LQ45 Stock Research Articles

The Affect of Firm Size, Debt Policy, Profitability on Stock Returns: Moderating Role Dividend Policy

This research aims to examine the influence of Firm Size, Debt Policy and Profitability on Stock Returns with Dividend Policy as a moderating variable in non-financial Firms included in the LQ45 stock index on the Indonesia Stock Exchange (BEI) for the 2017-2022 period. The sampling technique in this research is the purposive sampling technique. This research uses secondary data, namely the firm's annual financial report (annual report) obtained from the official website of the Indonesian Stock Exchange (IDX). The analytical method used is multiple linear regression techniques and moderated regression analysis (MRA) with testing using the SPSS version 29.0 application. The research results show that Firm size has a positive and significant effect on stock returns. Debt policy and profitability have a negative and insignificant effect on stock returns. Dividend policy can moderate Firm size on stock returns. However, dividend policy does not moderate debt policy and profitability on stock returns; however, dividend policy can moderate the influence of Firm size, debt policy, and profitability simultaneously on stock returns. The managerial implications of this research show that Firm size, debt policy, and profitability have a significant influence on stock returns, while dividend policy can moderate this relationship.

Error Correction Model (ECM) For Macroeconomic Factors And LQ45 Stock Price Index

This research aims to test the causal effects of the inflation rate empirically, the Rupiah exchange rate against the United States Dollar, and world crude oil prices on the LQ45 stock price index. In its testing, this research uses the Error Correction Model (ECM) and uses data every month from 2000 to 2022. The research results show that the inflation rate, exchange rate and world gold prices significantly affect the LQ45 stock price index. Furthermore, the inflation rate and exchange rate have a negative influence on the LQ45 index. Not optimal company growth due to high inflation and exchange rates will affect public market share demand, thereby reducing share prices. Then, world crude oil prices positively influenced the LQ45 index. These results support previous research findings that changes in world crude oil prices positively affect stock market prices in developing countries.

LQ45 Stock Price Forecasting: A Comparison Study of Arima(p,d,q) and Holt-Winter Method

The Holt-Winter method and ARIMA(p,d,q) are two frequently used forecasting techniques. When using ARIMA, errors are expected to be connected with earlier errors because it is based on data correlation with prior data (autoregressive) (moving average). The Holt-Winter model comes in two different forms: Multiplicative Holt-Winter and Additive Holt-Winter. No one has ever attempted to compare combined time series and cross-section data, despite the fact that there has been a great deal of prior study on ARIMA and Holt-Winter. In a combined time-series and cross-section dataset, the accuracy rates of Holt-Winter and ARIMA(p,d,q) will be compared in this study. LQ45 stock prices are used because they track the performance of 45 stocks with substantial liquidity, sizable market caps, and solid underlying businesses. The Mean Absolute Percentage Error (MAPE) method is used to gauge accuracy. This study contributes to MAPE exploration by using a Boxplot diagram from cross-sectional data. With the Boxplot diagram, we can see the MAPE spread, the MAPE's center point, and the presence of outliers from the MAPE of LQ45 stock. According to the findings of this empirical study, the average error rate for predicting LQ45 stock prices using ARIMA is 7,0390%, with a standard deviation of 7,7441%; for multiplying Holt-Winter, it is 29,3919%, with a standard deviation of 25,7571%; and for additive Holt-Winter, it is 18,0463%, with a standard deviation of 18,3504%. Apart from numerical comparisons, it can also be seen visually, based on the Boxplot diagram, that the MAPE of ARIMA(p,d,q) is more focused than Holt-Winter. In addition, in terms of accuracy distribution, it can be seen that the MAPE accuracy of the ARIMA method produces four outliers. Based on the MAPE accuracy rate, we conclude that Holt-Winter has a bigger error based on the MAPE value than ARIMA(p,d,q) at forecasting LQ45 stock prices.

Menguji Dampak Omicron terhadap Harga Saham dan Volume Transaksi: Studi Kasus pada Indeks Saham LQ-45 di Bursa Efek Indonesia

Rapid stock price movements, especially during the ongoing pandemic, demand heightened attention from investors. The unpredictability of stock prices and market reactions to COVID-19 and its variants poses a challenge in predicting their future trends. Beyond stock prices, examining the trading volume of listed companies is crucial. This research aims to analyze potential differences in stock prices and transaction volumes within the LQ-45 Stock Index following the Omicron announcement in Indonesia. The study focuses on the 14 days preceding and following the Omicron announcement, utilizing a saturated sampling approach. The data is analyzed through the Wilcoxon Signed Rank Test for hypothesis testing. Findings suggest significant disparities in stock prices and transaction volumes before and after the Omicron announcement. The research concludes that news or announcements related to the Omicron variant adversely impact the capital market, aligning with event study principles. Such announcements influence market responses, resulting in fluctuations in stock prices and transaction volumes.

FORMATION OF AN OPTIMAL PORTFOLIO OF LQ45 SHARES USING MARKOWITZ METHOD

Currently, there are still many investors who do not realize that stock management strategies are important. Therefore, as a practitioner, through this research, it is hoped that authors can help overcome this problem so that it can calculate maximum profits in-stock selection. This research aims at understanding stock management strategies by forming a Markowitz portfolio with the help of the K-Means grouping method. The population in this research included stock companies listed on the Indonesian Stock Exchange. The sample selection method used was the targeted sampling method. The sample data used was daily stock returns from LQ45 stock companies. The research results showed that based on data processing, stock grouping using the k-Means method and the Markowitz method was proven to produce maximum profits and low risk. Therefore, the method used in this research can be useful in the world of finance, especially to help investors.

Studi Komparatif: Harga Saham LQ45 Sebelum Dan Selama Masa Pandemi Covid-19

Research Aim: This study re-evaluates the impact of the COVID-19 pandemic on LQ45 stock prices Methods: Data from 2018-2021 are analyzed, with 2018-2019 representing pre-pandemic prices and 2020-2021 representing pandemic prices. Comparative analysis is conducted using average t-tests, with normality tests guiding the choice between paired sample t-tests and Wilcoxon signed-rank tests Findings: Data exhibited a non-normal distribution, revealing a significant 27% average decrease in LQ45 stock prices during the pandemic. This underscores the pandemic's substantial impact on stock prices, particularly within the LQ45 index Theoretical Contribution/Originality: This study corroborates earlier findings and provides empirical evidence of the significant difference in LQ45 stock prices before and during COVID-19, resolving previous inconsistencies Practitioner/Policy Implication: The COVID-19 pandemic negatively affects stock prices, signaling adversity to investors and prompting market reactions, leading to decreased stock values. The LQ45 index experienced a decline in share prices from 2020 to 2021, highlighting the pandemic's enduring impact on market dynamics Research Limitation: This study focuses solely on the COVID-19 impact on LQ45 stock prices using year-end closing prices. Future research could explore this influence further using monthly or quarterly stock data

LQ45 Stock Portfolio Optimization by Considering Return Predictions Using the Support Vector Regression (SVR) Method

Investors require stock portfolio optimization to achieve desired outcomes. This process involves prediction to obtain optimal weights in the expected portfolio. Historically, portfolio optimization has primarily considered risk and expected value. However, a new approach has integrated return prediction into traditional time series models, enhancing the performance of portfolio optimization models. Machine learning has proven effective in predicting stock market trends, with many researchers applying these models in portfolio formation with satisfactory results. This study employs Support Vector Regression (SVR) method to integrate return prediction in portfolio formation, utilizing stock data from the LQ45 index. Test results indicate that return prediction using SVR method yields a Root Mean Square Error (RMSE) of 0.34973. Portfolios considering return prediction demonstrate superior performance compared to the LQ45 index, as evidenced by portfolio return averages, standard deviation, and Sharpe ratio.

LQ45 Stock Portfolio Optimization by considering Return Predictions using the Holt Winter Method

A portfolio represents a selection of assets owned by individuals or groups with the goal of generating profit. Stocks are among the various forms of investments available. Investors typically consider two key factors: expected returns and associated risks. Portfolio optimization endeavors to maximize returns while minimizing risks. With the progression of time, portfolio optimization strategies increasingly incorporate return predictions using machine learning techniques. In this study, the Holt-Winter method is employed to forecast stock prices and returns, known for its accuracy in estimating seasonal time series data. Previous research primarily utilized the Mean- Variance method for portfolio optimization, but the outcomes were deemed unsatisfactory. Therefore, this study adopts a novel approach by integrating the Mean-Variance model with forecasting. Performance testing of the optimal portfolio emphasizes sensitivity, aiming for substantial average returns, minimal standard deviation, and a high Sharpe ratio. Comparative analysis with the LQ45 index portfolio reveals that the sensitivity-driven portfolio exhibits superior performance, yielding higher values in terms of average returns, standard deviation, and Sharpe ratio.

Algoritma Gated Recurrent Unit untuk Prediksi Harga Indeks Penutupan Saham LQ45

The Indonesia Stock Exchange (IDX) states that stocks, including LQ45 stocks, which constitute the stock market index for the IDX, have become one of the preferred investment options for the public. Investors need to have accurate analysis and information to gain significant profits as stock prices fluctuate due to company performance, industry factors, changes in interest rates, liquidity, global market conditions, market sentiment, and investor psychology. The Gated Recurrent Unit algorithm is suitable for application on historical stock data sets because they are time series data, can be computed and compared on a numerical scale. This algorithm is a variant of the Long Short-Term Memory algorithm or other types of processing modules for Recurrent Neural Networks. The data set used consists of closing price data or close features, comprising a training data set of 4,406 data and a test data set of 1,889 data that have undergone data preparation using various techniques, including data cleansing, data scrubbing, data splitting, data normalization, and data reshaping. The results showed that the Gated Recurrent Unit algorithm is the right strategy because it obtains a good evaluation of model performance, namely MSE of 0.0009; RMSE of 0.17325 and MAE of 0.0207.

Analisis Pembentukan Portofolio Optimal Dengan Menggunakan Model Indeks Tunggal DalamMengambil Keputusan Investasi Pada Saham LQ45 Yang Terdaftar Di BEI Periode 2019-2021

Investment is the main key in achieving increased economic growth, which is reflected in the ability to increase growth rates and income levels. The greater the investment of a country, the greater the level of economic growth that can be achieved. In this study the authors chose LQ45 companies as research objects. The LQ45 stock index was chosen because it is a stock that is quite attractive to investors. This is because LQ45 shares have high liquidity and market capitalization value. But at this time the LQ45 stock index is experiencing an unstable decline seen since 2019 of 3.2%, 2020 of -7.8%, 2021 -2.5% (IDX, 2021). This research was conducted using a quantitative descriptive research method that aims to find out which stocks are included in the optimal portfolio candidate. to find out how big the proportion of each share. And also to know the return and risk that will be accepted. The population used in this study is 45 shares of LQ45 companies listed on the Indonesia Stock Exchange for the 2019-2021 period. The sample selection in this study used a purposive sampling technique, namely a sampling technique with certain considerations or criteria Sugiyono (2010). With the criteria, namely companies that are active and consistently listed on the LQ45 Index during the 2019-2021 period, a total of 34 company shares. Based on the analysis that has been carried out by calculating using the formula in Microsoft Excel, the results in this study are from 34 samples of LQ45 company shares, there are 4 company shares that are candidates in the optimal portfolio, namely Aneka Tambang (Persero) Tbk. (ANTM), Erajaya Swasembada Tbk. (ERAA), Vale Indonesia Tbk. (INCO), and XL Axiata Tbk. (EXCL). Of the 4 companies, the proportion of shares selected was ANTM, the highest with 0.3669 (36.7%), then INCO with 0.2556 (25.6%), ERAA with 0.2182 (21.8%), and EXCL of 0.1593 (15.9%). Of the stocks that make up the optimal portfolio, it has a return of 0.0333 (3.33%) and a risk of 0.0459 (4.59%).

Portfolio Optimization: Application and Comparison of Markowitz Model and Single Index Model on LQ45 Stocks in Indonesia Stock Exchange

This paper examines the optimization of an Indonesian stock portfolio using two models: the Markowitz Model (Mean-Variance Model) and the Single Index Model. The data comprises historical returns of LQ 45 stocks from January 2016 to December 2021. The focus is on selecting stocks for the portfolio and determining their weights based on the two models. The study compares the performance of both optimized portfolios with the LQ45 Index benchmark, IHSG market, and each other using Sharpe and Treynor measurements. The paper tests whether the stock composition of the optimized portfolio from both models successfully and consistently generates a better performance in the future (1 January 2022 – 31 December 2022) and (1 January 2023 – 31 December 2023) compared to both LQ45 and IHSG. The results reveal a notable contrast in portfolio performance between 2022 and 2023. In 2022, both the Markowitz and Single Index portfolios exhibited remarkable returns, surpassing LQ 45 and IHSG. However, in 2023, both portfolios experienced substantial underperformance, with negative returns and unfavorable risk-adjusted metrics. These findings underscore the dynamic nature of financial markets and the need for continuous portfolio monitoring and adaptation. Investors are encouraged to reevaluate their portfolio strategies in response to changing market conditions. The study contributes valuable insights into the temporal variability of optimized portfolios and their sensitivity to evolving market dynamics.

Role of Indonesia’s G20 Presidency and Exchange Rate on LQ45 stock price: Dynamic Panel Model Approach

This study investigates the role of Indonesia’s G20 presidency and exchange rates on the stock prices of LQ45. Daily data were collected from companies listed on Indonesia’s stock exchanges between November 15 and December 24, 2022. Furthermore, we employed a dynamic panel model approach, specifically the system generalized method of moments regression. This analytical technique controls for unobserved heterogeneity, simultaneity, and dynamic endogeneity. The results illustrate that Indonesia’s G20 presidency positively impacts the stock prices in LQ45. In addition, Exchange rate negatively influences on the LQ45 stock prices, and the stock prices of LQ45 in the previous year often correspond to that in the current year. This study’s results provide recommendations for potential investors who wish to invest in the subsectors of LQ45 member companies. The policies implemented during Indonesia's G20 presidency can increase stock prices and create positive market sentiment to encourage migration flows. Additional studies are required to determine the impact of implementing the G20 priority agenda, which includes enhancing the global health framework, fostering employment prospects, transitioning to green and renewable energy, and advancing the digital economy to increase stock prices.

Optimizing the LQ45 Stock portfolio using Piecewise Linear Function: A Case Study from an Investor's Point of View

In optimization problem solving, both linear and nonlinear approaches can be used, with nonlinear programming considering constraints or not. One effective method of nonlinear programming is the Piecewise Linear approach, which breaks down complex nonlinear functions into straight-line segments to make their solution easier. This method can be applied in the financial sphere, such as in stock investment. This study discusses the application of piecewise linear function in optimizing investment portfolios in Bank Jago Tbk. (ARTO), Barito Pacific Tbk. (BRPT), and Go To Gojek Tokopedia Tbk. (GOTO) stocks. The purpose of this study is to provide insight that Piecewise Linear can provide optimal solutions in managing investment portfolios by calculating the risks and returns of selected stocks. The results showed a risk with a level as big as 0.01, the expected profit from the investment of the three stocks in the calculation period reached Rp 1,803,109. On the other hand, for investors who are more cautious and have a Level as big as 1, the anticipated profit in the same investment is around Rp 1,275,052.

Quadruple Witching Days and Abnormal Returns Analysis in The Indonesian Stock Market

The aim of this research is to analyze the Quadruple Witching Days phenomenon and its impact on abnormal returns on the Indonesian stock market. This phenomenon has attracted the attention of many investors and academics, because it is believed to be able to significantly influence stock price movements. It is hoped that the results of this research can provide an in-depth understanding of the influence of Quadruple Witching Days on the Indonesian capital market. Associative descriptive research tests the influence of Quadruple Witching Days. Using secondary data and library methods in the LQ45 and SRI-KEHATI stock indices listed on the Indonesia Stock Exchange for the period 2010 to 2022 with daily and weekly data. The hypothesis test that will be used in this research is the Paired Sample t - Test and the non-parametric Mann-Withney test. There are differences in abnormal returns before and after the Quadruple Witching Days event on the LQ45 stock index. There is no difference in abnormal returns before and after the Quadruple Witching Days event on the SRI-KEHATI stock index. High Vigilance, Quick Selling or Buying, Portfolio Diversification as a consideration for taking profits for investors LQ45, Long Term Investment Approach, Portfolio Stabilization may be more suitable with investment strategies because there is no significant difference in abnormal returns, investors in SRI-KEHATI.

Comparison of the Accuracy of Brown's and Holt's Double Exponential Smoothing in LQ45 Stock Price Forecasting

As of May 2022, 787 stocks are listed on the Indonesia Stock Exchange (IDX), and the number of stock indices in Indonesia to date is 38. One interesting and important stock index is the LQ45 index. Because this index is a very important reference index for investors, this research data focuses on stocks in the LQ45 index. There are two essential things in the forecasting process: the data and the right forecasting method. Two forecasting methods that can be used are Brown and Holt's Double Exponential Smoothing (DES). This study examines two methods with the lowest accuracy error in forecasting the LQ45 stock price data. Mean Absolute Percentage Error (MAPE) is used to measure the accuracy of the error. The analysis methods used to compare the MAPE of the two methods are the F test for variance similarity, Boxplot, t-test to test paired means with different cases of variance, and Wilcoxon signed rank test to test paired means nonparametric statistics. The result is that the MAPE average with Holt's DES method is smaller than the average MAPE with Brown's DES method. This is supported by the t-test for paired means with different cases of variance and also supported by the Wilcoxon signed exact rank test. Meanwhile, the MAPE standard deviation with Holt's DES method is smaller than the MAPE standard deviation with Brown's DES method. This is supported by the F test to test the variance similarity and is visually supported by a Boxplot diagram. From this study, LQ45 stocks with the smallest MAPE value accuracy are ICBP stocks. In general, based on the MAPE value, Holt's DES method is better than Brown's DES method in predicting the prices of stocks in the LQ45 index.

PENGARUH DEBT TO EQUITY RATIO, PRICE EARNING RATIO, EARNING PER SHARE, PRICE TO BOOK VALUE TERHADAP RETURN SAHAM SELAMA MASA PANDEMI COVID-19 (STUDI PADA PERUSAHAAN YANG AKTIF TERDAFTAR DALAM LQ45 TAHUN 2019-2021)

Abstract— The existence of Covid-19 caused the economy in Indonesia to become unstable, this caused the performance of the LQ45 stock index to experience a decline. This study's goal was to examine how financial ratios affected stock returns during the Covid-19 pandemic. The population of this study is the LQ45 Index which is found on the Indonesia Stock Exchange in 2019-2021 with a total of 54 companies. The samples used were 25 companies selected using purposive sampling method. This study uses secondary data obtained from annual reports published on the official website of the Indonesia Stock Exchange and the company's official website. Data analysis techniques use descriptive statistics and panel data regression. The results of this study indicate that the Debt to equity ratio (DER) and price earning ratio (PER) partially have no effect on stock returns. Meanwhile, earnings per share (EPS) and price to book value (PBV) have a positive effect on stock returns. Simultaneous test results show that the Debt to equity ratio (DER), price earning ratio (PER), earnings per share (EPS) and price to book value (PBV) have a positive effect on stock returns.
 Keywords: Debt to Equity Ratio (DER); Earnings per Share (EPS); Price Earning Ratio (PER); Price to Book Value (PBV); Stock Returns

Predicting the Direction of the Lq45 Stock Market in Indonesia

To cope with the unpredictability of the stock market, investors should have a solid understanding of the ups and downs of bullish and bearish phases. This is especially true when looking at Indonesia's LQ45 index, which consists of 45 of the most liquid stocks in Indonesia. This study carefully predicts the future direction of movement of the LQ45 stock price index by first figuring out its bearish and bullish periods, and then finding important macroeconomic factors. In this study, techniques such as X13 ARIMA SEATS, Bry-Boschan, and Binomial Logistic Regression are used to find clear bullish and bearish phases in the LQ45 index time frame. These techniques also point out certain macroeconomic indicators, such as Motor Sales, Retail Sales, Oil Price, BI Rate, 1-month Deposito Rate Working Capital Rate, Rupiah Exchange Rate, Inflation Narrow Money, Broad Money, US PMI Manufacture, and US Inflation, which have a great effect on these market conditions, as indicated by their p-values. Based on these insights, predictive models were created to show how the LQ45 index may change in the future. These models provide asset management companies with a solid basis for making smart investment decisions, with a tendency to be more aggressive when the market is up and more cautious when the market is down. This study is special because it combines the analysis of LQ45 index market trends with important macroeconomic factors in a way that has never been done before in the Indonesian market.
 

The Effect of Company Performance on Stock Returns in the LQ45 Stock Cluster in 2020-2022

In the Indonesian capital market, various securities are traded, but stock investors dominate. The LQ45 index illustrates the Indonesian capital market condition is better than the JCI (Jakarta Composite Index). Stocks on the LQ45 index have large market capitalization, high liquidity, and good company fundamentals, but have varying returns. It is necessary to group the stock returns of LQ45 index companies. The method used is time series clustering in the 2020-2022 period. Furthermore, the logistic regression analysis is used to determine the effect of company performance that is consistent in the LQ45 index on stock return status. The results showed that the selected algorithm for clustering was K-Means with 2 optimal numbers of clusters characterized as lagging stock and leading stock. Then, company stocks in the LQ45 index for the 2020-2022 period tend to be classified as leading stocks if they have a low Debt to Equity Ratio but have a high Net Profit Margin and Price Earnings Ratio.

Investigation of winner-loser portfolio anomalies and size effect anomalies in LQ45 index, Indonesia stock exchange

This study investigates the anomalies of winner-loser portfolios and anomalies of size effect on LQ45 stocks in the Indonesia Stock Exchange (IDX), period 2015-2020. The paired sample t-test (parametric) and Wilcoxon test (non-parametric) are used to test samples. Sample selection is carried out using the non-probability and purposive sampling methods. The shares of companies that met the sample criteria were 26. The primary data used cumulative abnormal returns and market capitalization as proxies for a company's stock size. The anomaly of the winner-loser effect phenomenon is observed by dividing the data into two periods: the formation period and the testing period. The periods are both short-term and long-term. The short-term period is six months, and the long-term is 12 months. The first finding shows reversals in average cumulative abnormal returns (CARs) on the winner-loser stock portfolio. In the testing period, the average CARs were turned down, and many average CARs became negative. Portfolio loser in the testing period, the average CARs reversed to positive. The second is a reversal in average CARs in winner-loser stock portfolios caused by the size effect. The performance of small winner-loser portfolios is proven to outperform big winners' portfolios. The reversal occurs in both the short-term and long-term. This finding suggests that behavioral finance can explain this market anomaly, and it is helpful for investors in coming up with strategies to get abnormal returns.

Window Dressing Phenomenon on LQ45 Stock Investment Decisions with a Modern Technical Analysis Approach

The anomalous phenomenon of the windows dressing market has the potential to distort stock prices amidst the issue of an economic recession which is predicted to occur in 2023. This research aims to analyze the trend of LQ45 index stock price movements amidst the windows dressing phenomenon. This type of research is descriptive with a qualitative approach using secondary data. The research subjects are the Top 5 Constituents on the LQ45 Fact Sheet as of September 2023 published by the BEI. The object of the research is graphic data and monthly stock price movement trends for 5 issuers in the fourth quarter starting from October-December 2023. The data is analyzed using a modern technical analysis approach using the Relative Strength Index and Bollinger Band indicators via the tradingview.com website. The research results show a weakening of the short-term share price trend for LQ45 index issuers, which is likely due to a temporary correction, while the long-term fundamental prospects for several issuers are still positive.