Can Data Scientists Save Hedge Funds? | AnacondaCON 2017

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Can Data Scientists Save Hedge Funds?CLO UDQUA NT - DEMO CR AT IZ ING T H E SEA R CH F O R A LPH A

MO R G A N SLA DECEO – CLO UDQUA NT

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What is CloudQuant?

•The world’s first free cloud-based high frequency trading strategy simulation platform

•An easy button for quant trading (python)

You create it, We fund it, We profit together!

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Section 1 : Hedge Fund Performance Overview

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Hedge Cumulative Returns vs. Passive Indices

***SPY+TLT is a combined portfolio consisting of 60% SPY and 40% TLT

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Daily Return Distributions

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Fact Sheet on Daily Return of IndicesDaily

Return Stats

Equity Hedge

Event Driven

Macro Index

Relative Value

ArbitrageSPY TLT SPY+TLT

Mean (bps) 1.25 0.50 0.21 0.74 4.69 2.93 3.98

Volatility (bps) 31.33 15.86 18.23 13.14 95.10 93.98 49.62

Skewness -0.84 -1.15 -0.54 -0.56 -0.53 -0.14 -0.33

Kurtosis 4.33 6.00 3.36 3.32 4.55 1.61 3.02

Sharpe Ratio 0.63 0.49 0.18 0.89 0.78 0.49 1.28

(statisticsofdatafrom2011-01-03to2017-01-12)

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1yr - Rolling Sharpe

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1yr - Rolling Sharpe

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Return Correlation between Hedge Funds

Equity Hedge Event Driven Macro IndexRelative Value

Arbitrage

Equity Hedge 1.000 0.773 0.602 0.904

Event Driven 0.773 1.000 0.509 0.697

Macro Index 0.602 0.509 1.000 0.700

Relative Value Arbitrage 0.904 0.697 0.700 1.000

(correlationsfordatafrom2011-01-03to2017-01-12)

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Fama-French 5 Factor Loadings

Intercept Mkt-RF SMB HML RMW CMA R-squared

Equity Hedge 1.98 187 -50.4 -27.9 -24.8 -32.7 0.34

Event Driven -0.539 93.1 -5.65 -22.4 -11.6 -10.8 0.35

Macro Index -1.87 57.7 -18.9 -50.5 3.46 19.7 0.08

Relative Value

Arbitrage4.14 59.3 -25.2 -12.2 -2.84 -4.66 0.18

***Intercept and all coefficients in a scale of 1e-05

(statisticsfordatafrom2011-01-03to2017-01-12)

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Fama-French 5 Factor + HF Index Loadings

Intercept

Mkt-RF SMB HML RMW CMAEquity Hedge

Event Driven

Macro Index

Relative Value

Arbitrage

R-square

d

Equity Hedge -2.27 57.5 -12.2 10.8 -17.1 27.7 - 44700 27600 121000 0.84

Event Driven -2.28 31.9 13.4 -13.9 -4.49 -0.635 24800 - -3620 28600 0.66

Macro Index -4.95 -20.2 8.98 -37.3 10.7 30.1 25000 -5910 - 61800 0.57

Relative Value Arbitra

ge

3.95 -12.8 -7.54 6.15 4.89 2.62 27800 11900 15700 - 0.79

***Intercept and all coefficients in a scale of 1e-05

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Conclusion

• Much of the alpha generated by hedge funds that is not captured by the systematic Fama-French factors is likely to be driven by exposures to unseen common factors.

• That suggests they are investing a set of similar investment ideas.

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Section 2: CloudQuant x Anaconda

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About CloudQuant

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Here is a traditional Hedge Fund strategy…

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Machine learning-enhanced strategy

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Comparison of the two strategiesOriginal Strategy ML-enhanced Strategy

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Comparison of the two strategies (continued)

Original Strategy ML-enhanced Strategy

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Data Sources and Resources

• Quandl: https://www.quandl.com/

• QuantNews (SSRN): http://www.quantnews.com/ssrn.php

• HFR: https://www.hedgefundresearch.com/family-indices/

• Fama-French factors: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_5_factors_2x3.html

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Section 3: Man vs. Machine (Learning)

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Overview of Systematic Funds and Discretionary Funds

Source: “Man vs. Machine Comparing Discretionary and Systematic Hedge Fund Performance”

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A Deeper Look at Macro Strategies

Source: “Man vs. Machine Comparing Discretionary and Systematic Hedge Fund Performance”

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A Deeper Look at Macro Strategies

Source: “Man vs. Machine Comparing Discretionary and Systematic Hedge Fund Performance”

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A Deeper Look at Macro Strategies

Source: “Man vs. Machine Comparing Discretionary and Systematic Hedge Fund Performance”

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And some recent performance data…

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Comparison during Key Market Events

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Conclusion

• Systematic/AI Hedge Funds are outperforming traditional discretionary Hedge Funds significantly

• Machine Learning and Regularization Techniques can help us to detect alpha better than discretionary and traditional regression approaches

• Data scientists are using new datasets and generating new investment ideas for the industry

• Crowd researchers are coming online. . . .

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References

1. CloudQuant LLC. "CloudQuant Simulation Database." CloudQuant, 2 Feb. 2017. <https://www.cloudquant.com/application/#/status>

2. Yahoo!. "SPY : Summary for SPDR S&P 500 -Yahoo Finance." Yahoo! , 15 Jan. 2017. <https://finance.yahoo.com/quote/SPY/?p=SPY>

3. Yahoo!. "TLT : Summary for IShares 20+ Year Treasury Bond -Yahoo Finance." Yahoo! 15 Jan. 2017.<https://finance.yahoo.com/quote/TLT/?p=TLT>

4. Hedge Fund Research. "HFRU Indices Performance Tables." HFRU | Hedge Fund Research, 02 Feb. 2017. <https://www.hedgefundresearch.com/family-indices/hfru>

5. French, Kenneth R. "Kenneth R. French - Data Library." Kenneth R. French - Data Library. Kenneth R. French, 2 Feb. 2017. <http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html>.

6. Harvey, Campbell R., and Otto Van Hemert (2016). "Man vs. Machine: Comparing Discretionary and Systematic Hedge Fund Performance." <Duke I&E Research Paper No. 2017-01>

7. Melin, Mark. "New Report Shows AI Hedge Funds Are Crushing Their Human Overlords."ValueWalk 18 Jan. 2017, Top Stories < http://www.valuewalk.com/2017/01/ai-hedge-fund-returns>

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Questions & Answers

CloudQuantAustin - Chicago - New York

312-690-2570

CloudQuant.com

Morgan Slade

mslade@cloudquant.com

https://www.linkedin.com/in/morganslade