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Simplify Asset Management on Tuesday introduced a new exchange-traded fund (ETF) that leverages machine learning to identify long and short investment opportunities by analysing hundreds of fundamental factors.
The Simplify Wolfe US Equity 150/50 ETF (WUSA) takes a long position in about 250 stocks and a short position in about 150 stocks. These strategies represent two key methods in the stock market.
What are long and short positions?
A long position occurs when an investor buys shares or assets expecting their value to rise over time. The goal is to sell later at a higher price, making a profit. For instance, if you purchase 100 shares of a company at Rs 100 per share, and the stock price rises to Rs 150, selling the stock would generate a Rs 50 per share profit.
A short position, on the other hand, involves borrowing shares and selling them with the expectation that their price will drop. The investor then buys back the shares at a lower price to return them, keeping the difference. However, short selling carries more risk, as rising stock prices can lead to substantial losses.
How does the ETF select its stocks?
WUSA’s stock picks are determined by a multi-factor machine learning model developed by Wolfe Research. This algorithm evaluates more than 300 factors and thousands of data points to detect patterns that could predict price movements in securities.
“As the algorithm learns, it identifies US equities with the highest and lowest forward expected returns,” said David Berns, co-founder and chief investment officer at Simplify in a press release. “These equities then form the long and short positions in the WUSA portfolio.”
Berns added that the ETF’s 150/50 allocation structure expands the potential returns—both positive and negative—that WUSA could capture.
Simplify’s growing ETF lineup
WUSA is the latest addition to Simplify’s existing lineup, which includes the $1.3 billion Simplify MBS ETF (MTBA) and the $1.2 billion Simplify Volatility Premium ETF (SVOL). Simplify currently manages about $6 billion across 30 funds.
Bloomberg reported that the launch of this ETF is part of a wider trend of research firms and major asset managers diving into the ETF space, which has seen rapid growth on Wall Street. Economists like Nouriel Roubini, Fundstrat’s Tom Lee, and Rob Arnott from Research Affiliates are just some of the prominent names getting involved in ETFs or planning to.
Wolfe Research, which serves as WUSA’s subadviser, is no stranger to the ETF space. The firm’s machine learning model also plays a role in Simplify’s Market Neutral Long/Short ETF (EQLS). Wolfe’s team, led by former Deutsche Bank quant Yin Luo, consists of over 30 analysts covering more than 750 companies.
“If I were them, I’d see this as a chance to diversify their business lines,” Todd Sohn, an ETF strategist at Strategas told Bloomberg. “ETFs are a growing segment within asset management, and teaming up with a firm that already has established ETF roots can be easier than starting from scratch.”
What is an ETF and a Quant ETF?
An exchange-traded fund (ETF) is a collection of investments, such as stocks or bonds, which can be bought and sold like a single stock. ETFs often have lower fees than other funds and offer diversification by investing in multiple assets at once.
Quant ETFs, or quantitative exchange-traded funds, take this a step further by using algorithms and data analysis to make investment decisions. Instead of relying on human fund managers, these funds follow pre-set rules based on factors like price trends, company fundamentals, and risk management.
How do Quant ETFs work?
Quant ETFs operate based on systematic, rules-driven approaches to identify investment opportunities. According to ETF.com, these algorithms examine factors such as:
Price trends: Identifying patterns in price movements.
Company fundamentals: Assessing data like earnings, revenue growth, and valuations.
Risk management: Evaluating risks and adjusting holdings.
Market conditions: Factoring in momentum, volatility, and other influences.
First Published: Sep 25 2024 | 2:01 PM IS