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.

We compile raw data from the Datastream database for all stocks traded on the Spanish equity market. Particularly, we compile the following data series: (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) dividend yield (DY series), (vii) price-to-earnings ratio (PE series), and (viii) effective tax rate (WC08346 series). We use the filters suggested by Griffin, Kelly, and Nardari (2010) for the Datastream database to exclude assets other than ordinary shares from our sample. Hence, our sample comprises 443 companies, including all firms that started trading within the time interval under study, as well as those that were delisted. As a proxy for the risk-free rate, we use the three-month Treasury Bill rate for Spain, as provided by the OECD. Accordingly, the dataset comprises the following series:

1. Spain_9_Portfolios_SIZE_BEME: Monthly returns for 9 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Spain_9_Portfolios_DY_PE: Monthly returns for 9 dividend yield-price-to-earnings ratio, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Spain_9_Portfolios_SIZE_TR: Monthly returns for 9 size-effective tax rate portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
4. Spain_FF_3_Factors: Monthly returns for the constituents of the three classic factors of Fama and French, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Spain_FF_5_Factors: Monthly returns for the constituents of the five factors of Fama and French, following the Fama and French (2015) methodology. (Raw data source: Datastream database)
6. Spain_RF: Three-month Treasury Bill rate for Spain. (Raw data source: OECD)
7. Spain_Avg_Tax_Rate: Value-weighted effective tax rate paid by companies traded in Spain. (Raw data source: Datastream database)

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.

Browse LSEG's I/B/E/S Estimates, discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivalled data and delivery mechanisms.

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) dividend yield (DY series). Following Griffing et al. (2010), we exclude non-common equity securities from Datastream data. 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_size-BEME_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_momentum_portfolios_M: Monthly returns for 20 momentum portfolios rebalanced in June of each year. (Raw data source: Datastream database)
3. Japan_3_Factors_M: Monthly returns for the constituents of the three classic factors of Fama and French, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
4. Japan_Consumption_Q: Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD)
5. Japan_Dividend_yield_M: Value-weighted dividend yield for the Japanese equity market. (Raw data source: Datastream database)
6. Japan_epsilon_DY_Q: Errors provided by the regression of consumption growth on the value-weighted dividend yield for Japan. (Raw data source: Datastream database and OECD)
7. Japan_RF_M: Three-month Treasury Bill rate for Japan. (Raw data source: OECD)

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. 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.

Context

This is the first live data stream on Kaggle providing a simple yet rich source of all soccer matches around the world 24/7 in real-time.

What makes it unique compared to other datasets?

• It is the first live data feed on Kaggle and it is totally free
• Unlike “Churn rate” datasets you do not have to wait months to evaluate your predictions; simply check the match’s outcome in a couple of hours
• you can use your predictions/analysis for your own benefit instead of spending your time and resources on helping a company maximizing its profit
• A Five year old laptop can do the calculations and you do not need high-end GPUs
• Couldn’t make it to the top 3 submissions? Nevermind, you still have the chance to get your prize on your own
• You can’t get accurate results on all samples? Do not worry, just filter out the hard ones (e.g. ignore international friendly) and simply choose the ones you are sure of.
• Need help from human experts for each sample? Every sample comes with at least two opinions from experts
• You wish you could add your complementary data? Just contact us and we will try to facilitate it.
• Couldn’t win “Warren Buffett's 2018 March Madness Bracket Contest”? Here is your chance to make your accumulative profit.

Simply train your algorithm on the first version of training dataset of approximately 11.5k matches and predict the data provided in the following data feed.

Fetch the data stream

The CSV file is updated every 30 minutes at minutes 20’ and 50’ of every hour. I kindly request not to download it more than twice per hour as it incurs additional cost.

You may download the csv data file from the following link from Amazon S3 server by changing the FOLDER_NAME as below,

https://s3.amazonaws.com/FOLDER_NAME/amasters.csv

*. Substitute the FOLDER_NAME with "**analyst-masters**"

Content

Our goal is to identify the outcome of a match as Home, Draw or Away. The variety of sources and nature of information provided in this data stream makes it a unique database. Currently, FIVE servers are collecting data from soccer matches around the world, communicating with each other and finally aggregating the data based on the dominant features learned from 400,000 matches over 7 years. I describe every column and the data collection below in two categories, Category I – Current situation and Category II – Head-to-Head History. Hence, we divide the type of data we have from each team to 4 modes,

• Mode 1: we have both Category I and Category II available
• Mode 2: we only have Category I available
• Mode 3: we only have Category II available
• Mode 4: none of Category I and II are available

Below you can find a full illustration of each category.

I. Current situation

Col 1 to 3:

Votes_for_Home Votes_for_Draw Votes_for_Away

The most distinctive parts of the database are these 3 columns. We are releasing opinions of over 100 professional soccer analysts predicting the outcome of a match. Their votes is the result of every piece of information they receive on players, team line-up, injuries and the urge of a team to win a match to stay in the league. They are spread around the world in various time zones and are experts on soccer teams from various regions. Our servers aggregate their opinions to update the CSV file until kickoff. Therefore, even if 40 users predict Real-Madrid wins against Real-Sociedad in Santiago Bernabeu on January 6th, 2019 but 5 users predict Real-Sociedad (the away team) will be the winner, you should doubt the home win. Here, the “majority of votes” works in conjunction with other features.

Col 4 to 9:

Weekday Day Month  Year  Hour  Minute

There are over 60,000 matches during a year, and approximately 400 ones are usually held per day on weekends. More critical and exciting matches, which are usually less predictable, are held toward the evening in Europe. We are currently providing time in Central Europe Time (CET) equivalent to GMT +01:00.

*. Please note that the 2nd row of the CSV file represents the time, data values are saved from all servers to the file.

Col 10 to 13:

Total_Bettors   Bet_Perc_on_Home    Bet_Perc_on_Draw   Bet_Perc_on_Away

This data is recorded a few hours before the match as people place bets emotionally when kickoff approaches. The percentage of the overall number of people denoted as “Total_Bettors” is indicated in each column for “Home,” “Draw” and “Away” outcomes.

Col 14 to 15:

Team_1 Team_2   

The team playing “Home” is “Team_1” and the opponent playing “Away” is “Team_2”.

Col 16 to 36:

League_Rank_1  League_Rank_2  Total_teams     Points_1  Points_2  Max_points Min_points Won_1  Draw_1 Lost_1 Won_2  Draw_2 Lost_2 Goals_Scored_1 Goals_Scored_2 Goals_Rec_1 Goal_Rec_2 Goals_Diff_1  Goals_Diff_2

If the match is betw...