The main purpose of this research is developing methods and models of decision-making to assess the stock market state, and predict the possible changes in the RTS index value. This article shows that the analytical models for assessing the stock market state do not give reliable results. The absence of the reliable estimates associated with the high degree of uncertainty, random, nonlinear and non-stationary process with a significant degree of aftereffect. In this paper, to formalize the securities market parameters it’s proposed the fuzzy sets method. To assess the stock market current state and make decisions the fuzzy situational analysis model (situational model) is applied. The analytical prediction results of the stock market and graph of the RTS index expected return changes in 2014-2016 are showed. The model of calculating the fuzzy inference rules truth degree to predict the RTS index is developed. The market parameters linguistic definition is given and the expert’s rules construction to predict the RTS index growth is shown. The program in Matlab environment is designed to perform research. The study result showed that the model allows for the RTS index prediction in the condition of incomplete initial data with a confidence level about 90%.
Forecasting Stocks with Multivariate Time Series Models.inventionjournals
This work seeks to forecast stocks of the Nigerian banking sector using probability multivariate time series models. The study involved the stocks from six different banks that were found to be analytically interrelated. Stationarity of the six series were obtained by differencing. Model selection criteria were employed and the best fitted model was selected to be a vector autoregressive model of order 1. The model was subjected to diagnostic checks and was found to be adequate. Consequently, forecasts of stocks were generated for the next two years.
Patience may be virtue, but impatience can frequently be profitable.
The attempt to determine future share price movement and its reliability by references to historical data.
Forecasting Stocks with Multivariate Time Series Models.inventionjournals
This work seeks to forecast stocks of the Nigerian banking sector using probability multivariate time series models. The study involved the stocks from six different banks that were found to be analytically interrelated. Stationarity of the six series were obtained by differencing. Model selection criteria were employed and the best fitted model was selected to be a vector autoregressive model of order 1. The model was subjected to diagnostic checks and was found to be adequate. Consequently, forecasts of stocks were generated for the next two years.
Patience may be virtue, but impatience can frequently be profitable.
The attempt to determine future share price movement and its reliability by references to historical data.
METHODOLOGICAL APPROACHES TO ANALYSIS OF CORRELATION RELATIONSHIPS BASED ON E...IAEME Publication
Clear economic activity management is one of the most essential aspects of any
company’s stable financial standing under contemporary conditions. Economic
activity management at a company is impossible without an in-depth and detailed
study of all the processes related to it. Analytics is of paramount importance for
managing a company’s settlement system.
This article is devoted to analyzing of correlations/correlation relationships
analysis methods based on economic statistics. The correlation analysis is used to
measure the strength of a relationship between variables and to evaluate the factors
that affect the result attribute the most, which distinguishes it from regression analysis
used to select the relationship form, model type, to determine rated values of the result
attribute. The regression and correlation analysis methods are used holistically. Pair
correlation aimed at studying correlations of a factor attribute and a result attribute
can be regarded as the most developed theoretically and used in practice. Such study
is called single-factor correlation and regression analysis used as the basis for
studying multi-factor stochastic relationships. The article also examines the approach
Applications of Artificial Neural Network in Forecasting of Stock Market Indexpaperpublications3
Abstract: Prediction in any field is a challenging and unnerving process. Stock market is a promising financial investment that can generate great wealth. However, under the impact of Globalization Stock Market Prediction (SMP) accuracy has become more challenging and rewarding for the researchers and participants in the stock market. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. ANN modeling of stock prices of selected stocks under NSE is attempted to predict the next day’s price. The network developed consists of one input layer, hidden layer and output layer with four, nine and one nodes respectively. The input being the closing price of the previous four days and output being the price for the next day. In the first section the adaptability of neural networks in stock market prediction is discussed, in the second section we discuss the traditional methods that were being used earlier for stock market prediction, in the third section we discuss the justification for using neural networks and how it is better over traditional methods, in the fourth section we discuss the basics of neural networks, section five gives an overview of data and methodology being used, in section six we have discussed the various forecasting errors methods to calculate the error, in section seven we have presented our results. The aim of this paper is to provide an overview of the application of artificial neural network in stock market prediction.
Weak Form of Efficient Market Hypothesis – Evidence from PakistanMuhammadFaizanAfridi
presentation on Weak Form of Efficient Market Hypothesis – Evidence from Pakistan presented by Muhammad Faizan Afridi & Sahibzada Muhammad Junaid.
Institute of Management Sciences
MBA(1.5)
The presentation explains that overall fundamental analysis in a company like economic, industry, and company analysis its gives a brief explanation about that.
A REVIEW OF STOCK TREND PREDICTION WITH COMBINATION OF EFFECTIVE MULTI TECHNI...IJMIT JOURNAL
It is important for investors to understand stock trends and market conditions before trading stocks. Both these capabilities are very important for an investor in order to obtain maximized profit and minimized losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends and market conditions. Technical analysis helps to understand stock prices behavior with regards to past trends, the signals given by indicators and the major turning points of the market price. This paper reviews the stock trend predictions with a combination of the effective multi technical indicator strategy to increase investment performance by taking into account the global performance and the proposed combination of effective multi technical indicator strategy model.
METHODOLOGICAL APPROACHES TO ANALYSIS OF CORRELATION RELATIONSHIPS BASED ON E...IAEME Publication
Clear economic activity management is one of the most essential aspects of any
company’s stable financial standing under contemporary conditions. Economic
activity management at a company is impossible without an in-depth and detailed
study of all the processes related to it. Analytics is of paramount importance for
managing a company’s settlement system.
This article is devoted to analyzing of correlations/correlation relationships
analysis methods based on economic statistics. The correlation analysis is used to
measure the strength of a relationship between variables and to evaluate the factors
that affect the result attribute the most, which distinguishes it from regression analysis
used to select the relationship form, model type, to determine rated values of the result
attribute. The regression and correlation analysis methods are used holistically. Pair
correlation aimed at studying correlations of a factor attribute and a result attribute
can be regarded as the most developed theoretically and used in practice. Such study
is called single-factor correlation and regression analysis used as the basis for
studying multi-factor stochastic relationships. The article also examines the approach
Applications of Artificial Neural Network in Forecasting of Stock Market Indexpaperpublications3
Abstract: Prediction in any field is a challenging and unnerving process. Stock market is a promising financial investment that can generate great wealth. However, under the impact of Globalization Stock Market Prediction (SMP) accuracy has become more challenging and rewarding for the researchers and participants in the stock market. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. ANN modeling of stock prices of selected stocks under NSE is attempted to predict the next day’s price. The network developed consists of one input layer, hidden layer and output layer with four, nine and one nodes respectively. The input being the closing price of the previous four days and output being the price for the next day. In the first section the adaptability of neural networks in stock market prediction is discussed, in the second section we discuss the traditional methods that were being used earlier for stock market prediction, in the third section we discuss the justification for using neural networks and how it is better over traditional methods, in the fourth section we discuss the basics of neural networks, section five gives an overview of data and methodology being used, in section six we have discussed the various forecasting errors methods to calculate the error, in section seven we have presented our results. The aim of this paper is to provide an overview of the application of artificial neural network in stock market prediction.
Weak Form of Efficient Market Hypothesis – Evidence from PakistanMuhammadFaizanAfridi
presentation on Weak Form of Efficient Market Hypothesis – Evidence from Pakistan presented by Muhammad Faizan Afridi & Sahibzada Muhammad Junaid.
Institute of Management Sciences
MBA(1.5)
The presentation explains that overall fundamental analysis in a company like economic, industry, and company analysis its gives a brief explanation about that.
A REVIEW OF STOCK TREND PREDICTION WITH COMBINATION OF EFFECTIVE MULTI TECHNI...IJMIT JOURNAL
It is important for investors to understand stock trends and market conditions before trading stocks. Both these capabilities are very important for an investor in order to obtain maximized profit and minimized losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends and market conditions. Technical analysis helps to understand stock prices behavior with regards to past trends, the signals given by indicators and the major turning points of the market price. This paper reviews the stock trend predictions with a combination of the effective multi technical indicator strategy to increase investment performance by taking into account the global performance and the proposed combination of effective multi technical indicator strategy model.
A REVIEW OF STOCK TREND PREDICTION WITH COMBINATION OF EFFECTIVE MULTI TECHNI...IJMIT JOURNAL
It is important for investors to understand stock trends and market conditions before trading stocks. Both
these capabilities are very important for an investor in order to obtain maximized profit and minimized
losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends
and market conditions. Technical analysis helps to understand stock prices behavior with regards to past
trends, the signals given by indicators and the major turning points of the market price. This paper reviews
the stock trend predictions with a combination of the effective multi technical indicator strategy to increase
investment performance by taking into account the global performance and the proposed combination of
effective multi technical indicator strategy model.
A REVIEW OF STOCK TREND PREDICTION WITH COMBINATION OF EFFECTIVE MULTI TECHNI...IJMIT JOURNAL
It is important for investors to understand stock trends and market conditions before trading stocks. Both these capabilities are very important for an investor in order to obtain maximized profit and minimized losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends and market conditions. Technical analysis helps to understand stock prices behavior with regards to past trends, the signals given by indicators and the major turning points of the market price. This paper reviews
the stock trend predictions with a combination of the effective multi technical indicator strategy to increase investment performance by taking into account the global performance and the proposed combination of effective multi technical indicator strategy model.
A REVIEW OF STOCK TREND PREDICTION WITH COMBINATION OF EFFECTIVE MULTI TECHNI...IJMIT JOURNAL
It is important for investors to understand stock trends and market conditions before trading stocks. Both
these capabilities are very important for an investor in order to obtain maximized profit and minimized
losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends
and market conditions. Technical analysis helps to understand stock prices behavior with regards to past
trends, the signals given by indicators and the major turning points of the market price. This paper reviews
the stock trend predictions with a combination of the effective multi technical indicator strategy to increase
investment performance by taking into account the global performance and the proposed combination of
effective multi technical indicator strategy model.
Macroeconomic and Institutional Determinants of Capital Market Performance in...Fakir Tajul Islam
This paper examines the institutional and macroeconomic
determinants of stock market performance using data in the last 20 years starting from 1995 to 2015. In this
paper, Gross Domestic Product (GDP), Consumer Price Index (CPI), inflation rate and Foreign Direct Investment
(FDI) inflows were used as the proxy of macroeconomic determinants, whereas market capitalization, total
issued capital and market turnover of Dhaka Stock Exchange were the proxy of institutional determinants of
capital market performance.
Enhanced Decision Support System for Portfolio Management Using Financial Ind...ijbiss
In many cases, financial indicators are used for market analysis and to forecast the future of stock prices. Due to the high complexity of the stock market, determining which indicators should be used and the reliability of their outcomes have always been a challenge. In this article, a hybrid approach in the form of a decision support system is being introduced that offers the best suggestions in buying and selling stocks. This system will help an investor to identify the best portfolio of stocks using a series of financial indicators. These indices act as a model that forecast the future price of a stock by examining its activities and status in the past. Therefore, using a combination of the indices enables us to make decisions with more certainty. Proficiency of this system has been evaluated through the collection of data from the stock market in Iran from 2001 through 2011. The results show that the use of indices and their combination have led to the decision support system to produce suggestions with very high precisions.
ENHANCED DECISION SUPPORT SYSTEM FOR PORTFOLIO MANAGEMENT USING FINANCIAL IND...ijbiss
In many cases, financial indicators are used for market analysis and to forecast the future of stock prices.
Due to the high complexity of the stock market, determining which indicators should be used and the
reliability of their outcomes have always been a challenge. In this article, a hybrid approach in the form of
a decision support system is being introduced that offers the best suggestions in buying and selling stocks.
This system will help an investor to identify the best portfolio of stocks using a series of financial
indicators. These indices act as a model that forecast the future price of a stock by examining its activities
and status in the past. Therefore, using a combination of the indices enables us to make decisions with
more certainty. Proficiency of this system has been evaluated through the collection of data from the stock
market in Iran from 2001 through 2011. The results show that the use of indices and their combination
have led to the decision support system to produce suggestions with very high precisions.
Stock market prediction of Bangladesh using multivariate long short-term memo...IJECEIAES
The prediction of stock market trends is a challenging task due to its dynamic and volatile nature. Research has shown that predicting the stock market, especially in developing nations like Bangladesh, is challenging due to the presence of multiple external factors in addition to technical ones. To address this, this study proposed a novel dataset that includes not only technical stock market data from 2014 to 2021, but also external factors such as news sentiment and other economic indicators like inflation, gross domestic product (GDP), exchange rate, interest rate, and current balance. The goal is to provide a comprehensive view of the Dhaka Stock Exchange (DSE), the largest stock market in Bangladesh. The main objective of this study is to predict the trend of DSE by taking into account both technical stock market data and relevant external factors, and to compare the predictions made with and without using external factors. The study utilized a multivariate long short-term memory (LSTM) neural network for the stock market trend prediction. The experimental results showed that the use of external factors improved the accuracy of the LSTM-based stock market trend predictions by approximately 24%.
The focus of this research is to explain whether investors prefer technical or fundamental analysis to analyze their investment options and to analyze factors influencing the selection of that investment analysis method. The research uses questionnaire with 125 participants. Six independent variables used to explain the choice of investment analysis method, namely investor’s education, investor’s experience, information accessibility by the investor, investor's time the horizon, trading activity frequency, and investor’s perception toward the disclosure done by the corporation. The result showed that Indonesian investors prefer technical analysis. The influencing factors that significantly the selection of analysis method are investor’s experience and investor’s time horizon.
Forecasting real economic growth by using the information contents of financial asset prices is one of the main themes in financial studies in recent years. Based on the micro-level stock data from Shenzhen Stock Exchange Market, the paper constructs a cross-section volatility measure using sample stocks, investigates the impact of stock price volatility on economic growth, and forecasts economic growth with stock prices volatility of different firm size. The empirical results indicate that stock price volatility is a good indicator for forecasting economic growth. The results also show that volatility of both large and small firms can be useful in forecasting economic growth. In addition, volatility of small firms can better predict economic growth.
Similar to The Decision-making Model for the Stock Market under Uncertainty (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Cosmetic shop management system project report.pdfKamal Acharya
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Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
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Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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There are many studies to predict the stock market state by using the analytical models, however,
the famous models with the trends extrapolation methods for prediction of the stock market state [1-3] do not
give good results in conditions of uncertainty (market volatility) [4]. The stock market state prediction by
using the neural networks requires a long time interval with fixed changes that is also unacceptable, since the
stock market changes occur over small time intervals.
The decision-making models depends on the expert's knowledge can be applied [5-6] to assess the
stock market state. Formalization of the stock market parameters is performed by the fuzzy sets methods and
using linguistic variable [7-9]. The situational analysis model from the decision-making models is applied [5-
6], [10]. This model gives better results for the stock market current state assessment as compared to the
famous analytical models.
The prediction problem of the stock market state is actual and very important problem. This problem
is not solved in conditions of uncertainty; despite there are many studies in this field of research. Find a good
mathematical model is not possible. There is only one option to develop models based on the knowledge
activation from the specialists’ experts.
The importance of the stock market state assessment problem is defined by the fact that Russia is
part of the global financial market system; it has an international credit rating. The prediction problem of
price dynamics is particularly relevant for Russia at the moment. Now the Russian stock market is
developing well, therefore it is necessary to use models and program for information support for investments
planning. Investment planning depends on the market situations analysis.
2. PREDICTION CRITERIA FOR THE STOCK MARKET
There are four groups of prediction methods in the stock market: Technical prediction method [11];
fundamental prediction method [12]; methods based on the gambling approach; intelligent method for
prediction in the stock market [13].
The technical prediction methods use the technical indicators for analysis and prediction the stock
market state. However, during the experience it was showed that the technical analysis does not give
effective results [4]. Fundamental analysis is based on the prediction of the prices behaviour. When
performing fundamental analysis Expert appears. The expert must be specializing in the global economy.
Expert's experience applied to create intelligent methods for prediction in the stock market. In fact, the
intelligent methods are using the technical and fundamental analysis, although the experts can carry out it on
a subconscious level. The numerous experiences for experts allow making the right decisions.
The criteria analysis of technical and fundamental methods of prediction was performed and some
examples of their application were shown. First of all, let’s see try for predicting by using correlation analysis
(the work of scientists under the auspices of the World Bank and the IMF [14]); trend extrapolation methods:
the moving average method, exponential smoothing method, method of trend correlation parabolic [15]. The
most commonly used correlation analysis to evaluate the stochastic relations between the different global
stock markets. The correlation analysis methods are used most often, but these methods are ineffective in
non-stationary conditions and numerous uncontrolled disturbances in the securities market.
To describe the stock market current state, the following situations were introduced: stable;
transient; market growth; unstable; stagnation.
Each of the situations can be determined by applying the following criteria (characteristic factors):
The average change in prices of a stock set (index), for example, SP500, NASDAQ, RTS, MICEX and
the other;
The current transactions volume (trading volume indicators);
Volatility, for example, the VIX index or RTSVX for RTS;
The stock market capitalization (monetary volume);
The oil price.
These factors are based on the exchange trading data in the stock market and determined on
heuristic formulas.
The Russian Federation stock market used two major exchange indexes-MICEX [16] and RTSI
[17]. These indicators show the average trading for the most liquid shares of the MICEX-RTSI Russian stock
exchanges. Moreover, the RTS index today is the second most important indicator. The indices data shows
the current status of certain sectors of the stock market. For example, RTSI2 index shows the prices
dynamics of less liquid stock of the 2nd tier, and the index MICEX10 shows price movements T10- of the
most liquid shares.
Market volatility was determined based on the amplitude of the price fluctuations for the selected
period of time. Market volatility is indicator shows how much the price jumping and how active of these
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securities trading [18]. The volatility calculates in the market by using the ATR indicator, it is for selected
time of VIX volatility index and its Russian analogue RTSVX. The stock market capitalization is the amount
of capital, expressed in the form of income securities. Market capitalization is the total capitalization of
revenues generated by individual marketable securities [19].
3. THE PREDICTION ANALYSIS FOR STOCK MARKET IN RUSSIA
In [20] it’s presented the analysis of the stock market indices and attempted to use the technical
prediction method for the period 2014–2016, the prediction results were interesting.
In [20] it’s implemented the analysis of the stock market development in the period 2005–2013. To
construct the model, the following macro-economic factors are taken: the oil prices growth rate; the growth
rate of non-cash money supply; the GDP growth rate. These factors are correlated with the resulting index is
significantly different from zero, alleged in [20]. As a result, in [20] it’s developed a three-factor model [14]
with use the analysis of regression and correlation for prediction the RTS index returns value. If the model's
parameters are distributed randomly and their distribution is statistically stationary, then it can get a linear
three-factor model of observations [21].
Three-factor model parameters were calculated, the yield of the RTS index was determined by the
formula
y=ln(RTS1/RTS0)4, (1)
Where y - the yield of the RTS index; RTS1, RTS0 - the last values of RTS index the current and
the previous quarter respectively.
The oil price growth pace at an annual rate is determined by the formula
x1=ln(P1/P0)4, (2)
Where x1 - the oil price growth pace on an annualized basis; P1, P0-the closing price of Brent crude
oil (NYMEX) the current and previous quarter respectively.
The growth rate of non-cash money supply at an annual rate is calculated by the formula
x2=ln(DM1/DM0)4, (3)
Where x3 - GDP growth at an annual rate, TGDP1, TGDP0-GDP real growth in the current and
previous quarter respectively, as a GDP percentage in 1995, ty and tq-the number of working days per year
and quarter, respectively.
In [20] it’s carried out these studies in the period of 2005-2013, an explanation of the expected
growth in Russian fuel and energy companies’ revenue, the RTS index growth increases the investment
activity of institutional investors and corporations, the growth of the stock market. The graph of the expected
return for RTS index in 2014-2016 is shown in Figure. 1. In [20] it’s carried out verification of the Russian
stock market prediction.
Figure 1. The expected return of the RTS index in 2014–2016
But now, it became obvious in 2016 that the prediction result is incorrect by using the technical
analysis method. Therefore, it has to be considered to the intelligent analysis methods in the stock market.
4. THE PROPOSED RESEARCH MODELS
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4. 1. Situational Model for Assessment the Stock Market State
In [5] it’s used the situational model with fuzzy inference to study the stock market state. There are
many other works [5-6] are using this model and give good results to solve the different problems. Also there
are other famous methods to construct models for assessing the current state of the stock market [5-7], [10].
The assessment problem remains important, especially in the conditions of uncertainty and the instability of
the stock market parameters.
The assessment problem of the stock market state is closely connected with the prediction problem
of changes in the stock market parameters. The prediction is done at interval of time, but when the time
interval of prediction is big, than the prediction result may be less accurate.
The experts knowledge was applied to solve the identification problems of the market state and
predict its parameters. This is due to the fact that the results of stochastic forecasting methods are
unsatisfactory [4]. The situational model for assessment the stock market current state is applied. In this
model, the experts are given the Fuzzy reference situations to determine the stock market situations and their
corresponding solutions.
It can be applied a variant, when a sets of fuzzy reference situations }
~
,...,
~
,
~
{ **
2
*
1
*
RSSSS is
divided into subsets
* * * *
j i jS S ,S ×S = ,i, j =1,n,i j
thus, each subset corresponds to one
decision of stock market state: h1 - stable; h2 - transition; h3 - growth; h4 – unstable and h5 - stagnation. The
experts mapped each fuzzy reference situation to a particular state of the stock market.
The experts give the linguistic and fuzzy variables on the basic sets and define the characteristic
factors parameters for decision making problem. The characteristic factors parameters are measured and the
measurement results correspond to the values on basic sets Xi
n,1i . The measured values of
characteristic factors correspond to the membership degree values of fuzzy variables
j
i . Measurements
the stock market parameters and calculate the membership degree of fuzzy variables allow us to determine
the real fuzzy situation in the stock market.
The situational models are comparing the real fuzzy situation with references fuzzy situations to
determine the stock market state.
The formalization and implementation the situational models to assess the current state of the stock
market exists the detail in [5].
The situational model has disadvantages. The main drawback is the lack of adequate guarantees of
the experts to describe the references fuzzy situations, the drawback of classification model is that the experts
must do exhaustive search for all decision-making rules in all combinations of fuzzy variables, the term-set
of linguistic variables. Therefore, the composition model is applied to predict the change values (estimates)
of the stock market parameters; it is also called the model of calculating the truth degree of fuzzy inference
rules [22]. Let’s consider the application of this model for prediction of the RTS index values for a given
period of time (7 days). The value of "7 Days" is selected based on the recommendations of experts.
4.2. Model of Calculation the Truth Degree of Fuzzy Inference Rules for the RTS Index Prediction
As noted above, the classification model has a complex application [22], which can be avoided by
limiting the number of rules for decision-making. As in in [5] to describe the stock market parameters on the
verbal level applied linguistic variables (LP) to the term-set:
-1 – RTS index, which has a term-set Т(1)={
1
1 -Very Small;
2
1 -Small;
3
1 -Medium;
4
1 -Big;
5
1 -Very Big};
-2–The volume of current transactions, which has a term-set Т(2)={
1
2 -Very Small;
2
2 -Small;
3
2 -Medium;
4
2 -Big;
5
2 -Very Big};
-3–Volatility in term-set of Т(3)={
1
3 -Very Low;
2
3 -Low;
3
3 -Medium;
4
3 -High;
5
3 -Very High};
-4 – Capitalization, which has a term-set Т(4)={
1
4 -Very Low;
2
4 -Low;
3
4 -Medium;
4
4 -High;
5
4 -Very High};
-5–The price of oil, which has a term-set Т(5)={
1
5 -Low;
2
5 -Medium;
3
5 -High;
4
5 -Very
High};
Stock market defined states: h1-stable; h2-transition; h3-growth; h4-unstable; h5–stagnation.
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All the rules (situations) to predict the RTS index by using the classification model is shown in
Table. 1.
Table 1. All the Rules to Predict the RTS Index by using the Classification Model
Rules
RTS
index
Volume of current
transactions
Volatility Capitalization Price of oil
The current
state
Forecast RTS
1
1
1 5
2 5
3 5
4 1
5 h4 drop
2
1
1 5
2 5
3 5
4 2
5 h4 drop
…… …… …… …… …… …… …… ……
12499
5
1 5
2 1
3 5
4 4
5 h1 strong growth
12500
5
1 5
2 1
3 5
4 3
5 h1 strong growth
As shown in Table.1, If the first, second, third and fourth of linguistic variables have five fuzzy
variables, and the 5th of linguistic variables has four fuzzy variables, then the total number of rules for the
classification model will equal 55554=12500.
Obviously, if every rule took from the expert 20 seconds, then the total time to fill the table. 1 will
be 69.4 hours. This number is unacceptable to apply this approach to predict the state of the stock market
parameters. When the expert works an 8-hour every day, then 69.4 hours will be 8.7 days, which it is more
than a week. During this time, the situation of the stock market will change and no longer needed to such
prediction.
Therefore, the model of calculating the truth degrees of inference fuzzy rules allow avoiding the
disadvantages of classification model [22] related to the increase in the number of rules.
For example, the results of analysis the stock market situation for prediction the change of RTS
index the expert only give the RTS index growth rules and uncertainties, which are shown in the Table. 2.
Any other rules will be drop for the RTS index.
As shown in Table. 2 the number of rules is 118. Therefore, the expert drawing up the rules for 20
seconds, it will take for this work 39.3 minutes. It is an acceptable time to predict.
Table 2. The Experts Rules for Prediction the RTS Index Growth
Rules
RTS
index
Volume of current
transactions
Volatility Capitalization
price of
oil
The current
state
Prediction RTS
1
5
1 5
2 1
3 5
4 4
5 h1 strong growth
2
5
1 5
2 1
3 5
4 3
5 h1 strong growth
…. …. …. …. …. …. …. ….
22
5
1 5
2 2
3 5
4 4
5 h1 growth
23
5
1 5
2 2
3 4
4 4
5 h3 growth
…. …. …. …. …. …. …. ….
117
4
1 3
2 2
3 5
4 3
5 h2 weak growth
118
4
1 3
2 2
3 4
4 2
5 h3 uncertainty
5. INFORMATION SUPPORT
The program in Matlab was developed to predict the changes of the RTS index in the stock market.
The algorithm of the program is shown in Figure. 2. The difference from the working results [5] is presence
the composition model (model calculation of the truth degree of fuzzy inference rules) in the algorithm for
prediction the RTS index value.
The algorithm show the identifying process of the stock market state: measuring characteristic
factors; fuzzification; inference; defuzzification; the current state of stock market; the prediction of RTS
index.
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Figure 2. The program algorithm
6. RESULTS AND DISCUSSION
To perform the study the program is used. The program is shown in Figure. 3.
Figure 3. The main interface of program
To assess the stock market current state in the Russian Federation, it is identified five linguistic
variables with their term-sets, references fuzzy situations of stock market, the decision-making rules of the
stock market state as in [5].
The user enters the characteristic factors parameters to predict the RTS index: the RTS index values,
transactions volume, the volatility, capitalization, oil prices. Figure 4 is shown the result of the input the
characteristic factors values.
Stock market
Fuzzification
References fuzzy situations
Real fuzzy situations
Rule base
The stock market
current state
Situational model
Composition model
Prediction of the RTS index
Rule base
Start
End
RTS Index
The volume of current
transactions
Volatility
Capitalization
The oil price
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Figure 4. Specific the characteristic factors values
Firstly, the program generates the stock market current state and then predicts the RTS index as
shown in Figure. 5.
Figure 5. The current status of the stock market and prediction for the RTS index
As a test, the prediction for the RTS index of stock market from 05.2014 to 04.2016 is performed.
The prediction results are shown in Table 3.
Table 3. The Study Results
Date
RTS
index
%
Volume of current
transactions
(million. $)
Volatility
Capitalization
(billion. $)
Price of
oil
Prediction
RTS
5.2014 12,12 39681 25,45 728,82 109,41 Growth
6.2014 5,43 39985 30,9 758,82 112,36 Strong growth
7.2014 -10,74 26221 36,39 707,71 106,2 Drop
8.2014 -2,39 23570 35,24 686,11 103,19 Drop
9.2014 -5,59 2821
4 33,26 656,76 94,67 Drop
10.2014 -2,87 25270 31,94 575,61 85,86 Drop
11.2014 -10,74 21215 40,21 556,52 70,15 Drop
12.2014 -18,84 26800 60,4 439,29 57,33 Drop
2.2015 21,60 26240 46,44 476,69 62,58 Growth
3.2015 -1,81 33700 40.78 465,12 55,11 Uncertainty
4.2015 16,91 30216 32,4 532,41 66,78 Uncertainty
5.2015 -5,88 29200 35,42 564,71 65,56 Drop
6.2015 -2,98 31200 33,83 518,18 63,59 Drop
7.2015 -8,63 28056 30,53 510,71 52,21 Drop
8.2015 -2,94 29200 37,68 455,38 54,15 Drop
9.2015 -5,26 31250 37,07 439,09 48,37 Uncertainty
10.2015 7,07 34200 34,24 473,02 49,56 Strong growth
11.2015 0,18 31200 38,53 465,25 44,61 Uncertainty
12.2015 -10,63 27170 34,23 418,58 37,28 Drop
2.2016 3,15 36180 43,63 393,51 35,97 Growth
3.2016 13,97 32200 34,02 461,72 39,60 Strong growth
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As shown in Table. 3 the situational model determines the stock market current state, then the model
of calculating the truth degree of inference fuzzy rules predicts the changes of the RTS index based on expert
rules which are given in the table. 2. For example, to predict the RTS index for month 06.2014 the user enters
the characteristic-factors parameters for the month 05.2014: the RTS index values, transactions volume, the
volatility, capitalization, oil prices, and then the program determines the parameters of the fuzzy real situation
as follows:
={<<0/VerySmall>, <0/Small>, <0/Medium>, <0,91/Big>, <1/VeryBig>/RTSindex>,
<<0/VerySmall>, <0/Small>, <0/Medium>, <0,75/Big>, <0,94/VeryBig>/ The volume of current
transactions >, <<0,85/VeryLow >, <0,95/Low>, <0/Medium>, <0/High>, <0/VeryHigh>/ Volatility >,
<<0/VeryLow>, <0/Low>, <0/Medium>, <0/High>, <1/VeryHigh>/ Capitalization >, <<0/Low>,
<0/Medium>, <0/High>, <1/VeryHigh>/ The price of oil >}.
For assessing the stock market current state in the Russian Federation, the five references fuzzy
situations of stock market, decision-making rules of the stock market state are determined in [5].
According to the situational model 𝑆̃ = 𝑆1
̃̇ , the decision was h1 - the current state is stable and
according to the model of calculation of the truth degree of fuzzy inference rules the prediction of the RTS
index will increase.
The prediction results using this model are shown in Table. 3.
In Table 3 the prediction for RTS index in 06.2014 was incorrect, the prediction result was a weak
growth, but in fact, it was noted a drop. Also, the prediction for RTS index in 02.2015 was wrong. The
prediction result from the model was growth the RTS index, in fact was the drop (Table 3 the highlight line).
The rest of the prediction results by using the proposed model were satisfactory, this corresponds with the
real events in the stock market.
Comparison the obtained results (RTS index) with the actual proved that the developed model for
prediction the changes of the RTS index has given good results compared with the famous models with the
trends extrapolation methods, which do not give good results in conditions of uncertainty (market volatility)
[4]. The reliability of the prediction results was approximately 90% of correct results.
7. CONCLUSION
In the article, the urgency of assessing the stock market current state to predict the index RTS of the
stock market is showed. The differences in this study: Firstly, the stock market current state is determined:
stable; transient; growth; unstable and stagnant by using the situational analysis model, then prediction for
the RTS index by using the model of calculation the truth degree of fuzzy inference rules.
According to the program results, it can be concluded that the proposed model and the program
allow predicting the RTS index of stock market in the condition of incomplete source data.
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