In The Investment Process Exploiting Alternative Data - MathWorks
Exploiting Alternative Datain the Investment ProcessBringing Semantic Intelligence to Financial Markets
Data is growing at an incredible speedSource: IDC - 2014, Structured Data vs. Unstructured Data: The Balance of Power Continues to Shift 90% of all data that existtoday has been generatedover the last 2 years. Nearly 80% comes as‘hard-to-consume’unstructured content. Offers an incredibleopportunity for investors toidentify new alpha sources.
A new wave of data sources.Source: CB Insights, RavenPack Thousands of data sourceshave become available. Early adopters have had areal edge by hiring dedicateddata hunters. The market is maturing withmore sellside researchbecoming available. Data hunting is becomingless of a differentiator.
Why alternative data?.intuitiveMore leadingonline/searchweb traffic,blogs, socialGPS traffic datasatellite imagese-transaction datacredit card dataMore accuratebooked sales
Proprietary vs. Public Information The edge is found in efficientprocessing not in being theonly one having the data. Old school thinking unlessyou’re Google, Facebook,Apple, Amazon, or Microsoft. Many correlated datasets areavailable. Alternative data is in mostcases about nowcasting offundamental data.
The alpha landscape has changed.Source: Data Capital Management (DCM) Over time, data driven alphasignals have shorter duration,while the number of alphasources have increased. As a result, investors need toconsume more data to createequally scalable strategies. There is pressure on cost,since each individual alphacontains less marginal value. More pressure oninfrastructure: storage,computing, and research. It’s a big numbers game
We makeunstructured content actionablefor financial professionals
Adding StructureCodifyRelevance TaggingNovelty DetectionSentiment AnalysisNamed entity recognition,topic categorization, andtemporal classificationQuantify importance, rank,and connectionsIdentify what is new,original, or unusualDetermine attitude, opinion,and impact
Documented Third-Party Use Cases.Creates global macro strategies focusing onEquity Index, Forex, and Sovereign Bond tradingEnhances a pairs-trading strategy using anabnormal news volume and sentiment overlayCreates supply and demand side sentimentindexes to trade crude oilApplies various machine learning algos forportfolio construction using news sentimentfactorsCreates delta and value sentiment indexes totrade G10 currenciesPioneers in alternative data within factorinvesting and event-driven strategiesCreates sentiment indexes for Equity Indexmarket timingConsiders news sentiment for intraday tradingCreates global macro strategies focusing on FXCarry and Sovereign Bond tradingShorts stocks based on negative sentiment(Sell on the news)
Positive versus Negative Sentiment StocksPositive sentiment stocks generally outperforms negative sentiment stocks. Inthis case looking at monthly investment horizons for the Russell 2000.
RavenPack Analytics show strong performance!RPA delivers attractive risk-return profiles (1-day holding) - taking averagesentiment across highly novel and highly relevant rge/Mid-CapEUSmall-CapAnnualized Return9.6%28.8%12.1%38.3%Annualized Vol.3.8%5.3%4.4%8.5%Information Ratio2.505.452.744.49Avg. Portfolio %Maximum drawdownTurnover
Factor Neutral performance: 1-day holdingControlling for factor exposure (MSCI USFAST) leads to stronger performanceIR: 2.50 - 4.15IR: 5.45 - 6.81
Factor Neutral: extending the holding periodSweet spot holding for large cap (upto 2 weeks), small cap (weeks to months)
Media Buzz: high buzz companies outperformCompanies with high event buzz yield 2x greater average per-trade and totalreturnsRussell 1000, IR: 1.80 - 2.08Russell 2000, IR: 2.79 - 3.74
Macro: Trading Energy and Metal FuturesTarget: Create two commodity baskets: Energy (5): crude oil, gasoil, gasoline, heating oil, natural gasMetals (9): aluminum, copper, gold, lead, palladium, platinum,rebar, silver, zinc
Results I: individual modelsThe KNN model delivers the best risk-adjusted return (IR: 1.46) for the energybasket while a Gradient Boosted Trees regression with student-t loss yields thehighest IR for the metals basket (3.36)
Results II: ensembleEnsembles are created by equal-weighting the predictions of the ten MachineLearning algorithms. This results in lower average prediction error bias for bothbaskets despite the simplicity of the approach
Results IV: ensemble - increasing volatilityWe define opportune periods asthose where volatility is increasing.This is determined by the 10-dayvolatility being above the 21-dayvolatility
Results V: ensemble - random portfoliosTo investigate the statistical significance of the increasing volatility approach, wecreate 1,000 random portfolios of comparable size to benchmark. The red line belowmarks the actual IR of the Increasing portfolio (Energy: 1.29, Metals: 3.11)
Questions?phafez@ravenpack.com
Data is growing at an incredible speed Source: IDC - 2014, Structured Data vs. Unstructured Data: The Balance of Power Continues to Shift 90% of all data that exist today has been generated over the last 2 years. Nearly 80% comes as 'hard-to-consume' unstructured content. Offers an incredible opportunity for investors to
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Le genou de Lucy. Odile Jacob. 1999. Coppens Y. Pré-textes. L’homme préhistorique en morceaux. Eds Odile Jacob. 2011. Costentin J., Delaveau P. Café, thé, chocolat, les bons effets sur le cerveau et pour le corps. Editions Odile Jacob. 2010. Crawford M., Marsh D. The driving force : food in human evolution and the future.
Le genou de Lucy. Odile Jacob. 1999. Coppens Y. Pré-textes. L’homme préhistorique en morceaux. Eds Odile Jacob. 2011. Costentin J., Delaveau P. Café, thé, chocolat, les bons effets sur le cerveau et pour le corps. Editions Odile Jacob. 2010. 3 Crawford M., Marsh D. The driving force : food in human evolution and the future.
