Quick Read
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WisdomTree launched WSLS and WTAL on the Texas Stock Exchange, bringing hedge fund-style long/short strategies to everyday investors at a 0.88% expense ratio.
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Both funds rely on AI-driven machine learning models from AlphaBeta, an Israeli fintech firm WisdomTree took a minority stake in during November 2025.
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WTAL targets market-neutral returns, meaning gains come from stock-picking spreads rather than overall market direction, in both U.S. and international equities.
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On October 8, 2026, WisdomTree announced the launch of the WisdomTree Efficient Long/Short U.S. SmallCap Equity Fund (TXSE:WSLS) and the WisdomTree Global Alpha Fund (TXSE:WTAL). Both funds have an expense ratio of 0.88%.
WisdomTree says the two funds give investors access to systematic long/short strategies “traditionally associated with hedge funds” through an ETF structure. They are WisdomTree’s first launches on the Texas Stock Exchange (TXSE), which already lists ETFs from other issuers.
How a Long/Short Fund Tries to Make Money
A long position makes money when a stock rises. A short position makes money when a stock falls. A fund that holds both can try to earn from the gap between the stocks it expects to do well and the ones it expects to lag.
A long/short strategy can seek returns from relative stock performance, but its sensitivity to the overall market depends on how the long and short exposures are balanced. WSLS also retains broad small-cap market exposure.
WisdomTree has used this approach before. The new funds build on the WisdomTree Efficient Long/Short U.S. Equity Fund (WTLS), which WisdomTree launched in January 2026.
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WSLS Stacks a Stock-Picking Layer on Top of Small Caps
WSLS holds broad U.S. small-cap exposure, either through stocks or through derivatives (contracts whose value tracks an underlying asset), and on top of that core it runs a dynamic long/short “overlay,” which is a second strategy layered over the main holdings. WisdomTree’s thinking is that individual small-cap stocks have more widely spread returns, which may give its models more room to add value.