Convergence Divergence Research Articles

Efficient Multiplicative Calculus-Based Iterative Scheme for Nonlinear Engineering Applications

It is essential to solve nonlinear equations in engineering, where accuracy and precision are critical. In this paper, a novel family of iterative methods for finding the simple roots of nonlinear equations based on multiplicative calculus is introduced. Based on theoretical research, a novel family of simple root-finding schemes based on multiplicative calculus has been devised, with a convergence order of seven. The symmetry in the pie graph of the convergence–divergence areas demonstrates that the method is stable and consistent when dealing with nonlinear engineering problems. An extensive examination of the numerical results of the engineering applications is presented in order to assess the effectiveness, stability, and consistency of the recently established method in comparison to current methods. The analysis includes the total number of functions and derivative evaluations per iteration, elapsed time, residual errors, local computational order of convergence, and error graphs, which demonstrate our method’s better convergence behavior when compared to other approaches.

Regional per capita income inequality and fiscal policy in Mexico, 1989–2021

AbstractIn this paper, we use trends in GDP gaps per capita or relative GDP per capita indicator as an aggregate alternative measure to show regional per capita income inequality between the states of Mexico. The indicator compares each state's gross domestic product per capita (GDPpc) with the annual average of each year and 2019; this allows for evaluating growth performance before and after the COVID-19 pandemic. The econometric method is a spatial dynamic panel, and the convergence–divergence model is conditioned to the behavior of the fiscal policy followed in each state: The fiscal policy indicators are current expenditure and public investment. In general, it is concluded that the amounts of current spending and public investment, in an active fiscal policy context, tend to favor states with higher relative per capita income, which is why an increase in regional inequality is observed. However, it tends to affect low-income regions more, with higher ratios of current spending and investment to GDP, which reduces regional inequality. Therefore, the short-term effects of active fiscal policy to reduce regional disparities can be neutralized.

PERBANDINGAN RETURN PENGGUNAAN MOVING AVERAGE CONVERGENCE DIVERGENCE DAN RELATIVE STRANGE INDEX PADA INDEKS SAHAM SYARIAH INDONESIA

This study compares the return levels from the use of the technical indicators Moving Average Convergence Divergence (MACD) and Relative Strength Index (RSI) applied to the simulation of the movement of the Indonesia Sharia Stock Index (ISSI). This research is a population study using panel data in the form of daily closing values over 12 years of the Indonesia Sharia Stock Index movement. The data was processed using a technical analysis approach with two momentum indicators, MACD and RSI, to obtain buy and sell signals based on the principles of each indicator, applied to the ISSI. The use of the MACD and RSI technical indicators on the ISSI shows that MACD yielded a profit of 207% with a success rate (win rate) of 72%, while RSI provided a profit of 125% with a win rate of 55%.

Market resilience in turbulent times: a proactive approach to predicting stock market responses during geopolitical tensions

PurposeThe Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.Design/methodology/approachHybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.FindingsOur study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.Originality/valueTo the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

Stock Market Prediction in Nepali Stock Market using Machine Learning Model

The stock market, a key driver of the global economy, presents a formidable challenge for accurate prediction due to its intricate, chaotic, and dynamic nature. This study explores machine learning approaches, specifically comparing three prediction models—Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The analysis incorporates historical stock trading data, including open, high, low, and close prices, and technical analysis indicators such as moving average, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Commodity Channel Index (CCI) so on. The evaluation focuses on the performance of these models in predicting future trends in stock prices, utilizing data from the Nepal Stock market spanning from 2013 to 2023. The selected stocks for analysis include two stock NIC Asia Bank Limited and, NMB Bank Limited, two index NEPSE and Hydropower index. Through rigorous assessment, it is determined that LSTM demonstrates superior overall performance compared to GRU and BPNN. This research contributes valuable insights into the effectiveness of machine learning models in forecasting stock market trends, with implications for investors and financial practitioners.

Simulated Impact of Time‐Varying River Runoff and Greenland Freshwater Discharge on Sea Level Variability in the Beaufort Gyre Over 2005–2018

AbstractGlobal mean sea level has been rising at a rate of 3.25 ± 0.4 mm yr−1 over 1993–2018. Yet several regions are increasing at a much faster rate, such as the Beaufort Gyre in the Arctic Ocean at a rate of 9.3 ± 7.0 mm yr−1 over 2003–2014. At interannual to decadal time scales, the Beaufort Gyre sea level is controlled by salinity changes due to sea ice melt and wind‐driven lateral Ekman convergence–divergence of freshwater. This study uses recent Greenland discharge and river runoff estimates to isolate and quantify the sea level response to freshwater fluxes variability over the period 1980–2018. It relies on sensitivity experiments based on a global ocean model including sea‐ice and icebergs. These sensitivity experiments only differ by the freshwater fluxes temporal variability of Greenland and global rivers which are either seasonal climatologies or fully time varying, revealing the individual and combined impact of these freshwater sources fluctuations. Fully varying Greenland discharge and river runoff produce an opposite impact on sea level trends over 2005–2018 in the Beaufort Gyre region, the former driving an increase, while the latter, a decrease. Their combined impact leads to a fairly weak sea level trend. The sea level response is primarily driven by salinity variations in the upper 300 m, which are mainly caused by salinity advection involving complex compensations between passive, active, and nonlinear advection. This study shows that including the temporal variability of freshwater fluxes in forced global ocean models results in a better representation of regional sea level change.

Regional Differences, Dynamic Evolution, and Convergence of Global Agricultural Energy Efficiency

Understanding the regional disparities, dynamic evolution, and convergence–divergence characteristics of global agricultural energy efficiency is crucial for enhancing agricultural energy efficiency, ensuring food security, and responding to global green development trends. This paper utilizes 2002–2021 panel data from 144 countries globally, employing the epsilon-based measure–global Malmquist–Luenberger (EBM-GML) model to estimate agricultural energy efficiency, considering unexpected output. The Dagum Gini coefficient, kernel density estimation, spatial Markov matrix, and spatial convergence model are employed to explain the spatial patterns and evolving trends of global and regional agricultural energy efficiency at three levels: regional disparities, dynamic evolution, and convergence. The results indicate significant spatial heterogeneity in global agricultural energy efficiency, with Europe exhibiting the highest efficiency, followed by Asia and the Americas, while Oceania and Africa demonstrate the lowest efficiency. Agricultural energy efficiency globally and in each region continues to improve, with increasing regional disparities, and difficulties in grade transitions in agricultural energy efficiency across regions. Each region exhibits β-convergence characteristics, but the convergence rates vary, and various factors influence growth rates of agricultural energy efficiency differently across regions. Therefore, countries should tailor their strategies based on local conditions, considering their own resource endowments and developmental stages, and strengthen international exchanges and cooperation.

Combine Study of Technical Indicator Bollinger Bands and Moving Average Convergence Divergence in Nepal Stock Exchange Limited

Stock price forecasting involves predicting a company's future condition based on time series analysis. Investors need precise forecasts of future indices or stock price changes to address the Buy-Sell-Hold problem effectively. Data were collected for Nepal Stock Exchange Limited, Nepse Alpha, and Chukul. For this study, data were collected over six months (December 2023 to May 2024), utilizing technical trading tools like Bollinger Bands (BB) and Moving Average Convergence Divergence (MACD). Combining BB and MACD on a single chart yields better results than analyzing each indicator separately. This integrated approach provides a more thorough understanding of market dynamics, improves decision-making accuracy, and leads to more profitable outcomes. Integrating the analysis of BB and the MACD on one chart can provide more benefits than using each indicator independently. This method offers market behavior and improves decision-making precision, resulting in more profitable outcomes.

A biological circuit to anticipate trend

Abstract Organisms gain by anticipating future changes in the environment. Those environmental changes often follow stochastic trends. The steeper the slope of the trend, the more likely the trend’s momentum carries the future trend in the same direction. This article presents a simple biological circuit that measures the momentum, providing a prediction about future trend. The circuit calculates the momentum by the difference between a short-term and a long-term exponential moving average. The time lengths of the two moving averages can be adjusted by changing the decay rates of state variables. Different time lengths for those averages trade off between errors caused by noise and errors caused by lags in predicting a change in the direction of the trend. Prior studies have emphasized circuits that make similar calculations about trends. However, those prior studies embedded their analyses in the details of particular applications, obscuring the simple generality and wide applicability of the approach. The model here contributes to the topic by clarifying the great simplicity and generality of anticipation for stochastic trends. This article also notes that, in financial analysis, the difference between moving averages is widely used to predict future trends in asset prices. The financial measure is called the moving average convergence–divergence indicator. Connecting the biological problem to financial analysis opens the way for future studies in biology to exploit the variety of highly developed trend models in finance.

A Comprehensive Study on Trend Analysis of Selected Stocks by Using Selected Technical Indicators

In this research, we delve into an in-depth exploration of the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and pivot points indicators to gauge their efficacy in forecasting stock price trends. The study draws upon a rich dataset sourced from reputable financial platforms, covering the period from January 2023 to April 2024. To ensure a representative sample, we adopt a stratified sampling approach, meticulously selecting five major companies based on their market capitalization. These companies—Reliance Industries, Tata Consultancy Services, HDFC Bank, ICICI Bank, and Infosys—serve as focal points for our analysis, offering insights into various sectors of the economy. By offering a nuanced understanding of the interplay between technical indicators and market dynamics, our study endeavors to contribute meaningfully to the discourse on stock price forecasting and investment strategies. Key Words: Stock price prediction, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Data analysis, Statistical methods, Market capitalization,

Impact of Visual Disturbances on the Trend Changes of COP Displacement Courses Using Stock Exchange Indices

This work aims to define a strategy for maintaining a vertical posture of the human body under conditions of conflicting sensory stimuli using a method of trend change analysis. The investigations involved 28 healthy individuals (13 females, 15 males, average age = 21, SD = 1.3 years). Measurements were conducted with eyes opened and closed and in the virtual environment with two sceneries oscillating at two frequencies. Values in the time domain were calculated—the mean center of pressure (COP) velocity and movement range in the AP direction—as well as values based on the moving average convergence divergence (MACD) computational algorithm—the trend change index (TCI), MACD_dT, MACD_dS, and MACD_dV. After dividing the analysis into distinct time periods, an increase in TCI values was identified in the oscillating scenery at 0.7 and 1.4 Hz during the 0.5–1 and 0.2–0.5 s time periods, respectively. Statistically significant differences were observed between measurements with an oscillation frequency of 0.7 Hz and those with an oscillation frequency of 1.4 Hz during the 0.2–0.5 s and 0.5–1 s periods. The use of stock exchange indices in the assessment of the ability to keep a stable body posture supplements and extends standard analyses in the time and frequency domains.

Compressed development, decompression, and diverging convergence in South Korea: which varieties of capitalism in contemporary Korea?

South Korea has achieved compressed growth and convergence of its income level to that of typical advanced economies. This paper adopts a narrow definition of compressed development as a form of late development that lacks political democracy and open markets, and then defines decompression as the process of democratization and economic liberalization. This paper applies this framework of “compressed development, decompression, and diverging convergence” to analyze the dynamics of Korean capitalism since the 1980s after the death of the modernizer-dictator, President Park. First, the middle class emerged in Korea in the 1980s as a result of compressed growth since the 1960s and served as a key intermediary in linking economic growth to democratization. The process of political decompression, or democratization, was peaceful because it was demanded and led not by the working class but by the middle class. Second, an ill-managed first wave of economic decompression (liberalization) led to a financial crisis, and then, the post-crisis reforms (the second, more radical decompression) imposed by the IMF planted the institutional seeds for convergence with Anglo-American capitalism. Third, Korea is experiencing the end of the East Asian miracle, as evidenced by slowing growth and rising inequality, and is undergoing a multifaceted convergence toward different varieties of capitalism. In terms of life expectancy and crime rates, Korea joins Japan, Italy, and Spain in the “safe capitalism” group, while it is an extreme outlier in terms of its large gender wage gap and long working hours. In terms of unemployment and long-term employment rates, Korea is closer to the Anglo-American model, but closer to Europe in terms of the degree of active (or passive) labor market policies and legal protection against layoffs. Korea seems to be realizing “divergent convergence” as it remains distinct from any particular form of Western capitalism.

Association rules forecasting for the foreign exchange market

Several association rule mining algorithms exist, and among them, Apriori is one of the most commonly used methods for extracting frequent item sets from vast databases and generating association rules to gain insights. In this research, we have applied a data mining technique to implement association rules and explore frequent item sets. Our study introduced a model that employs association rules to uncover associations between the foreign exchange market, the gold commodity, and the National Association of Securities Dealers automated quotations (NASDAQ). We suggested a method that used data mining to identify the good points of buying and selling in the foreign exchange market by utilizing technical indicators such as moving average convergence divergence (MACD) and the stochastic indicator to create association rules. The experimental findings indicate that the proposed model successfully generates strong association rules.

Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices

Predicting agricultural commodity prices accurately is of utmost importance due to various factors such as perishability, seasonality, production uncertainty etc. Moreover, the substantial volatility that may be exhibited in time series further adds to the complexity and constitutes a significant challenge. In this paper, a Hidden Markov (HM) guided Deep Learning (DL) models has been developed on nonlinear and nonstationary price data of agricultural commodities for forecasting by considering technical indicators viz., Moving Average (MA), Bollinger Bands (BB), Moving Average Convergence Divergence (MACD), Exponential MA (EMA) and Fast Fourier Transformation (FFT). HM Models (HMMs) can effectively handle the sequential dependencies and hidden states, while DL approach can learn complex patterns and relationships within the price series and thus the drawback of lack of generalization capability in the DL model has been overcome by HMM. In this study, the Potato price data of the Champadanga district of West Bengal, India has been utilized to assess the performance of the proposed technique. HMM has been combined with six baseline DL models viz., Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Bidirectional LSTM (BiLSTM) and Bidirectional GRU (BiGRU) for forecast modeling. Performance evaluation metrics viz., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and the insightful Diebold–Mariano (DM) test revealed that Hidden Markov hybridized with DL models surpassed baseline DL models in forecasting accuracy for 1-week, 4-week, 8-week and 12-week ahead DL predictions. The proposed approach holds significant promise for enhancing the precision of agricultural commodity price forecasting with far-reaching implications for various stakeholders such as farmers and planners.

Insular dwarfism in horses from the Aegean Sea and the Japanese archipelago

The horse (Equus caballus) varieties from Skyros and Rhodes islands (Greece) in the Aegean archipelago are extremely small, reaching shoulder heights of only about 1 m. Furthermore, the Japanese archipelago is home to eight small, native horse breeds. We investigated the evolutionary morphology and provided a review of historical documentations of these horses of cultural interest in Greece and Japan, thus providing a comparison of the independent evolution of small size in islands. We integrate cranial data from historical literature with data from newly gathered and curated skulls and analyse a measurement dataset featuring various domestic and mainland horse breeds and varieties. We use non-invasive imaging to study and measure 3D models of the bony labyrinth, housing the inner ear, and the braincase endocast. When considering the effects of allometry by regressing each PC1 scores (for each set of measurements) with the cranial geometric mean from linear measurements as a body size proxy, we show that size explains a large amount of the shape variation in horse crania, the bony labyrinths and brain endocasts. We found high intrabreed variation in all the analysed datasets. Globally, there are at least 30 distinct horse populations on islands, offering the chance to further study processes of convergence in morphological divergence and evaluate the effect of drift and the environment.

Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam

The aims of this study are to predict the stock price trend in the stock market in an emerging economy. Using the Long Short Term Memory (LSTM) algorithm, and the corresponding technical analysis indicators for each stock code include: simple moving average (SMA), convergence divergence moving average (MACD), and relative strength index (RSI); and the secondary data from VN-Index and VN-30 stocks, the research results showed that the forecasting model has a high accuracy of 93% for most of the stock data used, demonstrating the appropriateness of the LSTM model and the test set data is used to evaluate the model’s performance. The research results showed that the forecasting model has a high accuracy of 93% for most of the stock data used, demonstrating the appropriateness of the LSTM model in analyzing and forecasting stock price movements on the machine learning platform.

MACHINE LEARNING FOR STOCK MARKET FORECASTING: A REVIEW OF MODELS AND ACCURACY

As financial markets become increasingly complex and dynamic, the application of machine learning (ML) techniques for stock market forecasting has garnered significant attention. This paper presents a comprehensive review of various ML models employed in the realm of stock market forecasting, focusing on their methodologies and the accuracy achieved in predicting market trends. The review begins by examining traditional time-series models such as autoregressive integrated moving average (ARIMA) and moving average convergence divergence (MACD) and their limitations in capturing the intricate patterns present in financial data. Subsequently, the discussion transitions to more advanced ML models, including support vector machines (SVM), artificial neural networks (ANN), and ensemble methods like random forests and gradient boosting. Each model's strengths and weaknesses are scrutinized in the context of stock market forecasting. The paper explores the pivotal role of feature selection and engineering in enhancing the predictive power of ML models. Feature sets encompassing financial indicators, macroeconomic variables, sentiment analysis from news articles, and social media data are analyzed for their impact on forecasting accuracy. Additionally, the incorporation of technical indicators and alternative data sources is explored as potential avenues to improve model robustness. A critical aspect of this review is the assessment of accuracy in predicting stock market movements. The evaluation is conducted through a comparative analysis of model performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and accuracy rates. The study also addresses the challenge of model overfitting and proposes strategies to mitigate this issue for more reliable predictions. This review provides a nuanced understanding of the landscape of ML models for stock market forecasting, highlighting the diverse approaches, challenges, and opportunities in the quest for improved accuracy. It contributes valuable insights for researchers, practitioners, and investors seeking to leverage the potential of ML in navigating the complexities of financial markets.
 Keywords: Machine Learning, Stock Market, Forecasting, Models, Review.

Hybrid model for rainfall prediction with statistical and technical indicator feature set

As excessive rain may cause numerous disasters, rainfall prediction is very crucial and the prediction should be realistic since it encourages individuals to take preventative steps. This work proposes a new rainfall prediction model by following three major phases: Preprocessing, Extraction of Features and Prediction. The Improved Data Normalization technique is carried out for preprocessing the data. Then, it extracts the technical Indicator features like Average Directional Movement (ADX), Moving Average Convergence Divergence (MACD), RSI, Welles Wilder's Smoothing Average (WWS), statistical & higher order statistical features. According to these features, prediction takes place by a hybrid model that includes the Improved DBN & LSTM methods. By fine-tuning the best weights, the training will be carried out optimally to increase the prediction model's efficacy. For this, the Brownian Motion-based Pelican Optimization Algorithm (BMPOA) is implemented. Finally, the proposed technique has contrasted over existing models concerning different metrics.

Artificial intelligence and corporate carbon neutrality: A qualitative exploration

AbstractMany firms have established formal carbon neutrality (CN) targets in response to the increasing climate risk and related regulatory requirements. Subsequently, they have implemented various measures and adopted multiple approaches to attain these goals. Academic research has given due attention to firms' efforts in this direction. However, past studies have primarily focused on non‐digital and process‐oriented approaches to achieving CN, with the potential of digital technologies such as artificial intelligence (AI) remaining less explored. Our study aims to address this gap by qualitatively examining the use of AI for pursuing CN, drawing insights from firms with prior experience in the area. We analyzed the collected qualitative data to identify four key dimensions that capture different nuances of applying AI for achieving CN: (a) implementing AI for direct and indirect control of emissions, (b) accepting the strategic trade‐offs related to funding, data and systems concerns, and social priorities, (c) overcoming organizational and human‐related impediments, and (d) acknowledging the significant impact of AI in terms of gains in business model efficiency and measurable CN target attainment, which ultimately contribute to CN. Based on our findings, we propose a convergence–divergence model encompassing the positive aspects, inhibiting factors, synergies, and offsets necessary for firms to leverage AI to achieve net‐zero emissions effectively. Overall, our study contributes to the discourse on the utilization of AI for CN in a comprehensive manner.

Trend-Enhanced Improved Bollinger Bands Trend-Following High-Frequency Trading Strategy for Futures Market

The application of digital scientific technologies in the global financial markets has become widespread, which has led to the prevalence of the concept of quantitative trading. Among various quantitative trading strategies, high-frequency trading has gained significant popularity. In this study, we propose an improved Bollinger Bands trend-following high-frequency trading strategy based on trend enhancement. Instead of using the simple moving average (SMA), we replace it with the exponential moving average (EMA). Furthermore, we introduce a 3-times average true range (ATR) limit price rebound exit system to achieve timely profit-taking and stop-loss. By incorporating trend measurement indicators such as the average directional index (ADX), moving average convergence divergence (MACD), and commodity channel index (CCI), we determine the futures trend. Subsequently, this study formulates suitable parameters for the high-frequency trading strategy and conduct backtesting on the 2022 CSI 300 stock index futures, resulting in a remarkably high annualized return. These results present a novel approach to high-frequency trading strategy and provide valuable insights for the market.