Using all stocks listed in the Tokyo Stock Exchange and macroeconomic data for Japan, the dataset comprises the following series:

1. Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Monthly returns for 20 momentum portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Monthly returns for 25 price-to-cash flow-dividend yield portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
4. Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology for all factors, except for RMW, which is determined using the return on assets as sorting variable, as in Hou, Xue and Zhang (2014). (Raw data source: Datastream database)
6. Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD)
7. Consumer Confidence Index (CCI) for Japan. (Raw data source: OECD)
8. Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
9. Gross Domestic Product (GDP) for Japan. (Raw data source: OECD)
10. Consumer Price Index (CPI) growth rate for Japan. (Raw data source: OECD)

We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) price-to-cash flow ratio (PC series), and (vii) dividend yield (DY series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations in the period from July 1992 to June 2018. Accordingly, our sample comprises a total number of 5,312 stocks.

REFERENCES:

Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277. Hou K, Xue C, Zhang L. (2014). Digesting anomalies: An investment approach. Review of Financial Studies, 28, 650-705.

Data collected from Datastream, a proprietary commercial database containing financial data, published by Thomson Reuters. The dataset consists of fundamental stock data; return, price, unadjusted price, in two frequencies: annual and daily. Daily set contains price index, return index, unadjusted price, the annual set contains stock fundamentals, time series data and static data such as geographical location and others. The data is used for research purposes, but also for teaching in the school of economics and finance and for staff training

Graph and download economic data for Domestic Finance Companies, All Other Assets and Accounts and Notes Receivable, Flow (STFAFOXDFBANA) from Q2 1984 to Q1 2025 about notes, finance companies, accounting, companies, flow, finance, financial, domestic, assets, and USA.

Compare financial information of companies from different industries around the globe with Worldscope Fundamentals, providing essential insights and analysis.

The data are supplied commercially and format may change over time. The Chicago Mercantile Exchange dataset consisted of csv files containing columns of tick times with associated trade prices. The Cantor Fitzgerald database was available in ASCII format, comma delimited with varying numbers of fields over the time frame. The data were cleaned to extract the time stamp of trades and transaction price. Thomson Datastream is a large provider of economic and financial data available by commercial subscription

Cash Network is a financial services company that specializes in providing secure online platforms for users to manage their finances. Founded with a mission to empower users to take control of their financial futures, Cash Network has established itself as a reputable name in the industry, boasting a comprehensive platform that caters to a wide range of financial needs.

Throughout its platform, users can expect to find a vast array of financial data, from market trends to personal financial records. With its focus on security and user experience, Cash Network's online presence provides a reliable and efficient way for users to access and manage their financial information, making it an essential resource for anyone looking to stay on top of their financial game.

Flat file data set of the data found in the Austin Finance Online eCheckbook application. The data contained in this dataset is for informational purposes only and contains expenditure information for the City of Austin. Certain Austin Energy transactions have been excluded as competitive matters under Texas Government Code Section 552.133 and City Council Resolution 20051201-002; therefore, the line amounts may not reflect the total check amount if certain Austin Energy invoices were included on the check. Please visit the Austin Finance Online website for a searchable front end to this data set.

We compile raw data from the Datastream database for all stocks traded on the Tokyo Stock Exchance, Osaka Exchange, Fukuoka Stock Exchange, Nagoya Stock Exchange and Sapporo Securities Exchange. Particularly, we collect the following data series, on a monthly basis: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), and (iv) primary SIC codes. Following Griffing et al. (2010), we exclude non-common equity securities from Datastream data. Additionally, we remove all companies with less than 12 observations in RI series for the period under analysis. Hence, our sample comprises 5,627 stocks, considering all companies that started trading or were delisted in the period under analysis. We use the three-month Treasury Bill rate for Japan, as provided by the OECD database, as a proxy for the risk-free rate. Accordingly, the dataset comprises the following series:

1. Japan_25_Portfolios_MV_PTBV_M: Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Japan_20_Portfolios_MOM_M: Monthly returns for 20 momentum portfolios rebalanced in June of each year. (Raw data source: Datastream database)
3. Japan_61_Portfolios_SECTOR_M: Monthly returns for 61 industry portfolios. (Raw data source: Datastream database)
4. Japan_RF_M: Three-month Treasury Bill rate for Japan. (Raw data source: OECD)
5. Japan_C_Q: Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD)
6. Japan_Trade_Y: Trade openness for Japan, as measured by the variation rate of exports plus imports. (Raw data source: OECD)
7. Japan_RD_Y: Variation rate of R&D investment for Japan. (Raw data source: OECD)
8. Japan_IK_Y: Investment-capital ratio for Japan., determined using the methodology suggested by Cochrane (1991) (Raw data source: OECD)
9. Japan_CCI_M: Consumer confidence index for Japan. (Raw data source: OECD)

REFERENCES:

Cochrane, J.H. (1991), Production-based asset pricing and the link between stock returns and economic fluctuations. The Journal of Finance, 46, 209-237. Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.

United States Discrepancy: Flow: Finance Companies: saar data was reported at 21.387 USD bn in Mar 2018. This records an increase from the previous number of -54.407 USD bn for Dec 2017. United States Discrepancy: Flow: Finance Companies: saar data is updated quarterly, averaging -0.728 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 109.478 USD bn in Jun 2003 and a record low of -141.224 USD bn in Jun 2013. United States Discrepancy: Flow: Finance Companies: saar data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB029: Funds by Sector: Flows and Outstanding: Finance and Mortgage Companies.

This dataset includes loan specific data of funded and matured loans from 2012-2014 on the online peer-to-peer lending platform 'Lending Club'.

United States Assets: Flow: Finance Companies (FC) data was reported at -21.442 USD bn in Mar 2018. This records a decrease from the previous number of 11.325 USD bn for Dec 2017. United States Assets: Flow: Finance Companies (FC) data is updated quarterly, averaging 1.903 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 84.882 USD bn in Jun 2003 and a record low of -88.378 USD bn in Mar 2010. United States Assets: Flow: Finance Companies (FC) data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB029: Funds by Sector: Flows and Outstanding: Finance and Mortgage Companies.

Graph and download economic data for Total Business Loans and Leases Owned and Securitized by Finance Companies, Flow (DTBTXDFBAM) from Jul 1985 to Apr 2025 about securitized, owned, finance companies, companies, leases, flow, finance, financial, business, loans, and USA.

CN: Flow of Funds: Household: Use: Loan: Short Term and Bill Financing data was reported at 0.000 RMB bn in 2011. This stayed constant from the previous number of 0.000 RMB bn for 2010. CN: Flow of Funds: Household: Use: Loan: Short Term and Bill Financing data is updated yearly, averaging 0.000 RMB bn from Dec 1992 (Median) to 2011, with 20 observations. The data reached an all-time high of 0.000 RMB bn in 2011 and a record low of 0.000 RMB bn in 2011. CN: Flow of Funds: Household: Use: Loan: Short Term and Bill Financing data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under China Premium Database’s National Accounts – Table CN.AD: Flow of Funds Accounts: Financial Transaction: Household.

Japan Assets: Flow: Finance Companies (FC): Total data was reported at 207.900 JPY bn in Mar 2018. This records a decrease from the previous number of 812.100 JPY bn for Dec 2017. Japan Assets: Flow: Finance Companies (FC): Total data is updated quarterly, averaging -476.100 JPY bn from Mar 1998 (Median) to Mar 2018, with 81 observations. The data reached an all-time high of 3,500.500 JPY bn in Mar 1999 and a record low of -6,385.600 JPY bn in Mar 2000. Japan Assets: Flow: Finance Companies (FC): Total data remains active status in CEIC and is reported by Bank of Japan. The data is categorized under Global Database’s Japan – Table JP.AB051: SNA08: Financial Institution: Other Financial Intermediaries: Non Banks: Finance Companies: Flow.

United States Finance Co: Rec: B: Owned Assets: Flow: Others data was reported at 1.102 USD bn in Sep 2018. This records an increase from the previous number of 0.608 USD bn for Aug 2018. United States Finance Co: Rec: B: Owned Assets: Flow: Others data is updated monthly, averaging 0.175 USD bn from Jul 1980 (Median) to Sep 2018, with 459 observations. The data reached an all-time high of 9.961 USD bn in Sep 2015 and a record low of -8.351 USD bn in Nov 2003. United States Finance Co: Rec: B: Owned Assets: Flow: Others data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.KB038: Finance Companies: Owned and Managed Receivables.

GE: General Government: Cash Receipts: Operating Activities: Taxes: Other data was reported at 6.000 GEL mn in Jul 2018. This records an increase from the previous number of -78.800 GEL mn for Jun 2018. GE: General Government: Cash Receipts: Operating Activities: Taxes: Other data is updated monthly, averaging 2.700 GEL mn from Jan 2006 (Median) to Jul 2018, with 151 observations. The data reached an all-time high of 196.800 GEL mn in Mar 2016 and a record low of -172.600 GEL mn in Jan 2017. GE: General Government: Cash Receipts: Operating Activities: Taxes: Other data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Georgia – Table GE.IMF.IFS: Government Finance: Cash Flow Statement.

RO: General Government: Net Acquisition of Financial Assets data was reported at 179.200 RON mn in 2017. This records an increase from the previous number of 115.700 RON mn for 2016. RO: General Government: Net Acquisition of Financial Assets data is updated yearly, averaging -5.300 RON mn from Dec 2007 (Median) to 2017, with 11 observations. The data reached an all-time high of 179.200 RON mn in 2017 and a record low of -406.800 RON mn in 2008. RO: General Government: Net Acquisition of Financial Assets data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Romania – Table RO.IMF.IFS: Government Finance: Cash Flow Statement: Annual.

Column definitions for the flat file data set - Austin Finance Online eCheckbook - found on the data portal . The data contained in this dataset is for informational purposes only. Please visit the Austin Finance Online website for a searchable front end to this data set.

A financial market is a platform to produce data streams continuously and around 1. 145 Trillion MB of data per day. Estimation and the analysis of unknown or dynamic behaviors of these systems is one the challenging tasks. Analysis of these systems is very much essential to strengthen the environmental parameters to stabilize society activities. This can elevate the living style of society to the next level. In this connection, the proposed paper is trying to accommodate the financial data stream using the sliding window approach and random forest algorithm to provide a solution to handle concept drift in the financial market to stabilize the behavior of the system through drift estimation. The proposed approach provides promising results in terms of accuracy in detecting concept drift over the state of existing drift detection methods like one class drifts detection (OCDD), Adaptive Windowing ADWIN), and the Page-Hinckley test.

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