As of its 2024 fiscal year, Salesforce.com’s largest revenue-generating service offering was its “Service cloud” which earned the company over ******billion U.S. dollars in total revenue. Other cloud services offered by the company include the Sales Cloud, Salesforce Platform, Marketing Cloud and Data. Historically, Sales Cloud has generated most of Salesforce’s revenue, but this year it generated the second most revenue out of the segment, while still generating more compared to the previous year. Salesforce.com Salesforce is a software company which focuses its business around cloud related software-as-a-service (Saas). The company has experienced rapid growth in recent years, with revenues increasing from around ****billion dollars in 2010 to ******billion in 2021. This rapid growth is spread across all of the company’s regional markets, but the Americas region remains the company’s largest, with sales of more than *****billion dollars. Software as a service (SaaS) market One of the many reasons for Salesforce's success is the rapid growth of the SaaS market itself. Bringing in less than ***billion dollars in 2010, SaaS market revenues have grown to an estimated ****billion as of 2020. The SaaS model involves the licensing of software to businesses, allowing these companies to make use of a huge range of advanced business applications without the need to host the software themselves.

The Salesforce CRM document generation software was valued at USD 850 million in 2022 and will reach USD 1.95 billion by 2030, registering a CAGR of 11% for the forecast period 2023-2030. Factors Affecting Salesforce CRM Document Generation Software Market Growth

Technological advancement in salesforce CRM document generation software

The market may explore new dynamics and improve company documentation experiences by integrating cutting-edge technologies like artificial intelligence, predictive learning, and machine learning with Salesforce CRM document-generating software. According to a global survey, the market for salesforce CRM document creation software is propelled by a high rate of return on investment (ROI), lower prices, a rise in the use of web-based documents, and simple installation procedures. Businesses are also implementing Salesforce CRM document-generating tools in an effort to increase productivity and operational effectiveness. With these platforms, anyone can easily create, modify, and print specific documents to meet all their documentation needs. However, it's crucial for organizations to prioritize the protection of these documents, especially since they may contain sensitive consumer information. Fortunately, the CRM document creation software has robust security measures in place that make it challenging for unauthorized access. Additionally, different customer authorities have established compliance requirements for data safety regulations to ensure optimal protection of consumer data. All these factors are boosting the growth of the salesforce CRM document generation software market.

Increased focus on customer experience, data, and automation

With Salesforce CRM document generation, businesses can create customized documents tailored to their customer's specific needs. This improves the customer experience by providing relevant and helpful information. Additionally, the software enables businesses to collect and analyze data more effectively, leading to better decision-making regarding sales and marketing strategies. Furthermore, salesforce CRM document generation automates the document creation process, saving businesses valuable time and resources.

The Restraining Factor of Salesforce CRM Document Generation Software:

Data security and privacy concerns

The market for Salesforce CRM Document Generation Software is experiencing rapid growth, but it faces some significant obstacles that are impeding its progress. The primary restraints include concerns regarding data security and privacy, a lack of awareness among small and medium-sized enterprises, and high initial investment costs. Additionally, ensuring the quality of documents generated by the software is a major challenge.

Impact of the COVID-19 Pandemic on the Salesforce CRM Document Generation Software Market

The COVID-19 pandemic has caused significant disruption across several global markets due to restrictions on logistics and market limitations imposed by governments. However, a few markets have benefited from the pandemic's effects. The need for real-time online documentation of transactions has led to increased demand for customer data retention. The pandemic has resulted in the closure of manufacturing facilities and limited logistical operations, putting a strain on several sectors. As a result, there has been a growing demand for improved customer experience due to remote employment. Companies can use Salesforce CRM document-generating software to document client information and sales. Introduction of Salesforce CRM Document Generation Software

Many companies are switching from using Excel spreadsheets to using Customer Relationship Management (CRM) software to improve their customers' experiences. This change is happening thanks to the advancement of technology and the widespread availability of high-speed internet. As businesses become more complex, they are looking for ways to simplify their operations while still being efficient and cost-effective. One solution that has become increasingly popular is using Salesforce CRM document generation software to securely and reliably document large amounts of data. Many companies are embracing new technologies and experimenting with different components of their business, such as sales, HR, business intelligence, and operations, to find ways to function with limited resources. The company has impo...

Sale-Or-Purchase-of-Stock Time Series for Salesforce.com Inc. Salesforce, Inc. provides customer relationship management (CRM) technology that connects companies and customers together worldwide. The company offers Agentforce, an agentic layer of the salesforce platform; Data Cloud, a data engine; Industries AI for creating industry-specific AI agents with Agentforce ; Salesforce Starter, a suite of solution for small and medium-size business; Slack, a workplace communication and productivity platform; Tableau, an end-to-end analytics solution for range of enterprise use cases and intelligent analytics with AI models, spot trends, predict outcomes, timely recommendations, and take action from any device; and integration and analytics solutions. It also provides marketing platform; commerce services, which empowers shopping experience across various customer touchpoint; and field service solution that enables companies to connect service agents, dispatchers, and mobile employees through one centralized platform to schedule and dispatch work, as well as track and manage jobs. Salesforce, Inc. was incorporated in 1999 and is headquartered in San Francisco, California.

Trust but Verify: Programmatic VLM Evaluation in the Wild

  Viraj Prabhu, Senthil Purushwalkam, An Yan, Caiming Xiong, Ran Xu

Explorer | Paper | Quickstart Vision-Language Models (VLMs) often generate plausible but incorrect responses to visual queries. However, reliably quantifying the effect of such hallucinations in free-form responses to open-ended queries is challenging as it requires visually verifying each claim within the response. We propose Programmatic VLM… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/PROVE.

This report explores Credit Andorra’s deployment of Salesforce’s cloud platform to minimize the complexity resulting from different business lines and make the company more prepared to meet customer needs. The report identifies the main challenges Credit Andorra faced with earlier methods, highlights the use of a cloud solution to address these challenges, and outlines the key benefits being achieved. Read More

Factori houses an extensive dataset of US Person data, providing valuable insights into individuals across various demographic and behavioral dimensions. Our US Person Data section is dedicated to helping you understand the breadth and depth of the information available through our API.

Data Collection and Aggregation Our Person data is gathered and aggregated through surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points. This ensures that the data you access is up-to-date and accurate.

Here are some of the data categories and attributes we offer within US Person Graph: - Geography: City, State, ZIP, County, CBSA, Census Tract, etc. - Demographics: Gender, Age Group, Marital Status, Language, etc. - Financial: Income Range, Credit Rating Range, Credit Type, Net Worth Range, etc. - Persona: Consumer type, Communication preferences, Family type, etc. - Interests: Content, Brands, Shopping, Hobbies, Lifestyle, etc. - Household: Number of Children, Number of Adults, IP Address, etc. - Behaviors: Brand Affinity, App Usage, Web Browsing, etc. - Firmographics: Industry, Company, Occupation, Revenue, etc. - Retail Purchase: Store, Category, Brand, SKU, Quantity, Price, etc.

Here's the data schema: Person_id first_name last_name gender age year month day full_address city state zipcode zip4 delivery_point_bar_code carrier_route walk_sequence_code fips_state_code fips_county_code country_name latitude longtitude address_type metropolitan_statistical_area core_based_statistical_area census_tract census_block
census_block_group primary_address pre_address street post_address address_suffix address_secondline address_abrev
census_median_home_value home_market_value property_build_year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type household_id census_median_household_income
household_size occupation_home_office
dwell_type household_income marital_status length_of_residence number_of_kids pre_school_kids single_parent working_women_in_house_hold homeowner children
adults generations net_worth education_level education_history occupation occuptation_business_owner credit_lines credit_card_user newly_issued_credit_card_user credit_range_new credit_cards loan_to_value and alot more...

LOTSA Data

The Large-scale Open Time Series Archive (LOTSA) is a collection of open time series datasets for time series forecasting. It was collected for the purpose of pre-training Large Time Series Models. See the paper and codebase for more information.

  Citation

If you're using LOTSA data in your research or applications, please cite it using this BibTeX: BibTeX: @article{woo2024unified, title={Unified Training of Universal Time Series Forecasting Transformers}… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/lotsa_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.

Salesforce's Revenue Growth Stalls as Businesses Tighten Their Belts

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 Salesforce (CRM) Stock Regain Its Former Glory?

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

Salesforce AppExchange Tools Market Outlook

The global Salesforce AppExchange Tools market size was valued at USD 6.2 billion in 2023 and is expected to reach USD 15.4 billion by 2032, growing at a CAGR of 10.8% during the forecast period. This robust growth can be attributed to the increasing adoption of Salesforce CRM solutions by enterprises globally, the growing demand for customized business solutions, and the proliferation of cloud computing technologies.

One of the primary growth factors in the Salesforce AppExchange Tools market is the increasing need for businesses to streamline their operations and enhance customer relationships. As companies strive to maintain a competitive edge, they are increasingly turning to digital transformation initiatives. Salesforce AppExchange provides a platform where businesses can find a myriad of tools tailored to meet specific business requirements. This reduces time to market and enhances operational efficiencies, which in turn drives the demand for these tools.

The rise in remote working and the subsequent need for collaborative tools have also contributed significantly to this market's growth. With the shift towards remote and hybrid work models becoming a permanent fixture in many organizations, there is a pressing need for tools that facilitate seamless collaboration and communication among teams. Salesforce AppExchange offers a wide range of such tools, including project management and communication apps, which are witnessing high adoption rates.

Furthermore, the increasing integration of artificial intelligence (AI) and machine learning (ML) capabilities in Salesforce AppExchange Tools is another driver of market growth. These advanced technologies enable businesses to gain deeper insights from their data, automate repetitive tasks, and provide more personalized customer interactions. The growing focus on data-driven decision-making is encouraging more enterprises to adopt AI and ML-integrated tools from the AppExchange, thereby fueling market expansion.

The integration of a Data Exchange Tool within the Salesforce AppExchange ecosystem is becoming increasingly vital for businesses aiming to enhance their data management capabilities. These tools facilitate seamless data transfer between various systems and applications, ensuring that businesses can maintain data integrity and consistency across their operations. By enabling real-time data synchronization, Data Exchange Tools help organizations make informed decisions quickly, thereby improving operational efficiency and customer satisfaction. As the volume of data continues to grow, the demand for robust data exchange solutions is expected to rise, making them a critical component of the Salesforce AppExchange Tools market.

Regionally, North America holds the largest share of the Salesforce AppExchange Tools market, driven by high digital adoption rates and a strong presence of Salesforce users. However, the Asia Pacific region is expected to witness the highest growth during the forecast period, driven by increasing investments in cloud technologies, rapid digital transformation of enterprises, and the expansion of regional business ecosystems.

Tool Type Analysis

The Salesforce AppExchange Tools market is segmented by tool type into sales tools, marketing tools, customer service tools, IT & administration tools, collaboration tools, and others. Sales tools constitute a significant portion of the market due to the primary focus of Salesforce CRM on sales and customer relationship management. These tools help sales teams in lead generation, opportunity management, sales forecasting, and performance analytics, thereby driving their demand.

Marketing tools are also gaining traction, primarily due to the increasing need for businesses to manage multi-channel marketing campaigns effectively. Salesforce Marketing Cloud, integrated with marketing tools from the AppExchange, allows businesses to personalize customer experiences, track campaign performance, and optimize marketing efforts in real-time. The integration capabilities of these tools with existing business systems further enhance their value proposition.

Customer service tools are seeing increased adoption as businesses recognize the importance of delivering superior customer support. Tools such as chatbots, helpdesk software, and customer feedback systems help businesses manage customer queries efficient

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Salesforce CRM Document Generation Software Market Outlook

The global Salesforce CRM Document Generation Software market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 5.2 billion by 2032, growing at a robust CAGR of 15.2% during the forecast period. This impressive growth can be attributed to the increasing demand for efficient and automated document management systems across various industries, driven by the need to streamline business processes and enhance productivity.

One of the significant growth factors for this market is the rapid digital transformation seen across various industries. Businesses are increasingly adopting cloud-based solutions to optimize their operations and improve customer experience. Salesforce CRM document generation software is becoming critical for companies looking to automate and simplify their document-related workflows. This software enables companies to generate, manage, and share documents efficiently, thereby reducing manual effort and minimizing errors. The growing integration of AI and machine learning within CRM platforms further enhances the capabilities of document generation software, making it a vital tool for modern businesses.

Another driver for the market's growth is the rising need for compliance and data security. Regulatory bodies across the globe have stringent requirements for document management, especially in sectors like finance, healthcare, and legal services. Salesforce CRM document generation software helps organizations comply with these regulations by ensuring that documents are accurately generated, stored, and easily retrievable. This not only aids in compliance but also enhances data security, reducing the risk of data breaches and associated penalties. As regulatory landscapes evolve, the demand for sophisticated document generation tools is set to increase further.

Furthermore, the increasing trend of remote work and the growing number of small and medium-sized enterprises (SMEs) are also significant growth factors. With remote work becoming more prevalent, businesses require reliable software solutions to manage documentation processes seamlessly from different locations. Salesforce CRM document generation software, with its cloud-based deployment options, offers the flexibility and accessibility needed for remote operations. Additionally, the proliferation of SMEs, which often have limited resources and require cost-effective solutions for efficient document management, is driving the adoption of this software.

Regionally, North America holds a dominant position in the Salesforce CRM document generation software market, primarily due to the early adoption of advanced technologies and the presence of major market players in the region. Europe and Asia Pacific are also witnessing substantial growth, with increasing investments in digital transformation and the growing awareness of the benefits of CRM document generation software. Emerging economies in Latin America and the Middle East & Africa are gradually catching up, driven by the growing need for efficient business operations and regulatory compliance.

Component Analysis

The Salesforce CRM document generation software market is segmented into two main components: software and services. The software segment encompasses the core functionalities of document generation, including template creation, automated document generation, and integration with other CRM tools. This segment is witnessing significant growth due to the increasing demand for automation in document management processes. Businesses are continuously seeking efficient software solutions that can reduce manual efforts and improve accuracy in document generation. Additionally, the integration of advanced technologies like AI and machine learning into these software solutions is enhancing their capabilities, making them more attractive to enterprises across various industries.

On the other hand, the services segment includes implementation, consulting, training, and support services. This segment is crucial for the successful deployment and utilization of Salesforce CRM document generation software. As the complexity of document generation processes varies across different industries, the need for tailored consulting services to ensure optimal software performance is growing. Implementation services are essential for setting up the software according to specific business requirements, while training services help employees become proficient in using the software. Support services ensure that any issues encountered during the usage o

Salesforce, SWOT, PESTLE, CRM, ESG, financials, cloud computing: “ Read More

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.

Salesforce Stock (CRM): Another Strong Quarter Ahead?

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 Salesforce CRM Document Generation Software market is experiencing robust growth, driven by the increasing need for automation in sales and contract processes. Businesses are seeking to streamline document creation, reduce manual errors, and improve overall efficiency. The integration of document generation directly within the Salesforce CRM ecosystem enhances productivity by eliminating the need for data re-entry and ensuring data consistency across systems. This seamless integration is a key driver for adoption, along with the growing demand for personalized and branded documents to improve customer experience. The market is segmented by deployment (cloud-based and on-premise), organization size (SMEs and large enterprises), and industry vertical (e.g., healthcare, finance, technology). While the precise market size for 2025 is unavailable, considering similar SaaS market growth trends, a reasonable estimate would be around $2 billion, projecting a Compound Annual Growth Rate (CAGR) of approximately 15% between 2025 and 2033. This growth is fueled by continuous innovation, including the integration of AI-powered features like intelligent document assembly and e-signature capabilities which are becoming increasingly prevalent. Market restraints include the initial investment required for software implementation and integration, as well as the potential learning curve associated with adopting new technologies. However, the long-term cost savings and increased efficiency outweigh these initial hurdles for many businesses. Competition is relatively high, with established players like Conga, Nintex, and SpringCM vying for market share alongside newer entrants. Differentiation often comes down to specific features, integrations, pricing models, and the level of customer support offered. The future of the market will likely see continued consolidation, further innovation in AI and automation, and an increasing focus on data security and compliance. The projected market value in 2033, based on the estimated 15% CAGR, would exceed $7 billion. This demonstrates significant growth potential for this vital software segment within the broader CRM market.

ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models

ProVision is an extendable data generation engine which produces instruction data for large multimodal language models (MLMs). In particular, it synthesizes instruction data via data generators (Python programs) and scene graphs rather than proprietary models. It also includes a scene graph generation pipeline consisting of various state-of-the-art models (eg, object detection model). Thus… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/ProVision-10M.

The Salesforce Services market has become a pivotal segment in the evolving landscape of customer relationship management (CRM) and enterprise solutions. As businesses increasingly recognize the need to streamline operations, enhance customer engagement, and derive actionable insights from data, Salesforce's suite o

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.

Salesforce Inc. Expected to Report Earnings of $1.60 per Share on May 31

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

Dataset Card for "wikitext"

  Dataset Summary

The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License. Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over 110 times larger. The WikiText dataset also features a far larger… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/wikitext.

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.

Salesforce Soars Above Estimates, Signaling Strong Demand for Cloud Software

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