AI Startup Vestun Launches Hedge Fund Navigating Market Turbulence

Swiss-based AI company Vestun opens its market agnostic hedge fund strategy to new outside investors.

Vestun

Swiss-based AI company Vestun opens its market agnostic hedge fund strategy to new outside investors.
Swiss-based AI company Vestun opens its market agnostic hedge fund strategy to new outside investors.
Swiss-based AI company Vestun opens its market agnostic hedge fund strategy to new outside investors.

Zurich, Switzerland, Oct. 07, 2020 (GLOBE NEWSWIRE) — Vestun, a Swiss-based financial and technology company has now opened the launch of its hedge fund to new outside investors. The firm which until now has been only managing its own capital announced that its investment vehicle will open to institutional investments including banks, multi-family offices and asset managers within certain jurisdiction. 

The company flagship strategy trades liquid US equities systematically. The strategy is designed to autonomously adapts its portfolio and risk exposure dynamically to the prevailing market conditions. In contrast to traditional systematic strategies, Vestun’s approach does not rely on statistical rules and historical events to generate signals. Instead, the strategy aggregate domain specific intelligence with datasets that individually perform in their own economics while remaining uncorrelated against each other.

Chayan Asli, the founder and CEO of the company commented: “Nowadays, everyone has access to the same financial datasets and machine learning models. If everyone uses the same smart systems with the same recipe for success, it will undermine the competitive advantage obtained by using computer-driven models to invest. In our belief, relying on signals generated from statistical rigid rules and backtesting history are not sustainable for delivering consistent long-term market outperformance”.

Over the past decade, there has been an increasing number of funds involving data scientists or so-called “quants” using machines to build large statistical models. While they are usually more disciplined than their discretionary counterpart, the problem is that they tend to remain static. These models are usually not able to perform