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Asymmetric Investing: From Research to Realized Gains

Exceptional investment opportunities are rare. LMM Labs’ asymmetric investing research uses stringent gates to identify situations where historical patterns, fundamentals, and payoff potential align—with disciplined limits on capital at risk. When that evidence comes together, the investment case becomes clear, even though the outcome remains uncertain. SOXL provided one such opportunity, translating our research into substantial realized gains. Our Asymmetric Opportunity Screener makes this selective methodology available for exploration.

Explore the Asymmetric Opportunity Screener →

LMM Labs: From AI Conversations to Agentic Creation

LMM Labs has advanced from using chatbots for productivity gains to directing AI agents that build, test, and deploy cloud-based applications. The results are tangible: work that once would have taken development teams weeks or months now yields working prototypes in hours to days. Our ideas and technical judgment guide the process; agentic AI dramatically expands our ability to execute.

See agentic creation in action. Explore our growing collection of working applications:

LMM Labs Prototype Showcase →

LMM Labs Gets an Assist from ChatGPT!

The emergence of generative AI is a game changer for LMM Labs. We all have our aspirations, beliefs, fears, and doubts about where AI is going, but for us it is simply and primarily a productivity play. We have ideas and a fair amount of knowledge that we just do not have time to develop or explore. Generative AI is providing us the workforce multiplication capability to expand our horizons and make much more progress than we otherwise would be able to.

LMM Labs Looks at Blockchain

While cryptocurrencies get most of the attention surrounding blockchain, there is increasing attention being paid to blockchain as a core, enabling technology in many industries.  Financial services, telecommunications and security are a few examples.  The notion of a secure, highly distributed, immutable ledger has the potential to disrupt and change many current business practices and organizations.  As such, it deserves a deep dive into its potential.

LMM Labs Goes Algo for Alpha

LMM Labs adopts modern portfolio theory (MPT) to build well-diversified and optimized portfolios.  Covariance, correlation, the Capital Asset Pricing Model (CAPM), the Security Market Line (SML), and the Sharpe ratio, along with other quantitative methods, will be utilized to assess portfolios.  The intent here is not to take sides in the passive versus active portfolio management debate but to use cutting-edge techniques and algorithms to reduce risk and maximize return.  The core belief is that it is possible to find significant alpha and to beat the market over defined time intervals, if not necessarily in the long run.  This is in line with LMM Labs’ goal of using machine learning algorithms in the financial vertical.

The Iterative Organization

The potential for existing and emerging technologies to radically change what is considered a state-of-the-art organizational structure is enormous.  The notion of a constantly iterating, self-learning, fully automated organization that has a flat and ever-changing network structure is upon us.  This type of organization will challenge the fairly static hierarchical structures of today and provide those who adopt it with a clear strategic and competitive advantage.  LMM Labs hopes to advance the thinking and technology in this area.

© Permission granted to reproduce for personal and educational use only. Commercial copying, hiring, and lending are prohibited.  This copyright applies to The Iterative Organization and is effective as of 10/1/2018.  It is solely owned by Lee J. Farretta.  Rights will extend to LMM Labs once its LLC is approved.  All text, outlines, articles, diagrams, chapters and computer programs related to The Iterative Organization will be posted as comments to this thread, and/or stored on GitHub.

Machine Learning Initiative

LMM Labs launches a machine learning initiative.  The stretch goal is to develop solutions in two business verticals by the second half of 2019.  Between now and then, the team will survey and test existing machine learning and deep learning algorithms.  Finance, marketing, cybersecurity, and electoral politics are the focal points of this effort.  Aside from developing a core competency in machine learning algorithms, the central assertion here is that there is a large and growing addressable market for a wide variety of machine learning appliance software.  In other words, simple to complex machine learning appliances are a good idea.