ABSTRACT: The machine learning model LSTM was applied and compared with the linear regression machine
learning model for the CSX index of the Cambodia Securities Exchange. The time span covered in this study was
from February 1, 2019, to October 12, 2023, totaling 1146 days. Out of these, 917 days were classified as Train
data, accounting for 80% of the total sample size, while 229 days were classified as Test data. Additionally, the
same techniques were used to forecast the tax revenues of the Royal Government of Cambodia, which were
collected by the General Department of Taxation. Despite using monthly data, the tax revenues were analyzed
over a longer period of 25 years, starting from January 1998 to August 2023. The Train data consisted of 247
months, equivalent to 80% of the total sample size, while the Test data accounted for 61 months, approximately
20% of the total sample size. The machine learning model, LSTM, demonstrated superior performance over the
linear regression machine learning model in predicting the daily movement of the CSX Index of the Cambodia
Securities Exchange, as evidenced by the root mean square error. The Test data revealed that the estimated root
mean square error of the linear regression machine learning was 64.78, while the LSTM machine learning
produced a lower error of 33.08. However, when applied to tax revenues data, the linear regression machine
learning outperformed the LSTM machine learning, with estimated root mean square errors of 219.31 and
1601.87, respectively.
KEYWORDS: CSX Index, Tax Revenues, Machine Learning, Linear Regression, LSTM
A novel hybrid deep learning model for price prediction IJECEIAES
Β
Price prediction has become a major task due to the explosive increase in the number of investors. The price prediction task has various types such as shares, stocks, foreign exchange instruments, and cryptocurrency. The literature includes several models for price prediction that can be classified based on the utilized methods into three main classes, namely, deep learning, machine learning, and statistical. In this context, we proposed several modelsβ architectures for price prediction. Among them, we proposed a hybrid one that incorporates long short-term memory (LSTM) and Convolution neural network (CNN) architectures, we called it CNN-LSTM. The proposed CNNLSTM model makes use of the characteristics of the convolution layers for extracting useful features embedded in the time series data and the ability of LSTM architecture to learn long-term dependencies. The proposed architectures are thoroughly evaluated and compared against state-of-the-art methods on three different types of financial product datasets for stocks, foreign exchange instruments, and cryptocurrency. The obtained results show that the proposed CNN-LSTM has the best performance on average for the utilized evaluation metrics. Moreover, the proposed deep learning models were dominant in comparison to the state-of-the-art methods, machine learning models, and statistical models.
This paper describes an architecture for predicting the price of cryptocurrencies for the next seven days
using the Adaptive Network Based Fuzzy Inference System (ANFIS). Historical data of cryptocurrencies
and indexes that are considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D), and
Ethereum Dominance (ETH.D) in
adaily timeframe. The methods used to teach the data are hybrid and
backpropagation algorithms, as well as grid partition, subtractive clustering
, and Fuzzy C-means
clustering (FCM) algorithms, which are used in data clustering. The architectural performance designed
in this paper has been compared with different inputs and neural network models in terms of statistical
evaluation criteria. Finally, the proposed method can predict the price of digital currencies in a short time
A novel hybrid deep learning model for price prediction IJECEIAES
Β
Price prediction has become a major task due to the explosive increase in the number of investors. The price prediction task has various types such as shares, stocks, foreign exchange instruments, and cryptocurrency. The literature includes several models for price prediction that can be classified based on the utilized methods into three main classes, namely, deep learning, machine learning, and statistical. In this context, we proposed several modelsβ architectures for price prediction. Among them, we proposed a hybrid one that incorporates long short-term memory (LSTM) and Convolution neural network (CNN) architectures, we called it CNN-LSTM. The proposed CNNLSTM model makes use of the characteristics of the convolution layers for extracting useful features embedded in the time series data and the ability of LSTM architecture to learn long-term dependencies. The proposed architectures are thoroughly evaluated and compared against state-of-the-art methods on three different types of financial product datasets for stocks, foreign exchange instruments, and cryptocurrency. The obtained results show that the proposed CNN-LSTM has the best performance on average for the utilized evaluation metrics. Moreover, the proposed deep learning models were dominant in comparison to the state-of-the-art methods, machine learning models, and statistical models.
This paper describes an architecture for predicting the price of cryptocurrencies for the next seven days
using the Adaptive Network Based Fuzzy Inference System (ANFIS). Historical data of cryptocurrencies
and indexes that are considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D), and
Ethereum Dominance (ETH.D) in
adaily timeframe. The methods used to teach the data are hybrid and
backpropagation algorithms, as well as grid partition, subtractive clustering
, and Fuzzy C-means
clustering (FCM) algorithms, which are used in data clustering. The architectural performance designed
in this paper has been compared with different inputs and neural network models in terms of statistical
evaluation criteria. Finally, the proposed method can predict the price of digital currencies in a short time
Forecasting stock price movement direction by machine learning algorithmIJECEIAES
Β
Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.
Module 04 ContentΒ· As a continuation to examining your policies, rIlonaThornburg83
Β
Module 04 Content
Β· As a continuation to examining your policies, review for procedures that may relate to them.
Β· In a 4-page paper, describe the procedures for each of the two compliance plans. NOTE: procedures are not the same thing as policies. Policies (Module 03) sets the parameters for decision-making, while procedures explains the "how". The procedures should be step-by-step instructions, and may include a checklist or process steps to follow.
Β· Break each procedure section into 2 pages each.
Β· Remember to support your procedures for each of two plans with a total of three research sources (1-2 per procedure), cited at the end in APA format.
Β· Write your procedures in a way that all employees will understand at a large medical facility where you are the Compliance Officer.
Β· Remember, you chose two compliance policy plans under the key compliance areas of Compliance Standards, High-Level Responsibility, Education, Communication, Monitoring/Auditing (for Safety), Enforcement/Discipline, and Response/Prevention. (Check them out if you forget! Remember, you may have written about different policies for the two different compliance plans.)
Β·
Β· Submit your completed assignment by following the directions linked below. Please check the Course Calendar for specific due dates.
Β·
Β· Save your assignment as a Microsoft Word document. (Mac users, please remember to append the ".docx" extension to the filename.) The name of the file should be your first initial and last name, followed by an underscore and the name of the assignment, and an underscore and the date. An example is shown below:
Β· Jstudent_exampleproblem_101504
1
8
Southeast Missouri State University
Department of Computer Science
Name of the Instructor: Dr. Reshmi Mitra
CS β 609 Graduate Project
December 2021
Team Member List
Jing Ma
Manoj Thapa
Nagendra Mokara
Contents
Machine learning on Stock Predition 0
Abstract 4
1 INTRODUCTION 4
1.1 LSTM Prediction 5
2 BACKGROUND AND RELATED WORK 6
2.1 Overview of the design 7
3 RESEARCH DESIGN AND METHODS 8
3.1 Stock Price Prediction Project using LSTM 8
3.2 Build the dashboard using plotly 9
4 EVALUTION, VALIDATION ANF KEYRESULTS 9
4.1 Screenshot of Result 10
5 CONCLUSIONS 11
References 12
Sound Classification using Deep Learning β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦...13
Abstract β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.14
6 INTRODUCTION β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.14
6.1 Keywords β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦15
7 COLLECTION OF DATA FROM THE DATASET β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..15
7.1 Dataset β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.15
7.2 Segregation of Data into Various Folders ..β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.15
8 WRITING CODE AND DATA MODELING/TRAININGβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦16
8.1 Design Diagram of the Projectβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.16
9 RESULT β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.17
10 CONCLUSION/FUTURE WORK β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..18
Machine learning on stock prediction
Jing Ma
December 2021
Southeast Missouri State University
Advisor: Dr Robert Lowe
Course: Researc ...
Artificial Intelligence Based Stock Market Prediction Model using Technical I...ijtsrd
Β
The stock market is highly volatile and complex in nature. However, notion of stock price predictability is typical, many researchers suggest that the Buy and Sell prices are predictable and investor can make above average profits using efficient Technical Analysis TA .Most of the earlier prediction models predict individual stocks and the results are mostly influenced by companyβs reputation, news, sentiments and other fundamental issues while stock indices are less affected by these issues. In this work, an effort is made to predict the Buy and Sell decisions of stocks, trends of stock by utilizing Stock Technical Indicators STIs As a part of prediction model the Long Short Term Memory LSTM , Support Virtual Machine SVM Artificial intelligence algorithms will be used with Stock Technical Indicators STIs. The project will be carried on National Stock Exchange NSE Stocks of India. Mr. Ketan Ashok Bagade | Yogini Bagade "Artificial Intelligence Based Stock Market Prediction Model using Technical Indicators" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd53854.pdf Paper URL: https://www.ijtsrd.com.com/management/other/53854/artificial-intelligence-based-stock-market-prediction-model-using-technical-indicators/mr-ketan-ashok-bagade
Efficient commodity price forecasting using long short-term memory modelIAESIJAI
Β
Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2 ), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
Prediction Of Cryptocurrency Prices Using Lstm, Svm And Polynomial Regression...ferisulianta.com
Β
The rapid development of information technology, especially the Internet, has facilitated users
with a quick and easy way to seek information. With these convenience offered by internet
services, many individuals who initially invested in gold and precious metals are now shifting
into digital investments in form of cryptocurrencies. However, investments in crypto coins are
filled with uncertainties and fluctuation in daily basis. This risk posed as significant challenges
for coin investors that could result in substantial investment losses. The uncertainty of the
value of these crypto coins is a critical issue in the field of coin investment. Forecasting, is one
of the methods used to predict the future value of these crypto coins. By utilizing the models of
Long Short Term Memory, Support Vector Machine, and Polynomial Regression algorithm for
forecasting, a performance comparison is conducted to determine which algorithm model is
most suitable for predicting crypto currency prices. The mean square error is employed as a
benchmark for the comparison. By applying those three constructed algorithm models, the
Support Vector Machine uses a linear kernel to produce the smallest mean square error
compared to the Long Short Term Memory and Polynomial Regression algorithm models, with a mean square error value of 0.02.
Keywords: Cryptocurrency, Forecasting, Long Short Term Memory, Mean Square Error,
Polynomial Regression, Support Vector Machine
The usage of Neural network s has determined a variegated area of packages in the present world. This has caused the
improvement of various fashions for economic markets and funding. This paper represents the idea the way to predict share
market fee the use of artificial Neural community with a given enter parameters of share marketplace. The proportion
marketplace is dynamic in nature approach to expect percentage fee could be very complex method by using trendy prediction
or computation method. Its predominant motive is that there is no linear relationship between market parameters and target last
price. Since there is no linear relationship between input patterns and corresponding output patterns, so use of neural network is
a desire of hobby for share market prediction.
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
Β
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASAβs turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...gerogepatton
Β
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASAβs turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
The effect of Institutional Ownership, Sales Growth and Profitability on Tax ...AJHSSR Journal
Β
ABSTRACT: This research aims to test, analyze and obtain empirical evidence about the influence of
institutional ownership, sales growth and profitability on tax avoidance. The object of this research is
manufacturing companies in the consumer goods industry sector listed on the Indonesia Stock Exchange (BEI)
in 2018-2022. This research used quantitative research methods and causal research design. The sampling
technique in this research used non-probability sampling with purposive sampling as the basis for determining
the sample so that a sample of 55 samples was obtained. The data used is secondary data obtained from the
official website of the Indonesia Stock Exchange (BEI) during the 2018-2022 period. The data analysis method
used was multiple linear regression analysis with several tests such as descriptive statistical tests, classical
assumption tests, and hypothesis testing using SPSS version 26 statistical software. The results showed that the
institutional ownership variable has no effect on tax avoidance, while the sales growth and profitability has a
negative and significant effect on tax avoidance.
KEYWORDS: Institutional Ownership, Sales Growth, Profitability, Tax Avoidance
MGA ESTRATEHIYA SA PAGTUTURO KAUGNAY SA PASALITANG PARTISIPASYON NG MGA MAG-A...AJHSSR Journal
Β
ABSTRAK: Ang mga estratehiya sa pagtuturo ay mahalagang kasangkapan sa paghahatid ng mabisang
pagtuturo sa loob ng silid. Tinukoy sa pag-aaral na ito ang antas ng kagustuhan ng mga mag-aaral sa pagsasadula,
pangkatang talakayan at paggawa ng mga koneksyon sa tunay na karanasan sa buhay bilang mga estratehiya sa
pagtuturo ng panitikan sa Filipino at pasalitang partisipasyon ng mga mag-aaral sa Baitang 7 ng Misamis
University Junior High School, Ozamiz City. Ang ginamit na disenyo sa pananaliksik na ito ay deskriptivcorrelational. Ang mga datos sa pag-aaral ay nagmula sa kabuuang populasyon na 120 na mag-aaral at tatlong
mga guro na tagamasid sa pasalitang partisipasyon ng mga mag-aaral. Ang Talatanungan sa Kagamitan sa
Pagtuturo ng Panitikan at Checklist batay sa Obserbasyon sa Pasalita na Partisipasyon ay ang instrumentong
ginamit sa pagkalap ng datos. Mean, standard deviation, Analysis of Variance at Pearson Product-Moment
Correlation Coefficient ang mga ginamit na estatistiko na sangkap. Inihayag sa naging resulta na ang tatlong piling
estratehiya sa pagtuturo ng panitikan sa Filipino ay may pinakamataas na antas ng kagustuhan ng mga mag-aaral.
Ang antas ng pakilahok ng mga mag-aaral sa paggamit ng tatlong estratehiya sa pagtuturo ng panitikan ay
pinakamataas na nagpapahiwatig na aktibong nakilahok ang mga mag-aaral sa mga gawain. Inihayag din na
walang makabuluhang kaibahan sa antas ng kagustuhan ng mga mag-aaral sa mga estratehiya sa pagtuturo ng
panitikan sa Filipino. Ito ay nangahulugan na gustong-gusto ng mga mag-aaral ang pagkakaroon ng mga
estratehiya sa pagtuturo. Walang makabuluhang kaugnayan ang kagustuhan sa mga estratehiya at antas ng
pakikilahok ng mga mag-aaral. Hindi nakaapekto sa kanilang pakikilahok ang anumang estratehiyang ginamit ng
guro.
KEYWORDS : estratehiya, karanasan, pagsasadula, pagtuturo, pangkatang talakayan
More Related Content
Similar to The Utilization of Machine Learning for The Prediction of CSX Index and Tax Revenues in Cambodia
Forecasting stock price movement direction by machine learning algorithmIJECEIAES
Β
Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.
Module 04 ContentΒ· As a continuation to examining your policies, rIlonaThornburg83
Β
Module 04 Content
Β· As a continuation to examining your policies, review for procedures that may relate to them.
Β· In a 4-page paper, describe the procedures for each of the two compliance plans. NOTE: procedures are not the same thing as policies. Policies (Module 03) sets the parameters for decision-making, while procedures explains the "how". The procedures should be step-by-step instructions, and may include a checklist or process steps to follow.
Β· Break each procedure section into 2 pages each.
Β· Remember to support your procedures for each of two plans with a total of three research sources (1-2 per procedure), cited at the end in APA format.
Β· Write your procedures in a way that all employees will understand at a large medical facility where you are the Compliance Officer.
Β· Remember, you chose two compliance policy plans under the key compliance areas of Compliance Standards, High-Level Responsibility, Education, Communication, Monitoring/Auditing (for Safety), Enforcement/Discipline, and Response/Prevention. (Check them out if you forget! Remember, you may have written about different policies for the two different compliance plans.)
Β·
Β· Submit your completed assignment by following the directions linked below. Please check the Course Calendar for specific due dates.
Β·
Β· Save your assignment as a Microsoft Word document. (Mac users, please remember to append the ".docx" extension to the filename.) The name of the file should be your first initial and last name, followed by an underscore and the name of the assignment, and an underscore and the date. An example is shown below:
Β· Jstudent_exampleproblem_101504
1
8
Southeast Missouri State University
Department of Computer Science
Name of the Instructor: Dr. Reshmi Mitra
CS β 609 Graduate Project
December 2021
Team Member List
Jing Ma
Manoj Thapa
Nagendra Mokara
Contents
Machine learning on Stock Predition 0
Abstract 4
1 INTRODUCTION 4
1.1 LSTM Prediction 5
2 BACKGROUND AND RELATED WORK 6
2.1 Overview of the design 7
3 RESEARCH DESIGN AND METHODS 8
3.1 Stock Price Prediction Project using LSTM 8
3.2 Build the dashboard using plotly 9
4 EVALUTION, VALIDATION ANF KEYRESULTS 9
4.1 Screenshot of Result 10
5 CONCLUSIONS 11
References 12
Sound Classification using Deep Learning β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦...13
Abstract β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.14
6 INTRODUCTION β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.14
6.1 Keywords β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦15
7 COLLECTION OF DATA FROM THE DATASET β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..15
7.1 Dataset β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.15
7.2 Segregation of Data into Various Folders ..β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.15
8 WRITING CODE AND DATA MODELING/TRAININGβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦16
8.1 Design Diagram of the Projectβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.16
9 RESULT β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.17
10 CONCLUSION/FUTURE WORK β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..18
Machine learning on stock prediction
Jing Ma
December 2021
Southeast Missouri State University
Advisor: Dr Robert Lowe
Course: Researc ...
Artificial Intelligence Based Stock Market Prediction Model using Technical I...ijtsrd
Β
The stock market is highly volatile and complex in nature. However, notion of stock price predictability is typical, many researchers suggest that the Buy and Sell prices are predictable and investor can make above average profits using efficient Technical Analysis TA .Most of the earlier prediction models predict individual stocks and the results are mostly influenced by companyβs reputation, news, sentiments and other fundamental issues while stock indices are less affected by these issues. In this work, an effort is made to predict the Buy and Sell decisions of stocks, trends of stock by utilizing Stock Technical Indicators STIs As a part of prediction model the Long Short Term Memory LSTM , Support Virtual Machine SVM Artificial intelligence algorithms will be used with Stock Technical Indicators STIs. The project will be carried on National Stock Exchange NSE Stocks of India. Mr. Ketan Ashok Bagade | Yogini Bagade "Artificial Intelligence Based Stock Market Prediction Model using Technical Indicators" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd53854.pdf Paper URL: https://www.ijtsrd.com.com/management/other/53854/artificial-intelligence-based-stock-market-prediction-model-using-technical-indicators/mr-ketan-ashok-bagade
Efficient commodity price forecasting using long short-term memory modelIAESIJAI
Β
Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2 ), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
Prediction Of Cryptocurrency Prices Using Lstm, Svm And Polynomial Regression...ferisulianta.com
Β
The rapid development of information technology, especially the Internet, has facilitated users
with a quick and easy way to seek information. With these convenience offered by internet
services, many individuals who initially invested in gold and precious metals are now shifting
into digital investments in form of cryptocurrencies. However, investments in crypto coins are
filled with uncertainties and fluctuation in daily basis. This risk posed as significant challenges
for coin investors that could result in substantial investment losses. The uncertainty of the
value of these crypto coins is a critical issue in the field of coin investment. Forecasting, is one
of the methods used to predict the future value of these crypto coins. By utilizing the models of
Long Short Term Memory, Support Vector Machine, and Polynomial Regression algorithm for
forecasting, a performance comparison is conducted to determine which algorithm model is
most suitable for predicting crypto currency prices. The mean square error is employed as a
benchmark for the comparison. By applying those three constructed algorithm models, the
Support Vector Machine uses a linear kernel to produce the smallest mean square error
compared to the Long Short Term Memory and Polynomial Regression algorithm models, with a mean square error value of 0.02.
Keywords: Cryptocurrency, Forecasting, Long Short Term Memory, Mean Square Error,
Polynomial Regression, Support Vector Machine
The usage of Neural network s has determined a variegated area of packages in the present world. This has caused the
improvement of various fashions for economic markets and funding. This paper represents the idea the way to predict share
market fee the use of artificial Neural community with a given enter parameters of share marketplace. The proportion
marketplace is dynamic in nature approach to expect percentage fee could be very complex method by using trendy prediction
or computation method. Its predominant motive is that there is no linear relationship between market parameters and target last
price. Since there is no linear relationship between input patterns and corresponding output patterns, so use of neural network is
a desire of hobby for share market prediction.
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
Β
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASAβs turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...gerogepatton
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In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASAβs turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
The effect of Institutional Ownership, Sales Growth and Profitability on Tax ...AJHSSR Journal
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ABSTRACT: This research aims to test, analyze and obtain empirical evidence about the influence of
institutional ownership, sales growth and profitability on tax avoidance. The object of this research is
manufacturing companies in the consumer goods industry sector listed on the Indonesia Stock Exchange (BEI)
in 2018-2022. This research used quantitative research methods and causal research design. The sampling
technique in this research used non-probability sampling with purposive sampling as the basis for determining
the sample so that a sample of 55 samples was obtained. The data used is secondary data obtained from the
official website of the Indonesia Stock Exchange (BEI) during the 2018-2022 period. The data analysis method
used was multiple linear regression analysis with several tests such as descriptive statistical tests, classical
assumption tests, and hypothesis testing using SPSS version 26 statistical software. The results showed that the
institutional ownership variable has no effect on tax avoidance, while the sales growth and profitability has a
negative and significant effect on tax avoidance.
KEYWORDS: Institutional Ownership, Sales Growth, Profitability, Tax Avoidance
MGA ESTRATEHIYA SA PAGTUTURO KAUGNAY SA PASALITANG PARTISIPASYON NG MGA MAG-A...AJHSSR Journal
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ABSTRAK: Ang mga estratehiya sa pagtuturo ay mahalagang kasangkapan sa paghahatid ng mabisang
pagtuturo sa loob ng silid. Tinukoy sa pag-aaral na ito ang antas ng kagustuhan ng mga mag-aaral sa pagsasadula,
pangkatang talakayan at paggawa ng mga koneksyon sa tunay na karanasan sa buhay bilang mga estratehiya sa
pagtuturo ng panitikan sa Filipino at pasalitang partisipasyon ng mga mag-aaral sa Baitang 7 ng Misamis
University Junior High School, Ozamiz City. Ang ginamit na disenyo sa pananaliksik na ito ay deskriptivcorrelational. Ang mga datos sa pag-aaral ay nagmula sa kabuuang populasyon na 120 na mag-aaral at tatlong
mga guro na tagamasid sa pasalitang partisipasyon ng mga mag-aaral. Ang Talatanungan sa Kagamitan sa
Pagtuturo ng Panitikan at Checklist batay sa Obserbasyon sa Pasalita na Partisipasyon ay ang instrumentong
ginamit sa pagkalap ng datos. Mean, standard deviation, Analysis of Variance at Pearson Product-Moment
Correlation Coefficient ang mga ginamit na estatistiko na sangkap. Inihayag sa naging resulta na ang tatlong piling
estratehiya sa pagtuturo ng panitikan sa Filipino ay may pinakamataas na antas ng kagustuhan ng mga mag-aaral.
Ang antas ng pakilahok ng mga mag-aaral sa paggamit ng tatlong estratehiya sa pagtuturo ng panitikan ay
pinakamataas na nagpapahiwatig na aktibong nakilahok ang mga mag-aaral sa mga gawain. Inihayag din na
walang makabuluhang kaibahan sa antas ng kagustuhan ng mga mag-aaral sa mga estratehiya sa pagtuturo ng
panitikan sa Filipino. Ito ay nangahulugan na gustong-gusto ng mga mag-aaral ang pagkakaroon ng mga
estratehiya sa pagtuturo. Walang makabuluhang kaugnayan ang kagustuhan sa mga estratehiya at antas ng
pakikilahok ng mga mag-aaral. Hindi nakaapekto sa kanilang pakikilahok ang anumang estratehiyang ginamit ng
guro.
KEYWORDS : estratehiya, karanasan, pagsasadula, pagtuturo, pangkatang talakayan
The Role of the Instruction of Reading Comprehension Strategies in Enhancing ...AJHSSR Journal
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ABSTRACT :Throughout my studies and teaching English in different language centers and higher studies
institutions, I have come to conclude that students consider Reading comprehension as a nightmare that
frightens them and hinders their language acquisition in the Moroccan EFL Context. This may cause them to
develop an internal psychological obstacle that grows as their lack of the necessary instruments or tools to
overcome are not equipped with. They become lost and unaware about or unfamiliar with the necessary reading
comprehension strategies that could help them to face the problem of misunderstanding or non-understanding
of English texts. Respectively, this article which is only one part of my whole study aims at showing the effect
of teaching reading strategies in enhancing the S1 studentsβ familiarity with reading strategies and raising their
frequency use. A sample of 283 University students in EFL context have been chosen randomly and have
attended the usual academic reading classes, yet only 76 are subject to this survey. 38 of them constitute the
experimental group who have attended the treatment regularly in one of the language centers and the other 38
participants are chosen randomly from the whole population to constitute the Control group. They all have
Psychosocial Factors and Deviant Behaviors of Children in Conflict with the L...AJHSSR Journal
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ABSTRACT:This study aims to determine the relationship between psychosocialfactors and deviant
behaviors among children in conflict with the law (CICL) inDavao Region. The researchers want to discover the
prevalent factors thatdrive these children to their behaviors. Further, the study sought to determinethe
manifestation of psychosocial factors in terms of life satisfaction, emotionalsupport, self-esteem, and personality
traits. The study's data came from N-83children in conflict with the law (CICL) at the Regional Rehabilitation
Center forYouth (RRCY) in Bago Oshiro, Davao City; all respondents are male. This studyused a total
enumeration sampling technique due to the relatively smallpopulation size. The researchers adapted the
Psychosocial surveyquestionnaires by Zabriskie & Ward (2013) and by John and Srivastava (1999)as well as the
Deviant Behavior Variety Scale (DBVS) by Sanches et al. (2016).Through the use of a validated questionnaire,
the mean and standard deviationare determined. The researchers modified this questionnaire and translated itinto
the respondents' mother tongue (Cebuano) for them to comprehend itbetter. The study discovered no significant
relationship between psychosocialfactors and deviant behaviors of children in conflict with the law (CICL) in
theDavao Region
KEYWORDS :Children in Conflict with the Law (CICL), deviant behaviors, psychosocial factors
Entropy: A Join between Science and Mind-SocietyAJHSSR Journal
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ABSTRACT: Entropy is join, intersection and interaction between natural science and human mind-society.
We proposed that if internal interactions exist in isolated systems, entropy decrease will be possible for this
system. Management in system is a typical internal interaction within the isolated system. The purpose of
management is to use regulating the internal interactions within the system, and to decrease the increasing
entropy spontaneously. We propose the principle of social civilization and the developing direction is: freedom
of thought, rule of action. Both combinations should be a peaceful revision and improvement of social rules and
laws. Different countries and nations, different religions and beliefs should coexist peacefully and compete
peacefully. The evolution of human society must be coevolution. Its foundation is the evolution of the human
heart and the human nature.
KEYWORDS: entropy, science, society, management, mind, evolution.
A Model of Disaster Resilience Among Colleges and Universities: A Mixed Metho...AJHSSR Journal
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ABSTRACT :This research paper aimed to create a comprehensive framework for measuring disaster
resilience in colleges and universities. The study used a mixed method through Exploratory Factor Analysis
(EFA), which involved analyzing data from a survey questionnaire. The questionnaire was developed based on
in-depth interviews with 12 selected participants from the University of Mindanao, as well as relevant literature
and studies. It was reviewed and validated by 10 experts using a method called Content Validity Ratio (CVR).
This questionnaire was then administered to 400 students from 10 different colleges in University of Mindanao.
After conducting the Exploratory Factor Analysis and performing rotations and iterations, the researchers
identified five main constructs that characterize disaster resilience among colleges (1) disaster preparedness, (2)
disaster awareness, (3) community readiness, and (4) disaster management, (5) disaster resilience. The
researchers aimed to create an organization called βCouncil of College Disaster Volunteers (CCDV)β which
consist of student volunteers. These factors can be used to develop effective management strategies and
strengthen efforts in preventing and managing disasters and accidents.
KEYWORDS:content validity ratio, criminology, disaster resilience, disaster management, exploratory factor
analysis, and Philippines.
Environmental Struggles and Justice Among Lumad Farmers of Davao CityAJHSSR Journal
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ABSTRACT : The study described the various environmental struggles experienced among the participants
and their status in accessing justice. The study followed a qualitative multiple-case study approach; the
participants are the Lumad farmers of Marilog, Davao City selected through a Critical sampling method and
aims to present the environmental violations experienced by the Lumad farmers in Davao City and how it
affected their families and sustenance further, their status in accessing justice is also explored. The study
concluded that the most common struggles the participant experience are Illegal logging and improper waste
disposal, which affect their farms, family, health, and income. Their preferred means to accessing justice is
through barangay settlement; the rigors of accessing courts, such as distance, expenses, fear of ruling, and the
hassle of being called to be present in court, are the most prevalent barriers that hinder the lead farmers from
accessing justice or seeking legal action. Nevertheless, the participants believed that the government would help
them in accessing justice.
KEYWORDS :access to justice, criminology,environmental justice, environmental struggles, lumadfarmers
CYBERBULLYING EXPERIENCES OF UNIVERSITY OF MINDANAO CRIMINOLOGY STUDENTSAJHSSR Journal
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ABSTRACT:This paper explores the cyberbullying experiences among Criminology students at the
University of Mindanao. A simple random sampling method was used to distribute the study's online
questionnaire to the respondents and to survey the target population. This study has four hundred (400)
respondents, and the respondents are Criminology students at the University of Mindanao. The findings of this
study revealed that the level of cyberbullying experiences is sometimes manifested. On the other hand, the
cyberbullying experiences of the students indicate a moderate level, which indicates that the cyberbullying
experiences of the respondents are sometimes manifested. Also, the computations showed that among the
indicators presented, the highest mean is obtained in the psychological effect, which implies that there is a
significant effect of cyberbullying experiences of the respondents in terms of the Gender level of the
respondents. Therefore, respondents with a low level of cyberbullying experiences tend to have a moderate level
of cyberbullying experience. However, there is no significant effect in terms of age and year level of the
respondents according to the results regarding the psychological, emotional, and physical impact of
cyberbullying.
KEYWORDS :cyberbullying, emotional, experiences, psychological,physical effect, and simple random
sampling method.
A philosophical ontogenetic standpoint on superego role in human mind formationAJHSSR Journal
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ABSTRACT: One of the most significant contributions of psychoanalysis to understand the human being is the
elaboration of a model about the mind from a topical and dynamic perspective. Freud explains the mind by the
constitution of the preconscious, conscious, and subconscious. Later, by three dynamic components: the id, the
ego and the superego. Such an organization of the psychic apparatus supposes not only individual elements, but
social influences along the process of hominization. In this paper, we recover the findings of the renowned
anthropologist Lewis Morgan, trying to link some of them to the psychoanalytic theory. Especially highlighting
the importance of superego in Haidtβs social intuitionism.
Keywords: evolutionism, intuitionism, psychoanalysis, Freud, Haidt, Morgan
Improving Workplace Safety Performance in Malaysian SMEs: The Role of Safety ...AJHSSR Journal
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ABSTRACT: In the Malaysian context, small and medium enterprises (SMEs) experience a significant
burden of workplace accidents. A consensus among scholars attributes a substantial portion of these incidents to
human factors, particularly unsafe behaviors. This study, conducted in Malaysia's northern region, specifically
targeted Safety and Health/Human Resource professionals within the manufacturing sector of SMEs. We
gathered a robust dataset comprising 107 responses through a meticulously designed self-administered
questionnaire. Employing advanced partial least squares-structural equation modeling (PLS-SEM) techniques
with SmartPLS 3.2.9, we rigorously analyzed the data to scrutinize the intricate relationship between safety
behavior and safety performance. The research findings unequivocally underscore the palpable and
consequential impact of safety behavior variables, namely safety compliance and safety participation, on
improving safety performance indicators such as accidents, injuries, and property damages. These results
strongly validate research hypotheses. Consequently, this study highlights the pivotal significance of cultivating
safety behavior among employees, particularly in resource-constrained SME settings, as an essential step toward
enhancing workplace safety performance.
KEYWORDS :Safety compliance, safety participation, safety performance, SME
Psychological Empowerment and Empathy as Correlates of ForgivenessAJHSSR Journal
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ABSTRACT: The study explores Psychological Empowerment and Empathy as Correlates of Forgiveness.
The two variables are regarded to have influence on the decision one makes to forgive another. The study aimed
at examining the relationships between psychological empowerment and forgiveness, empathy and forgiveness
and to identify which one of the two,Psychological Empowerment or Empathy, is the more powerful predictor of
forgiveness. The study took a survey design with a sample of 350 drawn from a population of university students
using a self-administered questionnaire with four sections: Personal information, Psychological empowerment
scale, Toronto Empathy questionnaire, and the Heartland Forgiveness Scale (HFS). Data analysis employed
Pearsonβs product moment correlation and regression analysis to test hypotheses. The results show significant
relationships between psychological empowerment and forgiveness as well as empathy and forgiveness.
Empathy was found to be the more powerful predictor of forgiveness.
KEY WORDS: Psychological empowerment, empathy, forgiveness
Exploring The Dimensions and Dynamics of Felt Obligation: A Bibliometric Anal...AJHSSR Journal
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ABSTARCT: This study presents, to our knowledge, the first bibliometric analysis focusing on the concept of
"felt obligation," examining 120 articles published between 1986 and 2024. The aim of the study is to deepen our
understanding of the existing knowledge in the field of "felt obligation" and to provide guidance for further
research. The analysis is centered around the authors, countries, institutions, and keywords of the articles. The
findings highlight prominent researchers in this field, leading universities, and influential journals. Particularly,
it is identified that China plays a leading role in "felt obligation" research. The analysis of keywords emphasizes
the thematic focuses of these studies and provides a roadmap for future research. Finally, various
recommendations are presented to deepen the knowledge in this area and promote applied research. This study
serves as a foundation to expand and advance the understanding of "felt obligation" in the field.
KEYWORDS: Felt Obligation, Bibliometric Analysis, Research Trends
ABSTRACT : In Africa, traditionalauthorities are the guardians of tradition. Recently, however, they have
been caughtbetween tradition and modernity in the exercise of political power in Chad. However, we are
witnessing the revival of chieftaincy and the hybridization of the politicalpowersexercisedwithinit. In this
cohabitation of powers, traditionalauthorityisescapingitsrole as guardian of tradition.
Traditionalauthorityisthereforepresented in itscurrent state, as a proxy for the modern state in traditional
administrative districts. The aim of thisstudyis to analyze the mutations and adaptability of
traditionalauthorityfrom the pre-colonialperiodthrough the colonial period to the post-colonial period. This
workanalyzes the mutations of authorities. The data collected and processedrevealthattraditionalauthorities have
survivedalmosteverywhere, the former chiefdomsdissolvedduringcolonization have been restored by
republicanheads of state, while more and more frequently civil servants, businessmen, academics and
othermembers of the literateelite, whopreviouslyhad no attraction for the position of traditionalchief, are
beingenthroned.
Key words:Authorities, Administration, colonization, Chad, Kanem.
A Conceptual Analysis of Correlates of Domestic Violence and Adolescent Risky...AJHSSR Journal
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ABSTRACT: The study explores domestic violence and how it influences adolescent risky behavior.
Domestic violence is a devastating social problem resulting in significant and enduring effects on children,
threatening both their health and emotional well-being. The study aimed at examining the relationships between
domestic Violence and Psychological Empowerment, Domestic Violence and Self-esteem, psychological
Empowerment and Self-Regulation, Self Esteem and Psychological empowerment, Self-Esteem and Selfregulation, Self-Regulation and Adolescent Risky Behavior and identify the stronger predictor of self-regulation
between psychological empowerment and Self-esteem. Adolescent respondents who experienced domestic
violence were purposely selected and guided by teachers and administrators who had provided support to these
children.The questionnaire had six sections namely; personal information, the Child Exposure to Domestic
Violence Scale, the Psychological empowerment scale, the Rosenberg Self-esteem Inventory, and the Brief
Self-Control Scale. Data analysis employed Pearson's product-moment correlation (r) to test hypotheses 1,
2,3,4,5, and 6. Regression analysis was used for hypothesis 7.The results show a significant relationship
between domestic Violence and Psychological Empowerment, Domestic Violence and Self-esteem,
psychological Empowerment and Self-Regulation, Self Esteem and Psychological empowerment, Self-Esteem
and Self-regulation, Self-Regulation, and Adolescent Risky Behavior. The study documents that Psychological
empowerment is a stronger predictor of self-regulation than Self-esteem.
KEYWORDS:Domestic violence, psychological empowerment, self-regulation, and Adolescent risky behavior
Driving Sustainable Competitive Advantage Through an Innovative Aggregator Bu...AJHSSR Journal
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ABSTRACT : The aim of the research is to analyze the influence of the aggregation business model on
Sustainable Competitive Advantage (SCA). Through a survey of 216 MSMEs in the creative economy sector
selected randomly using an ex post facto causal research approach, an overview of the aggregator business
model and its impact on financial resources and SCA was obtained. The aggregator business model plays a role
in facilitating increased access to financial resources to meet both available and required working capital for
realizing SCA in Malang's Lokanima area. The strength of ABM lies in understanding the resources needed for
SCA and the effectiveness of mobilizing services while considering the most cost-effective options, including
providing various alternatives in their provision. Financial resources are an important factor supporting the
achievement of SCA. Access to financial resources is key to facilitating business growth and sustainability.
Theoretical implications: The concept of the aggregator business model emphasizes the efficient and effective
collection, aggregation, and distribution of resources in connecting service providers with consumers in an
economical and efficient manner. Practical implications: ABM can enhance the performance of financial
resource provision by optimizing relationships with MSMEs and financial institutions, leading to business
growth and sustainability for MSMEs.
KEYWORDS -Aggregator Business, Creative Economy, Financial Resources, Sustainable Competitive
Advantage
Accuracy of ChatGPT for Basic Values of Trigonometric FunctionsAJHSSR Journal
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ABSTRACT : This study analyzes the accuracy of ChatGPT, an artificial intelligence model based on GPT3.5, in determining the values of basic trigonometric functions. To this end, we examine ChatGPT's responses to
sine, cosine, tangent, and cotangent values for a wide range of angles. We compare the results provided by
ChatGPT with the accuracy values determined by basic trigonometry. We also explore differences in accuracy
depending on changes in question complexity and given context. The results show a high level of accuracy of
ChatGPT in determining the values of trigonometric functions, especially for common angles. However, it is
noted that accuracy may be affected in certain cases of extreme angles or complex questions. This analysis
provides an important representation of ChatGPT's capabilities in the field of mathematics, using a new method
for testing the accuracy of artificial intelligence models in determining trigonometric values.
Keywords -Accuracy, AI Model, ChatGPT, Trigonometric Functions, Trigonometry
Postmodern Marketing and Its Impact on Traditional Marketing Approaches: Is K...AJHSSR Journal
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ABSTRACT : The essay discusses the concept of postmodern marketing and its impact on marketing theory
and practice. It explores the characteristics of postmodernism, including openness, tolerance, hyper-reality,
fragmentation, and the lack of clear boundaries, and how they challenge traditional marketing approaches. The
paper also looks at the contributions of postmodern marketing to consumer and marketing research and how it
has redefined the way we think about marketing as a science. Ultimately, it raises the question of whether and
how marketing should adapt itself to the new conditions brought about by postmodernism.
KEYWORDS :Postmodernism, Postmodern Marketing, Kotler, Marketing Theory, Postmodern Consumer
Reorientation of Health Service Governance Toward the Fulfillment of Social J...AJHSSR Journal
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ABSTRACT: Health insurance is a human right. At the practical level, this health insurance program in
Indonesia is organized by BPJS Kesehatan (Social Security Administering Body for Health). The
implementation of BPJS Kesehatan is still not optimal and effective. Three problems are discussed in this
writing: the dynamics of health insurance governance in Indonesia, the implementation of the fulfillment of the
right to health by BPJS Kesehatan, and the reorientation of BPJS Kesehatan services toward social justice.
These problems are then answered by scientific research methods using a sociological juridical approach.
Complaintsoften occur regarding the regulations, the services provided by the health facility providers, and the
distance between the community and the health facilities. Such complaints affect the public interest in becoming
BPJS Kesehatan participants. The aforementioned conditions must be considered and evaluated for the
government's success in the aspired national health insurance plan.
KEYWORDS -BPJS Kesehatan, Health Insurance, Social Justice
βTo be integrated is to feel secure, to feel connected.β The views and experi...AJHSSR Journal
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ABSTRACT: Although a significant amount of literature exists on Morocco's migration policies and their
successes and failures since their implementation in 2014, there is limited research on the integration of subSaharan African children into schools. This paperis part of a Ph.D. research project that aims to fill this gap. It
reports the main findings of a study conducted with migrant children enrolled in two public schools in Rabat,
Morocco, exploring how integration is defined by the children themselves and identifying the obstacles that they
have encountered thus far. The following paper uses an inductive approach and primarily focuses on the
relationships of children with their teachers and peers as a key aspect of integration for students with a migration
background. The study has led to several crucial findings. It emphasizes the significance of speaking Colloquial
Moroccan Arabic (Darija) and being part of a community for effective integration. Moreover, it reveals that the
use of Modern Standard Arabic as the language of instruction in schools is a source of frustration for students,
indicating the need for language policy reform. The study underlines the importanceof considering the
childrenβs agency when being integrated into mainstream public schools.
.
KEYWORDS: migration, education, integration, sub-Saharan African children, public school
Abstract : The aim of thispaperis to report on the effects of physicalactivity and sport on the health of older
people. Based on a mixed-methodsapproach, several techniques, namelydocumentaryanalysis and semistructured interviews, wereused in thisresearch in order to obtain a range of data thatwasavailable, accessible
and relevant to the subjectunderstudy. This enabled us to arrive at the resultsaccording to which the
stakeholders' perceptions of theirhealth are based on the practice of physicalactivities and sport as a social
construct in a socio-cultural context. Older people see sport as a way of curingillnesses, but above all as a way
of givingtheir bodies vitality. Othersseeit as a way of reinvigoratingthemselvesafter retirement.
Key words: Ageing, Physical activities, Sports activities, Elderly people.
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Enhance your social media strategy with the best digital marketing agency in Kolkata. This PPT covers 7 essential tips for effective social media marketing, offering practical advice and actionable insights to help you boost engagement, reach your target audience, and grow your online presence.
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The Utilization of Machine Learning for The Prediction of CSX Index and Tax Revenues in Cambodia
1. American Journal of Humanities and Social Sciences Research (AJHSSR) 2023
A J H S S R J o u r n a l P a g e | 94
American Journal of Humanities and Social Sciences Research (AJHSSR)
e-ISSN : 2378-703X
Volume-07, Issue-11, pp-94-102
www.ajhssr.com
Research Paper Open Access
The Utilization of Machine Learning for The Prediction of CSX
Index and Tax Revenues in Cambodia
H.E. Prof. Dr. Ratana Eng1
, Siphat Lim2
1,2
CamEd Business School, Cambodia
ABSTRACT: The machine learning model LSTM was applied and compared with the linear regression machine
learning model for the CSX index of the Cambodia Securities Exchange. The time span covered in this study was
from February 1, 2019, to October 12, 2023, totaling 1146 days. Out of these, 917 days were classified as Train
data, accounting for 80% of the total sample size, while 229 days were classified as Test data. Additionally, the
same techniques were used to forecast the tax revenues of the Royal Government of Cambodia, which were
collected by the General Department of Taxation. Despite using monthly data, the tax revenues were analyzed
over a longer period of 25 years, starting from January 1998 to August 2023. The Train data consisted of 247
months, equivalent to 80% of the total sample size, while the Test data accounted for 61 months, approximately
20% of the total sample size. The machine learning model, LSTM, demonstrated superior performance over the
linear regression machine learning model in predicting the daily movement of the CSX Index of the Cambodia
Securities Exchange, as evidenced by the root mean square error. The Test data revealed that the estimated root
mean square error of the linear regression machine learning was 64.78, while the LSTM machine learning
produced a lower error of 33.08. However, when applied to tax revenues data, the linear regression machine
learning outperformed the LSTM machine learning, with estimated root mean square errors of 219.31 and
1601.87, respectively.
KEYWORDS: CSX Index, Tax Revenues, Machine Learning, Linear Regression, LSTM
I. INTRODUCTION
After the first election in 1993, Cambodia underwent a significant transformation from a socialist
political system to a democracy, and also transitioned from a planned economy to a free market economy. This
transition resulted in substantial economic growth during the early and mid-1990s, primarily driven by a
substantial influx of foreign direct investment. Notably, the country achieved a remarkable 10% economic growth
rate in 1995. Undoubtedly, financial resources play a pivotal role in facilitating various business activities. During
the early 2000s, although the number of banks operating in Cambodia was limited, both local and foreign investors
heavily relied on these financial institutions for their funding requirements. These banks can be categorized into
three distinct types: commercial banks, specialized banks, and micro-finance institutions (MFIs). As
intermediaries, banks accept deposits from customers and extend loans to the public. Consequently, borrowers are
obligated to pay interest rates to the bank, which compensates the institution for the cost of borrowing money and
remunerates depositors.
The Cambodia Securities Exchange (CSX) was established on July 11, 2011, with the Phnom Penh Water
Supply Authority being the first listed company. Currently, there are 11 listed companies on the exchange. The
establishment of the securities market has provided investors with another opportunity to invest in financial assets,
such as stocks and bonds. Instead of depositing money in a bank, investors with excess funds can purchase stocks
or bonds to earn higher returns or interest rates. Additionally, companies with a shortage of funds can issue stocks
or bonds to sell directly to the public. It is important for investors to stay informed about the movement of the
stock market index. Machine learning regression algorithms, including linear regression, K-Nearest Neighbor
(KNN) regressor, and Support Vector Machine (SVM) regressor, as well as deep learning algorithms such as
Gated Recurrent Units (GRU) and Long Short Term Memory (LSTM), can be used to forecast the stock market
index.
The aim of this study is to utilize a linear machine learning and a recurrent neural network known as
LSTM to forecast the Cambodia Securities Exchange Index, CSX Index. Additionally, these two algorithms will
be employed to predict tax revenues in Cambodia, which adds further intrigue to the research. The evaluation of
the effectiveness of these algorithms will be conducted by comparing the Root Mean Square Error (RMSE) of the
two techniques. The disparity between the projected and actual variables will be utilized to compute the RMSE.
A lower RMSE value indicates a superior model.
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This paper consists of five sections. The topic is introduced in the first section, followed by an extensive
review of literature in the second section. Section three presents the methodology adopted in the study. The fourth
section discusses the results and discussion, and the final section provides a concluding remark.
II. LITERATURE REVIEW
According to Seethalakshmi (2018), the forecasting of stock market prices is considered a regression use
case due to the continuous nature of stock prices. In their study, Di Persio and Honchar (2017) utilized a recurrent
neural network (RNN) to forecast Google stock prices. RNN, LSTM, and GRU are the three most efficient neural
networks for sequential data. RNN is typically used for historical data, while LSTM and GRU are capable of
avoiding the vanishing gradient problem through the use of forget, reset, and update gates. GRU has been found
to be faster due to its operation on reset and update gates.
Forecasting stock prices and predicting market trends pose significant challenges. Over the years, numerous
solutions have been proposed by researchers to tackle these challenges (Obthong et al., 2020; Polamuri et al.,
2019), and the following methods are briefly outlined. Machine learning (ML), deep learning, time series
forecasting, and ensemble algorithms are among the most widely adopted approaches for addressing these issues.
Ensemble algorithms have demonstrated the ability to enhance accuracy and reduce RMSE. Additionally, Hadoop
architectures have proven effective in handling large volumes of stock data (Jose et al., 2019), while deep learning
algorithms have shown promise in predicting financial markets (Hu et al., 2021). LSTM, a unique type of RNN,
has been successfully employed in stock forecasting to overcome long-term dependency (Qiu et al., 2020; Banik
et al., 2022). However, RNN-based architectures often encounter challenges such as the vanishing gradient and
exploding gradient problems, which necessitate careful consideration (Li and Pan, 2021; Zhu, 2020). Khan et al.
(2020) utilized linear regression and random forest techniques to achieve accuracy rates ranging from 80% to 98%
(Khan et al., 2020).
In a similar vein, Xu et al. (2020) employed bagging ensemble learning techniques to make predictions
about Chinese stocks. This methodology involves the integration of a two-stage prediction model, namely
ensemble learning SVM and random forest (RF), with KNN to cluster it with ten technical indicators. Another
proposed approach utilizes Ensemble LSTM with convolution neural network (CNN) on stock indexes to train
adversarial networks, which can aid in forecasting high-frequency stock and offers benefits such as adversarial
training, reduction of direction prediction loss, and forecast error loss (Zhou et al. 2018). To enhance the
effectiveness of the ensemble model of eXtreme gradient boosting (XG-Boost) and LSTM, XG-Boost is utilized
to select features applied to high-dimensional time series data, and LSTM is employed for stock price forecasting
(Vuong et al. 2022).
Machine learning ensemble methods have not only contributed to the enhancement of forecasting
performance, but they have also demonstrated promising results in the realm of neural network blending ensemble
models, as observed in certain research studies. In a study conducted by Yang (2019), a model comprising two
layers of RNN was implemented. The first layer employed a blending ensemble algorithm based on LSTM, while
the second layer utilized GRU. This model exhibited the lowest RMSE value of 186.32, accompanied by a
precision rate of 60% and an F1-score of 66.47, as reported by Li and Pan (2021).
Due to the influence of news and global events on stock prices, relying solely on price variation is insufficient for
the training of machine learning models. Additionally, artificial ANN and LSTM-based deep learning techniques
can be trained using both price values and text data. The Word2vec algorithm utilizes a neural network model to
extract word associations from a vast text corpus. Once trained, this model is capable of identifying synonymous
words or suggesting additional words to complete an incomplete sentence (Kumar and Ningombam 2018; Reddy
et al. 2020).
In an alternative methodology put forth by Li et al. (2020), an examination of past data through technical
analysis and a comprehensive analysis utilizing a deep learning technique were undertaken to yield enhanced
returns. In this context, the LSTM model was selected for prediction purposes due to its ability to retain memory
and its lack of susceptibility to the vanishing gradient problem. Comparable research has been presented in the
works of Agrawal et al. (2022), Sathish Kumar et al. (2020), and Umer et al. (2019).
III. METHODOLOGY
Linear Regression
Machine Learning is a field within Artificial Intelligence that concentrates on creating algorithms and
statistical models capable of acquiring knowledge from data and making predictions. Linear regression, a
supervised machine-learning algorithm, falls under the umbrella of Machine Learning. It learns from labeled
datasets and maps the data points to the most optimized linear functions, enabling accurate predictions on new
datasets. Linear regression is utilized in the prediction of stocks or financial markets to anticipate the future stock
price regression. This technique employs a model that is based on one or more attributes, such as closed price,
open price, volume, etc., to forecast the stock price. The objective of regression modeling is to replicate the linear
relationship between the dependent and independent variables. The linear regression model generates a line of
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best fit that describes the association between the independent factors and the dependent variable. When plotting
the values of the dataset on a graph, a straight line is mathematically fitted between the points in order to minimize
the square of the distance or difference between each point and the line. The hypothesis line is then utilized to
forecast the value of y for each given x. This forecasting technique is commonly referred to as linear regression
(Dospinescu and Dospinescu, 2019).
Long Short-Term Memory
Deep learning models have gained significant traction across various domains of science and
engineering. Their utilization is particularly prevalent in the field of stock price forecasting and trend prediction,
owing to their proficiency in capturing intricate patterns, managing substantial data volumes, and facilitating
feature learning and representation. Moreover, their adaptability to dynamic market conditions further enhances
their appeal. Within this subsection, we will delve into an exploration of several prominent deep learning models
within the finance domain. The LSTM model is a sophisticated variant of the RNN, which is a deep learning
algorithm. The LSTM model is capable of processing lengthy sequences of data units by retaining the data
sequence, which can be utilized for future inputs. A general LSTM cell structure is depicted in Figure 1. It consists
of three gates, namely the input gate, the forget gate, and the output gate. The sigmoid activation function is
employed by all of the gates.
Figure 1. LSTM Structure
Input gate (New information in cell state):
πππ = π(πππ[βπ‘β1, ππ] + ππ)
Forget gate (Useless information is eliminated):
πππ = π(πππ[βπ‘β1, ππ] + ππ)
Output gate (Activation to last block of final output):
πππ = π(π
ππ[βπ‘β1, ππ] + ππ)
The sigmoid function, denoted as s, is utilized in conjunction with the neuron gate weight, Wx, the preceding
LSTM block result, ht-1, the input, Xt, and the bias, bx. Figure 1 illustrates that the upper portion of the memory
line in each cell can be connected to the transport line through the assistance of the model. This connection
facilitates the handling of data from the previous memory to the current memory. Each LSTM node is equipped
with a set of cells dedicated to storing the data stream (Pramod and Pm 2021). To ensure ample training time for
a recursive network and the establishment of long-distance causal links, LSTM maintains errors at a consistent
level (Mukherjee et al. 2021). In various instances, neural networks and deep neural networks have demonstrated
superior forecasting performance compared to other machine learning models. However, in the realm of predicting
financial distress, the logistic regression model has exhibited better outcomes when compared to neural networks
(Zizi et al. 2021).
In this algorithm, the weights for each long short-term memory data point are adjusted through stochastic
gradient descent. It has been observed that LSTM is capable of handling long-term dependencies more effectively
than any other neural network algorithm (Pramod and Pm, 2021). LSTM is a sophisticated type of RNN and is a
deep learning technique. It is widely used in stock forecasting and when combined with sentiment analysis, it
yields better results than without sentiment analysis (Shahi et al., 2020).
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Data
The CSX Index, a daily stock market index, is utilized for conducting forecasting within the time span
of February 1, 2019, to October 12, 2023. The data for the CSX Index is obtained from the Cambodia Securities
Exchange and is divided into the Train and Test data. The study period encompasses a total of 1146 days, with
917 days classified as Train data, accounted for 80% of the total sample size, and 229 days classified as Test data.
In terms of Tax revenues, monthly data is analyzed from January 1998 to August 2023, covering a span of 308
months. Similar to the CSX Index data series, 80% of the total sample size, equivalent to 247 months, are classified
as Train data, while the remaining 20% (61 months) are classified as Test data. The data related to tax revenues
are collected from the Ministry of Economy and Finance.
Performance Measures
The evaluation of forecasting performance utilized the test data for both CSX Index and Tax Revenues
series. The measurement of π
Μ forecast and its corresponding π test data values of π are conducted through the
implementation of the following formula:
Mean Square Error (MSE)
πππΈ =
β(ππ‘ β π
Μπ‘)
2
π
Root Mean Square Error (RMSE)
π πππΈ = ββ(ππ‘ β π
Μπ‘)
2
π
Correlation Coefficient
π2
=
β(ππ‘ β π
Μ )(π
Μπ‘ β π
Μ
Μ )
ββ(ππ‘ β π
Μ )2(π
Μπ‘ β π
Μ
Μ )
2
IV. RESULTS AND DISCUSSION
The study encompasses a total sample size of 1146 days for the CSX Index series. On a daily basis, there
are four quoted data series: Open, High, Low, Close, and Volume. However, the focus of the CSX Index
forecasting lies solely on the Close index. Throughout the study period, the average, minimum, and maximum
values for the Close index are 578, 449, and 871, respectively. The 25th percentile is 488, the 50th percentile is
571, and the 75th percentile is 647. Additionally, the standard deviation is calculated to be 99.
Table 1. Descriptive Statistic, CSX Index
CSX Open High Low Close Volume
count 1146 1146 1146 1146 1.15E+03
mean 578 582 574 578 1.37E+05
std 99 101 97 99 1.11E+06
min 449 451 442 449 3.39E+02
25% 488 489 486 488 1.64E+04
50% 570 576 566 571 4.97E+04
75% 647 651 644 647 9.72E+04
max 871 877 869 871 3.26E+07
The Open data series displays an average index of 578, with a minimum index of 449 and a maximum index of
871. The standard deviation of the Open data is 99. Additionally, the 25th, 50th, and 75th percentiles of the Open
data series are 488, 570, and 675, respectively. The High data, on the other hand, showcases an average index of
582, accompanied by a minimum index of 451 and a maximum index of 877. The High data series also
demonstrates a standard deviation of 101. Furthermore, for the High data, the 25th, 50th, and 75th percentiles of
the series are 489, 576, and 651, respectively. Moreover, the Low data exhibits an average index of 574, with a
minimum index of 442 and a maximum index of 869. The standard deviation of the Low data is 101. The 25th,
50th, and 75th percentiles of the Low data series are 486, 566, and 644, correspondingly.
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Figure 2. Box, CSX Index
The data has been divided into two time periods, namely Train and Test. The Train period represents 80% of the
total sample size, while the Test period represents the remaining 20%. The results obtained from the
implementation of the linear regression machine learning algorithm reveal that during the Train period, the R-
square value is 0.60, the mean square error (MSE) is 3949.27, and the root mean square error (RMSE) is 62.84.
Similarly, during the Test period, the R-square, MSE, and RMSE values are 0.57, 4196.80, and 64.78,
respectively.
Figure 3. Machine Learning Regression Algorithm, CSX Index
The Test period demonstrated improved predicted results for the CSX Index when utilizing the Long Short-Term
Memory (LSTM) algorithm, as indicated by the reduction in RMSE from 64.78 to 33.08. This outcome highlights
the superior performance of the deep learning LSTM algorithm compared to the linear regression machine learning
approach in predicting the CSX Index.
Table 2. Linear Machine Learning Results, CSX Index
Metric Train Test
R-square 0.60 0.57
Mean Square Error (MSE) 3949.27 4196.80
Root Mean Square Error (RMSE) 62.84 64.78
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In addition to utilizing machine learning to forecast the CSX Index, this research also employed the same
methodology to predict the tax revenues of the Royal Government of Cambodia, which are collected by the
General Department of Taxation (GDT). The study spanned from January 1998 to August 2023, encompassing a
total of 308 months. On average, the monthly tax revenue amounted to 665.83 billion Riel. The standard deviation
was calculated to be 633.06 billion Riel. Furthermore, the minimum tax revenue recorded was 41.08 billion Riel,
while the maximum reached 3426.51 billion Riel. The 25th, 50th, and 75th percentiles were determined to be
121.28 billion Riel, 412.68 billion Riel, and 1104.35 billion Riel, respectively.
Figure 4. Deep Learning Algorithm, CSX Index
Figure 5. Machine Learning Regression Algorithm, Tax Revenues
The tax revenue machine learning regression algorithm was analyzed in Figure 5, showing a linear
predicted line that moved along the actual data during the study period. Further analysis revealed an R-square of
0.79 during the Train period, with a mean square error of 88409.23 and a root mean square error of 297.34.
Notably, the Test period showed an increase in R-square from 0.79 to 0.84, and a reduction in root mean square
error from 297.34 to 219.31 compared to the Train period.
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Table 3. Linear Machine Learning Results, Tax Revenues
Metric Train Test
R-square 0.79 0.84
Mean Square Error (MSE) 88409.23 48095.20
Root Mean Square Error (RMSE) 297.34 219.31
After transitioning from the linear regression machine learning algorithm to deep learning, specifically LSTM,
the algorithm's predictability has diminished. This decline is evident in the significant increase of the root mean
square error from 219.31 to 1601.87. Based on these findings, it can be inferred that the linear regression machine
learning algorithm outperforms the deep learning algorithm which is the LSTM.
Figure 6. Deep Learning Algorithm, Tax Revenues
V. CONCLUSION REMARKS
Stock market prediction has become increasingly challenging for investors, despite significant efforts
made over the past two decades to develop and improve time-series and machine learning forecasting models. In
this study, the machine learning model LSTM was applied and compared with the linear regression machine
learning model for the CSX index of the Cambodia Securities Exchange. The time span covered in this study was
from February 1, 2019, to October 12, 2023, totaling 1146 days. Out of these, 917 days were classified as Train
data, accounting for 80% of the total sample size, while 229 days were classified as Test data. Additionally, the
same techniques were used to forecast the tax revenues of the Royal Government of Cambodia, which were
collected by the General Department of Taxation. Despite using monthly data, the tax revenues were analyzed
over a longer period of 25 years, starting from January 1998 to August 2023. The Train data consisted of 247
months, equivalent to 80% of the total sample size, while the Test data accounted for 61 months, approximately
20% of the total sample size. The forecasted tax revenues provided valuable information for the General
Department of Taxation in preparing the government budget. To evaluate the performance of the proposed models,
root mean square error and correlation coefficient were used as performance measures.
The machine learning model, LSTM, demonstrated superior performance over the linear regression
machine learning model in predicting the daily movement of the CSX Index of the Cambodia Securities Exchange,
as evidenced by the root mean square error. The Test data revealed that the estimated root mean square error of
the linear regression machine learning was 64.78, while the LSTM machine learning produced a lower error of
33.08. However, when applied to tax revenues data, the linear regression machine learning outperformed the
LSTM machine learning, with estimated root mean square errors of 219.31 and 1601.87, respectively.
This study utilizes a specific set of machine learning models. However, it is important to note that other relevant
models can also be employed to achieve the same purposes. Additionally, various econometric models, such as
ARIMA, SARIMA, ARIMAX, ARCH, GARCH, TARCH, and PARCH models, can be combined with machine
learning models to enhance forecasting accuracy. These models can be applied not only to stock market indices
but also to tax revenues. It is worth mentioning that this study solely relies on index data for forecasting purposes.
Future research endeavors may consider incorporating share price data from companies listed in the Cambodia
Securities Exchange to provide valuable insights for prospective investors in their investment decisions.
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