Profitability Factors Research Articles

Determinant Factors of Profitability in Fintech Lending Companies in Indonesia from 2019 to 2022 Using Principal Component Analysis (PCA)

This reserach examines factors influencing the profitability of Fintech lending companies in Indonesia. A purposive sampling method was employed to select 45 companies annually. The data for this research are cross-sectional, comprising the years 2019, 2020, 2021, and 2022. The methods in this research are Principal Component Analysis (PCA) and multiple linear regression. Findings reveal that current ratio, cash ratio, working capital turnover, debt to assets ratio, short-term debt ratio, equity multiplier, total assets turnover, fixed assets turnover, tangible assets ratio, and effective tax rate significant determinants of profitability for Fintech lending companies in Indonesia. This reserach can help Fintech lending companies improve their profitability.

Polynomial Moving Regression Band Stocks Trading System

In this research, we attempted to fit a trading system based on polynomial moving regression bands (MRB) to Nasdaq100 stocks from 2017 till the end of March 2024. Since stocks movement does not follow a linear behavior, we used multiple degree polynomial regression models to identify the stocks’ trends and two standard deviations from the regression model to generate the trading signals. This way, the MRB was transformed into a momentum indicator designed to identify strong uptrends that can be used by a fully automated trading system. Our results indicate that the behavior of Nasdaq100 stocks can be tracked using all three examined polynomial models and can be traded profitably using fully automated systems based on those models. The best performing model was the model that used a four-degree polynomial MRB achieving the highest average net profit (USD 162.73). Regarding the risks involved, the third model has the lowest loss in dollar value (USD −95.52), and the highest minimum percent of profitable trades (41.51%) and profit factor (0.55) that indicates that this strategy is relatively less risky than the other two strategies.

Pricing Intellectual Capital in Asset Pricing Models: A study on Shanghai Stock Exchange

This study aims to examine the validity of intellectual capital as a priced risk factor in the asset pricing model. We have used the data of companies listed on the Shanghai Stock Exchange from 2000 to 2021. To test the models, time series as well as cross sectional analysis have been done using the Fama-MacBeth method. The results show that intellectual capital is a significant risk factor and after augmenting IC into asset pricing models improves the explanatory power to explain the variation in the stock returns. It further suggests that IC can be priced in the Chinese market, over and above what can be explained by covariance risk, size, value, investment and profitability factors.

DOES A REDUCTION IN TAX RATES INFLUENCE TAX AGGRESSIVENESS? AN ANALYSIS WITH ADDITIONAL FACTORS

This study investigates the effect of reduction tax rates in Indonesia and other factors that may impact tax aggressiveness. This study employs multiple linear regression analysis, using E-Views 12 as analytical software. A sample of fourteen real estate and property companies listed on the Indonesia Stock Exchange (IDX) for 2017 to 2023 was identified through purposive sampling. This study relies on secondary data derived from the company's financial statements. The results showed that the reduction in tax rates and the presence of independent commissioners did not significantly affect tax aggressiveness. In contrast, institutional ownership and profitability factors positively correlate with tax aggressiveness, while leverage shows a negative correlation. It is expected that the results of this study will contribute to the development of taxation policies for companies in Indonesia, aiming to reduce aggressive taxation practices and assist companies in their tax-related decision-making framework.

Analisis Tingkat Kesehatan Bank Nagari Syariah Menggunakan Metode RGEC

The purpose of this study was to measure the health level of Bank Nagari Syariah using the RGEC method. The research method used is descriptive quantitative method. The data collection technique used is documentation. The data analysis technique uses ratio analysis contained in the assessment of the bank's health level using the RGEC method. The results showed that the health level of Bank Nagari Syariah in 2017-2022 was in a good rating. In 2017-2018, the Financing to Debt Ratio was in an unhealthy state. But in 2019-2022 the bank has been able to improve its performance. The Net Performing Financing ratio is in a healthy state from year to year. The profitability factor shows that the bank is able to make a profit from its operational activities. Operating Expenses to Operating Income are categorized as healthy. The Good Corporate Governance factor shows that the implementation of the bank's operational structures and processes is in accordance with the regulations set by the OJK. The Capital factor is calculated by the Capital Adequacy Ratio and is in a very healthy state. The health level of Bank Nagari Syariah is in a healthy position.

Data-driven based financial analysis of concentrated solar power integrating biomass and thermal energy storage: A profitability perspective

This study utilizes decision tree algorithms to estimate the financial feasibility of concentrated solar power (CSP). The main focus of CSP is on solar tower (ST) technology combined with two backup systems, such as biomass boilers and thermal energy storage (TES). The main goal is to develop three decision tree algorithms to predict the power plant's profitability factor (PF) for each of the following three operational scenarios: solar tower-base case-no biomass (ST-BC-NB), solar tower-operation strategy 1-medium biomass (ST-OS1-MB), and solar tower-operation strategy 2-full biomass (ST-OS2-FB). PF was predicted according to main input parameters, including direct capital costs, biomass cost, annual escalation rate, hourly electricity price, annual escalation rate, and peaks and troughs for daily electricity prices. Thermal energy storage was in five different capacities: no-thermal energy storage (No-TES), 5 h, 10 h, 15 h, and 20 h. The decision tree models demonstrated accurate predictions with low errors, high confidence levels, and most data falling within the 95% confidence interval for the "No-TES" case. Solar power plants with biomass backup had a 30% reduction in generation costs compared to conventional plants. The configurations without thermal energy storage had the highest profitability, with a maximum PF of −0.014 USD/kWh and a 25% chance of achieving profitability.

Modelling mobility as a service: A literature review

MaaS (Mobility as a Service) is often presented as a means to solve some of the most pressing mobility issues such as car dependency and increasing vehicular greenhouse gas emissions. In this context, adequate models and simulations are key to estimating the impact of potential MaaS implementations and support decision-making to reap the benefits MaaS could bring. This article aims at reviewing MaaS representation in existing academic literature pertaining to transportation modelling, in order to assess its progress and identify trajectories for future improvements. We do so by considering mobility as an economic system in which MaaS interacts with both travel demand and supply. After identifying an adequate set of indicators to analyze MaaS models, we review 31 contributions and find out that most studies consist of demand-centered usage models while the integration of the system's supply side's operational and profitability factors is still lacking, especially in-MaaS financial flows.

Synergizing quantitative finance models and market microstructure analysis for enhanced algorithmic trading strategies

In today’s complex financial markets, “Algorithmic Trading” has become very important. The study delves into the amalgamation of four pivotal indicators - Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) to create and develop a potent trading strategy. Through intensive backtesting and parameter tuning, our study demonstrates 60.63 % profitable trades on the National Stock Exchange (NSE), India, surpassing the standalone indicators. The Weapon Candle Strategy created using the four indicators presents its efficiency as it was able to achieve a profit factor of 1.882. This suggests that when these four technical indicators combined to make a strategy, it can provide significantly more accurate and reliable trading signals compared to using a combination of two or three indicators. Algorithmic traders should use a multi-indicator approach to achieve a more comprehensive understanding of the market and make informed trading decisions.

The construction of urban cultural and creative industries using deep learning and information management

The ongoing transition within the Chinese economy assumes a pivotal role in shaping the landscape of the cultural and creative industries (CCI). Renowned for its environmentally sustainable attributes, coupled with high productivity, CCI has garnered considerable attention across diverse societal strata. This study endeavors to delineate the determinants influencing the developmental trajectory of CCI, with a focal point on City A as the primary subject of investigation, juxtaposed against Cities G, D, B, H, and X for comparative analysis, leveraging developmental data from the year 2021. Initially, the study elucidates the conceptual framework underpinning CCI and its intrinsic significance in facilitating urban metamorphosis. Subsequently, the positive impact of CCI through deep learning and information management technology is emphasized, and a cultural and creative recommendation model based on LSTM algorithm is constructed. Through performance evaluation, the recommendation accuracy for cultural and creative projects reaches 94.74 %. A robust developmental assessment model for CCI is then constructed via meticulous factor analysis of pertinent influencers. Employing factor analysis techniques, the study identifies two primary determinants exerting sway over CCI development: sustainable profitability factors and cultural influence factors. Noteworthy among the factors influencing CCI development within City A are fixed asset investment, cultural industry financing, the proliferation of university-based research institutions, and per capita cultural expenditure by residents. Of these, fixed asset investment, cultural industry financing, and the density of university research institutions prominently impinge upon sustainable profitability, with a discernible impact weight of 0.738 in the evaluative framework of CCI development, thus significantly shaping its trajectory. Moreover, consumer psychological factors, particularly market consumption patterns, are observed to exert a discernible influence on CCI evolution. This study augurs fresh insights into the realm of CCI development, infusing it with renewed vigor and vitality. Moreover, it underscores the inherent interdependence and positive correlation among the various research factors, offering novel perspectives and methodologies germane to the advancement of urban CCI.

Machine learning Ethereum cryptocurrency prediction and knowledge-based investment strategies

This work proposes a novel methodology to help in decision making in the cryptocurrency market. Two investment strategies have been designed for Ethereum (ETH), based on predictions of the price and trend of this cryptocurrency using real data. The two Ethereum cryptocurrency prediction systems rely solely on past values of other contextual stock indices, market indicators and online trends, and ignore any technical indicators of price evolution. Real data from cryptocurrency market has been collected and processed with different feature selection methods. Applying a regression approach with Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks, prediction models for the ETH price for 1, 7 and 15 days are obtained and compared. Also, support vector machine (SVM) is applied to predict the ETH price trend by applying a classification approach. In both approaches, sentiment analysis has been included to check its effect on the prediction results. The reliability of these prediction models in the current market has been evaluated by designing two original knowledge-based investment strategies. They are tested over two different time periods with real cryptocurrency market data. The results show that it is possible to generate up to 5.16 profit factor with few operations using these models. Furthermore, adding sentiment analysis has shown to have little influence. In this way, we contribute to the advancement of our knowledge of this volatile and still young cryptocurrency market, and specifically of the evolution of Ethereum and the factors that can influence its behavior.

The risk–return tradeoff among equity factors

We examine the time-series risk–return tradeoff among equity factors. We obtain a positive tradeoff for profitability and investment factors, which is consistent with the APT. Such relationship subsists when we control by the covariance with the market factor, which represents consistency with Merton’s ICAPM. Critically, we obtain an insignificant risk–return relationship for the market and other factors. The tradeoff is weaker among international equity markets. The out-of-sample forecasting power tends to be economically significant for the investment and profitability factors. Our results suggest that the risk–return tradeoff is stronger within segments of the stock market than for the whole.

Peran Profitabilitas, Likuiditas, Aktivitas, dan Leverage dalam Harga Saham

This research stems from the need to investigate the influence of financial factors, such as profitability, liquidity, activity, and leverage, on the share price of a company. The growing complexity of financial markets drives the need for a deeper understanding of the contribution of each factor to stock price dynamics. This research aims to identify and analyze the role of each financial factor of profitability, liquidity, activity and leverage in determining stock price movements. The research method used involves multivariable statistical analysis using historical financial data of companies listed on the stock market. This quantitative approach will help in dissecting the complex relationship between profitability, liquidity, activity, leverage, and stock price. This research produces significant findings regarding the impact of each financial factor on stock prices. An in-depth analysis will show to what extent profitability, liquidity, activity, and leverage influence a company's stock valuation. These results can be the basis for developing investment strategies that are smarter and responsive to market dynamics.

Determinant factors of banking profitability: an application of quantile regression for panel data

This study examines the determinants of bank profitability using a quantile regression approach, offering insights into factors affecting banks across different percentiles of profitability. Utilizing a comprehensive database from Orbis covering 1200 top-market institutions across 101 countries, the research uniquely employs dynamic panel quantile regression while addressing sample survival bias. Our findings highlight that bank size and capital adequacy negatively impact profitability, whereas market value exerts a positive influence on higher profitability banks. Credit risk affects profitability differently across levels of profitability, and inflation rate shows significance only for higher profitability banks. The study contributes to the existing literature by offering valuable insights into the factors determining bank profitability and how they behave at different percentiles in the sample, suggesting the importance of bank efficiency and competition in promoting economic growth.

Evaluating private hospital performance before and during COVID-19 in China.

The coronavirus disease 2019 (COVID-19) pandemic had a tremendous impact on the global medical system. The development of private hospitals is an important measure to deepen the reform of China's medical and health system, and an important driving force to improve the effective supply of medical services. This study aims to compare the performance of China's private hospitals before and during COVID-19 and determine the factors that affect hospital profitability between the 2 periods. Data are collected from 10 private listed hospitals from 2017 to 2022, and ratio analysis is used to measure hospital performance in 5 aspects, namely profitability, liquidity, leverage, activity (efficiency), and cost coverage. Multiple regression analysis is used to determine the influencing factors of hospital profitability. The results show a negative impact of COVID-19 on private hospital performance. Specifically, regardless of region, hospital profitability, liquidity, and cost coverage were reduced due to COVID-19, while hospital leverage was increased. COVID-19 had also an impact on hospital efficiency. In addition, before COVID-19, current ratio and cost coverage ratio were the determinants of hospital profitability, while only cost coverage ratio affected hospital profitability during the COVID-19 outbreak. We provide evidence that COVID-19 had an impact on China private hospitals, and the findings will aid private hospitals in improving their performance in the post-COVID-19 era.

Cryptocurrency price fluctuation and time series analysis through candlestick pattern of bitcoin and ethereum using machine learning

PurposeCandlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.Design/methodology/approachThe data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.FindingsThe inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.Research limitations/implicationsTo statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).Originality/valueThis study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

BANK-SPECIFIC, ECONOMIC AND LEGAL DETERMINANTS OF PROFITABILITY IN THE REPUBLIC OF NORTH MACEDONIA

Background: Bank profitability is more than just a financial indicator; it is a reflection of the health and vitality of the banking sector and the economy as a whole. Profitable banks help to maintain financial stability by increasing resilience, facilitating capital formation and intermediation, promoting innovation and adaptability, and instilling confidence and trust. Hence, profitability is critical in the banking sector since it directly influences policymakers, regulators, and bank management. Therefore, the study will estimate the influence of specific bank and economic-legal determinants on return on assets in the Republic of North Macedonia. Thus, the study aims to estimate the influence of specific bank and economic-legal determinants on return on assets in the Republic of Northern Macedonia. Methods: The study employs the methodology of the Vector Error Correction Model (VECM) and covers quarterly data from 2007 to 2022. To conduct the empirical analysis required to identify and assess the factors of bank profitability, quantitative data were gathered primarily from the National Bank of the Republic of North Macedonia, the International Monetary Fund (IMF), and the World Bank. Return on Assets was used as a dependent variable. The set of factors is composed into two groups: the first includes bank-specific (controlled) factors such as the sectors’ size, credit risk, capital adequacy, liquidity, income diversification, efficiency of operations, and non-performing loans. The second group includes macroeconomic (uncontrolled) factors such as economic growth, inflation, and interest rates. Results and Conclusions: Results reveal that the size of the banking sector, the risk of the credit, liquidity, income diversification and non-performing loans have a meaningful but negative influence on the response variable. However, capital adequacy, operational efficiency, GDP and interest rate have an important positive impact. Hence, based on the empirical analyses, to boost profitability, the Macedonian banking system should prioritise asset management as a size indication, raising the non-income ratio to diversify revenue, reducing credit risk and non-performing loans, and maintaining a good liquidity ratio.Furthermore, there is clear evidence that second-level banks should extend beyond national borders. Better loan portfolio management, greater technology with database processing and communication improvements, and expanded technology with database processing and communication improvements can strengthen the ability to deal with the next crisis. Additionally, banks should increase non-interest revenue by employing it as a risk outlet in banking and distributing it across many income-generating enterprises, enhancing profitability. These findings offer insights for bank executives and regulators interested in increasing bank profitability and stability.

PROFIT EFFICIENCY OF INDIAN BANKS: POST-INTERNET ERA

The present study is mainly concerned with the analysis of profitability factors that influence the performance of banks after the introduction of internet banking. This study examines the trends in important banking indicators for 10 years period from 2013 to 2022. The study actually concentrates more on NPA and taken into consideration of profit efficiency variables in relation to internet banking of scheduled commercial banks. Therefore, this study focus on the detailed analysis of profit efficiency by incorporating all variable while keeping NPA’s ratios as central for study. The study have concluded the decline in NPA (Non-Performing Assets). Further, Reserves & Surplus is also analysed intensively to know the future prospects of scheduled commercial banks. Keywords: Profitability, Non-Performing Assets, Scheduled Commercial Bank and Internet Banking

The Impact of Unexpected FOMC Actions on Corporate Earnings Expectations

This paper fills a gap in the literature by examining the impact of unexpected Federal Open Market Committee (FOMC) actions on the markets expectations of U.S. corporate earnings by examining the sell-side analysts' quarterly earnings forecasts from 1989 to 2022. This paper reveals a significant negative correlation between analysts' earnings forecasts and unexpected changes in the target interest rate. This correlation weakened after the Federal Reserves implementation of Forward Guidance as a monetary tool in 2004, which appears to enhance the market's ability to predict target rate adjustments. In addition, this paper shows that with a given amount of unexpected target rate changes, value firms display greater earnings forecast revisions from analysts than growth firms. Debt levels or profitability factors cannot fully explain this phenomenon. The findings of this paper contribute to the knowledge of how FOMC actions exert influence on equity valuations and the transmission mechanisms of monetary policies.

Customer Churn Prediction

In today’s highly competitive business landscape, customer retention revenue preservation, customer experience improvement, and marketing optimisation are critical factors for sustained growth and profitability. Customer churn predictionis discontinuing their services or purchases, which presents asignificant challenge for businesses across various industries. This project focuses on developing a predictive model to expect customer churn in the banking sector using machine learning techniques. The project involves the collection and analysis of historical customer data, confined account activity, transaction history, demographic information, and customer service interactions. By extracting the right features from this data, a machine learning model is trained to forecast which bank customers are at the highest risk of churning. A critical step in this study was the selection of relevant features that influence customerchurn. Feature selection was guided by domain knowledge and feature importance analysis. The different classifiers were used and then trained on the training dataset further ensuringthe model’s optimal performance. The model’s performance is assessed through various evaluation metrics, including accuracy, precision, and recall. Additionally, the project explores a model illustration to uncover the influential factors contributing to customer churn within the banking context. This project’s outcomes can empower banks to take proactive measures in retaining customers, enhancing their overall experience, and thereby preserving revenue streams. By addressing customer churn, banks can foster long-term relationships, reduce customer acquisition costs, and boost their competitiveness in the financial industry. The results of this project are expected to assist businesses in proactively retaining customers by targeting those at the highest risk of churning. Ultimately, reducing customer churn can lead to increased customer satisfaction, revenue, and long-term business sustainability.

PENGARUH CSR, PROFITABILITAS TERHADAP NILAI PERUSAHAAN (STUDI: PERUSAHAAN MAKANAN MINUMAN DI BEI TAHUN 2018-2022)

Every business must ensure the sustainability of the company's survival, and one of the efforts that can be made is to strive to increase the company's value to sustain and grow. The value of a company can be determined through Corporate Social Responsibility (CSR) and Profitability factors. This study aims to determine the influence of CSR and profitability on the value of the company. Study: Food and Beverage Companies in the Indonesia Stock Exchange (BEI) for the years 2018-2022. This research is a quantitative descriptive study. The population in this study is food and beverage companies listed on the Indonesia Stock Exchange for the years 2018-2022. The data used is secondary data obtained from the official BEI website. Data analysis was conducted using the panel data regression method with the assistance of Eviews 12 software. The results of the study show that CSR has a positive effect on the value of the company because companies are able to manage their social responsibilities effectively. Meanwhile, profitability also has a positive effect on the value of the company because companies can generate net profit from sales with well-managed operational costs, ultimately increasing the value of the company.