Information technology has changed the very nature of the financial markets, sparking a FinTech revolution that cuts across traditional banking and investing to deliver new ways of accessing the world of finance.
Artificial Intelligence (AI) powered by big data analytics and machine learning has fast become the go-to method for buying and selling assets on the open market. The year 2016 has seen the rise of the artificially intelligent hedge fund, a trend that will only intensify as machine learning and big data expand in size and capability.
The buzz surrounding AI has existed for several years, but has only recently taken off as an investment vehicle. Established asset managers and hedge funds are pouring money into emerging technologies and new data management techniques to stay ahead of the yield curve in an increasingly volatile international market.
In the market of Exchange Traded Funds (ETFs), AI uptake has been slow, as many market capitalization and price-weighted indices still rely on traditional methods. The ETF market has expanded rapidly over the past decade and will continue to do so for the foreseeable future. As indexing methodologies grow and evolve, many more asset managers will be keen to explore AI capabilities for boosting portfolio growth and diversifying across markets.
BUZZ Indexes: Early AI Adopter
To say that BUZZ Indexes is an early adopter of AI capabilities would be an understatement. BUZZ uses social media’s big data analytics to identify the best investment opportunities in the market. According to the BUZZ Indexes website, online investment discussions have grown 500% over the past three years. Within the context of big data, that’s 500% more ways to convert raw information into investing opportunities.
The BUZZ Social Media Insights ETF (BUZ ) tracks which investments are trending on social media through the lens of artificial intelligence and machine learning. Index creator Jamie Wise told ETF Database earlier this year that the BUZZ ETF differentiates itself from the pack by going beyond simplistic keyword analysis to capture the way people are thinking about brands from both investment and business perspectives.
Jamie Wise adds, “BUZ is the only investible solution that provides investors with exposure to a portfolio of securities of large-cap U.S. equities which exhibit the highest degree of positive investor sentiment. Concentrated positive sentiment positioning based on insights derived from social media’s big data, that’s what BUZ is all about.”
The ETF’s methodology utilizes big data analytics to sift through social media, uncovering the average total number of online mentions for U.S. listed equities over the previous four quarters. The top 125 to 150 securities with a market capitalization of at least US$5 billion and average daily trading volume of at least $1 million comprise the eligible investment universe. BUZ’s proprietary methodology then assigns each of the 125 to 150 securities a unique Insight Score. Securities with the top 75 Insight Scores are included in the index. Securities are weighted by Insight Score and the max weighting is 3% per holding.
Currently, there are no other ETFs that utilize AI methodologies to pick holdings other than BUZ. For more details on how the BUZZ Index is using social media to outperform the S&P 500, read “BUZZ Index: Using Social Media Sentiment to Outperform the S&P 500”.
Magha CGI 30 Index: Another Case of AI in Action
The Magha CGI 30 Index, which is designed to tap into large-cap U.S. equities with an average market cap of about $150 billion, also utilizes AI for making investment decisions. While the index has outperformed the S&P 500 over the last two years, it remains untracked by the ETF market. According to Shaunak Khire, Partner at Magha Holdings, that will soon change.
“I think AI will ultimately make financial products more accessible,” Khire told ETF Database in an exclusive interview in April 2016.
“The great thing about the technology behind AI, neural networks, and supervised or reinforcement learning approaches is that you now have an entity whose goal is sort of predefined. This goal is to allocate capital as efficiently as possible while maximizing returns.” Khire adds. “It can do this way more quickly than is remotely feasible for a team of humans. Therefore, I believe over the next three to five years, most asset managers will start having people to oversee an AI’s investment strategy and making sure there are no ‘fat finger’ trades for an algorithm as opposed to actively investing in the markets.”
Already today, several ETFs that track underlying AI technologies exist, such as the iShares Exponential Technologies ETF (XT ), the Global X Robotics & Artificial Intelligence Thematic ETF (BOTZ ) and the Robo-Stox Global Robotics and Automation Index ETF (ROBO ). Artificial intelligence technology is improving on a daily basis. Here we have an article that describes the best ETFs to capitalize on the rise of artificial intelligence. These ETFs track companies that invest heavily in AI technology.
To find more unique technology and related ETFs, use our ETF Screener. Filter ETFs using criteria such as asset class, sector, region, liquidity and expenses, to name a few.
For more detail on the Magha CGI 30 Index, read our Q&A with Shaunak Khire, Partner at Magha Holdings, which is a FinTech company that utilizes cutting edge technology with a focus in finance.
For a full list of Artificial Intelligence ETFs, click here.
The Bottom Line
Emerging technologies such as AI, big data analytics and machine learning are creating new possibilities for ETF investors. This market is expected to expand manifold as fund managers continue to explore data-intensive strategies. For information on new ETFs, including ETFs that utilize AI for picking their holdings, visit our ETF Launch Center to stay up to speed on the latest developments in the market.