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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.

The Impact of Selected Financial Ratios on The Market Performance of Listed Companies on The Acceleration Board Indonesia Stock Exchange (Period 2020-2023)

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This research aims to analyze the impacts of selected financial ratios on the market performance of listed companies in the Acceleration Board IDX (Period 2020-2023) and find if IDX need  to add the financial requirements in IDX Listing Regulations no. I-V to increase the company’s market performance in Acceleration Board IDX. The data of selected financial ratios and market performance were collected from the publication of listed companies’ financial reports and market trading data in 2020-2023 that can be accessed publicly on IDX’s official website. The population in this research is the whole Acceleration Board listed companies that were listed in IDX from 1st January 2020 – 31st December 2023. The samples used are limited to companies listed in Acceleration Board IDX period 2020-2023. Multiple regression analysis is used in this research to examine the relationship between the dependent variable and two or more independent variables. From five independent variables, partially, only the Net Profit Margin has a significant impact on the company’s market performance (stock price). From the results of the ANOVA test, there is a simultaneous significant effect of the independent variables ROA, ROE, DER, NPM, and Cash Flow from Operation on the dependent variable Market Performance (stock price).

Study Case: Company Valuation and Forecasting Financial Trends at PT PP London Sumatera Indonesia (LSIP)

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PT PP London Sumatra Indonesia Tbk (LSIP) is a company that has been established in Indonesia since 1906 and started its IPO in 1996 with an IPO price of Rp4.650 for each share. Even though the company’s performance is going well and the trend of palm oil consumption in Indonesia is increasing, the share price of PT PP London Sumatra Indonesia Tbk has fluctuated and even decreased when compared to the initial IPO price and share prices in the last 5 years. So this raises the question of how the company’s stock performance will be in the next few years. In this research, the author begins by analyzing the macro-environment which may have an impact on the company and industry, then the author carries out an analysis of the financial statements of the company and its competitors. Apart from that, the author also tries to forecast the company’s financial performance and then continue with company valuation analysis using the discounted cash flow method. After getting the valuation results, the author tries to see the company’s level of sensitivity using scenario analysis and also carries out capital structure analysis to find the optimal capital structure. Based on the results of financial performance analysis, Lonsum (LSIP) has better financial performance compared to its competitors except for assets turnover. And then based on the results of discounted cash flow analysis, this company is not yet worthy of being a place for us to invest. And the last based on all the results of the previous analysis, when compared with competing companies, LSIP is a company that has quite a lot of potential as a place to invest. However, currently the company still has to look for a catalyst in order to become a company that is worthy of being a place for us to invest.  Based on the result, the author suggest the reader to continue to collect information related to PT PP London Sumatra Indonesia Tbk, other competitors and plantation in similar industries to find catalysts that have positive impact on the company, and invest when the company has a positive catalyst.

Prediction of Stock Price Volatility Using the Long Short Term Memory (LSTM) Model for Investment Portfolio Selection Strategy

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Volatility is an important variable in financial data models. Predicting volatility in financial data is helpful for investors to make good decisions to reduce risk and to gain investment returns. In predicting volatility, many researchers have conducted research in building prediction models using data mining. This research uses a deep learning algorithm, namely Long Short Term Memory (LSTM) which has high accuracy compared to other models. The research aims to predict stock price volatility and for investment portfolio selection. The object of this study is the historical stock price of PT. Unilever Indonesia Tbk. (UNVR), PT. Fast Food Indonesia Tbk. (FAST) which manages KFC and PT. MAP Boga Adiperkasa Tbk. (MAPB) which manages Starbucks in the period 2023 to 2024, when there was a boycott caused by the war between countries that occurred in the Middle East. The data is analysed using the LSTM model where stock price volatility was determined by the variance of the return and log return on the next seven days, then using LSTM the stock price volatility data was predicted. The results show that the MSE and RMSE values are very small, which means that the volatility prediction results are almost the same as the actual data. And the average volatility prediction results in UNVR stock of 0.00841, MAPB stock of 0.01717, and FAST stock of 0.01323. From these results can be used as a reference for the selection of investment portfolios.

Correlation between Financial Performances with the Stock Price in Indonesia Stock Exchange on Telecommunication Industry for 2017-2021 (Case Study: PT Telkom, PT XL Axiata, PT Indosat Ooredoo)

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Over the past few decades, telecommunication in Indonesia has experienced enormous growth and development, playing a crucial role in supporting the advancement of society, economic prosperity, and human connectivity. A couple major companies dominate the market including PT Telekomunikasi Indonesia and private firms like PT Indosat Ooredoo and PT XL Axiata. These companies provide a wide range of services, including mobile phone service, fixed-line services, internet connectivity, and digital solutions, adapting to the diverse needs of businesses and individuals across the country. The purpose of this research is to determine how financial ratios affect stock prices on the Indonesian stock exchange. Additionally, this study compares the financial health of PT Telekomunikasi Indonesia, PT Indosat Ooredoo, and PT XL Axiata based on ratio comparison of Decree No. KEP-100/MBU/2002.

The results indicates that PT Telekomunikasi Indonesia has the healthiest financial performance by obtained AA category compared to PT Indosat Ooredoo and PT XL Axiata. This study finds the effect of eight financial ratios on the telecommunication industry for five years period. Based on the multiple linear regression test namely T test, it resulted that total asset turnover and total equity have a positive significant effect on stock price partially with a value less than 0.05. The F tests shows that all independent variables have effect to the stock prices by 80% and the remaining 20% are influenced by models outside this study.

Analysis of Changes in Food and Beverage Sector Stock Prices on the Indonesia Stock Exchange

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Stock prices on the Indonesia Stock Exchange (IDX), always fluctuate, so it is necessary to analyze any variables that affect stock prices. The purpose of this study is to analyze the factors that affect stock prices, the Food and Beverage Sector on the IDX for the period 2015 to 2019. The analysis method uses correlation, regression, t test, Anova test and multiple linear analysis with several classical assumption tests. The research population was 18 companies and the sample studied was 12 companies and data processing using SPSS Version 22.

The conclusions of the study are (a). Interest rates have no significant effect on stock prices, while firm size and earnings per share value have a significant effect on stock prices. Simultaneously, the three independent variables (Interest rate, company size and Earning per share), have a significant effect on the stock price of Food and Beverages on the Indonesia Stock Exchange.