Articles

Technical Analysis of Moving Average Convergence Divergence, Stochastic Oscillator, Relative Strength Index and Money Flow Index on The Stock Prices of Companies Listed on The IDX30 Index on The Indonesia Stock Exchange

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Every year, the number of investors in Indonesia is growing rapidly. To reduce the risk of loss, it is important to have a strategy in investing, one of which is by utilizing technical analysis. This study aims to assess the extent to which the technical analysis of the Moving Averages Convergence Divergence (MACD), Stochastic Oscillator (SO), Relative Strength Index (RSI), and Money Flow Index (MFI) indicators is effective in providing accurate signals related to stock prices during the 2024 Presidential Election on stock prices listed on the IDX30 Index. This study uses descriptive research with a quantitative approach. The method used in this study is technical analysis that focuses on understanding the relationship between the signals generated by the four indicators and stock price movements. Data analysis used is by accuracy testing and statistical difference testing. The results of the study showed that the four indicators have fairly good accuracy values, where the order of accuracy is the first MACD indicator 89%, the second SO indicator 81%, the third RSI indicator 79%, and the last MFI indicator 63%. However, based on statistical tests conducted, only the Moving Averages Convergence Divergence (MACD) indicator is effective in providing accurate signals related to stock prices during the 2024 Presidential Election for companies listed on the IDX30 Index. The results of this study are expected to provide insight into the signal strength of these technical indicators in predicting stock price movements, which in turn can be used by investors to make better investment decisions in the Indonesian capital market.

Stock Price Forecasting on Time Series Data Using the Long Short-Term Memory (LSTM) Model

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Stock price forecasting on time series data is a complex task due to the dynamic and uncertain nature of financial markets. This research aims to forecast stock prices by applying an advanced machine learning model, namely Long Short-Term Memory (LSTM), a deep learning architecture that excels in capturing long-term dependencies in time series data. The dataset used in this study consists of 1221 daily ANTM.JK stock price data over the period April 30, 2019 to April 30, 2024. The model was trained and evaluated using performance metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) in measuring the level of forecasting accuracy. The results show that the LSTM model can accurately predict stock prices on time series data, as evidenced by the MAPE accuracy evaluation value of 2.52% and RMSE of 54.64. These findings indicate that the LSTM model is effective in predicting stock prices on time series data and can be used as a supporting tool in making the right investment decisions.

Comparison of Diagnostic Accuracy of CT scan and USG in Right Upper Quadrant Pain: A Review Analysis

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Purpose: Acute right upper quadrant (RUQ) pain is a common presenting symptom in emergency departments and outpatient medical practices, and is most commonly attributable to biliary and hepatic pathology. The main objective of the study is to systematically analyse the comparison of diagnostic accuracy of ultrasound and computed tomography in right upper quadrant pain.

Material and methods: This study was conducted using a systematic search on Google scholar, Pubmed and Web of science published until 20th June 2020. The cited references of retrieved articles and previous reviews were also manually checked to identify any additional eligible studies with indexed search terms for cholecystitis, US, cholescintigraphy, CT, and MR imaging.

Results: After excluding duplicates and articles that did not meet the inclusion criteria, we obtained 30 articles with full-texts which were read for further evaluation, where another 60 were excluded as irrelevant. Overall, we included 30 articles that directly match on the inclusion criteria.

Conclusion: It is concluded that RUQ pain were not as good as sensitivities reported in prior studies. CT was statistically significantly better for the diagnosis of RUQ pain than US, most likely because of an unclear clinical picture, the patient population, and a high proportion of poor-quality US examinations. However, US is still our first test of choice if RUQ pain is suspected clinically, whereas CT is performed when the clinical picture is unclear.