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Journal articles on the topic 'Trading indicators'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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1

Limowa Lie, Ronald, Murtiyanto Santoso, Felix Pasila, Raymond Sutjiadi, and Resmana Lim. "The Development of Prediction Indicators on Currency Market Using Neuro-Fuzzy Method." E3S Web of Conferences 188 (2020): 00021. http://dx.doi.org/10.1051/e3sconf/202018800021.

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A technical indicator is an analysis instruments to help traders analyzing forex price movements through charts. Prediction indicators are artificial technical indicators that can help traders to analyse forex price movements in the future. This prediction information becomes one of the bases in making trading decisions. This project aims to develop prediction indicators on MetaTrader that can provide information on forex price predictions using Neuro-Fuzzy method. The Neuro-Fuzzy System requires input parameters in the system prediction process obtained from the system training process. These

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SÜMER, Kutluk Kağan. "AN EARLY WARNING MODEL FOR TECHNICAL TRADING INDICATORS." Eurasian Academy of Sciences Social Sciences Journal 1, no. 1 (2015): 1–20. http://dx.doi.org/10.17740/eas.soc.2015-v1-01.

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Tóth, Balázs Levente, and Balázs Fazekas. "Profitability of exponential and simple moving average crossover strategy and the profitability of trend trading." Economica 14, no. 3-4 (2023): 34–42. http://dx.doi.org/10.47282/economica/2023/14/3-4/13490.

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The study focused on trading based on the signals of indicators, specifically examining a strategy using moving average crossovers. One of the main purposes of the paper was to analyse the efficiency differences between the simple and exponential moving average crossover strategies. Additionally, the study explored the impact of trend-following trading on the performance of indicators. The research tested the performance of indicators using the example of companies with the highest market capitalization. The main conclusion of the article is that there was no superior indicator in cases of the

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Sharma, Shiva. "Effectiveness of Technical Indicators in Stock Trading." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49516.

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Abstract This research investigates the practical efficacy of technical indicators in stock trading by examining their application within the Indian equity markets. These indicators— mathematical tools derived from price and volume data—are widely used to support entry and exit decisions. Yet, their predictive reliability remains debated, especially in emerging markets like India. Using historical data from 2020 to 2025, the study evaluates four key indicators: RSI, SMA, EMA, and Supertrend. Performance is assessed through Sharpe ratio, win-loss ratio, average return, and maximum drawdown. A c

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Ali, I., S. Z. Mahfooz, N. Q. Mehmood, and M. N. Mehmood. "Deployment of a Smart Trading System for Intelligent Stock Trading." Nucleus 60, no. 1 (2022): 1–8. https://doi.org/10.71330/nucleus.60.01.1254.

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In this article we evaluate the deployment of a smart trading system that exploits the features of different technical indicators for intelligent stock trading. Depending on their behaviors, these indicators help in trading under various market conditions. Our smart trading system uses a unified trading strategy that selects five indicators from three well-known categories referred as leading, lagging, and volatility indicators. The trading system looks for common trend signals from at least three indicators within a certain period of time. Collectively generated signals from the technical ind

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McHugh, Catherine, Sonya Coleman, and Dermot Kerr. "Technical indicators for energy market trading." Machine Learning with Applications 6 (December 2021): 100182. http://dx.doi.org/10.1016/j.mlwa.2021.100182.

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Dukas, Stephen, and Jinwoo Park. "Trading activity indicators and market timing." Applied Financial Economics 5, no. 5 (1995): 337–44. http://dx.doi.org/10.1080/758522761.

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Thompson, Colin J., Anthony J. Guttmann, and Ben J. P. Thompson. "Trading indicators with information‐gap uncertainty." Journal of Risk Finance 9, no. 5 (2008): 467–76. http://dx.doi.org/10.1108/15265940810916120.

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Xie, Anran, Yichong Huang, Yian Bian, Shucen Zhao, and Jiaxin Lin. "Research on the Performance of the Trend Following Trading Strategy in the Chinese Commodity Market." Wireless Communications and Mobile Computing 2022 (September 24, 2022): 1–8. http://dx.doi.org/10.1155/2022/5296678.

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Trend following strategy is a popular strategy that investors often use in trading around the world. Stocks are bought during an upswing and sold during a decline, the two main phases of the trend-following trading technique. This research evaluates the performance of the trend-following strategy in the Chinese commodity market by systematically employing quantitative methods to trade and get back test results for performance evaluation. The main trading indicator for this research is DMAC (Dual Moving Average Crossover) with a trend indicator called ADX for adjustment. As a kind of technical

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P. Kumar and H. N. Archana. "A comparative study of Bollinger Bands and rate of change with reference to NIFTY." Prayukti – Journal of Management Applications 02, no. 02 (2022): 113–20. http://dx.doi.org/10.52814/pjma.2022.2205.

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The purpose of this study is to compare Bollinger Bands (BB) and Rate of Change (ROC) indicators on Nifty50 for the financial year starting from 01/04/2021 to 31/03/2022 to find out which one of the indicator or the combination of the indicator can generate better returns with the use of Streak software. Streak is an algo trading platform which facilitates traders to back test trading strategies by giving some entry and exit conditions and deploy the same for automatic trading. A good technical indicator should give fewer signals with high accuracy as there will be more brokerage costs involve

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Zhalezka, B., A. Stadnik, and V. Siniauskaya. "TECHNICAL ANALYSIS AND INDICATORS APPLICATION IN ALGORITHMIC MARKETING." Экономическая наука сегодня, no. 15 (May 19, 2022): 119–30. http://dx.doi.org/10.21122/2309-6667-2022-15-119-130.

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This paper is devoted to the investigation of the stock exchange decision making process by means of technical analysis on the base of indicators. Methods of the basic technical indicators calculation are considered. Trading strategy of the stock exchange decision making based on the new complex specific technical indicator is suggested, which is accounting values of the following basic technical indicators: moving average convergence divergence, parabolic stop and reverse, average directional moving index rating, momentum volume weighted average price. An example of trading strategy automatio

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Tiwari, Ashmita, Anand Kumar Sah, Gaurav Pandeya, and Laxmi Prasad Bhatt. "Stock Trading Indicator using Reinforcement Learning." Journal of Advanced College of Engineering and Management 10 (March 11, 2025): 147–61. https://doi.org/10.3126/jacem.v10i1.76326.

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Traditional trading strategies often struggle to consistently achieve profitable results in highly volatile and complex financial markets. This research explores the potential of Reinforcement Learning (RL) to develop a trading indicator capable of learning and adapting to market changes. By leveraging RL, the trading indicator aims to improve decision-making and enhance trading performance through continuous learning from experiences. The outcomes of this research include a flexible and adaptive RL-based trading indicator, trading strategies that generate higher returns with better risk manag

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Ardiyanti, Ni Putu Winda, Irma Palupi, and Indwiarti Indwiarti. "Trading Strategy on Market Stock by Analyzing Candlestick Pattern using Artificial Neural Network (ANN) Method." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 4 (2021): 1273. http://dx.doi.org/10.30865/mib.v5i4.3266.

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Technical analysis plays an important role in a stock market. Traders using technical analysis to find the trading strategy on the market stock. There are some technical indicators tools that can support the technical analysis, such as Moving Average, Stochastic, and others. Candlestick pattern also parts of the tools that used in technical analysis to develop the trading strategy since Candlestick represents the stock behavior. Therefore, understanding the Candlestick pattern and technical indicator tools will be valuable for the traders to predict the trading strategy. This study performs th

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Lin, Tse-Mao, Jing-Long Yu, Jhih-Wei Chen, and Chin-Sheng Huang. "Application of Machine Learning With News Sentiment in Stock Trading Strategies." International Journal of Financial Research 14, no. 3 (2023): 1. http://dx.doi.org/10.5430/ijfr.v14n3p1.

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This study empirically tested the feasibility of machine learning in trading strategies using technical indicators and news information as the feature variables for machine learning. Six indicators were adopted in this study, including moving average (MA), moving average convergence/divergence (MACD), relative strength index (RSI), stochastic oscillator (KD), and on-balance volume (OBV), and news sentiment ratio (SR) developed in this study via text mining. Selected machine learning models, including support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), recurrent neural network (R

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Suprapto, Adi Teguh, Mulyono Mulyono, and Danang Prihandoko. "Price Fraction Changes Impact on Stock Trading Indicators: An Events Study on Indonesia Stock Exchange." Winners 20, no. 1 (2019): 1. http://dx.doi.org/10.21512/tw.v20i1.5103.

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This research presented differences of stock price fraction system to stock trading indicator variables such as volume, value, and frequency of stock trading transactions on companies listed in Indonesia Stock Exchange. The purpose of this research was to measure and analyze the difference of stock price fraction system to stock trading indicator variables. Sample determination based on the sampling method was saturated, i.e., the technique of determining the sample by using all members of the population as a sample. The sample in this research used JCI data as it represents the 115 issuers li

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Burhop, Carsten, and Sergey Gelman. "Trading costs and trading quantity at the Berlin Stock Exchange, 1892–1913." Zeitschrift für Unternehmensgeschichte 67, no. 1 (2022): 21–42. http://dx.doi.org/10.1515/zug-2022-0014.

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Abstract We estimate two components of stock market liquidity, namely trading costs and trading volume, for the Berlin Stock Exchange, 1892–1913. Our three trading cost indicators are highly correlated with each other. To estimate trading volume, we use turnover tax revenues and turnover data from the Berliner Kassenverein. We use information on stock exchange turnover taxes collected in Berlin and we have compiled monthly data. We show that the three indicators of trading volume are highly correlated.

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Matrokhin, Aleksey Yu, Elena N. Vlasova, and Larisa V. Mikhailova. "ASSESSMENT OF MARKETPLACE COMPETITIVENESS INDICATORS FOR THE SALE OF KNITTED GOODS." Technologies & Quality 55, no. 1 (2022): 19–23. http://dx.doi.org/10.34216/2587-6147-2022-1-55-19-23.

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The article raises the question of the choice of key indicators that reflect the functionality of online trading platforms – marketplaces, their effectiveness from the point of view of users – sellers and consumers of goods and services. The ranking of the selected indicators, quantitative assessments of electronic sites and comparison of marketplaces for the sale of knitted underwear products have been carried out. The authors investigate the specifics of the work of marketplaces, the positive and negative aspects of their work. The single indicators of marketplace competitiveness were quanti

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Yu, Jia-Wei, Qin-Qin Huang, Yong-Han Guo, Zhi-Qiang Jiang, and Wen-Jie Xie. "Systemic Risk and Trading Strategy Based on Correlation-Based Networks in Stock Markets." Fluctuation and Noise Letters 19, no. 03 (2020): 2050028. http://dx.doi.org/10.1142/s0219477520500285.

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In this paper, we construct five systemic risk indicators and test their performances based on four different datasets. It is observed that the five indicators can accurately indicate the increment of systemic risks during the periods of sub-prime crisis and European debt crisis. Trading strategies based on the risk indicators are further designed to test the warning ability of future price drops. The backtests reveal that trading based on the five indicators provides satisfied excess returns when the trading costs are included. Our results provide insights to find new network-based risk indic

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Vrînceanu, Bogdan-Petru, and Florentin Șerban. "From Data to Decisions: A Trading Bot for Ethereum." International Journal of Research and Scientific Innovation XII, no. II (2025): 107–18. https://doi.org/10.51244/ijrsi.2025.12020010.

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This research focuses on the development and evaluation of an automated trading strategy for cryptocurrency markets, specifically Ethereum, using Pine Script version 5 on the Trading View platform. The strategy incorporates a variety of technical indicators, including the Relative Strength Index (RSI), Commodity Channel Index (CCI), Average True Range (ATR), Directional Movement Index (DMI), Aroon Indicator, and Exponential Moving Average (EMA), to capture diverse market conditions such as trend direction, volatility, and overbought/oversold levels. To enhance decision-making, a multiple logis

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Muhammad Khawar Bashir. "Trading Algorithm Model Based on Technical Indicators." Lahore Garrison University Research Journal of Computer Science and Information Technology 5, no. 1 (2021): 47–54. http://dx.doi.org/10.54692/lgurjcsit.2021.0501176.

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Today the rapid proliferation of the internet provides an environment where efficient e-commerce solutions can be developed. The electronic market is gaining more attention in the global economy, it gives buyers and sellers more liberty to trade cost-effectively and allows access to an adequate amount of data for analysis. New trading agents have been developed for the best utilization of such data. These agents design strategies using financial analysis techniques such as technical indicators. Two very well-known technical indicators used to develop strategies are Convergence-Divergence (MACD

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King, Juan C., Roberto Dale, and José M. Amigó. "Blockchain metrics and indicators in cryptocurrency trading." Chaos, Solitons & Fractals 178 (January 2024): 114305. http://dx.doi.org/10.1016/j.chaos.2023.114305.

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Ibrahim, Alaa Eldin. "Developing a system for trading in the stock markets." مجلة جامعة الشارقة للعلوم الانسانية والاجتماعية 12, no. 2 (2022): 36–53. http://dx.doi.org/10.36394/jhss/12/2/10.

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This research work provides a quantitative approach to measure the accuracy of a buy or sell signals of stocks in the US stock market. It describes several buy/sell indicators then measures the accuracy of each indictor by testing it on a number of filtered US historical data between 2000 and 2014. We show how each indicator weighs then by summing up scores of successful indicators; we end up with a score for each stock at a particular time. This would give a much more accurate buy or sell signal. In past stock market research [2, 3, 4, 5, 9, 10], typically researchers measure the probability

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Chen, Chien-Ming, Yuxiao Gong, and Jimmy Ming-Tai Wu. "Impact of Technical Indicators and Leading Indicators on Stock Trends on the Internet of Things." Wireless Communications and Mobile Computing 2022 (May 27, 2022): 1–15. http://dx.doi.org/10.1155/2022/9916310.

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The Internet of things (IoT) has had an enormous impact on the financial industry. With IoT, people can obtain real-time financial information; moreover, investment and financial management have become more flexible and diverse. Because of their high returns and strong liquidity, stocks have become essential commodities through which people invest and manage money. However, high returns are often associated with high risks. Therefore, it is important for investors to forecast the trends of future stock prices. This study uses a new stock trend prediction framework to predict changes in the sto

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Giribone, Pier Giuseppe, Simone Ligato, and Francesco Penone. "Combining robust dynamic neural networks with traditional technical indicators for generating mechanic trading signals." International Journal of Financial Engineering 05, no. 04 (2018): 1850037. http://dx.doi.org/10.1142/s2424786318500378.

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Forecasting assets’ prices is the aim of each trader, although the trading approaches employed may vary a lot. The development of machine learning techniques has brought the opportunity to design mechanic trading systems based on dynamic artificial neural networks. The aim of this paper is to combine traditional technical indicators [such as exponential weighted moving average (EWMA), percentage volume oscillator (PVO) and stochastic indicator — %K and %D] with the nonlinear autoregressive networks (NAR and NARX). The first part of the paper describes how neural networks designed for forecasti

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Sai Ashish, Malla Venkata. "Algorithmic Trading Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48961.

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Abstract: The Algorithmic Trading using Machine Learning is a financial trading system that integrates technical analysis indicators and future predictions with news sentiment analysis through reinforcement learning algorithms. The system trains autonomous trading agents to make effective buy, sell, or hold decisions in complex market environments by combining historical stock data, technical indicators, and sentiment analysis from financial news. It implements and compares three state-of-the-art reinforcement learning algorithms: Proximal Policy Optimization (PPO), Deep Q-Network (DQN), and A

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Sibiryatkina, I. V. "TOOLS FOR ASSESSING THE COMPETITIVENESS OF A COMMERCIAL ENTERPRISE." Scientific Review: Theory and Practice 10, no. 9 (2020): 2047–61. http://dx.doi.org/10.35679/2226-0226-2020-10-9-2047-2061.

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The article presents the systematization of approaches to assess the enterprises' level of competitiveness. The advantages and disadvantages of the most famous models for assessing the competitiveness of business entities are considered. The identified shortcomings made it possible to form a hypothesis for studying the enterprises' competitiveness, taking into account the industry factor, financial criteria and quality indicators of the enterprise. For the formation of an objective adaptation model for assessing the competitiveness of an enterprise, taking into account its industry specificity

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Meng, Hao. "A Brief Analysis of The Impact of Investor Sentiment on Stock Prices in The Chinese Stock Market." Frontiers in Business, Economics and Management 11, no. 2 (2023): 61–64. http://dx.doi.org/10.54097/fbem.v11i2.12559.

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This article describes the challenges that behavioral finance poses to traditional financial assumptions, from questioning the rationality of human behavior to explaining the classifications and three indicators of investor sentiment in behavioral finance. Furthermore, it confirms the combination of theory and practice by examining turnover ratio as a proxy indicator, the trading volume of closed-end funds, and stock price regression. It demonstrates that investor sentiment significantly influences stock prices. Moreover, it indicates that both investor sentiment and stock prices can mutually

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Bezsmolnyi, Yu V., and M. M. Seniv. "A decision support software system for cryptocurrency traders on the Trading View platform." Ukrainian Journal of Information Technology 6, no. 1 (2024): 9–16. http://dx.doi.org/10.23939/ujit2024.01.009.

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The work carried out a comparative analysis of scientific publications regarding the possibility of predicting the direction of the cryptocurrency exchange rate using the data of open numerical indicators, based on the results of which it can be concluded that due to the volatility of the cryptocurrency market and the need for accurate forecasting, there is a need to create an aggregated indicator that will take into account the current price exchange rate asset, parameters of simple indicators, trading volume, etc. In addition, this indicator will be a parameter for the application of a multi

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Cohen, Gil, and Mahmoud Qadan. "The Complexity of Cryptocurrencies Algorithmic Trading." Mathematics 10, no. 12 (2022): 2037. http://dx.doi.org/10.3390/math10122037.

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In this research, we provided an answer to a very important trading question, what is the optimal number of technical tools in order to achieve the best trading results for both swing trade that uses daily bars and intraday trade that uses minutes bars? We designed Machine Learning (ML) systems that can trade four major cryptocurrencies: Bitcoin, Ethereum, BNB, and Solana. We found that more indicators do not necessarily mean better trading performance. Swing traders that use daily bars should trade Bitcoin and Solana using Ichimoku Cloud (IC) plus Moving Average Convergence Divergence (MACD),

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Jayaraman, Gopu, Hanaa Mahmoud Sid Ahmed, Imran Azad, Mohammed Shahfaraz Khan, and Muawya Ahmed Hussien. "Enhancing market forecast accuracy: A structural equation model analysis of technical indicators in the Bank Nifty index." Asian Economic and Financial Review 15, no. 3 (2025): 404–34. https://doi.org/10.55493/5002.v15i3.5346.

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The growing intricacy of international financial markets requires sophisticated approaches to managing investments and minimizing losses. This paper evaluates the use of Structural Equation Modeling (SEM) to improve forecast accuracy by integrating multiple technical indicators within the Bank Nifty Index. The study employs SEM to estimate the effect of key technical indicators such as the Simple Moving Average (SMA), Relative Strength Index (RSI), Volume Weighted Average Price (VWAP), and Moving Average Convergence Divergence (MACD) on trading volumes and closing values. The model considers b

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Skremetov, Nikita A. "KEY PERFORMANCE INDICATORS FOR EVALUATING DIGITAL TRANSFORMATION IN A COMPANY." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 6/2, no. 147 (2024): 261–67. http://dx.doi.org/10.36871/ek.up.p.r.2024.06.02.028.

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One of the main directions of company development today is the digitalization of business processes. Recently, there has been an active introduction and use of digital innovations in the activities of companies. To solve the problems of increasing competitiveness within the framework of digitalization, trading companies actively use digital technologies that help to quickly promote goods and build relationships with customers and partners. Digital transformation has become an integral part of modern business and, in particular, trading activities, providing organizations with competitive advan

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Setiawan, Adhe Raka, and Bandi Bandi. "REAKSI PASAR TERHADAP PERUBAHAN DIVIDEN DENGAN INDIKATOR ABNORMAL RETURN DAN TRADING VOLUME ACTIVITY." Jurnal Economia 11, no. 2 (2015): 200. http://dx.doi.org/10.21831/economia.v11i2.8291.

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Abstrak: Reaksi Pasar Terhadap Perubahan Dividen dengan Indikator Abnormal Return dan Trading Volume Activity. Penelitian ini bertujuan untuk mengetahui reaksi pasar terhadap perubahan dividen, yaitu dividen tetap, dividen naik, dividen turun, dividen inisiasi, dan dividen omisi dengan indikator abnormal return dan trading volume activity pada perusahaan yang terdaftar di Bursa Efek Indonesia pada sektor properti, real estate, dan konstruksi bangunan periode 1998-2015. Penelitian ini menggunakan desain event study, di mana dilakukan pengamatan 5 hari sebelum dan 5 hari sesudah peristiwa. Anali

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Nasser, Muhammad H. "IRAQ STOCK EXCHANGE PERFORMANCE INDICATORS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 1/4, no. 154 (2025): 65–73. https://doi.org/10.36871/ek.up.p.r.2025.01.04.008.

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The Iraq Stock Exchange data for the period 2013–2021 were presented for the industrial, agricultural, hospitality, service, investment, insurance and banking sectors, as well as the gross domestic product data. Some financial indicators were applied, including market value rate, trading volume, total value rate, stock turnover rate and growth rate comparison during the study period. This showed the importance of the Iraq Stock Exchange index, which is considered one of the important and vital sources of stimulation for various sectors of the economy. The results also showed the weak performan

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Aloud, Monira Essa. "An Intelligent Stock Trading Decision Support System Using the Genetic Algorithm." International Journal of Decision Support System Technology 12, no. 4 (2020): 36–50. http://dx.doi.org/10.4018/ijdsst.2020100103.

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The authors present a simple data-driven decision support system for stock market trading using multiple technical indicators, decision trees, and genetic algorithms (GAs). It assembles technical indicators set into a decision tree based on stock trading rules and generates buy, hold, and sell classes that represent trading decisions. The main contribution of this study is the use of GAs based on a two-step classification method. This allows for selecting the relevant inputs and adapting them to the market dynamic. The GAs are used at the data input selection step and the weight selection step

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Крыленко, Е. Е., and И. А. Острикова. "Assessment of the financial condition of the enterprise taking into account industry specifics." Экономика и предпринимательство, no. 10(135) (January 10, 2022): 1093–96. http://dx.doi.org/10.34925/eip.2021.135.10.210.

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Деятельность торгового предприятия, структура его финансов, показатели ликвидности, финансовой устойчивости и рентабельности могут сильно отличаться от тех же показателей, характерных для предприятий других отраслей. Проводя финансовый анализ деятельности такого предприятия, необходимо учитывать факторы, влияющие на деятельность торгового предприятия и применять дополнительно методы и показатели, которые дадут более полное представление о финансовом состоянии торгового предприятия. Описанные в статье методы и показатели анализа финансового состояния торгового предприятия подходят как для крупн

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Qi, Yong, Hefeifei Jiang, Shaoxuan Li, and Junyu Cao. "ConvLSTM Coupled Economics Indicators Quantitative Trading Decision Model." Symmetry 14, no. 9 (2022): 1896. http://dx.doi.org/10.3390/sym14091896.

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Time series prediction methods based on deep learning have been widely used in quantitative trading. However, the price of virtual currency represented by Bitcoin has random fluctuation characteristics, which is extremely misleading for time series prediction. In this paper, a virtual currency quantitative trading model is established, which uses a convolution long short term memory (ConvLSTM) deep learning method to predict the transaction price, and uses the evaluation model composed of Chandler momentum oscillator (CMO), percentage price oscillator (PPO), stop and reverse(SAR) and other eco

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Puji Lestari, Novi, and Bertyapati Agra Aslama. "Mastering Market Movements: Technical Indicators in IDX30 Stock Selection." JBMP (Jurnal Bisnis, Manajemen dan Perbankan) 10, no. 2 (2024): 236–49. http://dx.doi.org/10.21070/jbmp.v10i2.1972.

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This study aims to analyze the role of trading volume, foreign money flows, and technical chart patterns in shaping investment decisions for IDX30 companies. Utilizing descriptive quantitative research, data was collected from Stockbit and IPOT, focusing on stock price charts, trading volume, and broker summaries from January 1, 2020, to December 31, 2022. The findings reveal that purchase decisions were made when stock prices broke out of a chart pattern, accompanied by increased trading volume and foreign accumulation. Conversely, sell decisions occurred when stock prices broke down from a p

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Apte, Mohit. "Enhancing Momentum Trading with Macroeconomic Indicators- A Strategic Approach." International Journal of Innovative Research in Computer Science and Technology 12, no. 4 (2024): 70–73. http://dx.doi.org/10.55524/ijircst.2024.12.4.11.

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Traditional momentum trading strategies capitalize on existing market trends but often overlook broader macroeconomic contexts, potentially limiting their effectiveness during periods of economic fluctuation. This paper introduces an enhanced momentum trading strategy that incorporates key economic indicators—GDP, inflation, unemployment rates, and interest rates—to provide a more robust framework capable of adapting to changing economic conditions. By integrating these macroeconomic factors, the strategy aims to improve predictive accuracy and performance stability. Using data from the S&

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Padilha, Victor Alexandre, Vinícius Magnani, Rafael Confetti Gatsios, Fabiano Guasti Lima, and Rafael Moreira Antonio. "Trend and Momentum Technical Indicators for Investing in Market Indices." EkBis: Jurnal Ekonomi dan Bisnis 8, no. 1 (2024): 74–86. http://dx.doi.org/10.14421/ekbis.2024.8.1.2163.

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The purpose of this study is to evaluate the effectiveness of technical analysis indicators in investment strategies.We conducted an empirical study using four trend indicators and four momentum indicators across seven market indices. Our methodology employs a parameter optimization process for each indicator and compares the results with classical parameters reported in the economics literature.Our findings indicate that no technical indicator consistently outperforms the buy-and-hold strategy in the long run. In addition, the optimization procedures did not yield significant improvements in

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Mahfooz, S. Z., Iftikhar Ali, and Muhammad N. Khan. "Improving Stock Trend Prediction using LSTM Neural Network Trained on a Complex Trading Strategy." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 4361–71. http://dx.doi.org/10.22214/ijraset.2022.45961.

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Abstract: Technical analysis in stock trading addresses the crucial matter of making optimal trading decisions promptly. Predicting directional movement in the target market using technical indicators is quite common. Besides its many other applications, machine learning helps to solve the algorithmic trading problem of determining optimal trading positions, and some types of deep neural networks have been proven as up-and-coming methods for forecasting the returns of the stock market. The current work presents the idea of training a neural network on a new trading strategy, named, Unified Tra

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Matkarimov, Bakhyt, Alibek Barlybayev, and Didar Karimov. "Enhancing Analytical Precision in Company Earnings Reports through Neurofuzzy System Development: A Comprehensive Investigation." Journal of Electrical and Computer Engineering 2024 (March 18, 2024): 1–19. http://dx.doi.org/10.1155/2024/8515203.

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The object of research is the fundamental and technical indicators of companies after the release of the earnings report. This study attempts to address the issue of understanding the impact of fundamental and technical analysis indicator dynamics on profits and loss news releases. This research provides an in-depth analysis of stock price forecasting models, focusing on the influence of earning report seasons as catalysts for stock price growth. The study explores the relationship between key financial indicators, including earnings per share (EPS), revenue, and the maximum price observed in

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Natalia, Natalia, Mulyono Mulyono, and Dian Kurnianingrum. "Impact in Changing Price Fraction to the Stock Trading Indicators in Indonesia Stock Exchange." Binus Business Review 7, no. 3 (2016): 241. http://dx.doi.org/10.21512/bbr.v7i3.1783.

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The purpose of this study was to determine how the impact of changes the price fraction to the stock trading indicator that is volume, value, and frequency of trading transactions. Data were analyzed using the Mann-Whitney U test. The results show that the volume of stock trading is not affected significantly by the implementation of the tick size, whereas for the value of trade and frequency of trade significantly affected.

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Riswan, Adi, and Dadang Iskandar Mulyana. "IMPLEMENTASI EXPERT ADVISOR UNTUK TRADING OTOMATIS FOREX MENGGUNAKAN INDIKATOR RSI DAN MA DENGAN METODE MARTINGALE DI PLATFORM METATRADER 4." Jurnal Indonesia : Manajemen Informatika dan Komunikasi 4, no. 3 (2023): 1463–71. http://dx.doi.org/10.35870/jimik.v4i3.324.

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Among the many digital businesses, forex trading is one of them. To trade forex, various types of software are used which are tools for traders, such as Metatrader 4. The core business of forex trading is to buy or sell consistently to trade forex, making a profit. In order to get the desired profit, it is important to adjust the forecast towards a growing market. Therefore, we need the right forex indicators to predict prices that are not wrong. However, traders who follow predictive indicators signals often fail to execute trades on time and therefore are unable to make consistent profits. T

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Al-Jadoui, Hamida Muslim Hussein, Abdulkhleq Dubai Abdulmahdi, and Ehab A. Mahmood. "The Relationship between Financial Indicators and the Trading Volume of the Iraqi National Bank Listed on the Iraq Stock Exchange." Journal of Management World 2025, no. 2 (2025): 490–99. https://doi.org/10.53935/jomw.v2024i4.981.

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The relationship between the financial indicators of the Iraqi National Bank and the trading volume of its shares is of great importance in supporting investment decisions and stimulating economic growth. Accordingly, the research problem is represented by the extent of the relationship between the financial indicators of the Iraqi National Bank in the Iraq Stock Exchange and the trading volume of its shares. Based on this, the research aims to analyze the quantitative relationship between the financial indicators and the trading volume of shares for the Iraqi National Bank listed on the Iraq

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Deep, Akash, Abootaleb Shirvani, Chris Monico, Svetlozar Rachev, and Frank Fabozzi. "Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading." Journal of Risk and Financial Management 18, no. 3 (2025): 142. https://doi.org/10.3390/jrfm18030142.

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Because of the theoretical challenges posed by the Efficient Market Hypothesis with respect to technical analysis, the effectiveness of technical indicators in high-frequency trading remains inadequately explored, particularly at the minute-level frequency, where the effects of the microstructure of the market dominate. This study evaluates the integration of traditional technical indicators with Random Forest regression models using minute-level SPY data, analyzing 13 distinct model configurations. Our empirical results reveal a stark contrast between in-sample and out-of-sample performance,

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Xiujun, Sun. "Evaluation Research on Development Level of Energy Internet." International Journal of Engineering and Management Research 8, no. 6 (2018): 12–16. https://doi.org/10.31033/ijemr.8.6.2.

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Considering the process of energy development and utilization and based on the development objectives of low carbon, high efficiency and electrification, this paper selects key indicators from three aspects, i.e., energy supply, energy consumption and energy trading. On the basis of the established indicator system, the structural entropy and factor analysis optimization model is built to select and optimize the final indicators. The selected indicators can provide a theoretical research framework for the study of development level and the prediction of development trend of global energy inter

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MIRONOV, Roman Yu, and Ivan V. SOFILKANICH. "Developing a trading strategy for investment portfolio construction based on leading indicators." Finance and Credit 31, no. 1 (2025): 194–222. https://doi.org/10.24891/fc.31.1.194.

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Subject. The article considers a trading strategy for investment portfolio construction, using leading indicators. Objectives. The aim of the study is to shape an investment strategy with the preservation of capital in the stock market during crisis periods, based on leading indicators. Methods. We employed traditional methods of cognition (analysis and synthesis, analogy, generalization, etc.), the graphical method, and backtesting. Results. The paper highlights the main disadvantages of the existing methods of investment portfolio creation, classifies the most popular leading indicators acco

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Irum Saba, Maria Shams Khakwani, Rehana Kouser, and Abdul Wahab. "Investor Sentiments and Trading Volume’s Asymmetric Response: a Non-linear ARDL Approach Tested in PSX." Journal of Accounting and Finance in Emerging Economies 5, no. 1 (2019): 47–56. http://dx.doi.org/10.26710/jafee.v5i1.720.

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The research paper entitled “Investor sentiments and trading volume’s asymmetric response: A non linear ARDL approach tested in PSX” is an attempt to investigate the dynamic linkages between trading volume and investor sentiments for Pakistan Stock Exchange (PSX) 100 index. Two sentiments indicators have been used to enlighten the linkages. These indicators are overconfidence and net optimism and pessimism. Trading volume has been used as a proxy for the measurement of market liquidity. Non-Linear Asymmetric Autoregressive Distributed Lag (NARDL) as well as Dynamic Conditional Correlation (DCC

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Mukund Harsha, Achuta, and Vaddi Venkata Sundara Kesava Rao. "Exploring profitable opportunities: Analysing technical indicators combinations for profitable trading." Corporate and Business Strategy Review 5, no. 1 (2024): 148–60. http://dx.doi.org/10.22495/cbsrv5i1art15.

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This study investigates the effectiveness of three technical indicators, namely Simple moving average (SMA), On-balance volume (OBV), and Commodity channel index (CCI), in identifying profitable trading opportunities. Drawing inspiration from the work of Naved and Srivastava (2015b), this research uses historical price data from 50 undervalued companies in comparison with the returns of NIFTY 50 companies. To assess the long-term feasibility of these indicator combinations, a performance analysis is carried out over 10 years, encompassing a sizable 8,50,209 trades. The analysis focuses on trad

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Cheng, Ching-Hsue, Ming-Chi Tsai, and Chin Chang. "A Time Series Model Based on Deep Learning and Integrated Indicator Selection Method for Forecasting Stock Prices and Evaluating Trading Profits." Systems 10, no. 6 (2022): 243. http://dx.doi.org/10.3390/systems10060243.

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A stock forecasting and trading system is a complex information system because a stock trading system needs to be analyzed and modeled using data science, machine learning, and artificial intelligence. Previous time series models have been widely used to forecast stock prices, but due to several shortcomings, these models cannot apply all available information to make a forecast. The relationship between stock prices and related factors is nonlinear and involves nonstationary fluctuations, and accurately forecasting stock prices is not an easy task. Therefore, this study used support vector ma

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