Articles

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.

Analysing Risk & Return Profiles: A Comparative Study of the Indonesian Stock Market against International Benchmarks

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The Indonesian stock market, also known as IDX, has emerged as a prominent player in the financial landscape of Southeast Asia. It has attracted the interest of investors who regard it as a potential source of massive returns. Nevertheless, empirical research has consistently demonstrated that developing nations’ stock markets tend to exhibit a notable degree of volatility. This volatility is well recognised for its propensity to amplify risk levels for domestic investors, potentially leading to a decrease in the demand for stock market investments. The observed phenomenon in Indonesia reveals a very low degree of stock market engagement when compared to benchmark nations. This suggests that the high volatility in the market may be a contributing factor.

Understanding the risk-return characteristics of the IDX in comparison to recognised benchmark indexes is crucial for making educated investment decisions. This study will conduct a comparative analysis of the risk-return attributes of the IDX in comparison to six well recognised benchmark indexes, specifically the S&P 500 Index, the Straits Times Index, the FTSE 100 Index, the Shanghai Stock Exchange Composite, the BSE Sensex, and the BOVESPA Index. The study employs several risk and return measures, such as standard deviation, coefficient of variation, and the Sharpe ratio, to assess the relative performance of the IDX.

The objective of this study is to evaluate the comparative appeal of the Indonesian stock market when compared to international benchmarks, with a specific focus on risk and return. The aim is to determine the optimal level of stock market involvement and the inclination towards risk aversion or risk-seeking behaviour among retail investors in Indonesia.

Analysis and Prediction of Bollinger Bands to Predict Stock Prices in Determining Investment Strategies

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It is critical for stock investors to be able to forecast future stock values in order to determine potential gains or losses. A technical analysis approach can be used to anticipate stock prices in the realm of stock investment. A technical analysis approach was used to construct the Bollinger bands detecting application system. Technical analysis is a means of observing price variations over a specific time period by employing historical data and indicators. The goal of this research is to predict Bollinger bands and define the circumstances for buying and selling stocks in technical analysis. The object of this study is the closing price of BBRI.JK, BBCA.JK and BMRI.JK during 2022. Data is analyzed using Bollinger Bands and decision to buy the right stock is when the actual price of the stock intersects with the lower band because at that time the stock price is increasing, while the decision to sell the right is when the actual price of the stock intersects with the upper band because at that time the stock price will decrease. In addition, the results of the Bollinger bands prediction for n + 1 from the last day of the sample, December 30, are respectively the upper and lower Bollinger bands on January 2, 2023, which are 5.012,948 and 4.775,052 for BBRI.JK shares, 8.755,919 and 8.446,581 for BBCA.JK shares, and 5.321,991 and 4.764,259 for BMRI.JK shares.

Portfolio Rebalancing with GARCH Model at Jarvis Balanced Fund

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With high inflation economy, investors must find another way to minimize their risk, but also maximize their return of the portfolio. Various instruments used for finding the most suitable amount of portfolio allocation. Single instruments, such as stocks, bonds, and time deposits is chosen by investors to secure their assets from inflation. The other investors chose mutual funds to grow their investments. One of the solutions to find the portfolio allocation is to rebalance the portfolio. In other perspectives, time-series model will help investors to predict the volatility that will happen in the future. GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model is used for finding volatility and generalized it from the ARCH Model. With GARCH Model, the total amount of residual and GARCH has to be less than 1. Either way, it will be categorized as a volatile asset. From the top 5 balanced fund in 2021, Jarvis Balanced Fund is one of the best-balanced funds with return of 55.85%/year. Using Jarvis Balanced Fund (JBF) portfolio from 2021 prospectus as the sample of this research, it is concluded that JBF has to reduce the number of stocks and increase the risk-free assets to prevent volatility that happen in the portfolio. Following with the macro economy of high-inflation economy era.

Stock Valuation and Financial Performance of Nickel Mining Company in Indonesia (Case Study: PT Vale Indonesia Tbk)

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Indonesia is home to 22% of the world’s nickel deposits, and its restriction on nickel ore exports since 2020 has resulted in significant changes to the supply chains of vital items such as electric vehicles and the stainless steel sector. Approximately 75% of nickel is used in the manufacturing of stainless steel, the most common use of nickel. However, nickel is also essential for the fabrication of electric vehicle (EV) battery cathodes, which are required for the shift to green energy. Current EV battery demand accounts for around 7% of worldwide output, but anticipated increases in EV demand will result in an exponential increase in nickel demand. The exponential increase in nickel demand led to volatility in global nickel prices. Volatility in global nickel prices is affecting companies that operate nickel mining businesses. PT Vale Indonesia Tbk (INCO) is listed on the Indonesia Stock Exchange as one of the nickel mining firms operating in the nickel mining business (IDX). The volatility of the global nickel price is both a breath of fresh air and a problem for the firm. INCO may also participate in and benefit from the rising worldwide demand for nickel in the foreseeable future.

The primary objective of this study is to evaluate the intrinsic value of a nickel mining company in order to assist investors in making decisions in the current market environment. Evaluation of financial performance over the last five years and projections for the next five years using absolute and relative valuation methodologies. The author suggests investors to purchase this stock using a risk-reward assessment suited to each investor’s circumstances and the potential return earned. Referring to the stock valuation evaluation, investors are recommended to purchase if the price of INCO falls below the range of IDR 6,051 to IDR 6,335. When the market price is inside and above the intended range, it is not advisable for an investor to purchase INCO.