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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.

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

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This data was utilised in answering the following research hypothesis: The Debt ratio within the contemporary Shariah Stock Screening procedure significantly impact the corporate financial behaviour of Shariah compliant firms, so much so that their asset pricing behaviour will be different compared to conventional firms.

The data (and subsequent regressions) will show that samples of Shariah compliant firms will share similar asset pricing behaviour vis-a-vis the conventional sample, however, some clear differences will also manifest. The most striking is that the Shariah compliant samples will tend to have significant intercepts, which imply that the five-factor model fails to completely explain the variation of average excess returns within Shariah compliant samples. In short, there exists more room to add additional variables, alongside the five-factor model, when explaining the asset pricing behaviour of Shariah compliant samples in the US.

The data comprises of monthly risk factor premiums of four samples (defined in the Steps-to-reproduce section). All data are sourced from Thompson Reuters Datastream. Please note that the data are in STATA .dta format, therefore, use the STATA program to open them. The data is ready to use as-is for regression purposes.

➡️A data set on select, monthly commodity prices made available by the World Bank in its so-called "pink sheet." These data are potentially useful for applications on data gathering, inflation adjustments, indexing, cointegration, general economic riff-raff, and more.

Details

All data are in nominal USD. Adjust (to taste) accordingly.

Data compiled by the World Bank for its historical data on commodity prices. The oil price data come from a combination of sources, supposedly Bloomberg, Energy Intelligence Group (EIG), Organization of Petroleum Exporting Countries (OPEC), and the World Bank. Data on coffee prices come from Bloomberg, Complete Coffee Coverage, the International Coffee Organization, Thomson Reuters Datastream, and the World Bank. Data on tea prices for Colombo auctions come the from International Tea Committee, Tea Broker's Association of London, Tea Exporters Association Sri Lanka, and the World Bank. Data on tea prices for Kolkata auctions come from the International Tea Committee, Tea Board India, Tea Broker's Association of London, and the World Bank. Tea prices for Mombasa/Nairobi auctions come from African Tea Brokers Limited, International Tea Committee, Tea Broker's Association of London, and the World Bank. EU sugar price data come from International Monetary Fund, World Bank. Sugar price data for the United States come from Bloomberg and World Bank. Global sugar price data come from Bloomberg, International Sugar Organization, Thomson Reuters Datastream, and the World Bank.

This data set effectively deprecates the sugar_price and coffee_price data sets in this package. Both may be removed at a later point.

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.

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