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The datasets for the Role of Financial Investors on Commodity Futures Risk Premium are weekly datasets for the period from 1995 to 2015 for three commodities in the energy market: crude oil (WTI), heating oil, and natural gas. These datasets contain futures prices for different maturities, open interest positions for each commodity (long and short open interest positions), and S&P 500 composite index. The selected commodities are traded on the New York Mercantile Exchange (NYMEX). The data comes from the Thomson Reuters Datastream and from the Commodity Futures Trading Commission (CFTC).

Using all stocks listed in the London Stock Exchange for the period from January 1989 to December 2018, the dataset comprises the following series:

1. Annual returns for 20 asset growth portfolios, following Fama and French (1993) methodology.
2. Annual returns for 25 portfolios size-book to market equity, following Fama and French (1993) methodology.
3. Annual returns for 62 industry portfolios, using two-digit SIC codes.
4. Fama and French (1993) factors for their three-factor model (RM, SMB and HML).
5. Fama and French (2015) factors for their five-factor model (RM, SMB, HML, RMW, and CMA).
6. Variation of the Amihid illiquidy measure for the London Stock Exchange, following Amihud (2002) methodology.
7. Three-month interest rate of the Treasury Bill for the United Kingdom, as provided by the OECD database.

We have produced these series using the following data from Thomson Reuters 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) tax rate (WC08346 series), (vii) primary SIC codes, (viii) turnover by volume (VO series), and (ix) the market price (P series). Following Griffin et al. (2010), we use the generic rules provided by the authors for excluding non-common equity securities from Datastream data.

REFERENCES: Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5, 31–56. 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.

This dataset accompanies the article Testing for the footprints of stabilization economic policy in forecast errors and provides the minimal data necessary to replicate the empirical findings. It consists of six sheets:DailyBonds – Contains first differences of daily 10-year government bond yields for 33 countries, covering the period January 1, 2012 – December 31, 2019. The raw yield data were obtained from the Thomson Reuters Datastream (Refinitiv Eikon) database and transformed into first differences to ensure stationarity. These data are the input for the ARMA–GARCH models and the Policy Effects Lagrange Multiplier (PELM) test applied in the paper.Revenue – Contains government revenue data (as % of GDP) for the same set of countries, sourced from the IMF World Economic Outlook (WEO) database. A direct link to the corresponding IMF WEO page is included at the top of the sheet.Expenditures – Contains government expenditure data (as % of GDP) from the IMF WEO database, with the link included at the top of the sheet.Rev-Exp – Computed differences between government revenue and expenditures (Revenue – Expenditure), constructed to provide an additional fiscal indicator in line with referee suggestions. This series was used in supplementary tests, including analysis of the Brexit crisis.N_lending_borrowing – Contains data on Net lending/borrowing (general government, % of GDP), sourced from the IMF WEO database.P_net_lending_borrowing – Contains data on Primary net lending/borrowing (general government, % of GDP), also from the IMF WEO database.Methodology and TechniquesThe DailyBonds sheet is used to estimate ARMA–GARCH models for sovereign bond yields and to compute the PELM test statistics, which detect stabilization policy footprints in financial markets. Fiscal indicators from the IMF WEO (revenue, expenditure, net lending/borrowing, primary net lending/borrowing) are then employed to evaluate fiscal dynamics across countries, including additional robustness checks prompted by reviewers (such as the Brexit crisis case study). The computed series Rev-Exp (revenue minus expenditure) provides an alternative fiscal stance measure that complements the WEO variables.Legal and Ethical ConsiderationsThe dataset includes only derived or publicly shareable information. The bond yield data were originally obtained from Refinitiv Eikon (Thomson Reuters Datastream), which is subject to subscription licensing; we provide only the first differences required for replication, not the raw series. The IMF WEO data are publicly available and redistributed here in line with the IMF’s open data policy. No individual-level or sensitive data are included.