“Flyberry Capital was founded 2011 with the aim of delivering attractive risk-adjust returns, uncorrelated to most traditional and alternative investments, and unconditional to any single market or economic environment. The company relies heavily on research, using mathematics, “big data” and sentiment analysis techniques to develop proprietary trading models and strategies. Flyberry employs a quantitative trading program that opportunistically and tactically can hold long and short positions in global futures markets. Investment decisions are systematically generated in response to high-impact information shocks observed in news announcements and events. We believe asset prices are subject to information shocks, and while investors continually update their valuation of assets upon the arrival of new information, market prices do not always instantly adjust to novel information. We feel superior information gathering and processing, therefore, can lead to a “knowledge advantage,” and potentially to exceptional investment results. We believe less efficient markets exist in which systematic application of skill and effort should pay off for our clients.”
Flyberry’s Systematic Big Data program is systematic, quantitative Managed Futures program that exploits short-term price moves in response to high-impact information shocks as manifested in news and events. Nearly 60 trading models are in production. The current portfolio comprises 18 well regulated, highly liquid exchange traded futures encompassing 5 market sectors. Flyberry applies a three-pronged approach in designing and implementing trade models to exploit short-term price moves when news and events diverge from sentiment: First, we acquire, cleanse and sort massive amounts of data from news, government sources, sensory feeds and social media. Next, we systematically and quantitatively evaluate the affect these data have on asset prices via our proprietary high-throughput screening and testing software. Finally, we optimize the most promising models, while ensuring robust parameters. As for Risk Management, risk positions are sized according to each model’s historical VaR. Risk is modulated between 0.5% and 2.0% at 97.5th-tile and is proportional to a strategy’s opportunity sets and recent performance. Maximum portfolio-wide risk (“risk-to-stop”) is set to 5% of NAV.