Our Collection of the Leading Voices in Autonomous Learning Investment Strategies (ALIS)
MOV37, the research and investment platform for Autonomous Learning Investment Strategies (ALIS), has launched the ALIS in Dataland podcast to explore how the intersection of man, machine and data science is creating a revolutionary new wave of investment managers.
“Technology is having a disruptive impact across industries, and investment management is no different,” said Jeffrey Tarrant, Chairman and Founder of MOV37. “When transformation occurs it usually comes from outside an industry, and we’re seeing that now with coders, gamers, hackers and academics figuring out how to leverage machine learning and data to make money from markets while using a fraction of the people and resources needed by existing methods.”
ALIS in Dataland draws on the extensive network of MOV37’s principals and advisors to feature some of the most interesting and innovative voices in data science and artificial intelligence, as well as the people launching the ALIS managers that are applying these techniques in revolutionary new ways in investment management.
“The podcast will bring to light some of the leading minds in data and AI and let people hear firsthand what they’re doing and the impact they’re having,” said Adil Abdulali, Chief Data Scientist at MOV37.
The first episode, “Artificial Intelligence: The Nuclear Winter is Over,” features Hein Hundal, Chief Scientist at Random Order, Inc., a California-based software design and development team at the intersection of highly scalable machine learning methodologies. Among other things, the Random Order team has built an automated robotic trading system based on machine reading of live news and various financial reports, and designed a comprehensive non-parametric trading robot consisting of a battalion of intelligent trading agents who collected data, reduced model complexity, compensated for degrees of freedom, built and validated hypotheses, and side-stepped the curse of dimensionality.
“Hein is a visionary in the fields of machine learning, data and game theory,” said Michael Weinberg, Chief Investment Officer at MOV37. “We had a fascinating discussion about how machine learning came out of its nuclear winter and started to work, to the point where it’s now beating humans in an increasing range of tasks. A key point was how humans are no longer teaching the machines; the machines are now teaching themselves. This has profound implications not only for the investment industry but for many fields around the world.”
The next ALIS in Dataland podcast episode will feature MOV37 advisory board member Zubin Siganporia, founder of QED Analytics and Fellow in industrial and applied mathematics and lecturer in pure mathematics at Oxford University. In the podcast, MOV37 Founder Jeffrey Tarrant and Zubin discuss cutting edge data topics including homomorphic encryption, the impact of quantum computing on cryptography, the spread of data-driven approaches to an increasing number of fields, and how machine learning and AI tools are starting to outperform domain experts.
MOV37, LLC is a research and investment platform for Autonomous Learning Investment Strategies (ALIS). ALIS are the new wave of emerging managers using machine learning, new data and cheap computing power to run innovative investment strategies at lower costs versus traditional quantitative or fundamental managers. MOV37’s principals and advisors use their deep connections and backgrounds in finance, technology and academia to identify the best minds in autonomous learning, data analysis and blockchain, and the firm builds institutional-grade fund structures to help selected ALIS managers access investor capital. More information is at www.mov37.com.
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