Indonesia's main stock market index, the JCI, fell to 7345 points on July 22, 2025, losing 0.72% from the previous session. Over the past month, the index has climbed 8.22% and is up 0.42% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Indonesia. Indonesia Stock Market (JCI) - values, historical data, forecasts and news - updated on July of 2025.

Stock market return (%, year-on-year) in Indonesia was reported at 18.73 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Indonesia - Stock market return (%, year-on-year) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Key information about Indonesia Jakarta Composite

• Indonesia Jakarta Composite closed at 6,270.6 points in Feb 2025, compared with 7,109.2 points at the previous month end
• Indonesia Equity Market Index: Month End: Jakarta Composite data is updated monthly, available from Apr 1983 to Feb 2025, with an average number of 735.7 points
• The data reached an all-time high of 7,670.7 points in Aug 2024 and a record low of 61.7 points in Aug 1986

• In the latest reports, IDX recorded a monthly P/E ratio of 12.6 in Jan 2025

Indonesia's main stock market index, the JCI, rose to 7443 points on July 23, 2025, gaining 1.34% from the previous session. Over the past month, the index has climbed 8.35% and is up 2.48% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Indonesia. Indonesia Stock Market (JCI) - values, historical data, forecasts and news - updated on July of 2025.

Indeks Pasar Saham Indonesia dilaporkan sebesar 6,270.6 10Aug1982=100 pada 2025-02. Rekor ini turun dibanding sebelumnya yaitu 7,109.2 10Aug1982=100 untuk 2025-01. Data Indeks Pasar Saham Indonesia diperbarui bulanan, dengan rata-rata 735.7 10Aug1982=100 dari 1983-04 sampai 2025-02, dengan 503 observasi. Data ini mencapai angka tertinggi sebesar 7,670.7 10Aug1982=100 pada 2024-08 dan rekor terendah sebesar 61.7 10Aug1982=100 pada 1986-08. Data Indeks Pasar Saham Indonesia tetap berstatus aktif di CEIC dan dilaporkan oleh Indonesia Stock Exchange. Data dikategorikan dalam Indonesia Global Database – Table ID.ZA001: Indonesia Stock Exchange (IDX): Indices.

Indonesia Capital Market: Stock Trading: Average Daily Trading: Growth data was reported at 21.894 % in Feb 2025. This records an increase from the previous number of -16.869 % for Jan 2025. Indonesia Capital Market: Stock Trading: Average Daily Trading: Growth data is updated monthly, averaging 2.564 % from Feb 2011 (Median) to Feb 2025, with 153 observations. The data reached an all-time high of 230.564 % in Nov 2016 and a record low of -47.121 % in Nov 2017. Indonesia Capital Market: Stock Trading: Average Daily Trading: Growth data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI020: Financial System Statistics: Capital Market Sector.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Indonesia to Become a Global EV Battery Hub with $9 Billion Investment

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Prices for Jakarta Stock Exchange Composite Index including live quotes, historical charts and news. Jakarta Stock Exchange Composite Index was last updated by Trading Economics this July 23 of 2025.

Indonesia ID: Export Market for Goods and Services: Volume data was reported at 306.078 USD bn in 2026. This records an increase from the previous number of 293.780 USD bn for 2025. Indonesia ID: Export Market for Goods and Services: Volume data is updated yearly, averaging 179.364 USD bn from Dec 1995 (Median) to 2026, with 32 observations. The data reached an all-time high of 306.078 USD bn in 2026 and a record low of 64.989 USD bn in 1995. Indonesia ID: Export Market for Goods and Services: Volume data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Indonesia – Table ID.OECD.EO: Trade Statistics: Share in World Trade and Performance Indicators: Forecast: Non OECD Member: Annual. XMKT - Export market for goods and services, volume OECD calculation, see OECD Economic Outlook database documentation

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

INDO Indonesia Energy Corporation Limited Ordinary Shares

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Indonésie: Stock market return, percent: Pour cet indicateur, Global Financial Development Database fournit des données pour la Indonésie de 1984 à 2021. La valeur moyenne pour Indonésie pendant cette période était de 16.13 pour cent avec un minimum de -30.91 pour cent en 1998 et un maximum de 187.43 pour cent en 1989.

Context

This dataset contains historical data of stocks listed on IHSG with time ranges per minutes, hourly, and daily. The source of the dataset is taken from Yahoo Finance's public data and the IDX website which is listed in the metadata tab. This dataset was created with the intention of academic research purposes and not to be commercialized. If you have questions about the dataset, please ask in the discussion tab. Code snippet: https://github.com/muamkh/IHSGstockscraper

Content

Stock minutes data is taken from 1 November 2021 until 6 January 2023. Stock hourly data is taken from 16 April 2020 until 6 January 2023. Stock daily data is taken from 16 April 2001 until 6 January 2023. All of the data is using CSV format. Stock data isnt adjusted with dividend, stock split, and other corporate action.

Stocklist Structure

• Code = Stock code
• Name = Company name
• ListingDate = Listing date of stock on Indonesia Stock Exchange
• Shares = Amount of shares
• ListingBoard = Board category (Main Board, Development Board or Acceleration). More info: https://www.idx.co.id/en-us/products/stocks/
• Sector = Sector Category based on IDX-IC. More info: https://www.idx.co.id/en-us/products/stocks/
• LastPrice = Last stock price
• MarketCap = Market Capitalization.
• MinutesFirstAdded = Date the data first retrieved in minute range
• MinutesLastAdded = Date the data last retrieved in minute range
• HourlyFirstAdded = Date the data first retrieved in hourly range
• HourlyLastAdded = Date the data last retrieved in hourly range
• DailyFirstAdded = Date the data first retrieved in daily range
• DailyLastAdded = Date the data last retrieved in daily range

Struktur Data Saham

• timestamp = Date and time of stock transaction
• open = opening price
• low = lowest price in the timespan
• high = highest price in the timespan
• close = closing price
• volume = Total volume traded in the timespan

Indonesia Data Center Processor Market is Segmented by Processor Type (GPU, CPU and More), Application( Advanced Data Analytics, AI/ML Training and Inference, High-Performance Computing and More), Architecture (X86, ARM-Based, RISC-V and Power), Data Center Type (Enterprise, Colocation, Cloud Service Providers / Hyperscalers). The Market Forecasts are Provided in Terms of Value (USD).

The market for the halal food and beverage industry sector has experienced rapid growth in recent years, which indicate excellent investment opportunities. This paper examine the effect of Technical Efficiency (TE) on firm value in 5 selected influential countries in halal food and beverage sector based on Global Islamic Economy Report 2020. Two steps estimation was used to run the data, using the Stochastic Frontier Analysis (SFA) model to determine the company’s TE and panel data to test the effect of TE through firm value. The results show that Indonesia has the highest score for TE (62%), followed by Pakistan (59%), South Africa (57%), Malaysia (55%), and Singapore (52%), which means, in general, there is inefficiency in allocating resources over 38% up to 48% and needs to be improved by halal food and beverage companies in. Regarding panel data, all countries sample except Pakistan highlight that TE significantly affect company value. It indicates that the crucial part of managing efficiency can be a sign in stock market performance. The result shows that company managers should set efficiency strategies to their business process for creating sustainability and increase their value in the capital market. As for investors, this TE can be used as an indicator before choosing company stocks; if the company is efficient, then it is worthy of being one of the portfolio assets. Form the government side, the finding can help them to set appropriate policy setting to boost halal food and beverages industry such as giving subsidy or incentive to increase the efficiency ability of halal food and beverage companies and identify the industry’s strength by comparing the result of TE between 5 countries.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Telekomunikasi Indonesia: Riding the Digital Wave in (TLK)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This study aims to analyze the impact of carbon emissions, cryptocurrency volatility, and macroeconomic factors on stock prices, stock valuations, and stock volatility in Indonesia. Employing a dynamic panel data approach and the two-step system Generalized Method of Moments (GMM), the research estimates four primary models: (1) stock price, (2) price-to-earnings ratio, (3) stock return volatility, and (4) a moderation model evaluating the interaction between carbon emissions and macroeconomic variables. The analysis draws on panel data from companies listed on the Indonesia Stock Exchange over the period 2020–2024. The findings indicate that carbon emissions exert a significantly negative effect on stock valuations but do not directly influence stock prices or return volatility. The interaction between carbon emissions and macroeconomic variables is shown to be significant in explaining stock price dynamics, suggesting that economic conditions can amplify or mitigate market perceptions of environmental risks. The volatility of Bitcoin and Ethereum positively affects stock valuations, although it does not have a significant impact on stock prices or volatility. Macroeconomic factors such as exchange rates and global oil prices also exhibit significant effects on the stock market. Furthermore, the dividend payout ratio has a positive influence on stock prices and valuations, while dividend yield contributes to increased volatility. These findings have important implications for regulators, investors, companies, and capital market authorities in fostering a more resilient and sustainable financial system. This study also contributes to the literature on sustainable finance and digital finance in emerging markets.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

PT Telekomunikasi Indonesia Tbk Forecast & Analysis

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

• Risk free rate based on BI Rate (Indonesian central bank rate)
• Market data for each equity fund return
• Market data for JCI (Jakarta Composite Index - Indonesian Capital Market Index)
• Market data for each sectoral return in JCI
• prepared data to be uploaded to eviews for analysis
• summary of result : regression result, and portfolio value decomposition result

Indonesia's flexible plastic packaging market is projected to grow at a CAGR of 6.07% from 2025 to 2033, reaching a value of million by 2033. This growth is attributed to rising demand from the food and beverage, medical and pharmaceutical, and personal care and household care industries. Indonesia's growing population and increasing disposable income are also contributing factors. Key market trends include the growing adoption of sustainable packaging solutions and the increasing use of flexible packaging in e-commerce. The market is segmented by material type (polyethylene (PE), bi-oriented polypropylene (BOPP), cast polypropylene (CPP), polyvinyl chloride (PVC), ethylene vinyl alcohol (EVOH), and other materials); product type (pouches, bags, films and wraps, and other product types); and end-user industry (food, beverage, medical and pharmaceutical, personal care and household care, and other end-user industries). The food segment is the largest end-user industry, accounting for over 50% of the market share. Major market players include Amcor Plc, PT Toppan Indonesia Printing, Primajaya Eratama, PT DINAKARA PUTRA, Sonoco Products Company, PT ePac Flexibles Indonesia, PT ARTEC PACKAGE INDONESIA, and PT Plasindo Lestari. Recent developments include: May 2024: PT United Harvest Indonesia, a prominent Indonesian food processor, entered the Chinese snack food market by introducing a line of shrimp crackers. Headquartered in Jakarta, the company rolled out its 'Deep Ocean Treasure' brand of dried shrimp snacks, targeting retailers in northern China. Indonesia, benefiting from duty-free privileges in China as an ASEAN member, became a key provider of primary goods to its top trading partner, China., August 2023: PepsiCo, the US snacks and beverages giant, was expected to resume snack production in Indonesia, marking a return after exiting a previous joint venture in the country two years ago. Breaking ground in Cikarang, West Java, PepsiCo started the construction of a new production facility. The company announced a substantial long-term commitment of USD 200 million, emphasizing its dedication to developing the Indonesian market.. Key drivers for this market are: Shift Towards Light Weight and Small Packaging Aids to Demand. Potential restraints include: Shift Towards Light Weight and Small Packaging Aids to Demand. Notable trends are: Lightweight and Convenient Packaging is Expected to Aid the Demand.

As of April 2025, Oppo led the mobile vendor market in Indonesia, with a share of around **** percent. It was followed closely by Samsung, with a share of about **** percent. Collectively, Oppo, Samsung, and Xiaomi accounted for **** of the mobile market share in the country. Oppo's rising dominance Oppo’s market share in Indonesia has risen significantly over the past years. The brand's relatively affordable prices have been one of the key drivers of its growth in the country, as Indonesian consumers tend to look for value for their money, searching for smartphones that are reasonably priced with good performance. In 2023, Oppo managed to tail Samsung in terms of the market share of unit shipments, indicating strong competition in the rapidly evolving Indonesian smartphone market. Smartphone usage and the telecommunications market With a substantial number of mobile internet users, Indonesia’s telecommunications market is one of the largest in Southeast Asia. Smartphones in Indonesia are used for various activities that require reliable network capabilities, such as video streaming and online gaming. While users have reported positive experience with 5G networks for these activities, the development of 5G infrastructure in Indonesia is still faced with challenges, including high costs and spectrum shortages. This has led to significantly slower 5G network growth compared to Indonesia’s neighboring countries, such as Singapore, Malaysia, and Thailand.