An important point in our data is that the data must be cleaned and the valume column is better because there is a lot of data noise in it.

The main stock market index of United States, the US500, rose to 6644 points on September 26, 2025, gaining 0.59% from the previous session. Over the past month, the index has climbed 2.50% and is up 15.78% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on September of 2025.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

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.

SHL Telemedicine Ltd American Depositary Shares is assigned short-term B1 & long-term Ba3 estimated rating.

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 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.

AEL^A American Equity Investment Life Holding Company Depositary Shares each representing a 1/1000th interest in a share of 5.95% Fixed-Rate Reset Non-Cumulative Preferred Stock Series A

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

Diluted-Average-Shares Time Series for American Vanguard Corporation. American Vanguard Corporation, through its subsidiaries, develops, manufactures, and markets chemical, biological and biorational products for agricultural, commercial, and consumer uses in the United States and internationally. It synthesizes and formulates chemicals, and ferments and extracts microbial products, including insecticides, fungicides, herbicides, soil health, plant nutrition, molluscicides, growth regulators, soil fumigants, and biorationals in liquid, powder, and granular forms for crops, turf, ornamental plants, and human and animal health protection. The company also markets, sells, and distributes end-use chemical and biological products for crop applications; and distributes products for turf and ornamental markets. It distributes its products through national distribution companies, and buying groups or co-operatives; and through sales offices, sales force executives, sales agents, and wholly owned distributors. American Vanguard Corporation was incorporated in 1969 and is headquartered in Newport Beach, California.

Corporate Profits in the United States increased to 3259.41 USD Billion in the second quarter of 2025 from 3252.44 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus 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.

General American Investors - Cumulative Preferred Stock (GAM-B): Worth Investing?

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

Access historical and point-in-time financial statements, ratios, multiples, and press releases, with LSEG's S&P Compustat Database.

Lingcod (Ophiodon elongatus) populations along the West Coast of North America have recovered from overfishing, but the status of genetically distinct lingcod in Puget Sound, Washington is less clear. This project will use small-scale lingcod releases to learn about the benefits and risks of using stock enhancement as a tool to help rebuild marine fish populations. We have conducted experiments...

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.

BML^G Bank of America Corporation Bank of America Corporation Depositary Shares (Each representing a 1/1200th interest in a share of Floating Rate Non-Cumulative Preferred Stock Series 1)

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

Judgement on questions of security policy and the disarmament efforts of the USA and the USSR. Topics: Trust in the United States and the Soviet Union to solve problems in world politics; importance of the summit meeting between Reagan and Gorbachev; country responsible for a possible failure of a summit meeting; assessment of Reagan and Gorbachev regarding their trustworthiness, their negotiation flexibility, their understanding for European problems and the use of military strength to attain political objectives; judgement on the effects of the Marshall Plan; extent to which informed about the trade dispute between the United States and the EC; goals of arms controls between the USA and the Soviet Union; trust in the USA in the preservation of West German interests in the arms control negotiations; assessment of the seriousness of the disarmament efforts of the USA and the Soviet Union; importance of verification of disarmament measures; significance of nuclear weapons for deterrence of a Soviet attack; attitude to increase in defense expenditures and taxes in case of relinquishing nuclear weapons to increase defense with conventional weapons; attitude to a withdrawal of American and Soviet missiles from Europe and related greater dependence on conventional weapons; most important defense systems for the security of the Federal Republic; Americans or Sowjets as initiator of a comprehensive nuclear test ban; most important beneficiary of a nuclear test ban; Americans or Sowjets as the ones stock-piling the most nuclear weapons; knowledge about conducting underground or surface nuclear tests by the USA and the USSR; assessment of the credibility of the Soviet recommendation of a nuclear weapon ban; attitude to an agreement of the Americans to eliminate all nuclear tests; most important arguments for or against the immediate elimination of all nuclear weapons tests; attitude to a restriction of nuclear tests, even without the verification demanded by the Americans; attitude to the anti-missile defense system SDI; most important arguments for or against further research on this defense system; attitude to Soviet linking limitation on SDI and progress in arms control; knowledge of Soviet anti-missiles; comparison of the American or Soviet status of research on anti-missile defense systems; attitude to participation of the Federal Republic in the research on SDI; expected effect of an SDI on the security of the Federal Republic; necessity of the NATO for the security of the Federal Republic; attitude to withdrawal of the Federal Republic from NATO; assessment of the strength of conventional weapons and the nuclear weapon strength of the USA in comparison to the USSR; attitude to use of nuclear weapons by NATO in Europe; trust in the defense preparedness of the USA regarding the Federal Republic; most important trouble spots threatening western interests; knowledge about the geographic location of Nicaragua; positive or negative influence of the USA as well as the Soviet Union on political development in Central America and in the Caribbean; attitude to military support of the Contras in Nicaragua as well as of the opposition troops in Afghanistan by the American government; most important reasons for or against support of the opposition troops in Afghanistan; assessment of realization of human rights in the USA as well as in the Soviet Union; attitude to criticism of government leaders on violation of human rights in other countries. Beurteilung sicherheitspolitischer Fragen und der Abrüstungsbemühungen der USA und der UdSSR. Themen: Vertrauen in die Vereinigten Staaten und die Sowjetunion zur Lösung der weltpolitischen Probleme; Wichtigkeit des Gipfeltreffens zwischen Reagan und Gorbatschow; verantwortliches Land für ein mögliches Scheitern eines Gipfeltreffens; Einschätzung von Reagan und Gorbatschow bezüglich ihrer Vertrauenswürdigkeit, ihrer Verhandlungflexibilität, ihres Verständnisses für europäische Probleme und des Einsatzes der militärischen Stärke zur Erreichung von politischen Zielen; Beurteilung der Auswirkungen des Marschall-Plans; Informiertheit über den Handelsstreit zwischen den Vereinigten Staaten und der EG; Ziele einer Rüstungskontrolle zwischen den USA und der Sowjetunion; Vertrauen in die USA bei der Wahrung bundesrepublikanischer Interessen bei den Rüstungskontrollverhandlungen; Einschätzung der Ernsthaftigkeit der Abrüstungsbemühungen der USA und der Sowjetunion; Wichtigkeit der Überprüfbarkeit von Abrüstungsmaßnahmen; Bedeutung von Atomwaffen zur Abschreckung eines sowjetischen Angriffs; Einstellung zur Erhöhung von Verteidigungsausgaben und Steuern im Falle eines Verzichts auf Atomwaffen zur Erhöhung der Verteidigung mit konventionellen Waffen; Einstellung zu einem Abzug amerikanischer und sowjetischer Raketen aus Europa und einer damit einhergehenden größeren Abhängigkeit von konventionellen Waffen; wichtigste Verteidigungssysteme für die Sicherheit der Bundesrepublik; Amerikaner oder Sowjets als Initiator eines umfassenden Atomtestverbots; wichtigster Nutznießer eines Atomteststopps; Amerikaner oder Sowjets als stärkerer Aufrüster mit Atomwaffen; Kenntnisse über die unterirdische oder überirdische Durchführung von Atomtests durch die USA und die UdSSR; Einschätzung der Glaubwürdigkeit des sowjetischen Vorschlags eines Atomwaffenstopps; Einstellung zu einer Zustimmung der Amerikaner zur Abschaffung aller Atomtests; wichtigste Argumente für bzw. gegen die sofortige Abschaffung aller Atomwaffen-Tests; Einstellung zu einer Einschränkung von Atomtests, auch ohne die von den Amerikanern geforderte Überprüfbarkeit; Einstellung zum Anti-Raketen- Verteidigungssystem SDI; wichtigste Argumente für bzw. gegen die weitere Forschung an diesem Verteidigungssystem; Einstellung zum sowjetischen Junktim zwischen der Beschränkung von SDI und dem Fortschritt in der Rüstungskontrolle; Kenntnis von sowjetischen Anti-Raketen; Vergleich des amerikanischen bzw. sowjetischen Stands der Forschung über Anti-Raketen- Verteidigungssysteme; Einstellung zur Beteiligung der Bundesrepublik an der Forschung zum SDI; erwarteter Effekt eines SDI auf die Sicherheit der Bundesrepublik; Notwendigkeit der NATO für die Sicherheit der Bundesrepublik; Einstellung zum Rückzug der Bundesrepublik aus der NATO; Einschätzung der Stärke der konventionellen Waffen und der Atomwaffenstärke der USA im Vergleich zur UdSSR; Einstellung zum Atomwaffeneinsatz der NATO in Europa; Vertrauen in die Verteidigungsbereitschaft der USA gegenüber der Bundesrepublik; wichtigste Krisenherde zur Bedrohung der westlichen Interessen; Kenntnis der geographischen Lage von Nikaragua; positiver oder negativer Einfluß der USA sowie der Sowjetunion auf die politische Entwicklung in Mittelamerika und in der Karibik; Einstellung zur militärischen Unterstützung der Kontras in Nikaragua sowie der Oppositionstruppen in Afghanistan durch die amerikanische Regierung; wichtigste Gründe für bzw. gegen die Unterstützung der Oppositionstruppen in Afghanistan; Einschätzung der Verwirklichung der Menschenrechte in den USA sowie in der Sowjetunion; Einstellung zur Kritik von Regierungsführern an Menschenrechtsverletzung in anderen Ländern. Demographie: Alter (klassiert); Geschlecht; Familienstand; Schulbildung; Berufstätigkeit; Berufslaufbahn; Haushaltseinkommen; Haushaltsgröße; Haushaltszusammensetzung; Befragter ist Haushaltsvorstand; Charakteristika des Haushaltsvorstands; Ortsgröße; Bundesland.

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.

Pan American (PAAS) Silver Stocks: A Shining Opportunity?

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 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.

Cementos Pacasmayo S.A.A. American Depositary Shares (Each representing five Common Shares) is assigned short-term B1 & long-term Ba1 estimated rating.

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 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.

Can American Strategic Investment (NYC) Continue Its Market Outperformance?

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 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.

Americans' Mining Mastery: Will AMST Stock Strike Gold?

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 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.

Bank of America Corporation Depositary Shares (Each representing a 1/1200th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 5) is assigned short-term B3 & long-term Ba3 estimated rating.

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 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.

BAC^M Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 5.375% Non-Cumulative Preferred Stock Series KK

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 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.

BAC^O Bank of America Corporation Depositary shares each representing 1/1000th interest in a share of 4.375% Non-Cumulative Preferred Stock Series NN

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