Simplify Asset Management launched the Simplify Market Neutral Equity Long/Short ETF (EQLS ) on the New York Stock Exchange. The fund uses a machine learning stock selection model to deliver an equity long/short portfolio.
EQLS invests in baskets of global equities, primarily through total return swaps. These swaps provide the returns, long or short, of a basket of common stocks. Additionally, Simplify uses a multi-factor quantitative ranking system powered by machine learning to select the companies in these baskets.
The swaps provide the fund with equity exposure of approximately 200% long the stocks of companies exhibiting positive performance factors, and 200% short the stocks of companies exhibiting negative performance factors.
Furthermore, Simplify has also designed EQLS to have a dynamic de-leveraging strategy. This helps avoid severe drawdowns, which differentiates the fund from other market neutral approaches.
“Simplify has developed a strong reputation for bringing innovation into the ETF world,” said VettaFi’s head of research Todd Rosenbluth. “It is great to see them expand their lineup.”
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A New Way to Manage an Equity Long/Short Portfolio
Market neutral strategies aim to offer diversification benefits and compelling return profiles. However, these strategies have often “disappointed,” said Simplify’s co-founder and CIO David Berns. They miss “significant moves to the upside and [are] slow to react to downward pressures.”
Berns added that, with EQLS, the firm has “sought to update how an equity long/short portfolio can be constructed and managed.”
“There is a major shortage of true alternative strategies in the ETF market,” said Simplify’s chief revenue officer Brian Kelleher. “Equity long/short strategies have proven their worth in institutional portfolios to help diversify portfolios and provide attractive absolute returns.”
Kelleher added that machine learning and quantitative investing have driven “successful investment strategies for decades.”
“The rising awareness of AI has brought a broader understanding of harnessing large data models for specific purposes," Kelleher said. "As a result, the timing of our launch is opportune.”
Berns added: “Advancements in machine learning allow for entirely new approaches to detecting patterns and translating those patterns into formulas used to forecast securities prices. That technology, coupled with the use of swaps, allows EQLS to stay true to its mission and to do so with greater capital efficiency and an added income component via gains on the swaps.”
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