The data sets provide the text and detailed numeric information in all financial statements and their notes extracted from exhibits to corporate financial reports filed with the Commission using eXtensible Business Reporting Language (XBRL).

This dataset contains important financial information and accounting ratios of the top 200 US Companies. Source of data in Yfiannce

This dataset provides a structured and machine-readable collection of financial statements filed with the Companies Registration Office (CRO) in Ireland. It currently includes financial statements for the year 2022, with additional years to be added as they become available. The dataset aligns with the European Union’s Open Data Directive (Directive (EU) 2019/1024) and the Implementing Regulation (EU) 2023/138, which designates company and company ownership data as a high-value dataset. It is available for bulk download and API access under the Creative Commons Attribution 4.0 (CC BY 4.0) licence, allowing unrestricted reuse with appropriate attribution. By increasing transparency and enabling data-driven insights, this dataset supports public sector initiatives, financial analysis, and digital services development. The API endpoints can be accessed using these links - Query - https://opendata.cro.ie/api/3/action/datastore_search Query (via SQL) - https://opendata.cro.ie/api/3/action/datastore_search_sql

Introduction

This dataset contains 604 public company financial statement annually in IDX (Bursa Efek Indonesia), largest number that I can see in kaggle :D. Company that's not included in this dataset either do not report their financial statement or contains some irrelevant publishing date.

Usability

• EDA
• Classifier Stock
• Fundamental Analysis
• Financial Statement Analysis

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Appendix

Type:

Type	Description	Translate (in Indonesia)
BS	Balance Sheet/Statement of FInancial Position	Laporan Posisi Neraca / Laporan Posisi Keuangan
IS	(Consolidated) Income Statement	Laporan Laba/Rugi (Konsolidasian)
CF	Statement of Cash Flow	Laporan Arus Kas

Account:

Account	Type	Translate (in Indonesia)
Accounts Payable	BS	Utang Usaha
Accounts Receivable	BS	Piutang Usaha
Accumulated Depreciation	BS	Akumulasi Penyusutan
Additional Paid In Capital (PIC) / Share Premium	BS	Saham premium
Allowance For Doubtful Accounts Receivable (AFDA)	BS	Cadangan Piutang Usaha
Buildings And Improvements	BS	Bangunan dan Pengembangan
Capital Stock	BS	Saham
Cash And Cash Equivalents	BS	Kas dan Setara Kas
Cash Cash Equivalents And Short Term Investments	BS	Kas, Setara Kas, dan Investasi Jangka Pendek
Cash Equivalents	BS	Setara Kas
Cash Financial	BS	Kas yang berhubungan dengan aktiviatas keuangan
Common Stock	BS	Saham Biasa
Common Stock Equity	BS	Ekuitas Saham Biasa
Construction In Progress	BS	Konstruksi yang Sedang Berlangsung
Current Assets	BS	Aset Lancar
Current Debt	BS	Utang Lancar
Current Debt And Capital Lease Obligation	BS	Utang Lancar dan Kewajiban Sewa Kapital
Current Liabilities	BS	Liabilitas Lancar
Finished Goods	BS	Barang Jadi
Goodwill	BS	Nilai Tambah (Goodwill)
Goodwill And Other Intangible Assets	BS	Nilai Tambah (Goodwill) dan Aset Tidak Berwujud Lainnya
Gross Accounts Receivable	BS	Piutang Usaha Bruto
Gross PPE	BS	Aktiva Tetap Bruto (Properti, Pabrik, dan Peralatan)
Inventory	BS	Persediaan
Invested Capital	BS	Kapital yang Diinvestasikan
Investmentsin Joint Venturesat Cost	BS	Investasi dalam Usaha Patungan dengan Harga Perolehan
Land And Improvements	BS	Tanah dan Pengembangan
Long Term Debt	BS	Utang Jangka Panjang
Long Term Debt And Capital Lease Obligation	BS	Utang Jangka Panjang dan Kewajiban Sewa Kapital
Long Term Equity Investment	BS	Investasi Ekuitas Jangka Panjang
Machinery Furniture Equipment	BS	Mesin, Perabotan dan Perlengkapan
Minority Interest	BS	Kepentingan Minoritas
Net Debt	BS	Utang Bersih
Net PPE	BS	Aktiva Tetap Bersih (Properti, Pabrik, dan Peralatan)
Net Tangible Assets	BS	Aset Berwujud Bersih
Non Current Deferred Taxes Assets	BS	Aset Pajak Tangguhan Non Lancar
Non Current Deferred Taxes Liabilities	BS	Liabilitas Pajak Tangguhan Non Lancar
Non Current Pension And Other Postretirement Benefit Plans	BS	Rencana Pensiun Non Lancar dan Manfaat Pasca Pensiun Lainnya
Ordinary Shares Number	BS	Jumlah Saham Biasa
Other Current Liabilities	BS	Liabilitas Lancar Lainnya
Other Equity Interest	BS	Kepentingan Ekuitas Lainnya
Other Inventories	BS	Persediaan Lainnya
Other Non Current Assets	BS	Aset Non Lancar Lainnya
Other Non Current Liabilities	BS	Liabilitas Non Lancar Lainnya
Other Payable	BS	Hutang Lainnya
Other Properties	BS	Properti Lainnya
Other Receivables	BS	Piutang Lainnya
Payables	BS	Utang
Pensionand Other Post Retirement Benefit Plans Current	BS	Rencana Pensiun dan Manfaat Pasca Pensiun Lainnya Saat Ini
Prepaid Assets	BS	Aset Dibayar Dimuka
Properties	BS	Properti
Raw Materials	BS	Bahan Baku
Retained Earnings	BS	Laba Ditahan
Share Issued	BS	Saham yang Diterbitkan
Stockholders Equity	BS	Ekuitas Pemegang Saham
Tangible Book Value	BS	Nilai Buku Berwujud
Total Assets	BS	Total Aset
Total Capitalization	BS	Total Kapitalisasi
Total Debt	BS	Total Utang
Total Equity Gross Minority Interest	BS	Total Ekuitas Bruto dengan Kepentingan Minoritas
Total Liabilities Net Minority Interest	BS	Total Liabilitas Bersih dengan Kepentingan Minoritas
Total Non Current Assets	BS	Total Aset Non Lancar
Total Non Current Liabilities Net Minority Interest	BS	Total Liabilitas Non Lancar Bersih dengan Kepentingan Minoritas
Total Tax Payable	BS	Total Utang Pajak
Treasury Shares Number	BS	Jumlah Saham Treasuri
Work In Process	BS	Pekerjaan dalam Proses
Working Capital	BS	Modal Kerja / Kapital Jangka Pendek
Beginning Cash Position	CF	Posisi Kas Awal
Capital Expenditure	CF	Pengeluaran - Kapital
Capital Expenditure Reported	CF	Pengeluaran - Kapital yang Dilaporkan
Cash Dividends Paid	CF	Dividen Tunai yang Dibayarkan
Cash Flowsfromusedin Operating Activities Direct	CF	Arus Kas yang Digunakan dalam Aktivitas Operasional Langsung
Changes In Cash...

This dataset contains Income Statement data from IDA?s published financial statements It was compiled from data in our systems as well as by extracting the data from the published Financial Statements documents. The dataset goes as far back as the foundation of the association (1961). This data has been verified and validated for publication, but does not, in any capacity, replace the official published Financial Statements. Please note that this dataset includes certain rows that are calculated totals, summing up values from related individual records. These are included for completeness and ease of analysis. An archive for IDA?s annual Financial Statements is available at www.worldbank.org/financialresults

FINGAP07 NUMBER OF FINANCIAL STATEMENTS AND NOTES TO ACCOUNTS PRODUCED

The Russian Financial Statements Database (RFSD) is an open, harmonized collection of annual unconsolidated financial statements of the universe of Russian firms:

• 🔓 First open data set with information on every active firm in Russia.

• 🗂️ First open financial statements data set that includes non-filing firms.

• 🏛️ Sourced from two official data providers: the Rosstat and the Federal Tax Service.

• 📅 Covers 2011-2023 initially, will be continuously updated.

• 🏗️ Restores as much data as possible through non-invasive data imputation, statement articulation, and harmonization.

The RFSD is hosted on 🤗 Hugging Face and Zenodo and is stored in a structured, column-oriented, compressed binary format Apache Parquet with yearly partitioning scheme, enabling end-users to query only variables of interest at scale.

The accompanying paper provides internal and external validation of the data: http://arxiv.org/abs/2501.05841.

Here we present the instructions for importing the data in R or Python environment. Please consult with the project repository for more information: http://github.com/irlcode/RFSD.

Importing The Data

You have two options to ingest the data: download the .parquet files manually from Hugging Face or Zenodo or rely on 🤗 Hugging Face Datasets library.

Python

🤗 Hugging Face Datasets

It is as easy as:

from datasets import load_dataset import polars as pl

This line will download 6.6GB+ of all RFSD data and store it in a 🤗 cache folder

RFSD = load_dataset('irlspbru/RFSD')

Alternatively, this will download ~540MB with all financial statements for 2023# to a Polars DataFrame (requires about 8GB of RAM)

RFSD_2023 = pl.read_parquet('hf://datasets/irlspbru/RFSD/RFSD/year=2023/*.parquet')

Please note that the data is not shuffled within year, meaning that streaming first n rows will not yield a random sample.

Local File Import

Importing in Python requires pyarrow package installed.

import pyarrow.dataset as ds import polars as pl

Read RFSD metadata from local file

RFSD = ds.dataset("local/path/to/RFSD")

Use RFSD_dataset.schema to glimpse the data structure and columns' classes

print(RFSD.schema)

Load full dataset into memory

RFSD_full = pl.from_arrow(RFSD.to_table())

Load only 2019 data into memory

RFSD_2019 = pl.from_arrow(RFSD.to_table(filter=ds.field('year') == 2019))

Load only revenue for firms in 2019, identified by taxpayer id

RFSD_2019_revenue = pl.from_arrow( RFSD.to_table( filter=ds.field('year') == 2019, columns=['inn', 'line_2110'] ) )

Give suggested descriptive names to variables

renaming_df = pl.read_csv('local/path/to/descriptive_names_dict.csv') RFSD_full = RFSD_full.rename({item[0]: item[1] for item in zip(renaming_df['original'], renaming_df['descriptive'])})

R

Local File Import

Importing in R requires arrow package installed.

library(arrow) library(data.table)

Read RFSD metadata from local file

RFSD <- open_dataset("local/path/to/RFSD")

Use schema() to glimpse into the data structure and column classes

schema(RFSD)

Load full dataset into memory

scanner <- Scanner$create(RFSD) RFSD_full <- as.data.table(scanner$ToTable())

Load only 2019 data into memory

scan_builder <- RFSD$NewScan() scan_builder$Filter(Expression$field_ref("year") == 2019) scanner <- scan_builder$Finish() RFSD_2019 <- as.data.table(scanner$ToTable())

Load only revenue for firms in 2019, identified by taxpayer id

scan_builder <- RFSD$NewScan() scan_builder$Filter(Expression$field_ref("year") == 2019) scan_builder$Project(cols = c("inn", "line_2110")) scanner <- scan_builder$Finish() RFSD_2019_revenue <- as.data.table(scanner$ToTable())

Give suggested descriptive names to variables

renaming_dt <- fread("local/path/to/descriptive_names_dict.csv") setnames(RFSD_full, old = renaming_dt$original, new = renaming_dt$descriptive)

Use Cases

🌍 For macroeconomists: Replication of a Bank of Russia study of the cost channel of monetary policy in Russia by Mogiliat et al. (2024) — interest_payments.md

🏭 For IO: Replication of the total factor productivity estimation by Kaukin and Zhemkova (2023) — tfp.md

🗺️ For economic geographers: A novel model-less house-level GDP spatialization that capitalizes on geocoding of firm addresses — spatialization.md

FAQ

Why should I use this data instead of Interfax's SPARK, Moody's Ruslana, or Kontur's Focus?hat is the data period?

To the best of our knowledge, the RFSD is the only open data set with up-to-date financial statements of Russian companies published under a permissive licence. Apart from being free-to-use, the RFSD benefits from data harmonization and error detection procedures unavailable in commercial sources. Finally, the data can be easily ingested in any statistical package with minimal effort.

What is the data period?

We provide financials for Russian firms in 2011-2023. We will add the data for 2024 by July, 2025 (see Version and Update Policy below).

Why are there no data for firm X in year Y?

Although the RFSD strives to be an all-encompassing database of financial statements, end users will encounter data gaps:

We do not include financials for firms that we considered ineligible to submit financial statements to the Rosstat/Federal Tax Service by law: financial, religious, or state organizations (state-owned commercial firms are still in the data).

Eligible firms may enjoy the right not to disclose under certain conditions. For instance, Gazprom did not file in 2022 and we had to impute its 2022 data from 2023 filings. Sibur filed only in 2023, Novatek — in 2020 and 2021. Commercial data providers such as Interfax's SPARK enjoy dedicated access to the Federal Tax Service data and therefore are able source this information elsewhere.

Firm may have submitted its annual statement but, according to the Uniform State Register of Legal Entities (EGRUL), it was not active in this year. We remove those filings.

Why is the geolocation of firm X incorrect?

We use Nominatim to geocode structured addresses of incorporation of legal entities from the EGRUL. There may be errors in the original addresses that prevent us from geocoding firms to a particular house. Gazprom, for instance, is geocoded up to a house level in 2014 and 2021-2023, but only at street level for 2015-2020 due to improper handling of the house number by Nominatim. In that case we have fallen back to street-level geocoding. Additionally, streets in different districts of one city may share identical names. We have ignored those problems in our geocoding and invite your submissions. Finally, address of incorporation may not correspond with plant locations. For instance, Rosneft has 62 field offices in addition to the central office in Moscow. We ignore the location of such offices in our geocoding, but subsidiaries set up as separate legal entities are still geocoded.

Why is the data for firm X different from https://bo.nalog.ru/?

Many firms submit correcting statements after the initial filing. While we have downloaded the data way past the April, 2024 deadline for 2023 filings, firms may have kept submitting the correcting statements. We will capture them in the future releases.

Why is the data for firm X unrealistic?

We provide the source data as is, with minimal changes. Consider a relatively unknown LLC Banknota. It reported 3.7 trillion rubles in revenue in 2023, or 2% of Russia's GDP. This is obviously an outlier firm with unrealistic financials. We manually reviewed the data and flagged such firms for user consideration (variable outlier), keeping the source data intact.

Why is the data for groups of companies different from their IFRS statements?

We should stress that we provide unconsolidated financial statements filed according to the Russian accounting standards, meaning that it would be wrong to infer financials for corporate groups with this data. Gazprom, for instance, had over 800 affiliated entities and to study this corporate group in its entirety it is not enough to consider financials of the parent company.

Why is the data not in CSV?

The data is provided in Apache Parquet format. This is a structured, column-oriented, compressed binary format allowing for conditional subsetting of columns and rows. In other words, you can easily query financials of companies of interest, keeping only variables of interest in memory, greatly reducing data footprint.

Version and Update Policy

Version (SemVer): 1.0.0.

We intend to update the RFSD annualy as the data becomes available, in other words when most of the firms have their statements filed with the Federal Tax Service. The official deadline for filing of previous year statements is April, 1. However, every year a portion of firms either fails to meet the deadline or submits corrections afterwards. Filing continues up to the very end of the year but after the end of April this stream quickly thins out. Nevertheless, there is obviously a trade-off between minimization of data completeness and version availability. We find it a reasonable compromise to query new data in early June, since on average by the end of May 96.7% statements are already filed, including 86.4% of all the correcting filings. We plan to make a new version of RFSD available by July.

Licence

Creative Commons License Attribution 4.0 International (CC BY 4.0).

Copyright © the respective contributors.

Citation

Please cite as:

@unpublished{bondarkov2025rfsd, title={{R}ussian {F}inancial {S}tatements {D}atabase}, author={Bondarkov, Sergey and Ledenev, Victor and Skougarevskiy, Dmitriy}, note={arXiv preprint arXiv:2501.05841}, doi={https://doi.org/10.48550/arXiv.2501.05841}, year={2025}}

Acknowledgments and Contacts

Data collection and processing: Sergey Bondarkov, sbondarkov@eu.spb.ru, Viktor Ledenev, vledenev@eu.spb.ru

Project conception, data validation, and use cases: Dmitriy Skougarevskiy, Ph.D.,

Dataset Summary This dataset provides monthly synthetic financial statement data for McDonald's Corporation, spanning from January 2005 to December 2024 (20 years, 240 rows). The structure and field types closely follow actual historical reports, but all values are artificially generated to simulate realistic trends, growth, and variability in key financial metrics.

Disclaimer: This dataset is synthetic and was programmatically generated for educational and analytical purposes. It does not reflect actual financial results of McDonald's.

Columns & Descriptions Column Name Description Date Month of the record (YYYY-MM) Market cap ($B) Market capitalization (billion USD) Revenue ($B) Revenue (billion USD) Earnings ($B) Earnings/Net income (billion USD) P/E ratio Price-to-Earnings ratio P/S ratio Price-to-Sales ratio P/B ratio Price-to-Book ratio Operating Margin (%) Operating margin percentage EPS ($) Earnings per share (USD) Shares Outstanding ($B) Shares outstanding (in billions) Cash on Hand ($B) Cash on hand (billion USD) Dividend Yield (%) Dividend yield percentage Dividend (stock split adjusted) ($) Dividend per share, adjusted for splits (USD) Net assets ($B) Net assets (billion USD) Total assets ($B) Total assets (billion USD) Total debt ($B) Total debt (billion USD) Total liabilities ($B) Total liabilities (billion USD)

Data Generation Synthetic Approach: All values are programmatically generated to simulate plausible historical trends and volatility, based on actual McDonald's data structure and real-world financial logic.

Monthly Granularity: Data points are provided for every month, offering high temporal resolution suitable for time-series analysis.

No Real Data: No actual McDonald's confidential or proprietary data is included.

Example Use Cases Financial time series modeling & forecasting

Data visualization practice

Building dashboards and BI demos

Educational purposes (finance, data science, statistics)

Benchmarking financial data analysis algorithms

Acknowledgements Dataset inspired by public McDonald's annual financial reports.

Summary Over the past decade, many scholarly journals have adopted policies on data sharing, with an increasing number of journals requiring that authors share the data underlying their published work. Frequently, qualitative data are excluded from those policies explicitly or implicitly. A few journals, however, intentionally do not make such a distinction. This project focuses on articles published in eight of the open-access journals maintained by Public Library of Science (PLOS). All PLOS journals introduced strict data sharing guidelines in 2014, applying to all empirical data on the basis of which articles are published. We collected a database of more than 2,300 articles containing a qualitative data component published between January 1, 2015 and August 23, 2023 and analyzed the data availability statements (DAS) researchers made regarding the availability, or lack thereof, of their data. We describe the degree to which and manner in which data are reportedly available (for example, in repositories, via institutional gate-keepers, or on request from author) versus those that are declared to be unavailable We also outline several dimensions of patterned variation in the data availability statements, including describe temporal patterns and variation by data type. Based on the results, we also provide recommendations to both researchers on how to make their data availability statements clearer, more transparent and more informative, and to journal editors and reviewers, on how to interpret and evaluate statements to ensure they accurately reflect a given data availability scenario. Finally, we suggest a workflow which can link interactions with repositories most productively as part of a typical editorial process. Data Overview This data deposit includes data and code to assemble the dataset, generate all figures and values used in the paper and appendix, and generate the codebook. It also includes the codebook and the figures. The analysis.R script and the data in data/analysis are sufficient to reproduce all findings in the paper. The additional scripts and the data files in data/raw are included for full transparency and to facilitate the detection of any errors in the data processing pipeline. Their structure is due to the development of the project over time.

Question Paper Solutions of chapter Introduction to Financial Statements Analysis of Financial Reporting and Financial Statement Analysis, Semester VI , Bachelors of Commerce (General)

The Consolidated Financial Statements (CFS) since 1995-96 are available on the Department of Finance website at: \r http://www.finance.gov.au/publications/commonwealth-consolidated-financial-statements.\r \r The CFS for the Australian Government present the whole of government and general government sector (GGS) financial reports and are prepared in accordance with AASB 1049 Whole of Government and General Government Sector Financial Reporting. They are required by section 48 of the Public Governance, Performance and Accountability Act 2013 (formerly section 54 of the Financial Management and Accountability Act 1997).\r \r The CFS include the consolidated results for all Australian Government controlled entities as well as disaggregated information on the sectors of GGS, public non financial corporations and public financial corporations. \r \r This dataset provides an historical series of a collection of published CFS for the whole of government and GGS from 2008-09, including the: \r \r • Income Statement\r \r • Balance Sheet \r \r • Cash Flow Statement\r \r The Historical CFS series is provided to assist those who wish to access and analyse this data. \r \r Please note that this dataset represents published information and will not be recast. Figures may not be directly comparable over time due to changes of classification, accounting standards or budget treatments. \r \r This data is released by the Department of Finance.\r

Exploring this economic and financial data by studying their behavior patterns could affect how we allocate our wealth wisely.

jstonge1/data-statements-2024-05-31 dataset hosted on Hugging Face and contributed by the HF Datasets community

For this dataset, scientific peer-reviewed articles by Tampere University researchers from the years 2020 and 2021 were extracted from the TUNICRIS. A random sample of 40 percent was taken from the listed 4,922 publications according to faculties and years. There were 2,085 analyzed articles, i.e. more than 42 percent of the total number.

To find Data Availability Statements, articles were opened one by one and searched for mentions of research data and its availability. For each article, it was written down whether DAS existed and where in the article it was located. From the contents of DAS, information about data availability, location, openness and possible restrictions on use was written down.

Dataset also includes information about the journals and publications taken from TUNICRIS.

The prevalence of DAS and data openness were examined in relation to different variables. Tampere University faculty information has been removed from the dataset.

Related slides: https://doi.org/10.5281/zenodo.7655892

Related article (in Finnish): Toikko, T., & Kylmälä, K. (2023). Tutkimusdatan saatavuustiedot tieteellisissä artikkeleissa: Raportti Data Availability Statementien käytöstä Tampereen yliopistossa. Informaatiotutkimus, 42(1-2), 31–50. https://doi.org/10.23978/inf.126098

Get detailed Automatic Data Processing Financial Statements 2021-2025. Find the income statements, balance sheet, cashflow, profitability, and other key ratios.

jstonge1/data-availability-statements-llama3.2-20250820 dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset contains Income Statement data from IBRD?s published financial statements It was compiled from data in our systems as well as by extracting the data from the published Financial Statements documents. The dataset goes as far back as the foundation of the institution (1946). This data has been verified and validated for publication, but does not, in any capacity, replace the official published Financial Statements. Please note that this dataset includes certain rows that are calculated totals, summing up values from related individual records. These are included for completeness and ease of analysis. An archive for IBRD?s annual Financial Statements is available at www.worldbank.org/financialresults

Dataset resulting from an online panel experiment conducted in Brazil in 2021 with the financial and technical support of FLPFI. The research project was designed and directed by the Central Bank of Brazil (BCB) in partnership with Plano CDE. To run the experiment, the research project conducted a survey through an online panel. The 3,022 participants were allocated into either control or one of two treatment groups and were exposed to different credit card statement prototypes.

This dataset contains 259 vulnerability statements found with Prospector, an open-source repository mining tool developed by SAP Security Research and the AssureMOSS consortium.

The vulnerabilities covered by this dataset are a subset of a larger vulnerability dataset built by SAP Security Research while developing and operating Eclipse Steady.

Note: IDs starting with IDAB and IDAG are "Guarantees", IDs starting with IDAD, IDAH and IDAE are "Grants" and the rest are "Credits". The International Development Association (IDA) credits are public and publicly guaranteed debt extended by the World Bank Group. IDA provides development credits, grants and guarantees to its recipient member countries / economies to help meet their development needs. Credits from IDA are at concessional rates. Data are in U.S. dollars calculated using historical rates. This dataset contains historical snapshots of the IDA Statement of Credits and Grants including the latest available snapshot. The World Bank complies with all sanctions applicable to World Bank transactions.