Technical Analysis Indicators Research Articles

CPC-SAX: Data mining of financial chart patterns with symbolic aggregate approXimation and instance-based multilabel classification

In order to be able to classify financial chart patterns through machine learning, we introduced and applied a novel classification algorithm on time series data of different financial assets through SAX (Symbolic Aggregate approXimation), a transformation algorithm. After applying a linear regression model on the features of a dataset to reduce the number of parameters needed, converting real valued data to strings of characters through Piecewise Aggregate Approximation (PAA) and labelling each level increasingly with Latin alphabets characters, the new algorithm called CPC-SAX (Chart Pattern Classification) compares vectors describing the ASCII value changes along the string and classifies them using already labelled SAX-transformed data. The results show satisfying accuracy scores on data of different time windows and types of assets. We also obtain information on the appearance of said patterns. By reaching our goal of properly classifying chart patterns as they appear, we can have a better indication of the future price trend, allowing the investor/trader to make better informed decisions.

Enhancing Analytical Precision in Company Earnings Reports through Neurofuzzy System Development: A Comprehensive Investigation

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 the 52-week period of the previous year (MaxW52). A trading algorithm is developed based on the adaptive neurofuzzy inference system (ANFIS). Through a comprehensive analysis of the neural network’s training sample, it is concluded that abnormally large negative indicators have a profound impact on traders’ emotional reactions. This results leads to a hypothesis for further research, suggesting that report indicators may be processed by computational algorithms, potentially including artificial intelligence (AI). Consequently, the emergence of emotional trading robots managed by investment funds becomes a crucial area for investigation. Understanding the behavior of these algorithms enables proactive decision-making, allowing traders to leverage their knowledge and sell-purchased securities to these algorithms before their transactions occur. The implications of this research shed light on the evolving landscape of trading strategies and the role of emotionality in financial markets.

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.

OPTIMIZATION OF SHOPPING STRATEGY IN CRYPTOCURRENCY MARKETS BASED ON ARTIFICIAL NEURAL NETWORKS

This study is relevant in the context of modern finance, where neural networks play a key role in the analysis and forecasting of price dynamics. The research focuses on identifying buy signals for the BTCUSDT and ETHUSDT trading pairs in the cryptocurrency market. To construct a neural network model that can automate the identification of moments beneficial for purchasing selected assets, various technical analysis indicators and convolutional neural networks (CNN) were used. The research includes an analysis of scientific literature, data collection, indicator selection, signal algorithm development, and the construction of neural network models. The main contribution of this work lies in the development and testing of models capable of predicting buy signals with a high accuracy, confirmed by accuracy indicators of over 92%. The findings of this study can be useful for private investors and financial institutions in forming investment strategies based on machine learning.

Analysis of Machine Learning's performance in stock market prediction, compared to traditional technical analysis indicators

Analysis of Machine Learning's performance in stock market prediction, compared to traditional technical analysis indicators

Analysis of machine learning's performance in stock market prediction, compared to traditional technical analysis indicators

Analysis of machine learning's performance in stock market prediction, compared to traditional technical analysis indicators

Empirical Investigation of Abnormal Returns through Technical Analysis in the Nepalese Stock Market

This study comprehensively assesses the potential superiority of a technical trading strategy compared to the conventional buy and hold approach in the Nepalese stock market, where prior research is limited. It investigates the effectiveness of three widely recognized technical analysis indicators, both individually and in combination, using a 12-year historical dataset of 75 stocks, and 25.65-year NEPSE (Nepal Stock Exchange) index data. The back testing procedure evaluates the profitability and viability of the technical analysis strategy relative to buy and hold. The results show that, when factoring in trading-related costs, the technical trading strategy does not consistently outperform buy and hold. However, when trading fees are excluded, the technical strategy exhibits statistically significant outperformance. Future research can explore alternative technical rules and strategies to gain a more nuanced understanding of trading dynamics.

An Analytical Study on Super Trend Trading System on Bank Nifty Perpetual Futures Contract

To trade efficiently and profitably, we must understand the on-going trend in the market. While a many of popular technical analysis indicators attempt to provide a better understanding of the overall trend in the market, they are limited in both trend generation and momentum. Based on the Japanese Heiken Ashi (HA) candles we develop one trading system, which can reflect in a single number both the likelihood of trend generation and momentum in the market. We back tested the strategy using HA and Super Trend for BANKNIFTY’S perpetual futures contract for one major time frame in the market which is the daily time frame. We find if the strategy gives consistent profits for traders in the markets. The study contributes to economic literature as well as to practical trading scenarios by the development of a trading strategy using Heiken-Ashi charts as a charting technique combined with Super Trend as an indicator, which gives the trend generation and momentum in the market.

Optimal and Non-Optimal MACD Parameter Values and Their Ranges for Stock-Index Futures: A Comparative Study of Nikkei, Dow Jones, and Nasdaq

This study investigates the optimal and non-optimal parameter values of the MACD (Moving Average Convergence Divergence) technical analysis indicator for three major stock market index futures: the Nikkei 225, the Dow Jones, and the Nasdaq. Using a recently developed methodology, it reveals the existence of specific ranges of optimal and non-optimal values for each of the three parameters of the MACD indicator in these indices. Sample models employing the optimal parameter values in the three index futures generated significantly higher returns, outperforming both a non-technical buy-and-hold strategy and a random strategy that did not incorporate any market information. This discovery suggests that the three market indices may not be weak-form efficient. Therefore, this study contributes to the research on market efficiency by verifying inefficiency using a new approach. The highlight of this study is identifying that the ranges of optimal parameter values for the three indices are different from each other, but the optimal parameter value combinations for each of the three indices share a unique characteristic form. This issue and its finding have not been explored in the existing literature. Several interesting findings and valuable insights for market participants and researchers arise from this study. The new methodology is unique in finding optimal and non-optimal parameter values through the analysis of parameter sets used in well-performing and poorly performing sample models. Its validity and reliability have been confirmed by this study, making a useful contribution to the field of technical analysis research, particularly in parameter optimization insight.

Stock Price Prediction Model Using Deep Learning Optimization Based on Technical Analysis Indicators

Stock price prediction is one of the processes of analyzing and determining stock prices in the future. With technical analysis, future stock price predictions can be predicted through the pattern of fluctuations in the stock price in the past. In this study, the researcher predicts the stock price for the next week using the Deep Learning method, namely the Multilayer Perceptron, and combined with the day-shifting method. To expect the results of this stock, the author also observes the model's usefulness and proposes a Mean Error to Mean Price Ratio (MEMPR) to increase the insights processed by the model. Then to find out the accuracy of stock price predictions for each algorithm, testing is carried out using stock data which consists of new data which is then carried out by a training process to get an absolute error value. The experimental results show that the model can predict stock prices with an R2 metric of 0.995.

Forecasting NFT coin prices using machine learning: Insights into feature significance and portfolio strategies

With the rise in popularity of Non-Fungible Tokens (NFTs), the demand for NFT coins has also surged. NFT coins are cryptocurrencies that facilitate NFT ecosystems by supporting NFT trading and platform governance. Accurate price predictions of NFT coins are crucial for risk managing volatility and constructing optimal portfolios. This study employs machine learning techniques to predict the daily price direction of four key NFT coins, namely ENJ, MANA, THETA, and XTZ. The machine learning methods employed include three decision tree-based methods (random forests, extremely randomized trees, XGBoost), support vector machine, Lasso and Naïve Bayes. The findings show that random forests, extremely randomized trees, XGBoost, and support vector machine models have accuracy ranging between 80% and 90% for predictions in the 14 to 21 day range. This adds to the literature showing that machine learning methods have high prediction accuracy for cryptocurrency prices. Conversely, Lasso or Naïve Bayes models yield considerably lower prediction accuracy. Feature importance is assessed using Shapley values. The Shapley value feature importance calculated from random forests highlights that, for 14 and 21-day forecasts, four variables - five-year expected inflation, ten-year bond yields, the interest rate spread, and on balance volume - are consistently highly ranked across all NFT coins. Additionally, the MA50, MA200, and WAD also emerge as important features. These results highlight the importance of including macroeconomic variables which capture business cycle conditions and technical analysis indicators that capture investor psychology as features. NFT coin portfolios constructed using trading signals generated from Extra Trees outperforms a buy and hold portfolio. Extra Trees are easy and fast to implement and investors not making use of this information are likely making sub-optimal investment decisions.

A Technical Indicator for a Short-term Trading Decision in the NASDAQ Market

Purpose: The objective is to employ a stochastic model to develop a new technical analysis indicator that could compute the variation of any index. We demonstrate the superiority and applicability of our proposed model and show that our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions by using our proposed model to analyze the variation of the NASDAQ Composite Index (IXIC). Design/methodology/approach: This study uses a stochastic process without mean-reverting property to develop a stochastic model that could compute the variation of any index. To show the superiority and applicability of our proposed model in computing the variation of any index, we employ our proposed model to compute the daily closing values of the IXIC over 10 years and derive the variation of the IXIC index. Findings: Our findings indicate that, based on the mean absolute percentage error, the calibrated model we proposed provides a more accurate estimate of the short-term index that outperforms both the simple moving average and the MACD in predictive accuracy. It delivers a robust anticipation of the overall market trend by offering a 95% confidence interval for the value of the composite NASDAQ index. Practical Implications: Our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions. Our proposed model provides market analysts with a forecasting tool by using our proposed technical analysis indicator to anticipate the market trend, which outperforms some traditional indicators of technical analysis, including Simple Moving Averages and Moving Average Convergence Divergence. Originality/value: Our approach, results, and conclusions are original and new in the literature. Our proposed model is a new technical indicator for predicting any index based on a stochastic process, which has been found to outperform some classical indicators. This research makes significant contributions to the field of decision sciences because the indicator we have developed plays a crucial role. It enables better buying and selling decisions based on market trend predictions estimated by using our proposed model. In this way, the indicator offers added value to professionals in making investment decisions. The results of this research work contribute to the development of new technical analysis indicators. Here, the IXIC index is an example, the use of this indicator is wider and could concern any stock market index and any share. So, this work enriches the literature and opens up new avenues for any researcher who wants to use stochastic processes to develop new technical indicators for different financial assets.

ПОРІВНЯННЯ СИСТЕМ ПРОГНОЗУВАННЯ НАПРЯМУ ЗМІН КУРСУ ФІНАНСОВОГО ІНСТРУМЕНТУ З ВИКОРИСТАННЯМ ПРОСТИХ, ЕКСПОНЕНЦІЙНИХ ТА ЛІНІЙНО ЗВАЖЕНИХ КОВЗНИХ СЕРЕДНІХ

Several modern scientific studies point to the fact that technical analysis indicators have a predictive power of various types. Accordingly, trading systems for working in financial markets built using them may have some practical value. This work explores a specific range of topical issues of development, testing, and implementation of a trading system that generates an instruction to execute a stock transaction based on the signals of technical analysis indicators, particularly exponential, weighted, and simple moving averages. The works of modern scientists have been analyzed in which options for using these indicators are considered. In this context, the current research aims to analyze the influence of linearly weighted moving average settings and their combinations on the profitability of the trading system, as well as to compare the results of such a system with the results of a strategy built on a combination of simple and exponential moving averages. General scientific (analysis, synthesis, comparison, modeling) and special (testing, statistical analysis, graphical, tabular) research methods were used to achieve the goal. Based on this, the tasks solved using indicators of this kind were formed. A methodology for selecting moving averages and their settings is proposed when creating, testing, and implementing a trader's trading system. A methodology for selecting moving averages and their settings is proposed when creating, testing, and implementing a trader's trading system. Several approaches to forming and interpreting the signal regarding the change in the rate of a financial asset generated by the system are considered. The work also analyzes the criteria for comparing the results of strategies at the testing stage. The results of the application of various options of strategies were calculated and compared, and the optimal ones were selected according to the given selection criteria. The simulation of trading operations was performed for the currency pair EUR/USD, and weekly quotes from 1999 to 2023 were used, based on which the optimal combination of indicators for use in the trading strategy was determined. It is also stated that the system, built on moving averages, has shortcomings and needs additional optimization. Variants of possible optimization and the corresponding toolkit that can be analyzed are indicated. Based on the results of the research, it was concluded that the proposed approach to the development and use of the trader's trading system can be used to perform accurate exchange operations.

The validity of technical analysis in the cryptocurrency market: evidence from machine learning methods

Purpose- This study aims to assess the effectiveness of technical analysis indicators used by investors in the cryptocurrency market for making informed decisions. Emphasizing the importance of accurate decision-making methods in financial markets, this research particularly focuses on the cryptocurrency market, which has gained significant attention among investors in recent years. Methodology- The study specifically examines technical analysis, a widely employed method in various financial markets, with a focus on its predictive capabilities concerning Bitcoin price forecasts. Leveraging advanced technologies, such as big data analysis and machine learning, the research utilizes daily trading data from January 1, 2017, to June 30, 2022, presenting technical indicators and their associated error margins. Findings- The study highlights the significance of using Weighted Moving Average (WMA) and Stochastic Oscillator (STO) indicators in combination, demonstrating that multiple indicators outperform individual ones. This research underscores the effectiveness of technical analysis methods in the cryptocurrency market, aiding the development of enhanced investment strategies. Conclusion- In conclusion, this study delves into the potency of technical analysis techniques employed by investors in cryptocurrency markets. The insights indicate that combining indicators and technical analysis methods holds promise for future investment strategies. It is essential to note that even the best method can lead to losses, as evidenced by the presence of error margins, and absolute profitability cannot be guaranteed through technical analysis methods. Keywords: Cryptocurrency, technical analysis, machine-learning, classification algorithms, investment. JEL Codes: C38, C55, G17

Development neuro-fuzzy model to predict the stocks of companies in the electric vehicle industry

Adaptive neuro-fuzzy inference system (ANFIS) it is a type of neural network that combines the strengths of both fuzzy logic and artificial neural networks. ANFIS is particularly useful in stock trading because it can handle uncertainty and imprecision in the data, which is common in stock market data. In stock trading, ANFIS can be used for a variety of purposes, such as predicting stock prices, identifying profitable trades, and detecting stock market trends. One of the key advantages of using ANFIS for stock trading is that it can handle both linear and non-linear relationships in the data. This is particularly useful in the stock market, where the relationships between different variables are often complex and non-linear. ANFIS can also be updated and retrained as new data becomes available, which allows it to adapt to changing market conditions. Therefore, the main hypothesis of this work is to understand whether it is possible to predict the dynamics of prices for stocks of companies in the electric vehicle (EV) sector using technical analysis indicators. The purpose of this work is to create a model for predicting the prices of companies in the EV sector. The technical analysis indicators were processed by several machine learning models. Linear models generally perform worse than more advanced techniques. Decision trees, whether fine or coarse, tend to yield poorer performance results in terms of RMSE, MSE and MAE. After conducting a data analysis, the ANFIS and Bayesian regularization back propagation Neural Network (BR-BPNN) models were seen to be the most effective. The ANFIS was trained for 2000 epochs which yielded a minimum RMSE of 5.90926

Application of machine learning in algorithmic investment strategies on global stock markets

The research undertakes the subject of machine learning based algorithmic investment strategies. Several technical analysis indicators were employed as inputs to machine learning models such as Neural Networks, K Nearest Neighbor, Regression Trees, Random Forests, Naïve Bayes classifiers, Bayesian Generalized Linear Models, and Support Vector Machines. Models were used to generate trading signals on WIG20, DAX, S&P500, and selected CEE indices in the period between 2002-01–01 and 2023–03–31. Strategies were compared with each other and with the benchmark buy-and-hold strategy in terms of achieved levels of risk and return. Sensitivity analysis was used to assess the quality of the estimation on independent subsets. The findings of the study showed that algorithmic strategies outperformed passive strategies in terms of risk-adjusted returns and that for the analyzed indices, Linear Support Vector Machine and Bayesian Generalized Linear Model were the best-performing models. The Linear Support Vector Machine was chosen as the model that, on average, produced the best results using a more thorough rank approach based on the outcomes for all examined models and indices.

"Predicting the Movement of Stock Prices Using Technical Analysis Indicators": Applied Research in a Sample of the Shares of Iraqi Private Commercial Banks

The research aims to identify technical analysis indicators (simple moving average، relative strength index، estimation index)، and their impact on predicting the movement of stock prices for Iraqi private commercial banks، by examining the methods used in technical analysis and its various models, in general, and its indicators in particular. And the methods of benefiting from them in making the rational investment decision aimed at achieving rewarding returns in the financial markets, and diagnosing the obstacles and difficulties that the participants face in predicting stock prices، and proposing solutions and recommendations to overcome and overcome them from a scientific and practical perspective. As the research community consisted of Iraqi private commercial banks، which numbered (25) banks، while the research sample consisted of (7) banks with a rate of (28%) of the research community، the research used the analytical approach for the financial data in the period between (1/8 /2021-30/4/2022)، as the financial statements (closing prices) of the research sample banks were calculated by means of mathematical equations for technical indicators، and represented them in the form of graphs to clearly determine price trends and (buy and sell) signals. The research came out with a set of recommendations، the most important of which is the need for private Iraqi commercial banks to strengthen the position of technical analysis as a qualitative tool used to improve the investor's decision، and increase the ability to analyze and predict. Purpose، and urging academic authorities to shed more light on technical analysis by encouraging research and studies because of its great importance in the decision to invest in stocks in the future.

ПОРІВНЯННЯ СИСТЕМ ПРОГНОЗУВАННЯ НАПРЯМУ ЗМІН КУРСУ ФІНАНСОВОГО ІНСТРУМЕНТУ З ВИКОРИСТАННЯМ ПРОСТИХ ТА ЕКСПОНЕНЦІЙНИХ КОВЗНИХ СЕРЕДНІХ

The range of actual research points to the fact that some technical analysis indicators have predictive power, and therefore trading strategies based on them have some applied value. This work examines some topical issues of the development and use of a trader's trading system, which relies on signals generated by indicators of technical analysis, particularly exponential and simple moving averages, in deciding to execute a stock transaction. The works of modern researchers, which describe approaches to using these indicators, are analyzed. In this context, the purpose of the study is to analyze the impact of exponential moving average settings and their combinations on the profitability of the trading system, as well as to compare the results of such a system with the results of a strategy built on simple moving averages and a combination of simple and exponential moving averages. Based on this, tasks that are solved using slides of this kind were formed. The technique of selecting technical indicators and their settings when creating a trader's trading system is proposed. Several variants of the formation and interpretation of the signal regarding the subsequent change in the rate of the financial asset generated by such a system are considered. The article also discusses the criteria for comparing strategies at the testing stage. The results of using different techniques were calculated and compared, and the optimal ones were determined according to the selected selection criteria. Trading simulations were performed for the EUR/USD currency pair, using weekly quotes from 1999 to 2023, based on which the optimal combination of sliders for use in the trading system was determined. It was noted that the strategy, based on exponential moving averages, needs additional optimization. Options for possible optimization and the corresponding tools that can be used are indicated. Based on the research results, it was concluded that the proposed approach to developing a trader's trading system could be used to perform actual exchange operations.

Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market

Abstract Technical analysis indicators are popular tools in financial markets. These tools help investors to identify buy and sell signals with relatively large errors. The main goal of this study is to develop new practical methods to identify fake signals obtained from technical analysis indicators in the precious metals market. In this paper, we analyze these indicators in different ways based on the recorded signals for 10 months. The main novelty of this research is to propose hybrid neural network-based metaheuristic algorithms for analyzing them accurately while increasing the performance of the signals obtained from technical analysis indicators. We combine a convolutional neural network and a bidirectional gated recurrent unit whose hyperparameters are optimized using the firefly metaheuristic algorithm. To determine and select the most influential variables on the target variable, we use another successful recently developed metaheuristic, namely, the moth-flame optimization algorithm. Finally, we compare the performance of the proposed models with other state-of-the-art single and hybrid deep learning and machine learning methods from the literature. Finally, the main finding is that the proposed neural network-based metaheuristics can be useful as a decision support tool for investors to address and control the enormous uncertainties in the financial and precious metals markets.

Cryptocurrency price forecasting method using long short-term memory with time-varying parameters

Numerous research have been done to predict cryptocurrency prices since cryptocurrency prices affect global economic and monetary systems. However, investigations using linear connection approaches and technical analysis indicators frequently fall short of providing an explanation for changes in the pattern of BitCoin pricing. This paper is proposed to study time-varying parameters with long short-term memory (LSTM). The study is investigated on a dataset retrieved from Binance from March 2022 to April 2022. The proposed LSTM used a variety of hyperparameter settings, particularly time parameters, to predict the cryptocurrency price (BTC/USDT) on the dataset. Additionally, it is evaluated in terms of mean absolute percentage error (MAPE) in comparison to smooth moving average (SMA), weighted moving average (WMA), and exponential moving averages (EMA). From the investigation, using the previous 3 days for prediction gives the lowest of the MAPE values and the proposed LSTM outperformed the other models. When considering the last three days' value of pricing, the indicated LSTM offers the best accurate prediction, with a MAPE percentage of 0.0927%.