Sigmoid Management

Sigmoid Management

Lian Lian founded Sigmoid Management in 2016, launching the firm with capital from his personal network and early backing from allocators who tracked his work at Point72 Asset Management. Lian had previously focused on volatility trading and convertible arbitrage within the Cubist Systematic Strategies unit, and Sigmoid was his vehicle for pushing systematic vol trading into a pure machine-learning framework. The firm operates from New York. Its core mandate combines convertible bond arbitrage with systematic equity options trading. The strategy integrates deep neural networks — including convolutional and recurrent architectures — to price volatility surfaces and identify mispriced options across global equity markets, convertibles, and corporate credit. Sigmoid trades across North American and European markets, with opportunistic exposure to Asian convertible issuance. The approach is signal-driven and market-neutral by design, seeking to isolate volatility and credit premia while hedging directional equity and rate risk. Positions span single-stock options, index volatility, and convertible bonds, with the firm known for holding positions through idiosyncratic events where its models detect structural mispricing (per Bloomberg, 2019). Sigmoid has maintained a deliberately small headcount by industry standards, operating with a tight research team of quantitative scientists and engineers. Lian retains sole portfolio authority, consistent with the firm's view that centralized risk management is essential for multi-signal vol strategies. In its early years, Bloomberg reported the firm managed over $500 million in regulatory assets (per Bloomberg, 2019). The firm has not published updated regulatory filings with a larger AUM figure since that report, and its current deployment remains undisclosed. What distinguishes Sigmoid structurally among quantitative hedge funds is its refusal to expand mandate breadth. The firm does not diversify into equities, macro, or credit in the conventional sense. Its entire risk budget sits inside volatility arbitrage executed through proprietary deep-learning infrastructure — a posture closer to a specialized trading lab than a multi-strategy platform. This focus has made it one of a small cohort of pure AI-driven vol funds that institutional allocators monitor for uncorrelated return streams.