Australia's main stock market index, the ASX200, fell to 8556 points on December 2, 2025, losing 0.11% from the previous session. Over the past month, the index has declined 3.81%, though it remains 0.71% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Australia. Australia Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

Australian Securities Exchange is Australia's primary securities exchange and is one of the largest listed exchange groups by market capitalization.

The Australian Securities Exchange (ASX) was established in July 2006 after the Australian Stock Exchange merged with the Sydney Futures Exchange, making it one of the top 20 global exchange groups by market capitalization. ASX facilitates trading in leading stocks, ETFs, derivatives, fixed income, commodities, and energy, commanding over 80% of the market share in the Australian Cash Market, with the S&P/ASX 200 as its main index. We offer comprehensive real-time market information services for all instruments in the ASX Level 1 and Level 2 (full market depth) products, and also provide Level 1 data as a delayed service. You can access this data through various means tailored to your specific needs and workflows, whether for trading via electronic low latency datafeeds, using our desktop services equipped with advanced analytical tools, or through our end-of-day valuation and risk management products.

Monthly and long-term Australia Stock Market data: historical series and analyst forecasts curated by FocusEconomics.

Techsalerator's Corporate Actions Dataset in Australia offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 2200 companies traded on the Australian Securities Exchange* (XASX).

‍

Top 5 used data fields in the Corporate Actions Dataset for Australia:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

‍

Top 5 corporate actions in Australia:

Resource Sector Developments: Corporate actions in the mining and resource sectors, including new mineral discoveries, expansion of mining operations, and commodity price fluctuations, have a significant impact on Australia's economy.

Financial Services and Fintech: Corporate actions related to financial services, including the growth of fintech companies, digital banking solutions, and changes in financial regulations, play a crucial role in Australia's financial landscape.

Real Estate Investments: Corporate actions in the real estate sector, such as property development projects, commercial real estate investments, and urbanization efforts, are notable contributors to Australia's economy.

Renewable Energy Initiatives: Corporate actions involving investments in renewable energy projects, such as solar and wind farms, reflect Australia's commitment to transitioning to sustainable energy sources.

Healthcare and Biotechnology: Corporate actions in the healthcare and biotechnology sectors, including drug development, medical research, and healthcare technology advancements, are important contributors to Australia's innovation-driven economy.

‍

Top 5 financial instruments with corporate action Data in Australia

Australian Stock Exchange (ASX) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Australian Stock Exchange. This index provides insights into the performance of the Australian stock market.

ASX Foreign Company Index: The index that tracks the performance of foreign companies listed on the Australian Stock Exchange, if foreign listings are present. This index gives an overview of foreign business involvement in Australia.

GroceryLand Australia: An Australia-based supermarket chain with operations in multiple regions. GroceryLand Australia focuses on providing essential products to local communities and contributing to the retail sector's growth.

FinanceDown Under: A financial services provider in Australia with a focus on promoting financial inclusion and access to banking services, particularly among underserved communities.

AgriTech Australia: A company dedicated to advancing agricultural technology in Australia, focusing on optimizing crop yields, sustainable farming practices, and technological innovation in the agricultural sector.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Australia, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price ‍

Q&A:

How much does the Corporate Actions Dataset cost in Australia?

The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

How complete is the Corporate Actions Dataset cov...

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 2200 companies listed on the Australian Securities Exchange* (XASX) in Australia. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

‍

Top 5 used data fields in the End-of-Day Pricing Dataset for Australia:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

‍

Top 5 financial instruments with End-of-Day Pricing Data in Australia:

S&P/ASX 200 Index: The S&P/ASX 200 is the benchmark stock market index in Australia. It tracks the performance of the 200 largest publicly listed companies on the Australian Securities Exchange (ASX) and is widely used as a measure of the Australian stock market's overall performance.

Australian Dollar (AUD): The Australian Dollar is the official currency of Australia and is commonly abbreviated as AUD. It is one of the most traded currencies in the world and is used for both domestic and international transactions.

Reserve Bank of Australia (RBA): The central bank of Australia responsible for monetary policy, issuing currency, and maintaining financial stability. The RBA's decisions on interest rates and monetary policy have a significant impact on the Australian economy.

Australian Securities Exchange (ASX): The ASX is the primary stock exchange in Australia, where domestic and international companies are listed and traded. It plays a crucial role in facilitating capital raising and investment in Australia's financial markets.

Australian Government Bonds: These are debt securities issued by the Australian government to fund government operations and infrastructure projects. Australian Government Bonds are considered safe investments and are used as benchmarks for interest rates and economic sentiment.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Australia, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Australia ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Australia?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Australia exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, d...

The Australia Data Center Power Market Report Segments the Industry Into Component (Electrical Solutions, and Service), Data Center Type (Hyperscaler/Cloud Service Providers, and More), Data Center Size (Small-Sized Data Centers, Medium-Sized Data Centers, and More), and Tier Level (Tier I and II, and More). The Market Forecasts are Provided in Terms of Value (USD).

The Custody, Trustee and Stock Exchange Services has experienced dynamic shifts driven by globalisation, digital revolution and market volatility over the past few years. Although the number of stock market trades has climbed, investors and superannuation funds have gravitated towards international markets to diversify their portfolios over the past few years, slowing revenue growth for domestic stock exchanges and share registry services. Despite the trend, Guzman and Gomez's recent IPO, the largest on the ASX in three years - could signal a potential revival in domestic stock exchange interest. Competition within the industry has heightened over the past few years. The payment space has experienced fierce competition, but the growing digital payments and online shopping segments have propelled credit card usage. Despite the booming popularity of alternative payment methods like buy now pay later (BNPL), credit card providers have boosted their appeal through attractive loyalty and reward programs, spurring industry growth. The inherently volatile financial markets and consumer sentiment heavily influence services like stock exchanges share registries and credit card administration. Incidents like the pandemic have adversely impacted service providers' performance in the two years through 2020-21. However, despite market fluctuations, the industry's wide range of services has helped moderate revenue volatility. Therefore, revenue has risen at an annualised 0.7% to $13.0 billion over the five years through 2024-25, including a revenue uptick of 0.5% in the current year. The industry is on track to recover over the next few years. Consumer sentiment and business confidence are set to rise, encouraging more clients to seek out custody, trustee and stock exchange services. Anticipated growth of the All Ordinaries Index, the value of funds under management (FUM) and superannuation funds' assets under management (AUM) will fuel industry expansion. However, digitalisation in the financial services sector will introduce new entrants, creating a challenging environment for traditional service providers and placing downward pressure on profitability. Revenue is forecast to rise at an annualised 1.9% to $14.3 billion over the five years through 2029-39.

Investor uncertainty due to volatility in interest rates has underpinned the industry’s revenue growth over the past five years. Investors have shifted strategies, bolstering their demand for independent investment research through improved interest in portfolio comparison tools and holistic research platforms. This has extended the industry's reliance on subscription and consultation services. Morningstar, Marsh Mercer and Lonsec continue to control a significant market share. Growth in revenue among these players has contributed to a boost in the industry’s overall performance. High competition and incumbent firms' strong brand presence have challenged new entrants. Investment research companies battled financial research saturation over the last five years as AI and digitisation expanded free-to-access advice for retail investors. Saturated market conditions pushed companies to respond by strengthening the quality of their workforce, lifting wage costs while improving productivity. Productivity growth paired with strong demand helped the industry retain healthy profitability. Revenue is expected to grow at an annualised 4.0% over the five years through 2025-26, to $636.0 million, despite a slowing growth rate of 0.3% in 2025-26 as investors cycled from traditional equities into alternative asset classes and exchange-traded funds. The industry’s outlook remains promising despite potential challenges. Established companies will continue to benefit from ongoing uncertainty in investment markets, while elevated exposure to subscription models will sustain revenue growth. The industry’s reliance on highly skilled workers will boost average salaries. However, increasing AI and machine learning adaptation will drive productivity, insulating the industry's profitability. While technology will improve analysts' scope to cover more reports more efficiently, technological advancements may trickle into downstream financial advisors' workflows, potentially suppressing demand for research services. These dynamics will urge the industry to innovate and adapt by developing new tools and product delivery methods to integrate their human-verified institutional research into advisors' workflows. In the long run, productivity gains and improved product delivery will benefit from growing investment market volatility, leading to an annualised revenue growth of 1.9% in the five years through 2030-31, to $698.4 million.

Snapshot img

Predictive AI In Stock Market Size 2025-2029

The predictive AI in stock market size is valued to increase by USD 1.63 billion, at a CAGR of 21.8% from 2024 to 2029. Increasing availability and integration of alternative data will drive the predictive AI in stock market.

Market Insights

North America dominated the market and accounted for a 33% growth during the 2025-2029.
By Component - Solution segment was valued at USD 329.80 billion in 2023
By Application - Algorithmic trading segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 445.64 million 
Market Future Opportunities 2024: USD 1632.20 million
CAGR from 2024 to 2029 : 21.8%

Market Summary

Predictive AI in the stock market refers to the application of artificial intelligence (AI) algorithms and techniques to analyze historical market data and make predictions about future trends. This technology has gained significant attention in recent years due to the increasing availability and integration of alternative data sources and the advancement of generative AI and large language models for qualitative alpha generation. One real-world business scenario where predictive AI is making a significant impact is in supply chain optimization. For instance, a manufacturing company can use predictive AI to forecast demand for its products based on historical sales data, economic indicators, and other external factors.
By accurately predicting demand, the company can optimize its inventory levels, reduce carrying costs, and improve operational efficiency. However, the adoption of predictive AI in the stock market also presents several challenges. Data quality and overfitting are major concerns, as historical data may not accurately reflect future market conditions. Market reflexivity, or the phenomenon where market participants' actions influence market trends, can also make it challenging to make accurate predictions. Despite these challenges, the potential benefits of predictive AI in the stock market are significant, including improved risk management, increased operational efficiency, and enhanced investment strategies.

What will be the size of the Predictive AI In Stock Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

Predictive AI in the stock market is an evolving technology that leverages advanced algorithms and real-time analytics to identify trends and patterns, enabling data-driven decision-making for businesses. One significant trend in this domain is the integration of demand sensing technology, which improves accuracy by reducing false positive and false negative rates. For instance, model performance can be enhanced through algorithm performance improvements, feature engineering techniques, and model retraining frequencies. In the realm of supply chain optimization, predictive AI-powered forecasting plays a pivotal role in inventory control strategies. By monitoring data in real-time, businesses can implement automated ordering systems, ensuring stockout prevention and minimizing excess inventory.
This approach not only improves precision and recall but also enables better risk mitigation planning and compliance with data privacy regulations. Scalability testing and data quality management are essential aspects of deploying predictive AI models in the stock market. Hyperparameter tuning and error rate reduction are critical for maintaining model performance, while system monitoring tools facilitate predictive maintenance and performance benchmarks. By adhering to data governance policies, businesses can ensure the reliability and accuracy of their predictive AI models, ultimately leading to improved business intelligence and strategic decision-making.

Unpacking the Predictive AI In Stock Market Landscape

The market management employs advanced clustering techniques and predictive modeling to minimize lead time variability and enhance production planning. By integrating real-time data processing and scalable infrastructure, businesses can achieve significant improvements in inventory optimization and order fulfillment prediction. For instance, predictive models trained on model training datasets have demonstrated a 20% increase in demand prediction accuracy compared to traditional methods. Data security protocols are essential to safeguard sensitive stock market data. Predictive AI systems employ machine learning models, deep learning algorithms, and neural network architecture for model evaluation and classification. These advanced techniques enable real-time anomaly detection and statistical process control, ensuring risk assessment metrics align with business objectives. Cloud-based infrastructure and process automation tools facilitate seamless data integration pipelines, allowing for efficient supply chain analytics and stock level monitoring. P

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.

AR3 AUSTRALIAN RARE EARTHS LIMITED

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

Financial asset investors have benefited from a generally strong domestic sharemarket performance and robust profit margins over the past few years. Typically, industry funds are invested in equities, and industry revenue depends on various sharemarket performances. The COVID-19 pandemic and ensuing inflationary pressures significantly disrupted both local and global equity markets, which limited industry performance. Yet, total assets have continued to accumulate over recent years, compounding returns for investors, assisted by previously low interest rates. Overall, industry revenue is expected to climb at an annualised 6.2% over the five years through 2024-25, to $176.3 billion. The low-interest rate environment that characterised the trading landscape until recently affected fixed-income assets' performance, which changed the mix of funds held in various industry investment vehicles. More recently, market volatility and cash rate hikes have led to investors increasingly moving to cash management trusts because of their perceived safety as investment instruments. Related elevated interest rates and negative business confidence are set to hurt returns for many investors in 2024-25, particularly investment portfolios geared for higher risk. Despite these pressures, investor incomes are set to swell by 1.7% in the current year off the back of an anticipated strong domestic sharemarket performance, bumped by strong business profit. A falling MSCI world index and negative consumer sentiment have the potential to continue softening investment performance over the coming years. Yet, inflationary pressures and interest rates are set to gradually ease as trading conditions improve. Projected global financial stability and a sluggish appreciation of the Australian dollar may set the stage for a resurgence in overseas investment in Australian markets, yet continued changes implemented by the FIRB may limit the willingness of overseas investors to spend domestically. The influence of superannuation funds over the industry may continue to rise, drawing funds from retail investors, yet they themselves are a large market. For this reason, continued increases to the Superannuation Guarantee Scheme are likely to boost assets at the disposal of pension funds. Overall, financial asset investor incomes are projected to continue growing at an annualised 3.2% through 2029-30, to total $206.6 billion.

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.

ANZ AUSTRALIA AND NEW ZEALAND BANKING GROUP LIMITED

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

The size of the Australia Soy Protein Market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 3.99% during the forecast period. The soy protein market in Australia is steadily growing, fueled by the rising popularity of plant-based products and their health advantages. Key industry leaders such as Cargill and ADM are dominating the market through their wide range of products and strong distribution networks. The popularity of soy protein in food and beverages, sports nutrition, and dietary supplements is increasing due to the growing trend of vegan, vegetarian, and flexitarian diets. The cost-effectiveness and high protein content of soy protein make it attractive to both manufacturers and consumers. Ongoing research and development efforts continue to support the optimistic outlook of the market, despite obstacles like manufacturing difficulties and different regulatory standards. This path underscores the crucial importance of soy protein in the Australian market and its potential for continual expansion. Recent developments include: December 2022: Harvest B, an Australian B2B alternative protein company, announced its expansion in Australia by opening its plant-based meat ingredient facility in Australia. The facility, according to Harvest B, is a cutting-edge facility equipped with the most advanced manufacturing technology for the production of proteins like soy, pea, wheat, and oat. Additionally, the facility will include a new Research and Development laboratory, where researchers will focus on optimizing plant-based meat ingredients., March 2022: GrainCorp announced their collaboration with Australia's National Science Agency CSIRO and a renowned plant-based food manufacturer, 'v2food', for a research project that was valued at USD 4.4 million in the plant-based protein market. GrainCorp raised the funding amount from the Australian Government's Cooperative Research Centres Projects (CRC-P) Program to separate and manufacture proteins from soy, fava beans, canola, and chickpeas on a large scale., February 2021: DuPont merged its Nutrition Business with International Flavors & Fragrances (IFF), thus establishing a company that may become a leading supplier of ingredients to the food industry.. Key drivers for this market are: Rising Demand for Plant-based Protein Sources, Strategic Investments by Players Operating in the Market. Potential restraints include: Availability of Substitute Protein Sources. Notable trends are: Rising Demand for Plant-based Protein Sources.

The Australia Data Center Networking Market Report Segments the Industry Into Components (By Product, by Services), End-Users (IT & Telecommunication, BFSI, Other End-Users). By Data-Center Type(Colocation, Hyperscalers/Cloud Service Providers, and More). Bandwidth( ≤10 GbE, 25–40 GbE, and More). The Market Forecasts are Provided in Terms of Value (USD).

The Finance sector's operating environment was previously characterised by record-low interest rates. Nonetheless, high inflation prompted the Reserve Bank of Australia (RBA) to hike the cash rate from May 2022 onwards. This shift allowed financial institutions to impose higher loan charges, propelling their revenue. Banks raised interest rates quicker than funding costs in the first half of 2022-23, boosting net interest margins. However, sophisticated competition and digital disruption have reshaped the sector and nibbled at the Big Four's dominance, weighing on ADIs' performance. In the first half of 2025, the fierce competition has forced ADIs to trim lending rates even ahead of RBA moves to protect their slice of the mortgage market. Higher cash rates initially widened net interest margins, but the expiry of cheap TFF funding and a fierce mortgage war are now compressing spreads, weighing on ADIs' profitability. Although ANZ's 2024 Suncorp Bank takeover highlights some consolidation, the real contest is unfolding in tech. Larger financial institutions are combatting intensified competition from neobanks and fintechs by upscaling their technology investments, strengthening their strategic partnerships with cloud providers and technology consulting firms and augmenting their digital offerings. Notable examples include the launch of ANZ Plus by ANZ and Commonwealth Bank's Unloan. Meanwhile, investor demand for rental properties, elevated residential housing prices and sizable state-infrastructure pipelines have continued to underpin loan growth, offsetting the drag from weaker mortgage affordability and volatile business sentiment. Overall, subdivision revenue is expected to rise at an annualised 8.3% over the five years through 2024-25, to $524.6 billion. This growth trajectory includes an estimated 4.8% decline in 2024-25 driven by rate cuts in 2025, which will weigh on income from interest-bearing assets. The Big Four banks will double down on technology investments and partnerships to counter threats from fintech startups and neobanks. As cybersecurity risks and APRA regulations evolve, financial institutions will gear up to strengthen their focus on shielding sensitive customer data and preserving trust, lifting compliance and operational costs. In the face of fierce competition, evolving regulations and shifting customer preferences, consolidation through M&As is poised to be a viable trend for survival and growth, especially among smaller financial institutions like credit unions. While rate cuts will challenge profitability within the sector, expansionary economic policies are poised to stimulate business and mortgage lending activity, presenting opportunities for strategic growth in a dynamic market. These trends are why Finance subdivision revenue is forecast to rise by an annualised 1.1% over the five years through the end of 2029-30, to $554.9 billion

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.

Abrdn Australia Equity Fund: Poised for Consistent Returns? (IAF)

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

The Australia Data Center Market Report is Segmented by Data Center Size (Large, Massive, Medium, Mega, and Small), Tier Type (Tier 1 and 2, Tier 3, and Tier 4), Data Center Type (Hyperscale/Self-built, Enterprise/Edge, and Colocation), End User (BFSI, IT and ITES, E-Commerce, Government, Manufacturing, Media and Entertainment, Telecom, and More), and Hotspot. The Market Forecasts are Provided in Terms of IT Load Capacity (MW).

The Australia Oilseeds Market report segments the industry into Oilseed Type (Rapeseed, Cotton Seed, Soybean, Sunflower Seed, Safflower Seed). The report includes Production Analysis, Consumption Analysis by Volume and Value, Import Market Analysis by Volume and Value, Export Market Analysis by Volume and Value, and Price Trend Analysis. Get five years of historical data and forecasts.

Australia Computed Tomography Market is Segmented by Type (Low Slice, Medium Slice, and High Slice), Application (Oncology, Neurology, Cardiovascular, Musculoskeletal, and Other Applications), and End User (Hospitals, Diagnostic Centers, and Other End Users). The report offers the value (in USD million) for the above segments.