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EDHEC Investment Management Datasets

EDHEC Investment Management Course

@kaggle.yousefsaeedian_edhec_investment_management_datasets

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About this Dataset

EDHEC Investment Management Datasets

Are hedge funds worth your money?
Hedge funds have developed from investment funds that were designed to lower the risk of your portfolio to a multitude of different investment styles with different goals. Their heyday was probably during the 90s and early 2000s when several star hedge fund managers rose to prominence and their assets under management grew significantly. However, since then hedge funds have been under scrutiny as their investment returns have been lacking and their ability to function as a diversification to a traditional stock and bond portfolio was put into question. As hedge funds have their own set of leverage and investment rules it is no wonder they have been accused of being greedy, unsuccessful and secretive. However, with this dataset you can make your own analysis.

Content
This dataset covers monthly hedge fund returns starting from 1997. The date column refers to the last day of the month - the end date of the return period, if I understand correctly. There are 12 different hedge fund strategies covered and the return index series are formed as an aggregate of other hedge fund index providers.

The strategy explanations are in EDHEC website:

Convertible Arbitrage - https://risk.edhec.edu/conv-arb/
CTA Global - https://risk.edhec.edu/cta-global/
Distressed Securities - https://risk.edhec.edu/dist-sec/
Emerging Markets - https://risk.edhec.edu/emg-mkts/
Equity Market Neutral - https://risk.edhec.edu/equity-market-neutral/
Event Driven - https://risk.edhec.edu/event-driven/
Fixed Income Arbitrage - https://risk.edhec.edu/fix-inc-arb/
Global Macro - https://risk.edhec.edu/global-macro/
Long/Short Equity - https://risk.edhec.edu/ls-equity/
Merger Arbitrage - https://risk.edhec.edu/merger-arb/
Relative Value - https://risk.edhec.edu/relative-value/
Short Selling - https://risk.edhec.edu/short-selling/
Funds of Funds - https://risk.edhec.edu/fof/
Acknowledgements
All credit for the maintenance and upload of the data goes to EDHEC. You should check their website for additional resources:

https://risk.edhec.edu/all-downloads-hedge-funds-indices

Inspiration
The EDHEC hedge fund data is the data used in examples/vignettes of PortfolioAnalytics - a package for optimizing, testing and analyzing portfolio returns. You should be easily able to expand the analysis from the vignettes just by using the larger dataset available here:

https://cran.r-project.org/web/packages/PortfolioAnalytics/index.html

Tables

Assets 7

@kaggle.yousefsaeedian_edhec_investment_management_datasets.assets_7
  • 39.06 kB
  • 409 rows
  • 13 columns
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CREATE TABLE assets_7 (
  "unnamed_0" TIMESTAMP  -- Unnamed: 0,
  "us_equities" DOUBLE,
  "world_equities" DOUBLE,
  "us_treasuries" DOUBLE,
  "high_yield" DOUBLE,
  "real_estate" DOUBLE,
  "commodities" DOUBLE,
  "corp_bonds" DOUBLE,
  "t_bill" DOUBLE,
  "tips" VARCHAR,
  "regime_5" BIGINT,
  "regime_7" BIGINT,
  "regime_9" BIGINT
);

Brk A

@kaggle.yousefsaeedian_edhec_investment_management_datasets.brk_a
  • 291.25 kB
  • 9,783 rows
  • 7 columns
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CREATE TABLE brk_a (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "adj_close" DOUBLE,
  "volume" BIGINT
);

Brka D Ret

@kaggle.yousefsaeedian_edhec_investment_management_datasets.brka_d_ret
  • 126.12 kB
  • 7,307 rows
  • 2 columns
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CREATE TABLE brka_d_ret (
  "date" TIMESTAMP,
  "brka" DOUBLE
);

Data Oct2018 V2

@kaggle.yousefsaeedian_edhec_investment_management_datasets.data_oct2018_v2
  • 46.37 kB
  • 405 rows
  • 12 columns
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CREATE TABLE data_oct2018_v2 (
  "date" TIMESTAMP,
  "world_equities" DOUBLE,
  "us_treasuries" DOUBLE,
  "bond_risk_premium" DOUBLE,
  "inflation_protection" DOUBLE,
  "currency_protection" DOUBLE,
  "regime_5" BIGINT,
  "regime_7" BIGINT,
  "us_equities" DOUBLE,
  "real_estate" DOUBLE,
  "commodities" DOUBLE,
  "corp_bonds" DOUBLE
);

Edhec Hedgefundindices

@kaggle.yousefsaeedian_edhec_investment_management_datasets.edhec_hedgefundindices
  • 29.05 kB
  • 263 rows
  • 14 columns
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CREATE TABLE edhec_hedgefundindices (
  "date" TIMESTAMP,
  "convertible_arbitrage" DOUBLE,
  "cta_global" DOUBLE,
  "distressed_securities" DOUBLE,
  "emerging_markets" DOUBLE,
  "equity_market_neutral" DOUBLE,
  "event_driven" DOUBLE,
  "fixed_income_arbitrage" DOUBLE,
  "global_macro" DOUBLE,
  "long_short_equity" DOUBLE,
  "merger_arbitrage" DOUBLE,
  "relative_value" DOUBLE,
  "short_selling" DOUBLE,
  "funds_of_funds" DOUBLE
);

F F Research Data Factors M

@kaggle.yousefsaeedian_edhec_investment_management_datasets.f_f_research_data_factors_m
  • 24.6 kB
  • 1,110 rows
  • 5 columns
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CREATE TABLE f_f_research_data_factors_m (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "mkt_rf" DOUBLE,
  "smb" DOUBLE,
  "hml" DOUBLE,
  "rf" DOUBLE
);

Ind30 M Ew Rets

@kaggle.yousefsaeedian_edhec_investment_management_datasets.ind30_m_ew_rets
  • 182.03 kB
  • 1,110 rows
  • 31 columns
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CREATE TABLE ind30_m_ew_rets (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "food" DOUBLE,
  "beer" DOUBLE,
  "smoke" DOUBLE,
  "games" DOUBLE,
  "books" DOUBLE,
  "hshld" DOUBLE,
  "clths" DOUBLE,
  "hlth" DOUBLE,
  "chems" DOUBLE,
  "txtls" DOUBLE,
  "cnstr" DOUBLE,
  "steel" DOUBLE,
  "fabpr" DOUBLE,
  "elceq" DOUBLE,
  "autos" DOUBLE,
  "carry" DOUBLE,
  "mines" DOUBLE,
  "coal" DOUBLE,
  "oil" DOUBLE,
  "util" DOUBLE,
  "telcm" DOUBLE,
  "servs" DOUBLE,
  "buseq" DOUBLE,
  "paper" DOUBLE,
  "trans" DOUBLE,
  "whlsl" DOUBLE,
  "rtail" DOUBLE,
  "meals" DOUBLE,
  "fin" DOUBLE,
  "other" DOUBLE
);

Ind30 M Nfirms

@kaggle.yousefsaeedian_edhec_investment_management_datasets.ind30_m_nfirms
  • 64.05 kB
  • 1,110 rows
  • 31 columns
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CREATE TABLE ind30_m_nfirms (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "food" BIGINT,
  "beer" BIGINT,
  "smoke" BIGINT,
  "games" BIGINT,
  "books" BIGINT,
  "hshld" BIGINT,
  "clths" BIGINT,
  "hlth" BIGINT,
  "chems" BIGINT,
  "txtls" BIGINT,
  "cnstr" BIGINT,
  "steel" BIGINT,
  "fabpr" BIGINT,
  "elceq" BIGINT,
  "autos" BIGINT,
  "carry" BIGINT,
  "mines" BIGINT,
  "coal" BIGINT,
  "oil" BIGINT,
  "util" BIGINT,
  "telcm" BIGINT,
  "servs" BIGINT,
  "buseq" BIGINT,
  "paper" BIGINT,
  "trans" BIGINT,
  "whlsl" BIGINT,
  "rtail" BIGINT,
  "meals" BIGINT,
  "fin" BIGINT,
  "other" BIGINT
);

Ind30 M Size

@kaggle.yousefsaeedian_edhec_investment_management_datasets.ind30_m_size
  • 233.64 kB
  • 1,110 rows
  • 31 columns
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CREATE TABLE ind30_m_size (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "food" DOUBLE,
  "beer" DOUBLE,
  "smoke" DOUBLE,
  "games" DOUBLE,
  "books" DOUBLE,
  "hshld" DOUBLE,
  "clths" DOUBLE,
  "hlth" DOUBLE,
  "chems" DOUBLE,
  "txtls" DOUBLE,
  "cnstr" DOUBLE,
  "steel" DOUBLE,
  "fabpr" DOUBLE,
  "elceq" DOUBLE,
  "autos" DOUBLE,
  "carry" DOUBLE,
  "mines" DOUBLE,
  "coal" DOUBLE,
  "oil" DOUBLE,
  "util" DOUBLE,
  "telcm" DOUBLE,
  "servs" DOUBLE,
  "buseq" DOUBLE,
  "paper" DOUBLE,
  "trans" DOUBLE,
  "whlsl" DOUBLE,
  "rtail" DOUBLE,
  "meals" DOUBLE,
  "fin" DOUBLE,
  "other" DOUBLE
);

Ind30 M Vw Rets

@kaggle.yousefsaeedian_edhec_investment_management_datasets.ind30_m_vw_rets
  • 179.3 kB
  • 1,110 rows
  • 31 columns
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CREATE TABLE ind30_m_vw_rets (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "food" DOUBLE,
  "beer" DOUBLE,
  "smoke" DOUBLE,
  "games" DOUBLE,
  "books" DOUBLE,
  "hshld" DOUBLE,
  "clths" DOUBLE,
  "hlth" DOUBLE,
  "chems" DOUBLE,
  "txtls" DOUBLE,
  "cnstr" DOUBLE,
  "steel" DOUBLE,
  "fabpr" DOUBLE,
  "elceq" DOUBLE,
  "autos" DOUBLE,
  "carry" DOUBLE,
  "mines" DOUBLE,
  "coal" DOUBLE,
  "oil" DOUBLE,
  "util" DOUBLE,
  "telcm" DOUBLE,
  "servs" DOUBLE,
  "buseq" DOUBLE,
  "paper" DOUBLE,
  "trans" DOUBLE,
  "whlsl" DOUBLE,
  "rtail" DOUBLE,
  "meals" DOUBLE,
  "fin" DOUBLE,
  "other" DOUBLE
);

Ind49 M Ew Rets

@kaggle.yousefsaeedian_edhec_investment_management_datasets.ind49_m_ew_rets
  • 283.24 kB
  • 1,110 rows
  • 50 columns
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CREATE TABLE ind49_m_ew_rets (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "agric" DOUBLE,
  "food" DOUBLE,
  "soda" DOUBLE,
  "beer" DOUBLE,
  "smoke" DOUBLE,
  "toys" DOUBLE,
  "fun" DOUBLE,
  "books" DOUBLE,
  "hshld" DOUBLE,
  "clths" DOUBLE,
  "hlth" DOUBLE,
  "medeq" DOUBLE,
  "drugs" DOUBLE,
  "chems" DOUBLE,
  "rubbr" DOUBLE,
  "txtls" DOUBLE,
  "bldmt" DOUBLE,
  "cnstr" DOUBLE,
  "steel" DOUBLE,
  "fabpr" DOUBLE,
  "mach" DOUBLE,
  "elceq" DOUBLE,
  "autos" DOUBLE,
  "aero" DOUBLE,
  "ships" DOUBLE,
  "guns" DOUBLE,
  "gold" DOUBLE,
  "mines" DOUBLE,
  "coal" DOUBLE,
  "oil" DOUBLE,
  "util" DOUBLE,
  "telcm" DOUBLE,
  "persv" DOUBLE,
  "bussv" DOUBLE,
  "hardw" DOUBLE,
  "softw" DOUBLE,
  "chips" DOUBLE,
  "labeq" DOUBLE,
  "paper" DOUBLE,
  "boxes" DOUBLE,
  "trans" DOUBLE,
  "whlsl" DOUBLE,
  "rtail" DOUBLE,
  "meals" DOUBLE,
  "banks" DOUBLE,
  "insur" DOUBLE,
  "rlest" DOUBLE,
  "fin" DOUBLE,
  "other" DOUBLE
);

Ind49 M Nfirms

@kaggle.yousefsaeedian_edhec_investment_management_datasets.ind49_m_nfirms
  • 87.46 kB
  • 1,110 rows
  • 50 columns
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CREATE TABLE ind49_m_nfirms (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "agric" BIGINT,
  "food" BIGINT,
  "soda" BIGINT,
  "beer" BIGINT,
  "smoke" BIGINT,
  "toys" BIGINT,
  "fun" BIGINT,
  "books" BIGINT,
  "hshld" BIGINT,
  "clths" BIGINT,
  "hlth" BIGINT,
  "medeq" BIGINT,
  "drugs" BIGINT,
  "chems" BIGINT,
  "rubbr" BIGINT,
  "txtls" BIGINT,
  "bldmt" BIGINT,
  "cnstr" BIGINT,
  "steel" BIGINT,
  "fabpr" BIGINT,
  "mach" BIGINT,
  "elceq" BIGINT,
  "autos" BIGINT,
  "aero" BIGINT,
  "ships" BIGINT,
  "guns" BIGINT,
  "gold" BIGINT,
  "mines" BIGINT,
  "coal" BIGINT,
  "oil" BIGINT,
  "util" BIGINT,
  "telcm" BIGINT,
  "persv" BIGINT,
  "bussv" BIGINT,
  "hardw" BIGINT,
  "softw" BIGINT,
  "chips" BIGINT,
  "labeq" BIGINT,
  "paper" BIGINT,
  "boxes" BIGINT,
  "trans" BIGINT,
  "whlsl" BIGINT,
  "rtail" BIGINT,
  "meals" BIGINT,
  "banks" BIGINT,
  "insur" BIGINT,
  "rlest" BIGINT,
  "fin" BIGINT,
  "other" BIGINT
);

Ind49 M Size

@kaggle.yousefsaeedian_edhec_investment_management_datasets.ind49_m_size
  • 357.32 kB
  • 1,110 rows
  • 50 columns
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CREATE TABLE ind49_m_size (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "agric" DOUBLE,
  "food" DOUBLE,
  "soda" DOUBLE,
  "beer" DOUBLE,
  "smoke" DOUBLE,
  "toys" DOUBLE,
  "fun" DOUBLE,
  "books" DOUBLE,
  "hshld" DOUBLE,
  "clths" DOUBLE,
  "hlth" DOUBLE,
  "medeq" DOUBLE,
  "drugs" DOUBLE,
  "chems" DOUBLE,
  "rubbr" DOUBLE,
  "txtls" DOUBLE,
  "bldmt" DOUBLE,
  "cnstr" DOUBLE,
  "steel" DOUBLE,
  "fabpr" DOUBLE,
  "mach" DOUBLE,
  "elceq" DOUBLE,
  "autos" DOUBLE,
  "aero" DOUBLE,
  "ships" DOUBLE,
  "guns" DOUBLE,
  "gold" DOUBLE,
  "mines" DOUBLE,
  "coal" DOUBLE,
  "oil" DOUBLE,
  "util" DOUBLE,
  "telcm" DOUBLE,
  "persv" DOUBLE,
  "bussv" DOUBLE,
  "hardw" DOUBLE,
  "softw" DOUBLE,
  "chips" DOUBLE,
  "labeq" DOUBLE,
  "paper" DOUBLE,
  "boxes" DOUBLE,
  "trans" DOUBLE,
  "whlsl" DOUBLE,
  "rtail" DOUBLE,
  "meals" DOUBLE,
  "banks" DOUBLE,
  "insur" DOUBLE,
  "rlest" DOUBLE,
  "fin" DOUBLE,
  "other" DOUBLE
);

Ind49 M Vw Rets

@kaggle.yousefsaeedian_edhec_investment_management_datasets.ind49_m_vw_rets
  • 280.09 kB
  • 1,110 rows
  • 50 columns
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CREATE TABLE ind49_m_vw_rets (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "agric" DOUBLE,
  "food" DOUBLE,
  "soda" DOUBLE,
  "beer" DOUBLE,
  "smoke" DOUBLE,
  "toys" DOUBLE,
  "fun" DOUBLE,
  "books" DOUBLE,
  "hshld" DOUBLE,
  "clths" DOUBLE,
  "hlth" DOUBLE,
  "medeq" DOUBLE,
  "drugs" DOUBLE,
  "chems" DOUBLE,
  "rubbr" DOUBLE,
  "txtls" DOUBLE,
  "bldmt" DOUBLE,
  "cnstr" DOUBLE,
  "steel" DOUBLE,
  "fabpr" DOUBLE,
  "mach" DOUBLE,
  "elceq" DOUBLE,
  "autos" DOUBLE,
  "aero" DOUBLE,
  "ships" DOUBLE,
  "guns" DOUBLE,
  "gold" DOUBLE,
  "mines" DOUBLE,
  "coal" DOUBLE,
  "oil" DOUBLE,
  "util" DOUBLE,
  "telcm" DOUBLE,
  "persv" DOUBLE,
  "bussv" DOUBLE,
  "hardw" DOUBLE,
  "softw" DOUBLE,
  "chips" DOUBLE,
  "labeq" DOUBLE,
  "paper" DOUBLE,
  "boxes" DOUBLE,
  "trans" DOUBLE,
  "whlsl" DOUBLE,
  "rtail" DOUBLE,
  "meals" DOUBLE,
  "banks" DOUBLE,
  "insur" DOUBLE,
  "rlest" DOUBLE,
  "fin" DOUBLE,
  "other" DOUBLE
);

Portfolios Formed On Me Monthly Ew

@kaggle.yousefsaeedian_edhec_investment_management_datasets.portfolios_formed_on_me_monthly_ew
  • 110.95 kB
  • 1,110 rows
  • 20 columns
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CREATE TABLE portfolios_formed_on_me_monthly_ew (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "n__0" DOUBLE  -- \u003c\u003d 0,
  "lo_30" DOUBLE,
  "med_40" DOUBLE,
  "hi_30" DOUBLE,
  "lo_20" DOUBLE,
  "qnt_2" DOUBLE,
  "qnt_3" DOUBLE,
  "qnt_4" DOUBLE,
  "hi_20" DOUBLE,
  "lo_10" DOUBLE,
  "dec_2" DOUBLE,
  "dec_3" DOUBLE,
  "dec_4" DOUBLE,
  "dec_5" DOUBLE,
  "dec_6" DOUBLE,
  "dec_7" DOUBLE,
  "dec_8" DOUBLE,
  "dec_9" DOUBLE,
  "hi_10" DOUBLE
);

Sample Prices

@kaggle.yousefsaeedian_edhec_investment_management_datasets.sample_prices
  • 2.37 kB
  • 13 rows
  • 2 columns
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CREATE TABLE sample_prices (
  "blue" DOUBLE,
  "orange" DOUBLE
);

Sp500

@kaggle.yousefsaeedian_edhec_investment_management_datasets.sp500
  • 13.54 kB
  • 396 rows
  • 3 columns
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CREATE TABLE sp500 (
  "date" TIMESTAMP,
  "sp500" DOUBLE,
  "sp500tr" DOUBLE
);

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