This dataset contains historical stock price data for major banks from the year 2014 to 2024. The dataset includes daily stock prices, trading volume, and other relevant financial metrics for prominent banks. The stock prices are provided in IDR (Indonesian Rupiah) currency.

PT Bank Central Asia Tbk (BBCA.JK), more commonly recognized as Bank Central Asia (BCA). As one of Indonesia's largest privately-owned banks, BCA was founded in 1955 and provides a diverse array of banking services encompassing consumer banking, corporate banking, investment banking, and asset management. With a widespread presence throughout Indonesia, including numerous branches and ATMs, BCA is esteemed for its robust financial achievements, inventive banking offerings, and dedication to customer satisfaction.

Dataset Variables:

1. Date: The date of the stock price data.
2. Open Price: The opening price of the bank's stock on the given date.
3. Close Price: The closing price of the bank's stock on the given date.
4. High Price: The highest price reached by the bank's stock during the trading day.
5. Low Price: The lowest price reached by the bank's stock during the trading day.
6. Adjusted Low Price: The closing price on a given trading day, adjusted to reflect any corporate actions, such as stock splits, dividends, rights offerings, or other adjustments that may affect the stock price.
7. Volume: The number of shares traded on the given date.

Data Sources: The dataset is compiled from reliable financial sources, including stock exchanges, financial news websites, and reputable financial data providers. Data cleaning and preprocessing techniques have been applied to ensure accuracy and consistency. More info: https://finance.yahoo.com/quote/BBCA.JK/history/

Use Case: This dataset can be utilized for various purposes, including financial analysis, stock market forecasting, algorithmic trading strategies, and academic research. Researchers, analysts, and data scientists can explore the trends, patterns, and relationships within the data to derive valuable insights into the performance of the banking sector over the specified period. Additionally, this dataset can serve as a benchmark for evaluating the performance of machine learning models and quantitative trading strategies in the banking industry.

Other-Long-Term-Assets Time Series for Robinhood Markets Inc. Robinhood Markets, Inc. operates financial services platform in the United States. Its platform allows users to invest in stocks, exchange-traded funds (ETFs), American depository receipts, options, gold, and cryptocurrencies. The company offers fractional trading, recurring investments, fully-paid securities lending, access to investing on margin, cash sweep, instant withdrawals, retirement program, around-the-clock trading, joint investing accounts, event contracts, and future contract services. It also provides various learning and education solutions comprise Snacks, an accessible digest of business news stories for a new generation of investors.; Learn, which is an online collection of guides, feature tutorials, and financial dictionary; Newsfeeds that offer access to free, premium news from sites from various sites, such as Barron's, Reuters, and Dow Jones. In addition, the company offers In-App Education, a resource that covers investing fundamentals, including why people invest, a stock market overview, and tips on how to define investing goals, as well as allows customers to understand the basics of investing before their first trade; and Crypto Learn and Earn, an educational module available to various crypto customers through Robinhood Learn to teach customers the basics related to cryptocurrency. Further, it provides Robinhood credit cards, cash card and spending accounts, and wallets. The company also owns and operates a digital currency marketplace that allows companies and individuals from all around the world to buy and sell bitcoin, litecoin, ethereum, ripple, and bitcoin cash. Robinhood Markets, Inc. was incorporated in 2013 and is headquartered in Menlo Park, California.

Context The StockNet dataset, introduced by Xu and Cohen at ACL 2018, is a benchmark for measuring the effectiveness of textual information in stock market prediction. While the original dataset provides valuable price and news data, it requires significant pre-processing and feature engineering to be used effectively in advanced machine learning models.

This dataset was created to bridge that gap. We have taken the original data for 87 stocks and performed extensive feature engineering, creating a rich, multi-modal feature repository.

A key contribution of this work is a preliminary statistical analysis of the news data for each stock. Based on the consistency and volume of news, we have categorized the 87 stocks into two distinct groups, allowing researchers to choose the most appropriate modeling strategy:

joint_prediction_model_set: Stocks with rich and consistent news data, ideal for building complex, single models that analyze all stocks jointly.

panel_data_model_set: Stocks with less consistent news data, which are better suited for traditional panel data analysis.

Content and File Structure The dataset is organized into two main directories, corresponding to the two stock categories mentioned above.

1.joint_prediction_model_set This directory contains stocks suitable for sophisticated, news-aware joint modeling.

-Directory Structure: This directory contains a separate sub-directory for each stock suitable for joint modeling (e.g., AAPL/, MSFT/, etc.).

-Folder Contents: Inside each stock's folder, you will find a set of files, each corresponding to a different category of engineered features. These files include:

-News Graph Embeddings: A NumPy tensor file (.npy) containing the encoded graph embeddings from daily news. Its shape is (Days, N, 128), where N is the number of daily articles.

-Engineered Features: A CSV file containing fundamental features derived directly from OHLCV data (e.g., intraday_range, log_return).

-Technical Indicators: A CSV file with a wide array of popular technical indicators (e.g., SMA, EMA, MACD, RSI, Bollinger Bands).

-Statistical & Time Features: A CSV file with rolling statistical features (e.g., volatility, skew, kurtosis) over an optimized window, plus cyclical time-based features.

-Advanced & Transformational Features: A CSV file with complex features like lagged variables, wavelet transform coefficients, and the Hurst Exponent.

2.panel_data_model_set This directory contains stocks that are more suitable for panel data models, based on the statistical properties of their associated news data.

-Directory Structure: Similar to the joint prediction set, this directory also contains a separate sub-directory for each stock in this category.

-Folder Contents: Inside each stock's folder, you will find the cleaned and structured price and news text data. This facilitates the application of econometric models or machine learning techniques designed for panel data, where observations are tracked for the same subjects (stocks) over a period of time.

-Further Information: For a detailed breakdown of the statistical analysis used to separate the stocks into these two groups, please refer to the data_preview.ipynb notebook located in the TRACE_ACL18_raw_data directory.

Methodology The features for the joint_prediction_model_set were generated systematically for each stock:

-News-to-Graph Pipeline: Daily news headlines were processed to extract named entities. These entities were then used to query Wikidata and build knowledge subgraphs. A Graph Convolutional Network (GCN) model encoded these graphs into dense vectors.

-Feature Engineering: All other features were generated from the raw price and volume data. The process included basic calculations, technical analysis via pandas-ta, generation of statistical and time-based features, and advanced transformations like wavelet analysis.

Acknowledgements This dataset is an extension and transformation of the original StockNet dataset. We extend our sincere gratitude to the original authors for their contribution to the field.

Original Paper: "StockNet: A Probing Task for Measuring Stock Market Prediction" by Yumeng Xu and Mohit Bansal. (ACL 2018).

Original Data Repository: https://github.com/yumoxu/stocknet-dataset

Inspiration This dataset opens the door to numerous exciting research questions:

-Can you build a single, powerful joint model using the joint_prediction_model_set to predict movements for all stocks simultaneously?

-How does a sophisticated joint model compare against a traditional panel data model trained on the panel_data_model_set?

-What is the lift in predictive power from using news-based graph embeddings versus using only technical indicators?

-Can you apply transfer learning or multi-task learning, using the feature-rich joint set to improve predictions for the panel set?

Analysis of ‘FAANG- Complete Stock Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aayushmishra1512/faang-complete-stock-data on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

There are a few companies that are considered to be revolutionary. These companies also happen to be a dream place to work at for many many people across the world. These companies include - Facebook,Amazon,Apple,Netflix and Google also known as FAANG! These companies make ton of money and they help others too by giving them a chance to invest in the companies via stocks and shares. This data wass made targeting these stock prices.

Content

The data contains information such as opening price of a stock, closing price, how much of these stocks were sold and many more things. There are 5 different CSV files in the data for each company.

--- Original source retains full ownership of the source dataset ---

Stock-Based-Compensation Time Series for Robinhood Markets Inc. Robinhood Markets, Inc. operates financial services platform in the United States. Its platform allows users to invest in stocks, exchange-traded funds (ETFs), American depository receipts, options, gold, and cryptocurrencies. The company offers fractional trading, recurring investments, fully-paid securities lending, access to investing on margin, cash sweep, instant withdrawals, retirement program, around-the-clock trading, joint investing accounts, event contracts, and future contract services. It also provides various learning and education solutions comprise Snacks, an accessible digest of business news stories for a new generation of investors.; Learn, which is an online collection of guides, feature tutorials, and financial dictionary; Newsfeeds that offer access to free, premium news from sites from various sites, such as Barron's, Reuters, and Dow Jones. In addition, the company offers In-App Education, a resource that covers investing fundamentals, including why people invest, a stock market overview, and tips on how to define investing goals, as well as allows customers to understand the basics of investing before their first trade; and Crypto Learn and Earn, an educational module available to various crypto customers through Robinhood Learn to teach customers the basics related to cryptocurrency. Further, it provides Robinhood credit cards, cash card and spending accounts, and wallets. The company also owns and operates a digital currency marketplace that allows companies and individuals from all around the world to buy and sell bitcoin, litecoin, ethereum, ripple, and bitcoin cash. Robinhood Markets, Inc. was incorporated in 2013 and is headquartered in Menlo Park, California.

Stock Price Prediction

A stock market, equity market or share market is the aggregation of buyers and sellers of stocks (also called shares), which represent ownership claims on businesses; these may include securities listed on a public stock exchange, as well as stock that is only traded privately, such as shares of private companies which are sold to investors through equity crowdfunding platforms.

The secret of a successful stock trader is being able to look into the future of the stocks and make wise decisions. Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices.

Here, you are provided dataset of a public stock market for 104 stocks. Can you forecast the future closing prices for these stocks with your Data Science skills for the next 2 months?

Context

The dataset contains prices and volumes for different stocks

Here is an example: cat 201801_Amsterdam_AALB_NoExpiry.txt

01/02/2018,09:01:00, 42.39, 42.39, 42.21, 42.21, 737 01/02/2018,09:02:00, 42.28, 42.28, 42.27, 42.27, 277 01/02/2018,09:04:00, 42.24, 42.24, 42.24, 42.24, 177 01/02/2018,09:05:00, 42.23, 42.23, 42.22, 42.22, 1543 01/02/2018,09:06:00, 42.23, 42.23, 42.23, 42.23, 241

The dataset contains trading data for 2182 unique stocks, on 40 unique stock exchanges. The monthly data is provided by stocks with each stock being associated with a specific stock exchange and is initially stored in the .txt format. Each file contains a trading history of a stock in a particular month and has the following schema.

Content

The columns correspond to the following schema:

• Date (Calendar Year 2018)
• Time (in CET timezone) The following 5 columns describe the data, that came in until the last minute
• Opening price (The first price within this minute)
• Highest price (The highest price within this minute)
• Lowest price (The lowest price within this minute)
• Closing price (the last price within this minute)
• Sum of the volume of all transactions within this minute

Dataset is a zipped file of stocks from many stock markets and forex. It covers the whole of 2018. Notice the following: 1. All mentioned timestamps are CET. 2. There are missing records and irregularities on the updates – see the previous example. You need to decide how to handle the missing values/records. 3. Different stocks have different update frequencies.

Capital-Expenditures Time Series for Robinhood Markets Inc. Robinhood Markets, Inc. operates financial services platform in the United States. Its platform allows users to invest in stocks, exchange-traded funds (ETFs), American depository receipts, options, gold, and cryptocurrencies. The company offers fractional trading, recurring investments, fully-paid securities lending, access to investing on margin, cash sweep, instant withdrawals, retirement program, around-the-clock trading, joint investing accounts, event contracts, and future contract services. It also provides various learning and education solutions comprise Snacks, an accessible digest of business news stories for a new generation of investors.; Learn, which is an online collection of guides, feature tutorials, and financial dictionary; Newsfeeds that offer access to free, premium news from sites from various sites, such as Barron's, Reuters, and Dow Jones. In addition, the company offers In-App Education, a resource that covers investing fundamentals, including why people invest, a stock market overview, and tips on how to define investing goals, as well as allows customers to understand the basics of investing before their first trade; and Crypto Learn and Earn, an educational module available to various crypto customers through Robinhood Learn to teach customers the basics related to cryptocurrency. Further, it provides Robinhood credit cards, cash card and spending accounts, and wallets. The company also owns and operates a digital currency marketplace that allows companies and individuals from all around the world to buy and sell bitcoin, litecoin, ethereum, ripple, and bitcoin cash. Robinhood Markets, Inc. was incorporated in 2013 and is headquartered in Menlo Park, California.

The values of any financial assets held including both formal investments, such as bank or building society current or saving accounts, investment vehicles such as Individual Savings Accounts, endowments, stocks and shares, and informal savings.

Dataset Description: Microsoft Stock Prices (One Year)

-This dataset contains daily trading information for Microsoft Corporation (MSFT) stock over a one-year period. Each entry represents a single trading day and includes essential stock market data.

Columns and Descriptions:

-**Date:** The trading day in YYYY-MM-DD format.

-**Open:** Stock price at market open.

-**High:** Highest stock price during the day.

-**Low:** Lowest stock price during the day.

-**Close:** Stock price at market close.

-**Volume:** Number of shares traded on that day.

-This dataset is suitable for time series analysis, stock price forecasting, and machine learning projects focused on financial data.

Cash-and-Short-Term-Investments Time Series for SJW Group Common Stock. H2O America, through its subsidiaries, provides water utility and other related services in the United States. The company engages in the production, purchase, storage, purification, distribution, wholesale, and retail sale of water and wastewater services; and supplies groundwater from wells, surface water from watershed run-off and diversion, reclaimed water, and imported water purchased from Santa Clara Valley Water District. It also offers non-tariffed services, including water system operations, maintenance agreements, and antenna site leases; contracted services, sewer operations, and other water related services; and a Linebacker protection plan for public drinking water customers in Connecticut and Maine. In addition, the company provides water services to approximately 232,000 connections that serve approximately one million people residing in portions of the cities of San Jose and Cupertino and in the cities of Campbell, Monte Sereno, Saratoga and the Town of Los Gatos, and adjacent unincorporated territories in the County of Santa Clara in the State of California; water service to approximately 142,000 service connections, which serve a population of approximately 463,000 people in 81 municipalities with a service area of approximately 275 square miles in Connecticut and Maine and approximately 3,000 wastewater connections in Southbury, Connecticut; approximately 29,000 service connections that serve approximately 88,000 people in a service area comprising more than 271 square miles in the region between San Antonio and Austin, Texas and approximately 1,000 wastewater connections. Further, it owns undeveloped land in California; and commercial properties and parcels of land in Connecticut. The company was formerly known as SJW Group and changed its name to H2O America in Ma 2025. H2O America was incorporated in 1985 and is headquartered in San Jose, California.

Common-Stock-Shares-Outstanding Time Series for Allied Properties Real Estate Investment Trust. Allied is a leading owner-operator of distinctive urban workspace in Canada's major cities. Allied's mission is to provide knowledge-based organizations with workspace that is sustainable and conducive to human wellness, creativity, connectivity and diversity. Allied's vision is to make a continuous contribution to cities and culture that elevates and inspires the humanity in all people.

Stock Price Time Series for South Plains Financial Inc. South Plains Financial, Inc. operates as a bank holding company for City Bank that provides commercial and consumer financial services to small and medium-sized businesses and individuals. It offers deposit products, including demand deposit accounts, interest-bearing products, savings accounts, and certificate of deposits. The company also provides traditional trust products and services; debit and credit cards; retirement services and products, including real estate administration, family trust administration, revocable and irrevocable trusts, charitable trusts for individuals and corporations, and self-directed individual retirement accounts. In addition, it offers investment services, such as self-directed IRAs, money market funds, mutual funds, annuities and tax-deferred annuities, stocks and bonds, investments for non-U.S. residents, treasury bills, treasury notes and bonds, and tax-exempt municipal bonds. Further, the company provides commercial real estate loans; general and specialized commercial loans, including agricultural production and real estate, energy, finance, investment, and insurance loans, as well as loans to goods, services, restaurant and retail, construction, and other industries; residential construction loans; and 1-4 family residential loans, auto loans, and other loans for recreational vehicles or other purposes; and mortgage banking services. The company was founded in 1941 and is headquartered in Lubbock, Texas.

Long-Term-Investments Time Series for Stock Yards Bancorp Inc. Stock Yards Bancorp, Inc. operates as a holding company for Stock Yards Bank & Trust Company that provides various financial services for individuals, corporations, and others in the United States. It operates in two segments, Commercial Banking, and WM&T. The Commercial Banking segment offers a range of loan and deposit products to individual consumers and businesses in all its markets through retail lending, mortgage banking, deposit services, online banking, mobile banking, private banking, commercial lending, commercial real estate lending, leasing, treasury management services, merchant services, international banking, correspondent banking, credit card services, and other banking services. This segment also provides securities brokerage services through an arrangement with a third party broker-dealer. The WM&T segment provides investment management, financial and retirement planning, and trust and estate services, as well as retirement plan management for businesses and corporations. It provides services in Louisville, central, eastern, and northern Kentucky, as well as Indianapolis, Indiana and Cincinnati, Ohio metropolitan markets. The company was founded in 1904 and is headquartered in Louisville, Kentucky.

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.

JPMorgan (JII) Stock: Is the Indian Investment Trust a Winner?

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 dataset contains comprehensive historical trading data for Amazon, including daily open, high, low, and close prices, as well as trading volume, dividends, and stock splits. The data spans a significant time frame, offering insights into Amazon's stock performance over time. Ideal for investors, financial analysts, and data scientists, this dataset can be used for trend analysis, backtesting trading strategies, and understanding market behavior. Whether you're studying Amazon's stock history or developing predictive models, this dataset provides the essential data you need

Data Overview

1. Datetime: This column records the date and time when the stock prices were observed.

2. Open: This is the opening price of the stock for the given time period.

3. High: This represents the highest price at which the stock is traded during the specified time period

4. Low: This is the lowest price at which the stock is traded during the specified time period.

5. Close: This is the closing price of the stock for the given time period.

6. Volume: This column records the total number of shares of the stock that were traded during the specified time period.

7. Dividends: This column records any dividend payments that occurred on the specified date. Dividends are distributions of a company's earnings to its shareholders.

8. Stock Splits: This column records any stock splits that occurred on the specified date. A stock split is a corporate action in which a company increases the number of its outstanding shares by issuing more shares to its current shareholders.

Common-Stock Time Series for BPER Banca SpA. BPER Banca SpA provides banking products and services for individuals, and businesses and professionals in Italy and internationally. It offers current and saving accounts, loans, mortgages, insurance and social security, and digital banking and related services; cards; and investments and savings products and services. The company also provides financing and leasing, collection and payment, import and export, liquidity and investment management, digital, and other services. In addition, it offers wealth management services comprising portfolio management, consultancy, financial advice, and wealth advisory services; insurance investment products; and funds and SICAVs. The company was founded in 1867 and is headquartered in Modena, Italy.

The Investment / Foreign Direct Investment (FDI) dataset is collected or analyzed by the Food and Agriculture Organization of the United Nations (FAO) on foreign direct investment flows and stocks in the agriculture, forestry, and fisheries sectors. FDI is an investment which aims to acquire a lasting management influence (10 percent or more of the voting stock) in an enterprise operating in a foreign economy. FDI may be undertaken by individuals, as well as business entities. The foreign direct investor most often is aiming to gain access to natural resources, to markets, to labour supply, to technology, to ensure security of supplies or to control the quality of a certain product. FDI can be decomposed into two types of investments: mergers and acquisitions (MA) and greenfield investments. The latter type results in the creation of new entities and the setting up of offices, buildings, plants or factories from scratch in a foreign economy. FDI is the sum of equity capital, reinvested earnings and other FDI capital. Equity capital comprises equity in branches, all shares in subsidiaries and associates (except non-participating, preferred shares that are treated as debt securities and are included under other FDI capital) and other contributions such as the provision of machinery. Reinvested earnings consist of the direct investor's share (in proportion to equity participation) of earnings not distributed by the direct investment enterprise. Other FDI capital (loans) includes the borrowing and lending of funds, including debt securities and trade credits between direct investors and direct investment enterprises. FDI inflows and outflows are important for tracking the direct investment conditions each year. Outward Foreign Direct Investment (FDI) flows record the value of cross-border direct investment transactions from the reporting economy during a year. It represents transactions affecting the investment in enterprises resident abroad. Whereas, Inward Foreign Direct Investment (FDI) flows record the value of cross-border direct investment transactions received by the reporting economy during a year. It represents transactions affecting the investment in enterprises of a specific industry resident in the reporting economy. The data included in Data360 is a subset of the data available from the source. Please refer to the source for complete data and methodology details. This collection includes only a subset of indicators from the source dataset.

This dataset, 'Amazon Stock Data and Key Affiliated Companies,' provides comprehensive daily stock data for Amazon (AMZN) and several companies that have significantly contributed to Amazon's business growth and success. The dataset includes historical data for key players such as Intel (INTC), FedEx (FDX), United Parcel Service (UPS), Salesforce (CRM), NVIDIA (NVDA), Visa (V), and Mastercard (MA).

The stock data spans over various years, capturing important trading metrics like open, close, high, low, and volume. Amazon, a global leader in e-commerce, cloud computing, and AI, has thrived with the support of these affiliated companies. From Intel's processors powering Amazon Web Services (AWS) to Salesforce's CRM solutions used by Amazon, and the logistics support provided by FedEx and UPS, each company plays a critical role.

This dataset can be used for financial analysis, stock market prediction models, correlation studies between Amazon and its key partners, or any other research involving the financial performance of these major corporations. Whether you're interested in understanding Amazon's stock trends or the interdependency of companies in its ecosystem, this dataset provides valuable insights.

Stock Price Time Series for The Bank of New York Mellon Corporation. The Bank of New York Mellon Corporation provides a range of financial products and services in the United States and internationally. It operates through Securities Services, Market and Wealth Services, Investment and Wealth Management, and Other segments. The Securities Services segment offers custody, trust and depositary, accounting, exchange-traded funds, middle-office solutions, transfer agency, services for private equity and real estate funds, foreign exchange, securities lending, liquidity/lending services, and data analytics. This segment also provides trustee, paying agency, fiduciary, escrow and other financial, issuer, and support services for brokers and investors. The Market and Wealth Services segment offers clearing and custody, investment, wealth and retirement solutions, technology and enterprise data management, trading, and prime brokerage services. This segment also provides integrated cash management solutions, including payments, foreign exchange, liquidity management, receivables processing, payables management, and trade finance, as well as U.S. government and global clearing, and tri-party services. The Investment and Wealth Management segment offers investment management strategies, investment products distribution, investment management, custody, wealth and estate planning, private banking, investment, and information management services. The Other segment provides leasing, corporate treasury, derivative and other trading, corporate and bank-owned life insurance, tax credit investment, other corporate investment, and business exit services. The company serves central banks and sovereigns, financial institutions, asset managers, insurance companies, corporations, local authorities and high net-worth individuals, and family offices. The Bank of New York Mellon Corporation was founded in 1784 and is headquartered in New York, New York.