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J.P. Morgan’s Guide to Big Data and AI Strategies

Alternative data is all the rage in the investment world as hedge funds, private investors, and everybody in between try to deliver consistent alpha in an ever more competitive landscape.

At a conference in New York City hosted by J.P. Morgan (JPM) earlier this month, several high-profile speakers from the quant and Big Data space gave their views on the topic. The takeaway from this event: ”alternative data delivers proven alpha”.

To get the ball rolling, JPM’s Global Quantitative & Derivatives Strategy team released a tour de force, titled "Big Data and AI Strategies", in which they put a host of alternative data providers, including RavenPack, under the microscope. RavenPack features prominently in their report and the JPM quant team provides several examples of how to unlock alpha by taking advantage of RavenPack Analytics (RPA) for global macro trading.

Note that the main authors of this report will be speaking at the RavenPack 5th Annual Research Symposium: The Big Data & Machine Learning Revolution, taking place Sept 19th in NYC.

As a first step, JPM calculated daily sentiment scores for different asset classes of interest using the RavenPack dataset. The exact methodology is described in the grey box below.

Guide to Big Data and AI Strategies

To illustrate the potential use of sentiment data, JPM constructed a set of simple long-short strategies: going long assets with the top three sentiment scores, and short assets with the bottom three sentiment scores.

Trading Equity Indexes

Firstly, the JPM quant team constructed a daily long-short equity portfolio from nine country-specific equity indices using “economic sentiment’ as a confirming indicator to go long/short equity indexes. Sentiment scores were aggregated across various frequencies (e.g. 1 day, 1 week, 1 month, etc.) to create faster or slower moving signals.

Guide to Big Data and AI Strategiest

The strategy delivers alpha irrespective of the chosen frequency, but the weekly signal performs the best with an annualized return of 5.6% and a Sharpe Ratio of 0.44. However, the daily signal also behaves well, returning a Sharpe Ratio of 0.21. Importantly, the portfolio is uncorrelated with traditional risk factors, such as momentum, value, carry, and volatility. Given the decent Sharpe Ratio expressed in the weekly portfolio together with the low correlation to known risk factors, it seems interesting to incorporate the strategy into a broader portfolio to reap the offered diversification benefits.

Developed-Market Currencies

As an illustration of the various use-cases of RavenPack Analytics, JPM also considers a developed-market FX example, filtering based on RavenPack currency news. The result is a contrarian long-short portfolio where the three currencies with the most negative/positive sentiment have a long/short position vis-à-vis the U.S. Dollar from a basket of 10 currency pairs (using carry-adjusted returns).

Guide to Big Data and AI Strategies

The idea behind the strategy is that currencies reacts quickly to news events and hence are susceptible to reversal. Similar to what we saw above, the currency portfolio is uncorrelated with traditional risk factors and hence suitable in a broader portfolio.

Sovereign Bonds

Lastly, the quant team at JPM designed a bond portfolio, which borrows from the two strategies mentioned above in that firstly, it uses only events belonging to Economy and secondly, is contrarian. They report Sharpe Ratios similar to the two other strategies with a maximum of 0.45 over a 1 month aggregation window. Combined with the low correlation to classical risk factors, this portfolio could also be part of a broader portfolio.

Guide to Big Data and AI Strategies

Overall, the Quantitative and Derivatives Strategy team at J.P. Morgan demonstrates succinctly how RavenPack Analytics can be utilized to generate alpha streams that are uncorrelated to known risk factors.

You can request the report here

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