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The dynamic fluctuations in stock prices present a major challenge for investors in making informed decisions. To anticipate such uncertainties, forecasting methods that can provide accurate predictions are required. This study compares two time series forecasting methods Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (Holt) in predicting the stock prices of PT Telkom Indonesia (TLKM). The dataset consists of monthly closing prices from January 2018 to December 2023. The performance of each model is evaluated using three error metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that the ARIMA(1,1,1) model yields higher predictive accuracy than the Holt method, with MAE of 787.71, MSE of 771,844.2, and RMSE of 878.55. In contrast, the Holt method records a MAE of 837.19, MSE of 878,393.4, and RMSE of 937.23. These findings confirm that ARIMA is superior in capturing the complex patterns of stock price movements and is more effective in volatile market conditions such as the stock exchange.

This study applies the Mean Variance model, which aims to form an optimal portfolio composition in the health, property, and cyclical consumer sectors and combine the three sectors into one portfolio, then visualize its efficient frontier. This study analyzes the return profiles and compares the risks of each portfolio using alternative risk measures such as the Coefficient of Variation (CV), Value at Risk (VaR), and Conditional Value at Risk (CVaR). Daily closing price data for the three sectors listed on the Indonesia Stock Exchange (IDX) from March 2, 2020, to March 3, 2025, were used in this study. Stock selection was conducted using purposive sampling, followed by selecting seven stocks for optimization based on the lowest Coefficient of Variation (CV) value. Portfolio optimization analysis was conducted using the Python programming language with Visual Studio Code software. The findings of this study indicate that the combined portfolio incorporating the three sectors is the most efficient, with an expected return of 0.104%, standard deviation of 0.007, and alternative risk measures such as Coefficient of Variation (CV) 6.9328, Value at Risk (VaR) of -0.99%, and Conditional Value at Risk (CVaR) of -1.44%, which are lower than those of single-sector portfolios. Visualization of the efficient frontier curve confirms that the combined portfolio offers better results in terms of risk and return. The results of this study indicate that cross-sector diversification can significantly reduce risk and prevent significant losses.

This study aims to examine the effect of profitability ratios, namely Earnings per Share (EPS), Net Profit Margin (NPM), Return on Assets (ROA), and Return on Equity (ROE), on the stock price of PT Ciputra Development Tbk during the 2016–2023 period. The research employs a quantitative approach with a causal research design using secondary data derived from quarterly financial statements and stock closing prices published by the Indonesia Stock Exchange. The data were analyzed using multiple linear regression, supported by classical assumption tests, partial hypothesis testing (t-test), simultaneous testing (F-test), and the coefficient of determination (R²). The results show that EPS, NPM, and ROA do not have a significant effect on stock prices, while ROE has a positive and significant effect. Simultaneously, all profitability variables do not significantly influence stock prices. The coefficient of determination indicates that profitability ratios explain a relatively small proportion of stock price variation, suggesting that stock prices in the property sector are influenced more by external and market-related factors than by short-term profitability indicators. These findings imply that ROE is the most relevant profitability indicator for investors in assessing property sector stocks, while other profitability ratios play a limited role.

This study investigates the potential role of the Jakarta Islamic Index (JII) as a hedging instrument and safe haven asset against the Indonesia Composite Index (IHSG) during the period from January 2020 to April 2025, a time characterized by elevated market volatility. The main objective is to determine whether sharia-compliant stocks in Indonesia offer diversification benefits during periods of financial stress. Utilizing daily closing prices converted into log returns, the study employs the Asymmetric Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (A-DCC GARCH) model to capture time-varying correlations between JII and IHSG. Prior to applying the model, standard diagnostic tests were performed to ensure data quality, including tests for stationarity, autocorrelation, and ARCH effects.Empirical results reveal a persistently high correlation between IHSG and JII, with an average of 0.826 and values exceeding 0.95 during periods of market turbulence. These findings indicate that JII does not fulfill the characteristics of a hedge or safe haven asset. A robustness analysis using extended data from 2010 to mid-2025 further supports the conclusion, showing the continued presence of strong comovement between the two indices across different market regimes. This suggests a structural relationship rather than one driven solely by crisis events. The high correlation may be attributed to overlapping index constituents and similar investor responses to market shocks. These results challenge the prevailing notion that Islamic indices inherently offer protection during downturns. As such, investors seeking to mitigate portfolio risk may need to look beyond domestic sharia equities and consider broader asset classes or international diversification. Future research is encouraged to explore cross-market and multi-asset safe haven properties, especially in the context of emerging economies.

Global economic uncertainty has raised concerns among the public, triggering actions to secure economic stability by investing, especially in gold and bitcoin. This study uses a comparative quantitative method, with the aim of comparing the returns, risks, and performance of the two investment instruments. The population in this study is in the form of data on the monthly closing prices of gold and bitcoin for the period of January 2019 – April 2025 which totals 152 data, consisting of 76 data from each variable. Data analysis was carried out by calculating returns, risks, Sharpe, Treynor and Jensen indices. The three indices are widely used performance measurement methods and have included return and risk factors in their measurements. The results of this study show that there is a significant difference between the return of gold and bitcoin, while for the risk variable between the two, there is no significant difference. Performance based on the Sharpe method also showed no significant difference between the two. However, for performance measurement using the Treynor and Jensen methods, significant differences were obtained between the two instruments. Overall, bitcoin offers higher profit potential than gold, even more so for investors who can accept high volatility. However, the decision to choose an investment instrument still depends on the risk profile of each investor.

This study aims to analyze the reaction of the Indonesian capital market to the inauguration of President Prabowo-Gibran using the event study method. The important variables in this study are abnormal returns and trading volume activity, with the population of all companies listed in the LQ45 stock index. The observation period was 14 days. The data consists  of daily closing stock  price, daily closing price of LQ45 IHSG, daily trading volume, and the number of shares outstanding. Hypothesis testing was conducted using the non-parametric Wilcoxon Signed Rank Test, as the data is not normally distributed. The results showed that there was no significant difference in  the average abnormal return. However, a significant difference in the average trading volume activity was found between the periods before and after the presidential inauguration event.  

This study examines the presence of the Monday effect in companies listed in the IDX30 index on the Indonesia Stock Exchange (IDX) from February 2018 to January 2023. The Monday effect is a market anomaly where stock returns on Mondays tend to be systematically different from other trading days. This phenomenon, if proven, challenges the efficient market hypothesis. The main research problem is whether the Monday effect exists in IDX30 stocks during the specified period. The study aims to provide empirical evidence regarding this anomaly in the Indonesian stock market. The research employs a quantitative approach, utilizing secondary data in the form of daily stock closing prices. The sample consists of 15 companies that were consistently listed in the IDX30 index throughout the study period, selected through a purposive sampling method. The analysis is conducted using the One-Way ANOVA test with SPSS 27 statistical software to compare stock returns across different trading days. The findings confirm the presence of the Monday effect in IDX30-listed stocks, indicating that stock returns on Mondays exhibit statistically significant differences compared to other days. These results suggest that behavioral factors and market inefficiencies may influence stock price movements in the IDX30 index. This study contributes to the literature on stock market anomalies and provides insights for investors and policymakers regarding trading strategies and market efficiency in Indonesia.

When making investment decisions, it is crucial for investors to consider various risks that may arise, both in the short and long term. One method to measure risk is through volatility. Volatility represents a statistical measurement of the degree of price variation over a specific period, expressed as volatility (σ) (Aklimawati & Wahyudi, 2013). This study aims to discuss the pricing of European option contracts using Conditional Monte Carlo simulation and the Black-Scholes method. The data used in this study is secondary data obtained from Yahoo Finance. The data consists of quantitative information, namely the monthly closing prices of Toyota Motor Corporation (TM) stock, spanning 5 years from July 1, 2019, to July 1, 2024, yielding 60 data points. In this research, the pricing of European call option contracts was calculated using Conditional Monte Carlo simulation and the Black-Scholes method. The study concludes that European option contract pricing can be determined using two methods: Conditional Monte Carlo simulation and the Black-Scholes method. Conditional Monte Carlo simulation can be employed to calculate European option prices in a structured manner, utilizing stochastic volatility estimated through the Ordinary Least Squares (OLS) method. The two methods yield differing option prices; Conditional Monte Carlo simulation produces lower option price estimates with relatively lower error values compared to the Black-Scholes method at every strike price. The lower estimates from Conditional Monte Carlo simulation are due to its consideration of stochastic changes in volatility, whereas the Black-Scholes method results in higher prices due to its assumption of constant volatility. The comparison demonstrates that Conditional Monte Carlo simulation provides cheaper price estimates under market conditions with non-constant volatility, despite requiring higher computational time compared to the Black-Scholes method.   ,

This research applies the Monte Carlo simulation method to predict the movement of Apple Inc.'s stock price over a long period of time. Using historical data of Apple's stock price from 12 December 1980 to 24 March 2022, this study aims to generate a probability distribution of the future stock price. The method involves several steps, including data collection, log return calculation, parameter estimation, and simulation of the stock price path through random iterations based on the log return distribution. The simulation results show that the closing price of Apple stock can be predicted by following the historical trend, although there are differences with the real data due to the stochastic nature of the Monte Carlo technique. This research also applies a variance reduction method to improve simulation efficiency. The findings provide a valuable perspective for investors and financial analysts in identifying investment risks and opportunities through an in-depth understanding of the dynamics of stock price movements using Monte Carlo simulation. Suggestions for future research include the use of VaR methods with historical variance and covariance approaches, as well as considering longer data periods and more stock indices for more comprehensive results.

The aim of this research is to determine the application of the Price Earning Ratio and Price to Book Value as a basis for making stock investment decisions in coal sub-sector companies listed on the IDX for the 2020-2022 period. The population in this study are coal sub-sector companies listed on the IDX in 2020-2022. Sampling used a purposive sampling technique and eight company samples were obtained. Data collection techniques use documentation. Data analysis techniques use PER and PBV. The results of this research show that based on intrinsic value calculations using PER and compared with closing price, company shares that are undervalued include ADRO, BSSR, BYAN, GEMS, ITMG, MBAP, MYOH, and PTBA where the investment decision that can be taken is to buy shares or holding shares if already owned. Based on the intrinsic value calculation using PBV and compared with the closing price, company shares that are undervalued include ADRO, BYAN, GEMS, ITMG, MBAP, MYOH, and PTBA where the investment decision that can be taken is to buy shares or hold shares if they have owned, and one company, namely BSSR, is in an overvalued condition where the investment decision that can be taken is to sell shares.

The purpose of this study is to determine the effect of the economic recession resulting from an increase in interest rates on sales, cash flows, and stock prices of property companies listed on the Indonesia Stock Exchange. This study examines the impact of the economic recession on sales, cash flow, and stock prices before the issue of the economic recession in the Q3 2022 period and after the recession issue appears in the Q4 2022 period. The measurement of cash flow and sales takes data from financial reports, while stock prices use the closing price of shares. This study uses a sample of property companies that have published their financial reports on the Indonesia Stock Exchange. The analysis of this study uses multiple linear regressions with the help of (Statistical Package for Social Science) SPSS 25. The results of this study indicate that (a) sales of property companies in Indonesia affect the issue of economic recession. (b) operating cash flows of property companies in Indonesia affect the issue of global economic recession (c) stock prices of property companies in Indonesia have no effect on the global economic recession.

The purpose of the study was to determine and analyze the effect of GCG, CSR, and Financial Performance on Share Value. The research location is a Banking Company listed on the Indonesia Stock Exchange. The number of samples in this study was 120 financial reports. The method used is panel data regression analysis. The results showed that GCG has a positive effect on the value of shares in banking companies listed on the Indonesia Stock Exchange, CSR has a positive effect on the value of shares in banking companies listed on the Indonesia Stock Exchange and PER has no effect on the value of shares in banking companies listed on the Indonesia Stock Exchange.