Richard P. Roche, CAIA

Loading Events

← Back to Events

Richard P. Roche, CAIA

Rick Roche joined Little Harbor Advisors, LLC in April 2013 as a Managing Director. Little Harbor Advisors (LHA) is a sponsor of quantitative investment strategies. Rick has 39 years’ experience in investment management and is committed to lifelong learning.

Rick is the founder of Roche Invest AI, LLC, a consultancy that promotes the use of machine learning and alternative investment data in quantitative models. He’s considered a “Light Quant/Analytical Translator” – an individual knowledgeable about AI/Machine Learning & alternative investment data use in quantitative investment models.

From 4Q 2017 thru 2Q 2020, Rick was the featured speaker at 60 FPA Association (CFP) chapters, CAIA chapters, CFA Society and NY-AIR events. His Machine Learning in Investment Management presentation has been enthusiastically received by 3,100 credentialed advisors and analysts.

Rick Roche has consulted 95 major financial companies and has personally trained 75,5000 financial sales pros in customer loyalty marketing, capital market trends and investor behavior.

He has a unique ability to synthesize large amounts of academic and technical research and translate into actionable intelligence. His extensive investment background, encyclopedic command of market theory and staccato delivery have earned him the Top-Rated Speaker rating.

Rick holds Series 3 (Commodities), 7, 63 and 65 licenses. Rick earned his Chartered Alternative Investment Analyst charter designation in 2014. He is also a Member of Society of Quantitative Analysts (SQA), the New York Alternative Investment Roundtable (NY-AIR) and has a new Professional Membership at CFANY. Rick earned a B.A.-History from the University of Dayton.

From 1977 to 1980, Rick served two terms in the Massachusetts House of Representatives representing Springfield, MA.

September 2020

Sep
22
Tue

Epidemiology of Volatility Transmission and Regime Change Risk Models

Most investment and risk models have difficulty reflecting regime changes characterized by market turbulence where asset prices behave in an uncharacteristic fashion, given their historical pattern of behavior. This presentation will demonstrate how Machine Learning uncovers subtle insights about correlated sources of risk that may not be detected by off-the-shelf risk models or fundamental portfolio and security analysis.

Find out more »
Conference / Seminar, Performance & Risk Analytics, Virtual Events & Programming
+ Export Events