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.

Context

The algorithmic trading space is buzzing with new strategies. Companies have spent billions in infrastructures and R&D to be able to jump ahead of the competition and beat the market. Still, it is well acknowledged that the buy & hold strategy is able to outperform many of the algorithmic strategies, especially in the long-run. However, finding value in stocks is an art that very few mastered, can a computer do that?

Content

This Data repo contains the following datasets (in .csv format):

• 2014_Financial_Data.csv
• 2015_Financial_Data.csv
• 2016_Financial_Data.csv
• 2017_Financial_Data.csv
• 2018_Financial_Data.csv

Each dataset contains 200+ financial indicators, that are commonly found in the 10-K filings each publicly traded company releases yearly, for a plethora of US stocks (on average, 4k stocks are listed in each dataset). I built this dataset leveraging Financial Modeling Prep API and pandas_datareader.

Important remarks regarding the datasets:

1. Some financial indicator values are missing (nan cells), so the user can select the best technique to clean each dataset (dropna, fillna, etc.).
2. There are outliers, meaning extreme values that are probably caused by mistypings. Also in this case, the user can choose how to clean each dataset (have a look at the 1% - 99% percentile values).
3. The third-to-last column, Sector, lists the sector of each stock. Indeed, in the US stock market each company is part of a sector that classifies it in a macro-area. Since all the sectors have been collected (Basic Materials, Communication Services, Consumer Cyclical, Consumer Defensive, Energy, Financial Services, Healthcare, Industrial, Real Estate, Technology and Utilities), the user has the option to perform per-sector analyses and comparisons.

4. The second-to-last column, PRICE VAR [%], lists the percent price variation of each stock for the year. For example, if we consider the dataset 2015_Financial_Data.csv, we will have:

   • 200+ financial indicators for the year 2015;
   • percent price variation for the year 2016 (meaning from the first trading day on Jan 2016 to the last trading day on Dec 2016).

5. The last column, class, lists a binary classification for each stock, where

   • for each stock, if the PRICE VAR [%] value is positive, class = 1. From a trading perspective, the 1 identifies those stocks that an hypothetical trader should BUY at the start of the year and sell at the end of the year for a profit.
   • for each stock, if the PRICE VAR [%] value is negative, class = 0. From a trading perspective, the 0 identifies those stocks that an hypothetical trader should NOT BUY, since their value will decrease, meaning a loss of capital.

The columns PRICE VAR [%] and class make possible to use the datasets for both classification and regression tasks:

• If the user wishes to train a machine learning model so that it learns to classify those stocks that in buy-worthy and not buy-worthy, it is possible to get the targets from the class column;
• If the user wishes to train a machine learning model so that it learns to predict the future value of a stock, it is possible to get the targets from the PRICE VAR [%] column.

Inspiration

I built this dataset during the 2019 winter holidays period, because I wanted to answer a simple question: is it possible to have a machine learning model learn the differences between stocks that perform well and those that don't, and then leverage this knowledge in order to predict which stock will be worth buying? Moreover, is it possible to achieve this simply by looking at financial indicators found in the 10-K filings?

A league table of the 120 cryptocurrencies with the highest market cap reveals how diverse each crypto is and potentially how much risk is involved when investing in one. Bitcoin (BTC), for instance, had a so-called "high cap" - a market cap worth more than 10 billion U.S. dollars - indicating this crypto project has a certain track record or, at the very least, is considered a major player in the cryptocurrency space. This is different in Decentralize Finance (DeFi), where Bitcoin is only a relatively new player. A concentrated market The number of existing cryptocurrencies is several thousands, even if most have a limited significance. Indeed, Bitcoin and Ethereum account for nearly 75 percent of the entire crypto market capitalization. As crypto is relatively easy to create, the range of projects varies significantly - from improving payments to solving real-world issues, but also meme coins and more speculative investments. Crypto is not considered a payment method While often talked about as an investment vehicle, cryptocurrencies have not yet established a clear use case in day-to-day life. Central bankers found that usefulness of crypto in domestic payments or remittances to be negligible. A forecast for the world's main online payment methods took a similar stance: It predicts that cryptocurrency would only take up 0.2 percent of total transaction value by 2027.

Dataset Versioning Platform Market Outlook

According to our latest research, the global dataset versioning platform market size reached USD 1.32 billion in 2024, reflecting robust adoption across industries as organizations seek to manage and track complex data workflows. The market is expected to exhibit a strong compound annual growth rate (CAGR) of 18.6% over the forecast period, reaching a projected value of USD 6.13 billion by 2033. This dynamic growth is primarily fueled by the increasing reliance on data-driven decision-making, the proliferation of machine learning and artificial intelligence initiatives, and the need for enhanced data governance and compliance in a rapidly evolving digital landscape.

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Geo-Distributed Database Market Outlook

According to our latest research, the geo-distributed database market size reached USD 13.7 billion globally in 2024, reflecting the surging adoption of distributed data management solutions across diverse industries. The market is projected to expand at a robust CAGR of 23.4% from 2025 to 2033, culminating in a forecasted market size of USD 107.6 billion by 2033. This remarkable growth trajectory is primarily driven by the increasing need for real-time data access, enhanced business continuity, and seamless scalability to support global operations.

One of the key growth factors for the geo-distributed database market is the exponential rise in data volumes generated by enterprises operating across multiple geographies. As organizations embrace digital transformation, the proliferation of cloud computing, IoT devices, and mobile applications has resulted in unprecedented data generation and consumption. Geo-distributed databases enable enterprises to store, process, and analyze data closer to the source, thereby reducing latency and improving user experiences. This capability is particularly critical for sectors such as e-commerce, financial services, and telecommunications, where milliseconds can make a significant difference in customer satisfaction and operational efficiency.

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From a regional perspective, North America held the largest share of the geo-distributed database market in 2024, driven by a mature IT infrastructure, high cloud adoption rates, and a strong presence of leading technology vendors. However, Asia Pacific is poised to witness the fastest growth during the forecast period, fueled by rapid digitalization, expanding internet penetration, and increasing investments in smart city and IoT initiatives. Europe continues to demonstrate steady growth, supported by stringent data protection regulations and a focus on digital sovereignty. Latin America and the Middle East & Africa are gradually catching up, with growing awareness and adoption of distributed database technologies among enterprises and government agencies.

In the realm of geo-distributed databases, the concept of a Distributed Cache is gaining prominence as a crucial component for enhancing performance and scalability. Distributed Cache systems are designed to store frequently accessed data in a distributed manner across multiple nodes, thereby reducing data retrieval times and alleviating the load on primary databases. This approach not only improves the speed of data access but also enhances the overall efficiency of data processing in geo-distributed environments. By caching data closer to the end-users, organizations can significantly reduce latency and provide a seamless user experience, which is particularly beneficial for applications requiring real-time data processing and high availability. As businesses continue to expand their global operations, the integration of Distri

Data Marketplaces Market size was valued at USD 1.09 Billion in 2023 and is projected to reach USD 1.29 Billion by 2031, growing at a CAGR of 4.56% during the forecast period 2024-2031.

Global Data Marketplaces Market Drivers

The market drivers for the Data Marketplaces Market can be influenced by various factors. These may include:

Increasing Big Data Adoption: The proliferation of big data across industries has led to a significant rise in the need for data marketplaces. Organizations are increasingly aware of the value of data-driven decision-making, which has spurred demand for diverse data sources. Companies seek to harness large volumes of structured and unstructured data, driving them to marketplaces for innovative data solutions. The challenge of managing data internally encourages businesses to leverage external data assets, enhancing analytics capabilities and fostering better customer insights. Consequently, the growth of big data adoption directly influences the expansion and diversification of data marketplaces.

Enhanced Data Privacy Regulations: The introduction of stringent data privacy regulations, such as GDPR and CCPA, has transformed how businesses manage and exchange data. These regulations compel organizations to be more transparent in their data handling processes, fostering a need for compliant data sources. As companies prioritize adherence to legal standards, they are increasingly turning to data marketplaces with vetted datasets that ensure compliance. This shift is enhancing trust and encouraging more organizations to participate in data trading. Therefore, the evolution of privacy laws is a significant driver for the growth of the data marketplace ecosystem.

Global Data Marketplaces Market Restraints

Several factors can act as restraints or challenges for the Data Marketplaces Market. These may include:

Regulatory Compliance: Data marketplaces face significant challenges due to stringent regulations concerning data privacy and security. Regulatory frameworks such as GDPR and CCPA impose strict guidelines on how data can be collected, stored, and shared. Organizations must ensure that their data practices align with these regulations, which can lead to increased costs and complexity in operations. Non-compliance can result in severe penalties, reputational damage, and loss of consumer trust. This regulatory burden may deter businesses from participating in data marketplaces, as they grapple with evolving compliance standards and the potential for legal ramifications associated with mishandled data.

Data Quality and Integrity: The success of data marketplaces hinges on the quality and integrity of the data being offered. Poorly curated or inaccurate data can undermine the credibility of the marketplace and diminish user trust. Buyers seeking high-quality datasets may be deterred by the fear of investing in unreliable information. Additionally, maintaining data quality requires constant monitoring, validation, and updating, which can strain resources for marketplace operators. This challenge is further exacerbated by the proliferation of data sources, making it difficult to ensure consistency and accuracy across the offerings, ultimately affecting buyer satisfaction and marketplace growth.