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.

CBA COMMONWEALTH BANK OF AUSTRALIA.

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 benchmark interest rate in Armenia was last recorded at 6.75 percent. This dataset provides - Armenia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

LON:CBA Stock Forecast: A Sell For The Next 3 Month

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 Credit Card Issuance industry has contracted as the number of cards issued and balances accruing interest have fallen. Issuers have faced significant competition from other forms of payment like debit cards and BNPL services. The monthly value of debit card transactions has continued to surpass the monthly value of credit card transactions thanks to initiatives like the Reserve Bank of Australia's (RBA) least-cost routing initiative. BNPL services have also gained popularity with younger consumers who constitute a significant market for online sellers. That's why revenue is set to weaken by an annualised 5.3% over the five years through 2024-25, to $7.6 billion. To compete with sophisticated competition, credit card issuers have beefed up their reward and referral programs and integrated online payment, service and customer acquisition platforms into their operations. The Big Four banks dominate the industry and NAB's acquisition of Citigroup's Australian consumer banking business has expanded its collective market share. Economic conditions tied to inflationary pressures have ravaged consumer sentiment and appetites for spending through credit. Some customers have opted to pay down debt instead and have avoided taking on more. A sharp climb in interest rates over the past few years has compounded this dynamic, which is set to constrain industry performance in 2024-25, with revenue declining by an anticipated 0.9%. Credit card issuers' performance will improve over the coming years as economic conditions recover. Credit card issuance revenue is projected to expand at an annualised 2.0% through the end of 2029-30, to total $8.4 billion. The RBA is forecast to slash the cash rate once inflation falls within the central banks' target band, lifting credit card issuer profit margins as funding costs drop. Alternative payment methods, like BNPL services, debit transactions and other fintech solutions, are on track to sap away demand for credit cards. However, easing inflationary pressures and lower interest rates over the medium term are set to spur household consumption expenditure and credit card use. In response to the fierce competition, issuers will emphasise innovation and enhance their rewards and points systems to entice consumers.

India Base Rate: Foreign Banks: Commonwealth Bank of Australia data was reported at 9.100 % pa in Dec 2016. This stayed constant from the previous number of 9.100 % pa for Sep 2016. India Base Rate: Foreign Banks: Commonwealth Bank of Australia data is updated quarterly, averaging 9.500 % pa from Sep 2010 (Median) to Dec 2016, with 26 observations. The data reached an all-time high of 10.000 % pa in Mar 2013 and a record low of 8.000 % pa in Mar 2011. India Base Rate: Foreign Banks: Commonwealth Bank of Australia data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Interest and Foreign Exchange Rates – Table IN.MB002: Base Rate.

India Foreign Banks: Term Loan Rate: High: Commonwealth Bank of Australia data was reported at 9.900 % pa in Dec 2016. This records a decrease from the previous number of 11.400 % pa for Sep 2016. India Foreign Banks: Term Loan Rate: High: Commonwealth Bank of Australia data is updated quarterly, averaging 13.000 % pa from Sep 2010 (Median) to Dec 2016, with 26 observations. The data reached an all-time high of 14.000 % pa in Mar 2013 and a record low of 9.900 % pa in Dec 2016. India Foreign Banks: Term Loan Rate: High: Commonwealth Bank of Australia data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Interest and Foreign Exchange Rates – Table IN.MB041: Lending Rate: Other than Export Credit: Term Loan Rate: High.

Indonesia Prime Lending Rate: Retail Credit: Commonwealth Bank data was reported at 11.000 % pa in Jul 2019. This stayed constant from the previous number of 11.000 % pa for Jun 2019. Indonesia Prime Lending Rate: Retail Credit: Commonwealth Bank data is updated monthly, averaging 11.000 % pa from Oct 2011 (Median) to Jul 2019, with 94 observations. The data reached an all-time high of 12.000 % pa in Oct 2016 and a record low of 10.310 % pa in Jul 2012. Indonesia Prime Lending Rate: Retail Credit: Commonwealth Bank data remains active status in CEIC and is reported by Bank of Indonesia. The data is categorized under Indonesia Premium Database’s Interest and Foreign Exchange Rates – Table ID.MB002: Prime Lending Rate: By Banks.

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.

Ceiba Investments (CBA) - Soaring High or Crashing Down?

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

India Foreign Banks: Demand Loan Rate: High: Commonwealth Bank of Australia data was reported at 9.250 % pa in Sep 2016. This records a decrease from the previous number of 10.400 % pa for Jun 2016. India Foreign Banks: Demand Loan Rate: High: Commonwealth Bank of Australia data is updated quarterly, averaging 10.900 % pa from Sep 2011 (Median) to Sep 2016, with 20 observations. The data reached an all-time high of 12.000 % pa in Sep 2012 and a record low of 9.250 % pa in Sep 2016. India Foreign Banks: Demand Loan Rate: High: Commonwealth Bank of Australia data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Interest and Foreign Exchange Rates – Table IN.MB037: Lending Rate: Other than Export Credit: Demand Loan Rate: High.

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

India Foreign Banks: Term Loan Rate: Low: Commonwealth Bank of Australia data was reported at 9.900 % pa in Dec 2016. This records an increase from the previous number of 9.150 % pa for Sep 2016. India Foreign Banks: Term Loan Rate: Low: Commonwealth Bank of Australia data is updated quarterly, averaging 10.125 % pa from Sep 2010 (Median) to Dec 2016, with 26 observations. The data reached an all-time high of 14.000 % pa in Jun 2012 and a record low of 9.150 % pa in Sep 2016. India Foreign Banks: Term Loan Rate: Low: Commonwealth Bank of Australia data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Interest and Foreign Exchange Rates – Table IN.MB040: Lending Rate: Other than Export Credit: Term Loan Rate: Low.

India Foreign Banks: Cash Credit Rate: Low: Commonwealth Bank of Australia data was reported at 10.750 % pa in Jun 2016. This stayed constant from the previous number of 10.750 % pa for Mar 2016. India Foreign Banks: Cash Credit Rate: Low: Commonwealth Bank of Australia data is updated quarterly, averaging 11.500 % pa from Sep 2010 (Median) to Jun 2016, with 24 observations. The data reached an all-time high of 14.000 % pa in Sep 2012 and a record low of 10.650 % pa in Dec 2015. India Foreign Banks: Cash Credit Rate: Low: Commonwealth Bank of Australia data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Interest and Foreign Exchange Rates – Table IN.MB032: Lending Rate: Other than Export Credit: Cash Credit Rate: Low.

The Judo Bank Australia Services PMI fell to 50.3 in June 2023 from 52.1 in the previous month, final data showed. It marked the third consecutive month of expansion in the Australian services sector, albeit the slowest, driven by softer growth in new businesses which led to a slower rise in business activity. Firms continued to hire additional staff to cope with the increased workload. Amid higher demand, inflationary pressures intensified within the service sector. Input cost inflation climbed due to higher interest rates, wages and energy costs. Businesses remained broadly optimistic with regards to future activity but pared back their optimism. This dataset provides - Australia Commonwealth Bank Services PMI- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Foreign Banks: Demand Loan Rate: High: Commonwealth Bank of Australia在2016-09达9.250 % 每年，相较于2016-06的10.400 % 每年有所下降。Foreign Banks: Demand Loan Rate: High: Commonwealth Bank of Australia数据按季度更新，2011-09至2016-09期间平均值为10.900 % 每年，共20份观测结果。该数据的历史最高值出现于2012-09，达12.000 % 每年，而历史最低值则出现于2016-09，为9.250 % 每年。CEIC提供的Foreign Banks: Demand Loan Rate: High: Commonwealth Bank of Australia数据处于定期更新的状态，数据来源于Reserve Bank of India，数据归类于India Premium Database的Interest and Foreign Exchange Rates – Table IN.MB037: Lending Rate: Other than Export Credit: Demand Loan Rate: High。

Foreign Banks: Term Loan Rate: Low: Commonwealth Bank of Australia在2016-12达9.900 % 每年，相较于2016-09的9.150 % 每年有所增长。Foreign Banks: Term Loan Rate: Low: Commonwealth Bank of Australia数据按季度更新，2010-09至2016-12期间平均值为10.125 % 每年，共26份观测结果。该数据的历史最高值出现于2012-06，达14.000 % 每年，而历史最低值则出现于2016-09，为9.150 % 每年。CEIC提供的Foreign Banks: Term Loan Rate: Low: Commonwealth Bank of Australia数据处于定期更新的状态，数据来源于Reserve Bank of India，数据归类于India Premium Database的Interest and Foreign Exchange Rates – Table IN.MB040: Lending Rate: Other than Export Credit: Term Loan Rate: Low。

Foreign Banks: Cash Credit Rate: High: Commonwealth Bank of Australia在2016-06达10.750 % 每年，相较于2016-03的12.650 % 每年有所下降。Foreign Banks: Cash Credit Rate: High: Commonwealth Bank of Australia数据按季度更新，2010-09至2016-06期间平均值为13.000 % 每年，共24份观测结果。该数据的历史最高值出现于2013-03，达14.000 % 每年，而历史最低值则出现于2016-06，为10.750 % 每年。CEIC提供的Foreign Banks: Cash Credit Rate: High: Commonwealth Bank of Australia数据处于定期更新的状态，数据来源于Reserve Bank of India，数据归类于India Premium Database的Interest and Foreign Exchange Rates – Table IN.MB033: Lending Rate: Other than Export Credit: Cash Credit Rate: High。