasticity model (TARCH), Exponential generalized autoregressive conditional heteroscedastic model (EGARCH), Integrated Generalized Autoregressive conditional heteroscedasticity model (IGARCH) and Power Autoregressive conditional heteroscedasticity model (PARCH) since the sector indices are financial time series data. Findings revealed that the appropriate model to forecast the sector indices of Oil Palms sector, Services sector and Stores & Supplies sector as PARCH (2,1) model, Beverage, Food & Tobacco sector as PARCH (1,1) model, Chemicals & Pharmaceuticals sector as PARCH (2,2) model, Banking Finance & Insurance sector and Investment Trusts sector as IGARCH (2,2) model, Footwear & Textiles sector as EGARCH (1,1) model, the Manufacturing sector as EGARCH (1,3) model and Hotels & Travels sector as TARCH (1,1) model. The findings of this research study are useful to policymakers and investors for their decision-making.

A Financial Performance Analysis of Village-Owned Enterprise in Margo Mulyo Bringinan, Jambon 2018-2021

Village Owned Enterprises (BUMDesa) are institutions formed by village governments and communities that manage them based on the needs and economy of the village. The purpose of this research is to determine the financial performance of BUMDesa Margo Mulyo Bringinan Village in 2018-2021. The method used is descriptive quantitative with financial ratio analysis techniques including Current Ratio, Debt To Equity Ratio, Return On Equity Ratio, Net Profit Margin Ratio, Total Asset Turn Over, and Recevable Trunover Ratio. The population of this study is the Financial Report of Margo Mulyo Village Owned Enterprises (BUMDesa) for 2018-2021 with the sample being the Financial Report of Margo Mulyo Village Owned Enterprises (BUMDesa) for 2018-2021 which consists of balance sheets and reports of Remaining Business Results (SHU). . The results of this study state that the assessment of financial performance based on the Current Ratio is included in the healthy category, with an infinite ratio value. The Debt to Equity Ratio is included in the healthy category with a ratio value of 0%. The Return On Equity Ratio shows unhealthy financial performance results. Net Profit Margin Ratio is included in the healthy criteria. Total Asset Turn Over and Receivable Turnover are included in the very unhealthy criteria with a ratio value of < 1 time. This research shows a straight line relationship between the results of the Current Ratio analysis and Total Asset Turnover.

Analisis Kelayakan Finansial pada Industri Baglog Jamur untuk Peningkatan Produksi 30.000 Unit per Bulan pada Agroindustri

The agricultural sector is the main structure of national economic development. One of the sub-sectors in agriculture that has an important role for the welfare of farmers and the regional economy as well as the national economy is the Horticulture sub-sector. This sub-sector has been supported by the agro-industry. One of the products from this agro-industry is baglog of mushrooms. This study aims to analyze the financial feasibility of increasing production capacity from 20,000 to 30,000 baglogs of mushrooms per month. It caused the average demand is 29,070 baglogs of mushrooms per month. The research object is the manufacture of mushroom baglog. Descriptive quantitative with financial feasibility analysis techniques and sensitivity analysis is used as a method in this research. The results found that the investment period of 5 years required an investment cost of IDR 244,342,677. Financial feasibility analysis with a discount rate of 9% produces an NPV value of IDR 257,886,281, an IRR of 44.594%, a Profitability index of 2.06 and a Payback Period of 1.88 years. The results of the sensitivity analysis of an increase in the cost of raw materials by 17% and a decrease in production of 9% obtained the results of NPV > 0, IRR > 9%, Profitability index > 1 and Payback period < business life. Based on these results, the investment is financially feasible because IRR > MARR.

Comparison of the Financial Performance of SOEs in the Building Construction Sub-Sector Listed on the Indonesia Stock Exchange for the 2020 and 2021 Periods

This study aims to compare the financial performance of SOEs engaged in the infrastructure sector, the building construction sub-sector, and conduct stock offerings on the Indonesia Stock Exchange for the 2020 and 2021 periods. This research is a descriptive type of research. The data used is in the form of company financial report data obtained from the STIEM Bongaya Makassar Investment Gallery and visiting the website www.idx.co.id to obtain supporting information. Financial ratio analysis techniques use liquidity ratios, solvency ratios, activity ratios, profitability ratios, and market value ratios to calculate company financial data. Financial report data that has been calculated using financial ratios is then analyzed and interpreted based on a comparison between the financial ratios of each company and the industry average value. Then do a comparison of the financial performance of the four companies to find out which company has a better level of financial performance. The results of the analysis show that of the overall financial ratios used, PT. Pembangunan Perumahan (Persero) Tbk. is a company that has better financial performance than PT. Waskita Karya (Persero) Tbk., PT. Wijaya Karya (Persero) Tbk., and PT. Adhi Karya (Persero) Tbk.