How smart is Smart Beta?
How about Nobel prize smart?
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The world of investment — like every other niche in our economy— is undergoing a massive disruption powered by big data, AI, and increasingly sophisticated algorithms. This has been driven in part by better approaches to analyzing risk and diminishing the impacts of volatility. For some time, investment has been migrating out of actively managed funds to so-called passive funds.
Active funds rely on stock pickers making investment decisions based on handicapping stocks. The degree to which a stock exceeds the market is called the alpha.
Passive funds pull from a representative basket of stocks that index a market and track the market, without trying to beat it. The measure of a stock’s volatility is called its beta, so passive funds are organized around the beta: betting that the market or market segment will rise, but if it doesn’t, at least the downside is minimized.
The chart below shows transitioning to passive from active funds have become the major part of strategy for investors. in 2016, $423 billion moved from active management, and $390 billion moved into index funds, according to Morningstar. That’s a massive shift.
In recent years, a third option has emerged, called smart beta, and even some of the most vocal supporters of passive investing have started to adopt its techniques. In 1973, Burton Malkiel, a Princeton economist published A Random Walk Down Wall Street in which he famously debunked active investing, saying
a blindfolded monkey throwing darts at the stock listings could select a portfolio that would do just as well as one selected by the experts.
Malkiel was on the board at Vanguard a few years later when the firm introduced the first passive index fund, and now is the chief investment advisor for Wealthfront, which is employing so-called smart beta approaches that can outperform passive index funds.
The underlying science is based on the work of various Nobel laureates — William F. Sharpe and Eugene F. Fama — who discovered that some characteristics of stocks — so called factors — correlate with outperforming the market. One factor is momentum, where stocks that are doing better than the index will likely continue. Likewise, stocks with high dividends tend to outperform.
Here’s a chart showing factors for the period November 1975 to June 2015 for MSCI World fund:
Smart beta is partly passive, and partly active. A fund’s algorithm (or rules base) can be transparently shared, just as in traditional passive funds. However, because the selection of stocks is driven by the particular factors relevant to the market or market segment, smart beta can outperform the market, like actively traded funds can. Since the algorithm is (relatively) unchanging, there is no management overhead, although stock rebalancing can take place at whatever frequency desired.
And, of course, behind all this is big data and data analysis. The Nobel laureates did the foundational research to establish that smart beta really is smart. And now, that is becoming big data and AI smart.
Different funds can crunch the enormous data pools of stock information applying already well-understood factors, like momentum, but at the same time they can employ AI to look for other factors that might correlate with higher performance, factors that aren’t found in companies’ quarterly financial reports. Indeed, funds are using other factors already, like social media coverage, or other market ‘mood’ indicators. I read of a company that was analyzing the height of the floating tops of oil storage tanks from satellite images to estimate energy sector factors. Another tracks the numbers of cars in supermarket parking lots. Anything can tell a story about beta.
As big data and data analysis have revolutionized the world of investment, we are seeing a predictable result: less people, more AI and algorithms. In March, BlackRock announced it was transitioning over $30 billion in actively traded funds to smart beta:
Landon Thomas Jr, At BlackRock, Machines Are Rising Over Managers to Pick Stocks
On Tuesday, BlackRock laid out an ambitious plan to consolidate a large number of actively managed mutual funds with peers that rely more on algorithms and models to pick stocks.
The initiative is the most explicit action by a major fund management firm in reaction to the exodus of investors from actively managed stock funds to cheaper funds that track every variety of index and investment theme.
Some $30 billion in assets (about 11 percent of active equity funds) will be targeted, with $6 billion rebranded BlackRock Advantage funds. These funds focus on quantitative and other strategies that adopt a more rules-based approach to investing.
“The democratization of information has made it much harder for active management,” Mr. [Lawrence] Fink [founder and CEO of BlackRock] said in an interview. “We have to change the ecosystem — that means relying more on big data, artificial intelligence, factors and models within quant and traditional investment strategies.”
As part of the restructuring, seven of BlackRock’s 53 stock pickers are expected to step down from their funds. Several of the money managers will stay on as advisers. At least 36 employees connected to the funds are leaving the firm.
And more layoffs to follow, as BlackRock and other firms move on the inexorable path to smart beta, and AI finding new factors by combing through ever larger data sets.
Other Big Data News:
A better technique to predict the path of severe weather has been developed by a group of academics and Accuweather forensic meteorologist, James Wistar, based on tracking the so-called ‘bow echo’ in radar images, which are associated with violent winds.
Erin Casey, New research leverages big data to predict sever weather
“In a line of thunderstorms, a bow echo is one part that moves faster than the other.” As the name suggests, once the weather conditions have fully formed, it resembles the shape of a bow. “It can get really exaggerated,” he said. “It’s important because that’s where you are likely to get serious damage, where trees will come down and roofs get blown off.”
The research focused on automating the detection of the bow wave, based on continually monitoring radar imagery from NOAA. The goal is to give people a 10, or 15 minute headstart on conventional approaches.
Wasn’t that the plotline of ‘Twister’?
Car manufacturers are investing in chip development, AI, and quantum computing — areas formerly way outside their competency — in order to gain the computing capacity needed to deal with the torrent of big data as tomorrow’s digital and driverless cars come on line.
Jack Ewing, BMW and Volkswagen Try to Beat Apple and Google at Their Own Games
Cars will need to constantly communicate, absorbing and analyzing information from thousands of vehicles at once, to make decisions to smooth traffic flow, save fuel and avoid hazards.
That presents a huge new challenge for companies traditionally focused on manufacturing.
“The processing power needed to deal with all this data is orders of magnitude larger than what we are used to,” said Reinhard Stolle, a vice president in charge of artificial intelligence at the German automaker BMW, which is building a data center near Munich that is 10 times the size of the company’s existing facility. “The traditional control engineering techniques are just not able to handle the complexity anymore.”
Big data is a challenge for all automakers, but especially German companies because they target affluent customers who want the latest technology.
We’ll have to see if VW will actually need to create and manage its own quantum computer data centers. But it is clear that the volume of data being generated brings its own challenges, as BMW has discovered. The company — even with only 40 prototype driverless cars — is generating so much data that it can’t be processed in the cloud. This is one of the reasons Bosch thinks it must make its own chips, though, since procession of all the pertinent data will have to take place in real time, in the cars’ brains.
