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"Alpha from Alternative Data" by Emmett Kilduff, Founder and CEO of Eagle Alpha

From QuantCon 2017: At J.P. Morgan's annual quantitative conference 93% of investors said alternative data will change the investment landscape.

In this presentation, Emmett will discuss the rapidly increasing adoption of alternative data, give a detailed overview of the 24 different types of alternative data, outline the applications of alternative data for quantitative funds, discuss interesting datasets that are available (including Asian datasets) and present case studies that evidence value in alternative datasets.

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"Alpha from Alternative Data" by Emmett Kilduff, Founder and CEO of Eagle Alpha

  1. 1. Alpha From Alternative Data 29th September 2017 Presenter: Emmett Kilduff (Founder & CEO) Mobile: +353 (0)86 7772198 Email: emmett.kilduff@eaglealpha.com
  2. 2. Table Of Contents 1 1: Adoption 2: The Global Landscape 3: The Asian Landscape 4: Case Studies 5: Eagle Alpha 1
  3. 3. 1: Adoption
  4. 4. A1, 6% A2, 70% A3, 23% Alternative Data Will Fundamentally Change The Investment Landscape 2 Q: At JPMorgan’s annual quant conference last May, 237 asset managers were asked: what is your opinion of Big Data / Machine Learning? A1: 6% said “fad – investor interest will decline”. A2: 70% said “evolution – it’s importance will gradually grow for all investors”. A3: 23% said “revolution – will lead to rapid changes to investment landscape”.
  5. 5. Alternative Data Is Not New. >50 Innovative Firms Have Been Working With Alternative Data For Years e.g. WorldQuant 3
  6. 6. GSAM Is An Innovator 4
  7. 7. Blackrock Is An Innovator 5
  8. 8. Schroders Is An Innovator 6
  9. 9. Balyasny & Citadel Are Innovators 7
  10. 10. Blue Mountain & DeShaw Are Innovators 8
  11. 11. Alternative Data Adoption Will ‘Cross The Chasm’ By The End Of 2018 Quantitative funds Cutting- edge firms Large number of quant funds Bulk of firms Small minority Hedge funds Select few Quant-a- mental firms Bulk of firms Minority Mutual funds <5 firms Quant-a- mental firms The more dynamic mutual funds Bulk of firms (conservatives) The rest (skeptics) Source: Geoffrey Moore’s book ‘Crossing the Chasm’, Eagle Alpha 50+ firms have been working with alternative data for years. Among discretionary managers today, 24% are using ‘big data’ today. Source: Barclays. Today 20% of HFs >$1bn AUM have headcount or 50% of a persons time. Source: Jefferies Eagle Alpha currently has a dialogue with 226 firms. This is increasing rapidly. 70% of firms say importance of big data will gradually grow for all firms. Source: JPMorgan. 80% of firms want greater access to alternative data. Source: Greenwich survey. 9
  12. 12. A Key Driver Of Adoption Is Firms Adopting A ‘Quantamental’ Approach Consumer Insights Source: Morgan Stanley 10
  13. 13. A Key Driver Of Adoption Is To Increase Profit Margins Consumer Insights Source: Quinlan Associates 11
  14. 14. Benefits Of Using Alternative Data Consumer Insights Source: Quinlan Associates 12
  15. 15. 90% Of Alt Data Users Have Seen The Return They Hoped For, 95% Said It Helps Explain Their Strategy To Clients 13
  16. 16. Use Cases 14
  17. 17. 2: The Global Landscape
  18. 18. Eagle Alpha’s Database Currently Has 528 Datasets Split Into 24 Categories Of Alternative Data Pricing (78) Employment (11) Web Crawled (20) Mobile App Usage (33) Reviews & Ratings (41) Social Media (62) Sentiment (26) Online Search (19) Expert Views (15) Store Locations (15) Advertising (27) Event Detection (27) Trade (10) Data Aggregators (45) Consumer Credit (15) Open Data (53) Public Sector (54) B2B (13) Geo- Location (36) Satellite & Weather (35) Internet of Things (4) Consumer Transaction (31) Business Insights (83) Consumer Insights ESG (15) Note: the number in each octagon represents the number of alternative datasets that Eagle Alpha has identified in that category. 15
  19. 19. Where Does Consumer Transaction Data Come From? Online Transactions Point of Sale Transactions 16
  20. 20. Credit Card Company Personal finance apps, loyalty programs Customer Bank Merchant Bank POS terminal & technologies Merchant provides purchase data to market research firms Merchant financial programs & services Payment processor Merchant emails receipt to customer Credit card issuing bank Many Parties Involved In A Consumer Transaction 17
  21. 21. Consumer Transaction Data Sources (U.S.) Apps Eagle Alpha Data Partner (China) Eagle Alpha Data Partner Eagle Alpha Data Partner 18
  22. 22. Example Of A Dataset: Employment Dataset 19 Vendor Overview • The company aggregates labour market data from company websites in the U.S. and worldwide. • The data is updated daily and contains no duplicate listings or job pollution. • Eagle Alpha Labour Market Data partner has become a leading provider of labour market data and analytics. • The index includes every industry and every job type and doesn’t include jobs from job boards or job sites. Data Set Description • Disclaimer by vendor regarding data set: Information provided through this document is not advice and is subject to change. We may amend, update, suspend or delete any information in the content without notice at any time and at our sole discretion. The provider has represented to Eagle Alpha that the following information is true at the time of writing [June 2017] Data Set Overview Geography USA and worldwide Coverage 4 million job openings sourced from 30,000 company websites Mapped to Tickers Yes History Since 2007 Collection Frequency Real-Time Delivery Frequency Real-Time Lag Time Delivery Method Platform, AWS, FTTP Legal & Compliance • Granular: A proprietary dataset of high quality and real-time updates with dozen primary and some secondary fields. • Breadth: Index of 4 million job openings (~70% of all job openings in U.S.) and has jobs sourced from 30,000 companies.. • Representative: Index is updated daily for real-time jobs and data feeds and includes jobs and companies from 185 countries? Legal • The provider is authorized to sell the data set. • The data sets do not include any PII. Compliance • The data set does not contain any MNPI. • The data set is available to any buy side firm i.e. no exclusivity offered to interested parties. Related Tickers Alpha: Use Cases Trial & Subscription E.A Standard Trial Agreement No Trial Data Full Access to Historical Data Trial Duration Up to 90 days • Examine the crawled data covering approximately 15k companies, of which around 3.5k companies are publicly traded in the US. • Two categories of variables: Jobs Created and Jobs Active • Form monthly and annual portfolios by dividing the sample of firms based on the 10 variables into both deciles and quintiles. • The top portfolio is the portfolio of 10% of the firms and bottom portfolio is 10% of the firms where the variable is the lowest. • Calculate the hedge return which is the top portfolio average return minus the bottom portfolio average return No • Corresponds to 3500 tickers: examples include: AMZ:US, PYPL:US, GRUB:US, UBER:US, AAPL:US, NMG:US, SQ:US, LUV:US Pricing Contact Eagle Alpha at enquiries@eaglealpha.com Alpha: Example • Eagle Alpha’s predictive model, demonstrates alpha opportunity in the variables where Jobs Active produce the highest and most consistent returns. • Jobs Active: Calculated as is or normalized - Yearly hedge returns are between 6-8% • Growth-returns are U-shaped where the top and bottom portfolios are higher than the middle portfolios • Jobs Created: Calculated as is or normalized - Yearly hedge returns are between 2-4% (portfolio returns are not linear)
  23. 23. Eagle Alpha Recently Published An 82 Page Report That Outlines The Applications Of Alternative Data 20
  24. 24. The Report Includes 20 Case Studies That Are Categorised By Asset Class And Type Of Manager 21
  25. 25. 3: The Asian Landscape
  26. 26. The Alt Data Landscape In China Based On Our Current Database – We Have Just Hired A Person To Focus On China 22 Advertising 6 App Usage & Web Traffic 9 Business Insights 2 Consumer Credit 6 Consumer Transactions 6 Data Aggregators 15 Employment 1 ESG 1 Event Detection 1 Geo-Location 6 Open Data 6 Pricing 3 Public Sector 5 Reviews & Rating 1 Satellite & Weather 1 Sentiment 6 Store Location 1 Web Crawled Data 2
  27. 27. Several Datasets Provide Granularity That Traditional Datasets Do Not Offer e.g. China Autos Dataset 23 Vendor Overview • The provider is the leading provider of “data-supported business decisions” for the automotive industry in China. • With the largest market share in the autos segment in China, their key data products take 60% market share, which reaches 80% market share in JV automakers. • They have assembled a unique panel consisting of over 1,300 contributing co-operating Chinese dealerships. • Eagle Alpha have an exclusive partnership with this provider to distribute these powerful data sets to the finance vertical. Data Set Description • Disclaimer by vendor regarding data set: Information provided through this document is not advice and is subject to change. We may amend, update, suspend or delete any information in the content without notice at any time and at our sole discretion. The provider has represented to Eagle Alpha that the following information is true at the time of writing [17 Jan 2017] Data Set Overview Geography China Coverage National, with regional breakdowns Mapped to Tickers No History Since 2012 Collection Frequency Mixed – Month/ Bi- Monthly Delivery Frequency Mixed – Month/ Bi- Monthly Lag Time Between 5 & 20 days Delivery Method API, CSV Legal & Compliance • Transaction Price: Average transaction price of automobiles at a model, sub-model & version level (dealership sourced). National/ city level breakdown. • Rebate: Manufacturer promotion data, includes a breakdown of all promotional activity by OEM’s (dealership sourced). • Showroom Indicators: (1) Inventory Indicator, (2) Order Indicator, (3) Customer Intention Indicator. • Volume: CPCA volume data, adjusted using dealership data to provide sales volume mix at a version level. (Imported Models NOT Included). Legal • They are authorized to sell the data set. • The data set do not include any PII. Compliance • The data set does not contain any MNPI. • The data set is available to any buy side firm i.e. no exclusivity offered to interested parties. Related Tickers • Examples include: F:US, GM:US, TM:US, NSANY:US, DAI:GR, VOW:GR, BMW:GR, 2333:HK. Alpha: Use Cases • Predict revenue for domestic Chinese manufacturers and revenues generated by foreign manufacturers in mainland China. • Track discounting and promotional activity of OEM’s on a monthly basis. • Track inventory levels and average transaction price by brand (sourced from dealership panel). Alpha: Example • Eagle Alpha’s first-order autoregressive model for predicting Great Wall Motors revenue, incorporating provider transaction price and volume data, demonstrates a reduction of mean absolute percentage error of 5.37ppts from (10.28% to just 4.91%), over a baseline model. Directional accuracy is also markedly improved, increasing from 57.14% to 85.71%. Trial & Subscription E.A Standard Trial Agreement Yes Trial Data Restricted API Access Trial Duration Up to 6 weeks Pricing: Full API Access $120,000 p.a. per team
  28. 28. This Is An Example Of A Consumer Transaction Dataset 24 Vendor Overview • This provider is the professional services division one of the world’s largest payment networks by number of cards issued. • Operating in the Chinese market, they provides end-to-end services of big data analytics and strategy consulting services. • Their products and services leverage the intelligence derived from the analysis of over 20 billion transactions per year. Data Set Description • Disclaimer by vendor regarding data set: Information provided through this document is not advice and is subject to change. We may amend, update, suspend or delete any information in the content without notice at any time and at our sole discretion. The provider has represented to Eagle Alpha that the following information is true at the time of writing [1 Jan 2017] Data Set Overview Geography China Coverage 800 million card holders Mapped to Tickers Not yet History Since 2011 Collection Frequency Monthly, Weekly Delivery Frequency Monthly, Weekly Lag Time Monthly: 8 Days Weekly: 4 Days Delivery Method CSV Legal & Compliance • National Monthly/ Weekly Indices: Real Estate, Restaurant & Catering, Luxury Hotel, Economic Hotel, Luxury Automobile, Economic Automobile, Department Store, Jewellery, Apparel, Luxury, Gas Station, Entertainment, Movie Theatre, Home Appliance, Overseas Spending, E-Commerce. • Overseas Spending: Monthly/ Weekly Indices in Retail, Restaurant & Catering, Hotel, Duty Free Shops, ATM and more, down to Sector-level. • Ticker Level Indices (Not yet available): Eagle Alpha have begun testing and development of a suite of ticker level indices with this provider. Legal • Eagle Alpha are authorized to sell the data set. • The data sets do not include any PII. Compliance • The data set is available to any buy side firm i.e. no exclusivity offered to interested parties. Related Tickers • Examples include: EPA:RMS, EPA:MC, LON:BRBY, EPA:KER, BIT:BMW, ETR:VOW3, ETR:NSU, ETR:DAI Alpha: Use Cases • Monthly/Weekly Indices: Closely tracks official data published by the Chinese NBS for floor space of residential buildings sold. • Overseas Spending: Track spending by China bank card holders abroad at a sector and sub-sector level e.g. luxury retail in Japan or spend in Macau Casinos. • Business Intelligence: Predict revenue of companies based on consumer spending in the Chinese market. Trial & Subscription E.A Standard Trial Agreement No Trial Data Historical Indices Trial Duration Up to 6 weeks National Monthly Indices: Per Index $10,000 p.a. per team Overseas Spending: Per Index $25,000 p.a. per team Alpha: Example • Correlation between the provider’s data for a U.S. tech hardware company’s transactions in China and its sales was +96% from 2014 through 2016, with R-squared of 92%. Eagle Alpha’s autoregressive model built using the vendor’s data demonstrated an MAPE of 13%, and the model captured major inflection points in revenues over time. Ticker Level Indices Not yet National Weekly Indices: Per Index $15,000 p.a. per team
  29. 29. Delhi Mumbai Bangalore Business Insights 8 Consumer Credit 4 Consumer Transactions 2 Data Aggregators 5 Employment 1 Geo-Location 2 IoT 1 Open data 4 Pricing 12 Reviews & Ratings 1 Trade 1 Social Media 3 Store Location 1 Web Crawled Data 6 The Alt Data Landscape In India Based On Our Current Database. Last June We Hired A Person Who Speaks Hindi 25
  30. 30. 4: Case Studies
  31. 31. • Based on geo-location data from 50 million mobile phones. • R-Square from a regression with quarterly revenue growth of 50 US retailers was 0.39. • Average excess return for stocks in the highest quintile was 2.14%, lowest quintile was -1.26%. Revenue Growth vs. Consumer Activities March 2009 – July 2014 Source: National Bureau of Economic Research, June 2016 ,Froot, Kang, Ozik, Sadka Predictive Value Of Geo-location Data 16 26
  32. 32. • Based on search data from Google Trends. • Generate search terms, extract search volumes, process the data, test predictive power of each term. • Construct the index. • Measure improvement over a baseline autoregressive index. Search Data: Key Backtesting Results (Dec 16) Source: Google Trends, Eagle Alpha Indicator Comparison Dataset Correlation Improvement over baseline model Consumer Confidence (UK) Gfk Consumer Confidence 0.96 8% Mortgage Applications (UK) Mortgage Approvals 0.86 8% Unemployment (UK) Unemployment Rate (UK) 0.89 13% Jobs (UK) Claimant Count Change 0.67 5% Housing (UK) RICS House Price Index 0.6 5% Unemployment (US) Unemployment Rate (US) 0.9 14% Jobs (US) Nonfarm Payrolls 0.68 4% Retail Sales (US) Retail Sales Ex. Autos (US) 0.31 8% Predictive Value Of Search Data 17 27
  33. 33. • Based on Data collected from listings of jobs indexed exclusively from corporate websites. • Formed monthly and annual portfolios by dividing the firms into deciles and quintiles based on postings activity. • Calculated the hedge returns, i.e. top portfolio return minus bottom portfolio. • “Jobs Active” metric produced the highest and most consistent returns of 7-9%. Predictive Value Of Employment Data 18 28
  34. 34. Source: Email Receipt Data, Investment Bank, Eagle Alpha • Analysis Based on Email Receipt Data for 31 S&P 500 Companies. • Data was aggregated into a weekly score and week-over-week percentage change was calculated. • A cross-sectional comparison was performed, long the top 5 stocks and short the bottom 5 stocks. Annualised Returns 16% Sharpe Ratio 1.13 Email Receipt Data: Cumulative returns using the 4-week z-score Predictive Value Of Consumer Transaction Data 19 29
  35. 35. • This dataset shows a 99% correlation with reported revenue for Great Wall Motors and a 95% correlation with YoY revenue growth over a 5-year period. • Eagle Alpha’s model for predicting Great Wall Motors revenue, incorporating this dataset, demonstrates a mean absolute percentage error (MAPE) of just 4.9% (Figure 1). The error rate for market consensus estimates was 8.1% over the same period. • The information edge over the street estimates is potentially large. For example, as shown in Figure 2, in Q4 2014 the CAI data was forecasting a QoQ growth rate of 48% compared to the consensus estimate of 17% versus the reported QoQ growth rate of 42%. Figure 1: Great Wall Motors Revenue Prediction Figure 2: Great Wall Motors Revenue Growth vs CAI Dealership Data Using A Chinese Autos Dataset To Predict Revenue For Great Wall Motors 30
  36. 36. 5: About Eagle Alpha
  37. 37. Eagle Alpha’s Vision To Be The ‘Go-To’ Firm For The Alternative Data Needs Of Asset Managers 31
  38. 38. Eagle Alpha’s Solution Is Focused On Education And Alpha Tailored Teach-in Firms that want to catch up with the early adopters engage us for an up to 8 hour customized teach- in. We are the only provider of comprehensive teach-ins regarding alternative data. Thought Leadership Firms that want to stay ahead of competitors license this package. It includes events, lessons learnt, proprietary papers and industry developments. We are the only provider of a dedicated Thought Leadership offering regarding alternative data. Bespoke Projects Clients that: a) want to start small; or b) don’t have the skillset; or c) don’t have the capacity, engage Eagle Alpha to do bespoke projects. Given the other 5 parts of the Eagle Alpha solution we are uniquely positioned to deliver bespoke projects for buyside firms. Data Insights Data Insight reports and indicators give clients actionable ideas and demonstrate, how different types of datasets can be leveraged. We are one of 2 independent firms worldwide that publish “quantamental” research reports that are based on alternative data. Analytical Tools We have two tools (Web Queries, Digital Expert Network) that enable clients to conduct proprietary research. Clients value leveraging our 5 years of experience in order to help tailor the tools to give insight into their research questions. Data Sourcing This part of the solution includes: a) a database, b) an advisory service; c) vendor access; and d) proprietary datasets. We have an unrivalled solution, based on 5 years of experience, to navigate clients through the sea of data. Education / Best Practice Alpha OverviewUSP 32
  39. 39. We Are Hosting The BIGGEST Data Showcase Event On 5th December In NYC 33
  40. 40. We Have Already Become A Recognised Leader In The Alternative Data Space e.g. Citi’s Primer Had A 10 Page Profile 34
  41. 41. JPMorgan’s Primer Positioned Eagle Alpha As An Emerging Data Aggregator 35
  42. 42. BoA’s Primer Included Our Taxonomy Of Categories And Positioned Us As A Leading Aggregator 36

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