Profitability Factors Research Articles

Algorithmic Trading Bot Using Artificial Intelligence Supertrend Strategy

Abstract: This article presents a trading strategy that combines the Super Trend indicator with the K-Nearest Neighbors (KNN) algorithm, utilizing artificial intelligence (AI) to automate market decision-making and enhance trading accuracy. The strategy integrates the SuperTrend indicator, which dynamically tracks market volatility, with the KNN algorithm, allowing the system to classify market trends as bullish, bearish, or neutral based on historical data. This enables the strategy to make intelligent, data-driven decisions in real-time without human intervention. The AI-driven approach automates the entire trading process, from data analysis to trade execution, improving efficiency and removing emotional biases from trading decisions. The KNN algorithm plays a key role in this automation by analyzing past market conditions and identifying patterns that inform future price movements. This allows the system to adapt to changing market trends and react quickly to new data, ensuring timely and accurate decisions. The results of the strategy indicate strong performance, with a Net Profit of 959.38 USD and a Gross Profit of 3,005.71 USD, demonstrating the strategy's ability to generate consistent returns. The Profit Factor of 1.469 further highlights the system's ability to produce profits while managing risk effectively. Additionally, the Sharpe Ratio of 0.558 shows that the strategy provides positive risk-adjusted returns, making it a reliable tool for automated trading. In conclusion, this AI-powered Super Trend-KNN strategy showcases the potential of combining artificial intelligence with technical indicators for automated trading. By eliminating the need for manual intervention and leveraging AI to adapt to market conditions, the strategy provides an efficient and scalable solution for intelligent decision-making in trading.

Competitiveness of Sweet Orange Value Chain in Sindhuli District, Nepal

Government of Nepal declared sweet orange super zone in Sindhuli for increasing the productivity and better commercialization of product. This study analyzed sweet orange value chain in Sindhuli with the aim of identification of major value chain actors along with value chain competitiveness, structure, conduct and performance. A Survey was done with 150 producers, 5 retailers and 4 agro vets, 8 banks and co-operatives, 2 processing industry and 2 nurseries using random sampling method in 2021. The findings revealed the involvement of two channels in value chain stream of sweet orange. A total of 17% was transacted through first channel via collectors and second channel transacted 83% of fruits via local traders. Sweet orange market was found oligopolistic along the chain. The business was found profitable with B/C ratio 1.37 in study area. Relatively better institutional set up on extension services, better technological adoption rate was driving factors for profitability. Value chain stream of Sindhuli had better market margin, market efficiency and value addition. Policy efforts to strengthen local institutions, insurances, market information and credits are recommended to improve performance of value chain. Processing and value addition of sweet orange should be of concern to government for enhancing export potentiality.

Study on the Influencing Factors of Financing Ability of Real Estate Listed Companies

In recent years, due to the rich profits in the real estate industry, more and more companies join the ranks to share a share of the pie, which leads to the uneven quality of the real estate industry, and the continuous emergence of bad market behaviors such as real estate speculation and bubble real estate, which has brought a negative impact on the real estate industry. In order to reverse this unfavorable situation, the state has also introduced some policies to regulate the real estate industry. With the introduction of some national tax policies and financial policies, the real estate enterprises have some capital difficulties, which will eventually hinder the further development of the real estate industry. Therefore, the real estate industry should develop vigorously, especially the development of listed real estate companies are inseparable from stable financing. Therefore, it is of great theoretical and practical significance to refine the influencing factors of the financing ability of real estate listed companies and determine the influence intensity and direction, which will enrich the theory of enterprise financing and optimize the financing structure of real estate listed companies. This article first related affecting factors of real estate financing ability of listed companies at home and abroad literature, using the factor analysis method to the real estate listed company financing ability influence factors of multiple indexes for sorting, construct the real estate financing ability evaluation index system of influencing factors of listed companies, on the basis of this place, design with the use of multiple regression analysis the real estate listed companies financing ability and the factors influencing the regression model. Excel, SPSS and other software were used to conduct data operation and processing on the model, and the output results of operation were summarized and the following research conclusions were drawn. Firstly, using the factor analysis method to the real estate listed company financing ability will influence factors of multiple indices for sorting, finally get the real estate listed company financing ability of the five key factors of affecting factors namely profitability factor (F1), short-term debt paying ability factor (F2), capacity factor (F3), development capacity factor (F4), the company scale factor (F5). Secondly, the short-term solvency factor has a significant negative effect on the financing ability (asset-liability ratio) of real estate listed companies. Thirdly, the development ability factor has a significant positive effect on the financing ability (asset-liability ratio) of real estate listed companies. Fourthly, the company size factor has a significant positive effect on the financing ability (asset-liability ratio) of real estate listed companies. Fifthly, there is no significant causal relationship between profitability factor and operating ability factor and financing ability (asset-liability ratio) of real estate listed companies.

Determinant Factors of Profitability of the Indonesian Non-Financial SMEs PEFINDO Index

This research aims to determine the influence of determinant factors of non-financial SMEs' PEFINDO index profitability in Indonesia from 2018 to 2022, with independent variables including company size, company age, company efficiency, working capital, liquidity, leverage, volatility, and market risk on the dependent variables of ROE and ROA. The study utilizes secondary data in the form of financial reports from non-financial companies that have been included in the PEFINDO25 index of the Indonesia Stock Exchange during the 2018-2022 period. This research sample used purposive sampling and obtained 65 companies as samples. Data analysis for hypothesis testing involves multiple regression using E-views 9 software. The test results indicate that company size, company efficiency, and working capital have a positive impact on ROE, while company age, leverage, and market risk have a negative impact on ROE. Company size, company age, and volatility have a negative impact on ROA, while company efficiency and working capital have a positive impact on ROA. However, liquidity does not significantly affect either ROE or ROA. These factors need to be considered by companies to make informed decisions in maintaining or improving company profitability, avoiding financial distress leading to bankruptcy, and determining suitable investments for investors.

Faktor-Faktor Profitabilitas yang Mempengaruhi Tax Avoidance

This study aims to determine the profitability factors of tax avoidance. The research method used in this study used secondary data taken from the official website of the Indonesia Stock Exchange and the website of each company. The population in this study is consumer non-cyclicals companies listed on the Indonesia Stock Exchange for the period 2016-2022. The sampling method in this study is purposive sampling. Companies that meet the criteria are 13 companies from a total population of 118 companies, so that the total number of samples in this study is 91 company data that meet the criteria. The variables used in this study are gross profit margin (X1), net profit margin (X2), return on investment (X3), return on equity (X4) and earning per share (X5) as independent variables and tax avoidance (Y) as the dependent variable. The data analysis technique used is regression analysis of panel data using statistical eviews software. The results of this study state that: Net profit margin affects tax avoidance. Gross profit margin, return on investment, return on equity and earnings per share have no effect on tax avoidance. Gross profit margin, net profit margin, return on investment, return on equity and earning per share simultaneously affect tax avoidance.

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.

Analysis Determining Factors of Profitability The Infrastructure, Utilities and Transportation Sector Firms

The infrastructure, utilities and transportation sectors play an important role in a country's economy. Profitability is a key indicator in assessing the performance of companies in this sector. However, achieving profitability is faced with various challenges, including large capital investments, high operating costs, strict government regulations, and changes in global economic conditions. This study uses a literature study method to analyze the factors that affect the profitability of companies in the infrastructure, utilities, and transportation sectors. The results of the analysis show that financial factors, operational efficiency, technology, regulations, and external conditions such as market demand and global energy prices play a significant role in determining the level of profitability. Companies that are able to manage their capital structure, utilize technology, and adapt to regulations and dynamic economic conditions have a great opportunity to improve their financial performance. This study provides in-depth insights into how companies can manage internal and external factors to achieve sustainable profitability.

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.

Based on the DuPont analysis method under the IQIYI Study on Profitability Issues

With the improvement of material conditions and the increasing popularity of the Internet, video websites have become an important pastime for people. At present, most domestic video websites are losing money, and the research subject of this paper, Iqiyi, as a comprehensive reflection of a platform's operational efficiency, its profitability is the key to its survival and development. After years of development, video websites have formed three different profit models: "advertising revenue", "membership payment", and "IP industry chain”. However, the current problems facing the profitability of domestic video websites are: high cost, shortage of capital, frequent piracy infringement, and homogenization of content. This dissertation takes literature method and DuPont analysis as the research object, and explains the background, purpose and significance of the research. The main contents of the article include Dupont analysis, profitability factors, etc. Secondly, based on a comprehensive understanding of relevant literature and theories, this paper takes Iqiyi as the theme and provides an in-depth analysis of various profitability factors of Iqiyi from the perspective of profitability model; it finds the main problems under the current profitability model: uneven profit sources and consecutive years of losses. The main causes of these problems are: narrow profit target range, high costs and expenses, and decreasing number of member users. Finally, based on the analysis of this paper, suggestions are given to improve Iqiyi's 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.