Moving Average Convergence Divergence Research Articles

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%.

Stock Market Prediction Using Fuzzy Logic and Technical Indicators: A Case Study of Indian Banks

This study presents a decision support system for stock market investors that combines technical analysis with fuzzy logic. The system utilizes four essential technical indicators—Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator (SO), and On-Balance Volume (OBV)—to predict future price movements. These indicators are mapped as inputs into a fuzzy inference system, where fuzzy rules and membership functions are applied to generate trading recommendations, categorized as Bullish, Neutral, or Bearish. Experiments using MATLAB with data from 10 Indian banks demonstrate that the system effectively predicts stock movements and improves investment decisions when integrated with additional market information and investor insights

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.

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.

Efektifitas Penggunaan Analisis Stochastic Oscillator Dan Moving Average Convergence-Divergence Pada Perdagangan Saham-Saham Perbankan di Bursa Efek Indonesia

The objective of the research is to find out how effective stochastic oscillator and moving average convergence-divergence (MACD) technical analysis is in yielding capital gain and abnormal return, viewed from descriptive analysis, analysis of effects, and disparity test analysis by using some theories and previous research results. This research uses daily stock closing price data from the issuers in banking su-sector of the research period of 2015-2019. The research employs descriptive analytic approach, regression analysis and disparity test, supported by IBM SPSS version 25 software. The result of the descriptive analysis shows that buy and sell stochastic oscillator and MACD signals are effective in yielding capital gain. The result of regression analysis shows that gain signal yielded by indicator of Stochastic Oscillator and MACD has significant effects on capital gain in which the probability value (Asymp. Sig) of Stochastic Oscillator on capital gain is 0.000, and the probability value (Asymp.Sig) of MACD on capital gain is 0.000. The result of the disparity test analysis shows that there is no difference in abnormal return in the pre and post appearance of gain signal in Stochastic Oscillator and in MACD

Investigating profitability of Technical Trading Rules around COVID pandemic: Evidence from PSX

This paper investigates the role of technical analysis in predicting the stock prices of listed companies on the Pakistan Stock Exchange (PSX). Technical indicators such as Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), Bollinger Bands (BB), and Money Flow Index (MFI) are evaluated to explore their significance for stock gains in the Pakistan Stock Exchange. For this purpose, daily price data from 29 companies listed on the PSX was collected during the period from 2018 to 2022. This sample period provides a unique opportunity to analyze the impact of Covid-19 on the accuracy of technical trading signals. MACD, RSI, BB, and MFI are calculated, and the accuracy of the results is then assessed. Evidence from the PSX highlights that these indicators perform differently across various sectors. The t-test outcomes indicate significant differences in results before and after Covid-19, emphasizing the impact of the pandemic on stock movements. Based on these results an index has been developed and proposed to aid future decision-making processes.

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.

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.

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,

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.

Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions.

The cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations. In this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly. To make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.

Speed vs. efficiency: A framework for high-frequency trading algorithms on FPGA using Zynq SoC platform

Software-based technical indicators have been widely used for the stock market forecasting, aiming to predict market direction. Even though many algorithms for the software based technical indicators are presented, there are almost no hardware implementations reported in the literature. In this paper, the hardware implementation is presented for three commonly used technical indicators: Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), and Aroon. Latency evaluation is conducted for Bitcoin and Ethereum within a single-day timeframe, utilizing the Xilinx Zynq-7000 programmable SoC XC7Z020-CLG484-1 platform.Additionally, various hardware/software (HW/SW) partitioning strategies are explored to leverage the flexibility of software alongside the performance advantages of hardware via the Zynq SoC platform. The results show that the best performing technical indicator is MACD with a speedup of 30 times over its software only counterpart. Furthermore, a hybrid design integrating multiple technical indicators is proposed, pairing MACD with RSI due to their competitive throughput values, differing by only 0.38 microseconds. This hybrid approach capitalizes on the parallel processing capabilities of hardware, enabling multiple systems to operate simultaneously.

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.

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.

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.

How to Become Rich Using Quantitative Trading?

Bitcoin has become a very common investment product, also known as 'digital gold' because of its global discussion and rising prices. This paper develops a model that can only rely on these data to predict future price fluctuations and plan trading decisions. The trading decision planning model consists of three sub-models that are mutually undertaken. For model 1, in order to judge the price trend reliably, this paper uses the Long Short-Term Memory (LSTM) neural network, which is sensitive to the time series, to learn all the time series price data up to the trading day, and to predict the future price data of them respectively, which is provided to another sub-models for subsequent work. For Model 2, after obtaining the follow-up price data, all the technical indicators needed are calculated in this model, and then the moving average convergence divergence (MACD) rule is used to determine whether the transaction is carried out for the first time, and then the predicted price trend is used for the second judgment. For model 3, the model begins to make decision- making planning. This model mainly solves the bi-objective programming problem with the goal of maximizing returns and minimizing risks. In the range of controllable risks, the model will give the optimal solution of maximizing returns. Finally, using the model, the sensitivity of the model to transaction cost is studied by changing the transaction commission, and it is found that the final investment income is more sensitive to the transaction commission of bitcoin.

Technical Indicators of Major Healthcare Companies: A Comprehensive Analysis

This research aims to provide a comprehensive analysis of major healthcare stocks along with the nifty health care index within the healthcare sector. Employing technical indicators such as the Moving Average Convergence Divergence (MACD), Rate of Change (ROC) and Relative Strength Index (RSI) we conduct a detailed trend analysis to identify historical patterns and potential turning points in stock prices. Furthermore, advanced time series forecasting models and predictive analytics are utilized to forecast future share prices for the selected three companies, offering valuable insights for investors and market participants.
 In addition, we investigate the relationship between the stock prices of Apollo Hospital, Fortis Healthcare & Narayana Hruda and the Nifty Healthcare Index. This analysis sheds light on how individual stock prices are influenced by broader sectoral performance. We also explore the impact of MACD, ROC, and RSI technical indicators on the prices of all healthcare stocks through rigorous statistical analysis. Our findings provide healthcare sector stakeholders with a deeper understanding of stock behaviour, potential future movements, correlations with market indices and risk assessment.
 The results of this study enhance the decision-making process for investors and market participants in the healthcare sector. By examining historical trends, forecasting price dynamics, assessing sectoral correlations, evaluating technical indicator effects and risk measurement, this research contributes to a more comprehensive view of these stocks and their positioning within the healthcare industry.

Mechanisms of Stock Selection and Its Capital Weighing in the Portfolio Design Based on the MACD-K-Means-Mean-VaR Model

When designing a stock portfolio, investors must select stocks with different characteristics and increasing price trends and weigh each capital. Both are fundamental to diversifying loss and profit. Therefore, the mechanisms that accommodate both are needed. Based on this, this research aims to design a stock selection and capital weighing mechanism using the MACD-K-means-Mean-VaR model. The moving average convergence–divergence (MACD) is used to analyze stock buying time, providing trend, momentum, and potential price reversal insights. Then, stocks with increasing price trends are clustered using K-means, a grouping simple pattern data method based on specific characteristics. The best stocks from each cluster are capital weighted using the mean value at risk (mean-VaR), a portfolio optimization model adjusting loss possibility to the investor’s acceptance tolerance. The mechanism is then applied to Indonesia’s 100 stock index data to analyze variable sensitivities and compare it with another model. The application reveals that all variables significantly impact portfolio return mean and VaR, suggesting the need for clustering and analyzing stock price movements in stock portfolio design. This research academically develops a portfolio design mechanism by clustering stocks and analyzing price movement trends. It enables investors to practically diversify and choose stocks with increasing price trends, reducing losses and increasing profit opportunities.