Technical Indicators Research Articles

An Intelligent Approach for Predicting Stock Market Movements in Emerging Markets Using Optimized Technical Indicators and Neural Networks

Abstract Integrating big data analytics and machine learning algorithms has become increasingly important in the fast-changing landscape of stock market investment. The numerical findings showcase the tangible impact of our methodology on the accuracy and efficiency of stock market trend predictions. Identifying and selecting the most salient features (technical indicators) is critical in predicting the trend direction of exchange-traded funds (ETFs) in emerging markets, leveraging financial and economic indicators. Our methodology encompasses an array of statistical techniques strategically employed to identify critical technical indicators with significant implications for time series problems. We improve the efficacy of our model by performing systematic evaluations of statistical and machine learning methods across multiple sets of features or technical indicators, resulting in a more accurate trend prediction mechanism. Notably, our approach not only achieves a substantial reduction in the computational cost of the proposed neural network model by selecting only 5% of the total technical indicators for predicting ETF trends but also enhances the accuracy rate by approximately 2%.

Neural Network Based Technical Analysis of Football Games

Football is the most famous sports in the world, and English Premier League is the number one league in the world for three consecutive years (Fédération internationale de football association, FIFA). It is always interesting to apply technical analysis to understand what makes the best football player and football team. In this article, we try to answer the question "what kind of tactical play is the most advanced" through statistical analysis on the game data of English Premier League in the 21-22 season. To be more specific, firstly, we applied descriptive statistics to analyze the technical and tactical play of each team, and then screen out the technical and tactical indicators that significantly affect the outcome of the game through one-way analysis of variance (ANOVA) and discriminate analysis, and the preliminary target conclusions were obtained. BP neural network was then carried out to predict the rankings of the Premier League teams by using the indicators selected by ANOVA and discriminant analysis, as input value. BP neural network prediction model is then established to predict the ranking of each team in the 22-23 season. A general conclusion and make suggestions on the planning of the technical and tactical playing methods of our country's youth soccer sports.

Cryptocurrency assets valuation prediction based on LSTM, neural network, and deep learning hybrid model

Cryptocurrency, a digital currency managed by decentralized networks, has gained immense popularity since the inception of Bitcoin. These digital assets, often characterized by extreme price volatility, have generated substantial interest from investors. Traditional financial models struggle to account for the unique dynamics and complexities of cryptocurrencies, prompting the adoption of deep learning techniques. This study investigates the use of Long Short-Term Memory (LSTM), Neural Networks, and Deep Learning (CNN) in predicting cryptocurrency prices. These deep learning models leverage various data sources, such as technical indicators and sentiment analysis, to gain a comprehensive understanding of cryptocurrency markets. The research evaluates the performance of these models using Root Mean Squared Error (RMSE) as the primary metric. The results demonstrate that the hybrid model, combining LSTM, Neural Networks, and Deep Learning, exhibits the highest predictive accuracy across multiple cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB). However, challenges persist, such as model adaptability to unforeseen market events and data noise. Future developments may involve incorporating external factors and interdisciplinary collaboration to create more holistic valuation models. Despite these challenges, the study underscores the potential of hybrid deep learning models in enhancing cryptocurrency valuation accuracy and their relevance in risk management strategies for investors and traders.

Key technologies of earthquake early warning system for China’s high-speed railway

PurposeThe purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.Design/methodology/approachThe paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.FindingsThe study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.Originality/valueThe paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.

Study of the relationships between the technical and economic characteristics of river cargo ships during operational and economic feasibility studies

One of the problems of operational and economic feasibility is determining the economic efficiency of a new ships. The complexity of the problem increases for shipis od river and sea-river class due to the high variability of navigation conditions, which significantly affect efficiency indicators. The purpose of the article is to study the mutual influence of the technical indicators of sea-river cargo ship and the economic indicators of its operation, taking into account the navigation conditions on specific sections of waterways. Various technical indicators are considered and it is shown that the most comprehensive indicator is the specific fuel consumption per ton-kilometer. The influence of the error of forecast technical indicators on economic indicators was assessed. It is shown that the optimality of economic indicators depends on the characteristics of the waterway so that a vessel that is optimal on one waterway may not be optimal on another. It has also been established that optimal technical parameters do not always agree with optimal economic ones.

ENERGY POTENTIAL OF DISTRIBUTED GENERATION AT POWERFUL BOILER HOUSES OF UKRAINE IN CONDITIONS OF MILITARY AGGRESSION

The Integrated Power System of Ukraine (IPS) is purposefully destroyed by the russian aggressor and suffers significant damage, which has caused long-term emergency interruptions in the power supply of critical infrastructure facilities, enterprises and the population. One of the ways to increase the stability of the IPS of Ukraine is to increase the share of distributed generation in its composition. The conceptual idea of solving this problem is the transformation of existing critical infrastructure objects from electricity consumers into electricity producers/suppliers who will provide their own electricity needs and the needs of other electricity consumers. Boiler houses of centralized heat supply systems (DHS) of Ukrainian cities with their existing electric power and fuel infrastructure can become such objects in wartime conditions. The author proposes the widespread introduction of energy-efficient cogeneration gas-piston units (CGPUs) at boiler houses of the DHS. The container-type CGPU is a typical mass-produced equipment that is manufactured in Ukraine and abroad and has a short implementation period (up to 5 days from the date of delivery to the industrial site of the boiler house). The high maneuverability of the CGPUs allows for an increase the flexibility of the IPS of Ukraine. The article presents the main technical and economic indicators of distributed generation with the use of CGPU at existing boiler houses for further national planning of the development of distributed generation during the martial law and post-war reconstruction of Ukraine. The application of CGPU at boiler houses of the DHS of Ukrainian cities has the potential to provide additional distributed generation with a total capacity of up to 2365.7 MW, in particular, 1224.8 MW at 158 boiler houses with a thermal capacity of 100 Gcal/h and more (612 CGPUs with an average unit electric power of 2.0 MW each); 561.9 MW at 409 boiler houses from 20 to 100 Gcal/h (803 CGPUs of 0.7 MW each); 561.9 MW in 2101 boiler houses from 3 to 20 Gcal/h (4136 CGPUs of 0.14 MW each). Under the conditions of 8 hours a day of use of the installed capacity of CGPUs, 567.8 thousand MWh of electricity will be produced every month at the boiler houses of the DHS (taking into account the consumption of the boiler houses for their own needs and release to the network to other consumers) and 136–142 million cubic meters of natural gas will be consumed. According to the average specific cost of modular CGPU (turnkey supply) from 800 to 950 USD/kW of installed electric capacity, capital costs for implementing the CGPUs with a total capacity of 2,365.7 MW will amount to USD 1.89–2.25 billion. Bibl. 31, Fig. 3.

TECHNOLOGY AND EQUIPMENT FOR OBTAINING OF UNIVERSAL HYDROCARBON FUEL

An analysis of methods and equipment for obtaining hydrocarbon fuel was carried out. The purpose of the work is the development of technology and heat-mass exchange equipment for the production of universal hydrocarbon fuel, suitable for use in all types of thermal power plants. Heat and mass exchange equipment has been developed, with the help of which it is possible to obtain universal hydrocarbon fuel with a maximum size of coal particles no larger than 3 microns. The use of water-coal suspensions as an energy fuel allows to increase the technical, economic and environmental indicators of power plants. Thus, during the transition to coal-hydrogen fuel, it was possible to increase its thermal capacity from 13.2 to 21.1 MW when 70 % of coal was replaced by coal-hydrogen fuel. Currently, there are various methods and equipment for the preparation of hydrocarbon fuel. However, they all have certain drawbacks. The main disadvantages of the methods of obtaining hydro-coal fuels are multi-stage, metal-intensive, high energy costs, carrying out physico-mechanical and expensive chemical demineralization of coal, etc. Also, the disadvantages are the limited frequency of hydraulic shock pulses during processing in one cycle, which is insufficient when preparing highly concentrated suspensions that meet the requirements for liquid composite fuel intended for direct combustion. In order to improve the quality of the resulting suspension due to more effective grinding and increasing the amount of its solid component, heat and mass exchange equipment was developed, which can be used to obtain universal hydrocarbon fuel for burning in boilers, furnaces, various thermal power equipment, internal combustion engines, etc. As heat and mass exchange equipment, rotary pulsation devices operating according to the method of discrete-pulse energy input were used. Bibl. 18, Fig. 4, Tab. 3.

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.

Modeling of transportation and technological processes in automated systems

The purpose of the study is to create a problem-oriented model of transport and technological processes in automated systems. To achieve this goal, it is necessary to develop methodological aspects of creating an automated modeling system. The content of the basic principles of problem-oriented modeling in the study of an integrated automated system for managing stocks and transport and technological processes is revealed in the paper. At the same time, methodological aspects of creating automated modeling systems, namely, a conceptual mathematical model for the analysis and standardization of technical and economic indicators are considered. The functioning of logistics systems, namely, system modeling (supply — transport) is also considered. The problems of interaction and relationships, optimization of resource flows, and increasing the efficiency of management decisions making have been solved. It is shown that modeling resource flows involves adjustments through iterative redistribution of interregional and intraregional supplies among suppliers and consumers. Combining product items into one delivery leads to savings in stocks formation costs by reducing the number of orders. To create effective automated control systems, it is necessary to study in detail the primary object — the logistics and technical support system itself. The creation of applied mathematical and software is a promising direction for solving this problem and developing it as an automated modeling system. In this regard, when creating such automated control systems, it is most advisable to use a model-oriented approach, which is based on the principles of system analysis, standardization, classification and modularity. Thus, it is shown that the use of the problem-oriented approach to creating an automated modeling system makes it possible to reduce the labor intensity of its development and implementation, to provide the opportunity to unify algorithms for solving a problem, but also to apply standard methods for processing experimental results.

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.

Research on Building Power Quality Assessment and Control Technology Based on Online Monitoring

This study is to improve the assessment accuracy and control efficiency of building power quality by adopting online monitoring and integrating multiple technical indicators and control methods. A whole set of power quality technical index system is developed in the study, involving voltage deviation, harmonic voltage/current limit, and so on. The power quality indicators are quantitatively evaluated through standardization and weight determination. The assessment results show that the measured power quality indicators vary within the interval of 0 to 9, which verifies the effectiveness of the assessment system. In terms of control technology, this paper discusses in depth the static reactive power compensation (SVC) device and its role in improving building power quality, and confirms its efficacy in optimizing power quality through simulation experiments. This work not only successfully evaluates the building power quality, but also proposes practical control strategies, which provide theoretical and practical guidance for the stable operation of power systems and the further improvement of power quality.

Playing style of women and men elite table tennis players

ABSTRACT Background: Match and notational analysis are well-recognised methods to collect important information about technical-tactical indicators in racket sports (shot, footwork, ball placement, and outcome). The purpose of this study was to analyse the principal indicators, with a special focus on comparing the playing style of women and men high-level players. Methods: twenty matches played by 40 women, and twenty matches played by 40 men were analysed in slow motion by an expert coach. All the players were in the top 111(W) and 120 (M) positions of the ITTF world ranking; Results: women play more shots/rallies and shots/games compared to men. The most used shots are Top forehand and Serve for both groups. Women also prefer to play backhand block and drive. Conversely, men prefer to counterattack with forehand Top C Top. One step is often used by women, and they also prefer to hit the ball without performing any step. Conversely, men perform crossover and pivot footwork. Serves are generally directed to close-to-the-net areas, and the following shots are typically directed to the backhand corner during the rallies. Conclusions: Women showed a specific distribution of technical indicators with respect to men players, suggesting sex-specific training sessions.

Perovskite Quantum Dots for the Next‐Generation Displays: Progress and Prospect

AbstractThe “Nobel Prize in Chemistry 2023” is awarded to Moungi G. Bawendi, Louis E. Brus, and Alexey I. Yekimov for discovering and synthesizing Quantum Dots (QDs). Colloidal QDs possess fascinating size‐, morphological‐, composition‐, and assembly‐tunable electronic and optical properties, which makes them star materials for various optoelectronic applications, especially as luminescent materials for next‐generation wide color gamut ultra‐high‐definition displays. Perovskite QDs (PQDs) have gained widespread attention in recent years. In less than ten years, research on perovskite‐related materials and devices has basically been perfected in terms of quantum yield and external quantum efficiency (EQE). However, on the eve of its industrial application, some key technical indicators and technical processes need to be met and resolved. The development and transformation of QD materials and then focuses on the progress of luminescence linewidth and EQE of the PQD light‐emitting diode. Finally, several application avenues are reviewed for PQDs, and some challenges and opportunities in the field are proposed.

Developing Hybrid Deep Learning Models for Stock Price Prediction Using Enhanced Twitter Sentiment Score and Technical Indicators

Developing Hybrid Deep Learning Models for Stock Price Prediction Using Enhanced Twitter Sentiment Score and Technical Indicators

Magnetic Field Analysis and Development of Disk Axial–Radial Hybrid Excitation Generator for Range Extenders in Extended-Range Electric Vehicles

Magnetic Field Analysis and Development of Disk Axial–Radial Hybrid Excitation Generator for Range Extenders in Extended-Range Electric Vehicles

Harnessing Machine Learning for Predicting Cryptocurrency Returns

The study investigates the predictability of both the individual and basket of 10 major cryptocurrencies’ daily price changes between 2017 and 2023 by employing various machine learning classification algorithms such as random forests, k-nearest neighbour, decision trees, logistic regression, and Bernoulli naïve Bayes. These models utilize 15 different features based on historical price data and technical indicators as input features. The study estimates find logistic regression as superior over other models under consideration in predicting cryptocurrency daily returns. Overall, the study finds that on an average machine learning classification algorithms predictive accuracies have surpassed 50% when applied to daily frequencies on the basket of 10 major cryptocurrencies.

The prediction of Bitcoin price through gold price using long short-term memory model

<span>The majority of research on predicting the price of Bitcoin employs technical methods to enhance long short-term memory models' effectiveness. Although some studies employ different machine learning techniques, such as economic or technical indicators, their precision is inadequate. Thus, this research aims to introduce a model that predicts the price of Bitcoin by utilizing the long short-term memory (LSTM) technique and incorporating gold's economic and technical data as features. The research collected gold and Bitcoin price data from FinanceDataReader for around seven years, from January 1, 2016, to January 22, 2023, consisting of six categories: date, open, high, low, close, volume, and change (based on dollars). The normalized closing price data was trained for 50 epochs, resulting in the loss value reaching close to zero. The model's accuracy was measured by mean squared error, resulting in a score of 0.0004. This study's importance is two-fold: firstly, it can provide cryptocurrency-related businesses with more accurate predictions and improved risk management indicators. Secondly, incorporating economic metrics can address the limitations of overfitting and a single model's poor performance.</span>

Estimation of production rate distribution by overhaul periods of the wells

This article examines the issues of the distribution of production capacity over the actual intervals of the overhaul period of the wells under consideration. Production rates and overhaul intervals of wells are the main technical and economic indicators which are of great importance for increasing production and improving the efficiency of well stock operation in general, especially at the late stages of field development. On the basis of actual field data ranges of oil and water flow rates and values of overhaul period of wells of Balakhany-Sabunchu-Ramana field have been analyzed. Plots of distribution of flow rates and relative total production for all actual intervals of overhaul period were plotted. Mathematical models of distribution of production indicators by overhaul of wells have been obtained with the help of computer program. The results of the research allow to reveal regularities and to estimate production capacity of the considered well stock under the given operating conditions.

Dynamic design of technical and economic indicators of open-pit mining with the help of neuron network technologies

Dynamic design of technical and economic indicators of open-pit mining with the help of neuron network technologies

Refined Algorithm for Forecasting Technical Condition Index of a Transformer for Automating Maintenance and Repair Planning

Refined Algorithm for Forecasting Technical Condition Index of a Transformer for Automating Maintenance and Repair Planning