Herb Weiss, Cfa

At Random Forest Capital, we approach investment management from the perspective of data science, in which machine learning within fully non-parametric statistical models are applied to the problem of expected gains in financial investments. Rather than having humans look at each individual event within the marketplace, machine learning employs statistical algorithms over thousands of variables and millions of observations that are capable of detecting persistent effects across all aspects of data. We use not only a wide array of machine learning methods that simultaneously apply successfully to real-life applications from medical diagnoses to sports analysis, but additionally apply proprietary methods developed in house to optimize returns.

Servco Pacific Capital, LLC (“SPC”) is the direct investment, venture / private equity arm of Servco Pacific Inc. (“Servco”), one of the oldest multi-generational, privately held businesses in the Hawaiian Islands. Established in 1919, Servco is the fourth largest business in the state of Hawaii and has operations in the United States, Asia and Australia. Its businesses include automotive distribution and retail, car-sharing programs, retail hydrogen fueling stations, and appliance distribution. Servco also jointly controls Fender Musical Instruments, the world’s largest electric guitar and amplifier company, in partnership with TPG Growth. In addition to its focus on venture capital and private equity investments, SPC also leads corporate development and strategy efforts for Servco’s operating groups. SPC is based in San Francisco, California.

At Random Forest Capital, we approach investment management from the perspective of data science, in which machine learning within fully non-parametric statistical models are applied to the problem of expected gains in financial investments. Rather than having humans look at each individual event within the marketplace, machine learning employs statistical algorithms over thousands of variables and millions of observations that are capable of detecting persistent effects across all aspects of data. We use not only a wide array of machine learning methods that simultaneously apply successfully to real-life applications from medical diagnoses to sports analysis, but additionally apply proprietary methods developed in house to optimize returns.