These spreadsheets list tickers and date ranges for high-yield green bonds analyzed in the manuscript titled 'Green Dreams, Risky Assets? A Study of High-Yield Green Bonds.'

As the raw bond pricing data are sourced from Bloomberg Terminal, we are unable to publicly deposit the original dataset due to licensing restrictions. However, we provide spreadsheets containing tickers and dates to enable researchers with Bloomberg Terminal access to replicate our results.

The global financial database market is experiencing robust growth, driven by increasing demand for real-time data analytics and insights across various financial sectors. The market, currently estimated at $15 billion in 2025, is projected to expand at a compound annual growth rate (CAGR) of 8% from 2025 to 2033, reaching approximately $28 billion by 2033. This expansion is fueled by several key factors. The rise of algorithmic trading and quantitative finance necessitates access to high-quality, comprehensive financial data, driving demand for both real-time and historical databases. Moreover, regulatory compliance requirements are pushing financial institutions to invest in robust data management systems, contributing to market growth. The increasing adoption of cloud-based solutions and advanced analytical tools further accelerates market expansion. The market is segmented by application (personal and commercial use) and database type (real-time and historical). The commercial segment currently dominates, propelled by the needs of large financial institutions, investment banks, and asset management firms. However, the personal use segment is expected to witness significant growth driven by the increasing accessibility of financial data and analytical tools to individual investors. Geographical distribution shows a strong presence in North America and Europe, which are expected to remain dominant markets due to the established financial infrastructure and advanced technological capabilities. However, Asia-Pacific is anticipated to demonstrate the fastest growth, driven by increasing economic activity and the expansion of financial markets in emerging economies. Competition is intense, with established players like Bloomberg and Refinitiv (Thomson Reuters) alongside emerging niche players. The competitive landscape is marked by both established giants and agile newcomers. Established players, like Bloomberg, Thomson Reuters, and WRDS, leverage their extensive data networks and brand reputation. However, these are challenged by newer entrants offering innovative solutions and specialized datasets targeting specific niche markets. The ongoing technological advancements, such as the rise of big data analytics and artificial intelligence, presents both opportunities and challenges. While AI-powered analytics unlock deeper insights from financial data, the need to adapt to evolving technologies and data security concerns require substantial investment. Regulatory changes and data privacy concerns also represent potential restraints, requiring continuous adaptation and compliance measures. The future of the market hinges on the ability of players to innovate, adapt to evolving regulations, and meet the increasing demand for speed, accuracy, and comprehensive financial data insights. The market's trajectory strongly suggests a promising future for both established and emerging companies.

This dataset contains data on Russian public companies. Financial sector organizations were not included in the sample. It is worth describing separately the data collection process, which was divided into two parts. First of all, qualitative characteristics of corporate governance were searched and systematized. Due to the lack of access to the Bloomberg terminal, it was necessary to collect indicators "manually". Additional difficulties were caused by the fact that there is no regulated corporate governance disclosure form. Thus, companies provided data in different ways. The main sources of information were organizations' annual reports, issuer's quarterly reports, sustainability reports, IFRS financial statements, as well as relevant sections of companies' official websites. At the same time, the listed reports were not always contained on the official websites of the organizations under consideration, therefore the following information resources were used additionally: Interfax, Cbonds and LiveTrader. At the second stage of data collection we systematized financial indicators taken from IFRS statements of the companies. Corporate governance indicators: BSIZE (size of the Board of Directors), BIND (percentage of independent members of the Board of Directors), DUAL1 (combining the roles of CEO and member of the Board of Directors), DUAL2 (combining the roles of CEO and Chairman of the Management Board), YCEO (CEO irremovability - logarithm of the number of years in the position), COMT (presence of internal audit, remuneration and nomination committees), COMTIND (degree of independence of internal committees), AUDIT (dummy variable equal to 1 if audited by a Big4 company), REMUN1 (percentage of remuneration to members of the Board of Directors as a percentage of total personnel expenses), REMUN2 (percentage of remuneration to members of the Management Board as a percentage of total personnel expenses), REMUN3 (percentage of remuneration to key management personnel as a percentage of total personnel expenses). Financial indicators: ROCE (return on capital employed), SIZE (company size), QTOB (Tobin ratio), TANG (tangible fixed assets to total assets), AGE (company age), NDTS (annual depreciation to total assets), INT (interest rate). The sample includes data from 2012 through 2021. It was important to have reliable information for each indicator required for the analysis. Otherwise, the company was excluded from the sample. The final sample included 32 Russian public companies. All indicators were taken in annual terms due to the specifics of corporate governance factors (with a few exceptions, they change no more than once a year). Thus, 320 observations were available for the analysis.

The B2B Information Services market is experiencing robust growth, with a market size of $106 million in 2025 and a projected Compound Annual Growth Rate (CAGR) of 14.3% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing reliance of businesses on data-driven decision-making fuels demand for comprehensive and reliable information across diverse sectors. The finance, energy, medical and healthcare, and legal and tax industries are particularly significant consumers, leveraging these services for market research, risk assessment, regulatory compliance, and strategic planning. Secondly, technological advancements, such as the rise of big data analytics and AI-powered insights platforms, are enhancing the value and accessibility of B2B information services. This allows for more sophisticated analysis and more effective strategic decision-making. Finally, the growing globalization of businesses necessitates access to accurate and timely global market intelligence, further boosting demand. The market is segmented by type (Professional Publishing, Joint Information, Consultation Service) and application (Finance, Energy, Medical and Healthcare, Legal and Tax, Others), offering diverse solutions catering to specific industry needs. Major players like Bloomberg, Thomson Reuters, and Wolters Kluwer are at the forefront, driving innovation and competition within the market. The continued growth trajectory is expected to be influenced by factors such as increased investments in research and development within the information services sector, leading to innovative product offerings. However, challenges remain. Data security and privacy concerns are paramount and require robust solutions from providers. Additionally, maintaining the accuracy and reliability of the information provided is critical for maintaining client trust. Competition is fierce, and providers must continuously differentiate their offerings through superior data quality, insightful analytics, and user-friendly platforms. The geographical distribution of the market is broad, with North America and Europe currently holding significant market share, but growth opportunities exist in rapidly developing economies in Asia-Pacific and other regions, promising further market expansion in the coming years.

A minimal dataset from a survey of 226 health facilities in Uganda supported by PEPFAR. 206 of the facilities were transitioned from PEPFAR support and 20 were maintained. Variables pertain to service discontinuation and perceived changes in quality and access to HIV services after transition.

The global financial industry quantitative evaluation service market is experiencing robust growth, driven by increasing demand for sophisticated risk management tools, regulatory compliance needs, and the burgeoning adoption of advanced analytical techniques within financial institutions. The market, estimated at $5 billion in 2025, is projected to exhibit a compound annual growth rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This growth is fueled by several key factors: the proliferation of big data in finance, the rise of algorithmic trading, and the increasing complexity of financial instruments. The enterprise segment currently dominates the market, owing to the greater need for advanced analytics among large financial institutions. However, the personal segment is witnessing significant growth, driven by the increasing accessibility and affordability of quantitative evaluation tools and services. Cloud-based solutions are rapidly gaining traction, surpassing internal deployments due to cost-effectiveness, scalability, and ease of access. Leading players like Bloomberg, AQR Capital Management, and Renaissance Technologies are driving innovation and shaping market trends through continuous product development and strategic partnerships. Geographic expansion, particularly in rapidly developing economies in Asia-Pacific and the Middle East & Africa, offers significant growth potential. While the market faces restraints such as the high initial investment cost and the need for specialized expertise, ongoing technological advancements and increasing regulatory scrutiny are expected to offset these challenges, leading to continued market expansion. The North American market holds the largest share currently, followed by Europe, reflecting the concentration of major financial institutions and a higher level of technological adoption in these regions.

The global financial data services market is on a significant growth trajectory, driven by the increasing digitization of the financial industry and the escalating demand for data-driven insights for investment and risk management. This expansion is fueled by the growing complexity of global financial markets, stringent regulatory compliance requirements, and the proliferation of advanced technologies like AI and machine learning for predictive analytics. Key market players are focusing on providing real-time, accurate, and comprehensive data solutions to cater to a diverse clientele, including banks, asset management firms, and hedge funds. The Asia Pacific region is emerging as the fastest-growing market, presenting lucrative opportunities, while North America continues to hold the largest market share due to its mature financial infrastructure and high technology adoption rate.

Key strategic insights from our comprehensive analysis reveal:

The integration of Artificial Intelligence (AI) and Machine Learning (ML) is no longer a trend but a fundamental driver, enabling predictive analytics, algorithmic trading, and personalized financial advice, thereby creating significant value.
The Asia-Pacific region, led by China and India, is projected to witness the highest CAGR, driven by rapid economic growth, increasing foreign investment, and widespread digital transformation in its BFSI sector.
There is a surging demand for specialized data services, particularly in Environmental, Social, and Governance (ESG) criteria and alternative data (e.g., satellite imagery, social media sentiment), as investors seek a more holistic view for decision-making.

Global Market Overview & Dynamics of Financial Data Services Market Analysis The global financial data services market is experiencing robust growth, set to expand from $19,761.5 million in 2021 to an estimated $52,972.4 million by 2033, progressing at a compound annual growth rate (CAGR) of 8.564%. This growth is underpinned by the financial sector's digital revolution, where real-time, accurate data is crucial for maintaining a competitive edge, ensuring regulatory compliance, and managing complex risks. The increasing adoption of cloud computing and AI is further democratizing access to sophisticated analytical tools, broadening the market's reach. Global Financial Data Services Market Drivers

Increasing Regulatory Complexity and Compliance Demands: Stringent regulations like MiFID II, Dodd-Frank, and Basel III mandate greater transparency and robust reporting, compelling financial institutions to invest heavily in reliable data services to ensure compliance and manage risk effectively.
Growth of Algorithmic and High-Frequency Trading: The rising prevalence of automated trading strategies that rely on instantaneous access to vast amounts of market data to execute trades in microseconds is a primary driver for real-time data feed services.
Digital Transformation in the BFSI Sector: The broad shift towards digital platforms in banking, wealth management, and insurance necessitates sophisticated data services for everything from customer analytics and personalized services to fraud detection and operational efficiency.

Global Financial Data Services Market Trends

Adoption of AI and Machine Learning for Predictive Analytics: Financial firms are increasingly leveraging AI/ML to analyze market trends, forecast asset performance, and automate investment decisions, driving demand for high-quality, structured datasets.
Surge in Demand for ESG Data: A growing investor focus on sustainability and ethical investing has created a massive trend for specialized ESG (Environmental, Social, and Governance) data services to assess corporate performance beyond traditional financial metrics.
Rise of Cloud-Based Data Platforms: The shift towards cloud-based solutions offers financial institutions greater flexibility, scalability, and cost-efficiency in accessing and analyzing large datasets, moving away from legacy on-premise systems.

Global Financial Data Services Market Restraints

Data Security and Privacy Concerns: The high sensitivity of financial data makes it a prime target for cyberattacks. The risk of data breaches and the need to comply with data privacy regulations like GDPR pose significant challenges and operational costs.
High Cost of Premium Data Services: Subscriptions to premium, real-time financial data feeds and sophisticated...

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Context

Numerous people had asked me to study the role retail trading plays in driving asset prices. Using this as my inspiration, I found a dataset with hourly tick data for ~9,000 stocks and another one with hourly Robinhood user participation data (aka how many Robinhood users own a stock in a particular time period) . Here you will not only find the data used to perform my research, but also a copy of the notebook I ended up using. Excited to see what the community does with this!

Content

2 major sources were used to acquire this data: - Stooq - While not written in English, this website hosts numerous free stock tick datasets. I was able to directionally confirm accuracy of the data vs what my personal brokerage account reported over this time period. I cannot speak to the preciseness of this data. - RobinTrack - This website collects Robinhood user participation data for stocks that trade on their platform. Per Bloomberg, it does appear Robinhood will stop providing access to this data in the near future (as of August 2020)

Additionally, you can find the notebook I used to prepare the research for my article here

The data covers the time period between September 2019 and July 2020.

Understanding the Files

I originally tried to input this information directly in the Data Explorer section but Kaggle kept bugging out.

Robinhood_Master_v1.csv

This is the master dataframe that includes hourly tick and Robinhood user participation data for ~9,000 stocks going back ~1 year - #: Index column; it can be ignored - Clean_Datetime: This column can also be ignored. - Close: Closing price for the stock noted in the Ticker column during this row's time period - High: Highest price reached for the stock noted in the Ticker column during this row's time period - Low: Lowest price reached for the stock noted in the Ticker column during this row's time period - Close: Closing price for the stock noted in the Ticker column during this row's time period - Open: Opening price for the stock noted in the Ticker column during this row's time period - OpenInt: This column can be ignored - its almost all 0 - Ticker: The stock ticker analyzed in a given row. For example, if this shows 'AAPL' then this row is reporting data on Apple stock. - users _ holding _ first: The initial amount of Robinhood users who owned the stock noted in the Ticker column during this row's time period - users _ holding _ last: The final amount of Robinhood users who owned the stock noted in the Ticker column during this row's time period - users _ holding _ max: The highest amount of Robinhood users who owned the stock noted in the Ticker column during this row's time period - users _ holding _ min: The lowest amount of Robinhood users who owned the stock noted in the Ticker column during this row's time period

df_apple_final.csv

This is the pre-processed dataframe that includes the cleaned predictors I used for my Apple time series modeling. All columns (except "y", "Clean _ Datetime _ PST" and "ds") were shifted back 1 day. The idea here is that all predictors need to occur on or before the target data. Otherwise, you end up using future data to predict the past. I'll only describe columns below that are not also found in the master dataframe. - users _ holding _ 1D _ change: the day-over-day change in Robinhood stock ownership for Apple - users _ holding _ 13D _ change: the 13 day change in Robinhood stock ownership for Apple - Open 6D_change: the 6 day change in Apple’s stock market opening price - Open 13D_change: the 13 day change in Apple’s stock market opening price - SPY users _ holding _ 1D _ change: the day-over-day change in Robinhood stock ownership for SPY - SPY Open 1D _ change: the day-over-day change in SPY’s stock market opening price - SPY Open 13D _ change: the 13 day change in SPY’s stock market opening price

custom_functions.py

In my notebook, I had to create a couple custom functions to run the graphs used there (this file is explicitly imported into my notebook with all the other python libraries). If you want to run my notebook, make sure it can find this file so it can run these functions.

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