Quantitative strategies have been growing; will they eventually take over from humans?
ARE rockstar fund managers a thing of the past?
You know, the super brainy fund managers with Messiah status. Those chosen few men with their ability to pick multibagger stocks.
In the 80s and early 90s, we had Peter Lynch, Warren Buffett and Mark Mobius.
In the new millenium, with the advent of financial technology and digital disruption, could it be machines taking over instead?
Should that be the case, does this also mean that expensive actively managed funds are a thing of the past?
Over the last few years, machines are once again giving their human counterparts a run for their money.
Recall that in the mid-90s, there was a sudden emergence of quantitative funds such as LSV Asset Management and Renaissance Technologies. With the global financial crisis in 2008, many of these quant funds crashed and burn.
However, the trend of digital disruption means that some form of big data stategy is the new reality for the investment ecosystem today,
This could be the case, especially when the largest fund company in the world, BlackRock appears to be embracing machine over man in a big way.
The rise of technology and exchange-traded funds have changed the investment world. Now more than ever, normal investors have more options at cheaper fees, hence putting more pressure on the world’s biggest fund managers.
Quantitative strategies have been growing in absolute and relative terms. They’ve also recently been outperforming humans, attracting even more investment dollars. Market share controlled by machine-based algorithmic hedge fund strategies have more than tripled since 2009.
According to Novus Investments, quants have also grown relative to their hedge fund peers. They now control over 13% of total long market value for all hedge funds, where it was only 8% in 2011.
In the most significant move that the hedge management world is embracing machine over man, Laurence D. Fink, founder and chief executive of BlackRock laid out an ambitious plan in March, to consolidate a large number of actively managed mutual funds with peers that rely more on algorithms and models to pick stocks.
This is in reaction to the exodus of investors from actively managed stock funds to cheaper funds that track every variety of index and investment theme.
Based on reports, some US$30bil in assets (about 11 percent of active equity funds) will be targeted, with US$6bil rebranded BlackRock Advantage funds. These funds focus on quantitative and other strategies that adopt a more rules-based approach to investing.
In interviews, Fink was quoted as saying: “The democratisation of information has made it much harder for active management. We have to change the ecosystem — that means relying more on big data, artificial intelligence, factors and models within quant and traditional investment strategies.”
So what does this mean for the former active fund managers?
The report also said that 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.
When it comes to fund management, you’d be surprise nowadays that more and more investments are being put into technology, rather than fund managers.
Finding a good fund manager nowadays is increasingly hard. This is especially with the huge influx of global small cap companies, how can any one person write such detailed analysis on the thousands of stocks available?
Now, the main reason a quantitative process is pursued is because it offers tighter risk control, more stable returns, and better overall performance
Also, a fund manager’s input isn’t really needed. The quant method depends on significantly less information than the typical qualitative approach.
Gerald Ambrose, managing director of Aberdeen Asset Management Sdn Bhd says that some 10% of Aberdeen’s global asset under management now consist of quant funds.
“While our team in Kuala Lumpur is still predominantly bottoms up and long term, this is a service that the global team is providing. I wouldn’t say you should go 100% either way, Our strategy is to be a supermarket and offer all the available products to our clients,” said Ambrose.
He said that the good thing about quant or passive management is that it has brought in more people who previously wouldn’t have participated in capital markets.
“Yes passive investment has brought fees down. That is the good thing. I think there is room for quants in the market. But there are limitations too,” he said.
He feels that passive management looks mainly at historical data.
“When you steer a ship, you can’t be steering from the back. How does a computer read things such as transparency, corporate governance and future profitability? So with quants, what you get is a lot of these funds flocking to the same big cap stocks which are highly liquid and have a certain market cap size. I personally think this is one the reasons why there is the case of overvaluation in many of the big caps. Passive investing many be sowing the sees for its own downfall,” he says.
Affin Hwang Asset Management Head of Equity Strategies & Advisory Gan Eng Peng says that quant funds are not common in Malaysia.
“There might be some marketing of offshore products towards high net worth individuals but it is also not a big market at this point in time. It will only take off in a bigger manner if the products come through and show performance success,” he says.
In the US now, it accounts for around 15-20% of actively managed monies and growing.
Gan said that the growth of the quant fund there indicates value add positive performance experience and it is a viable product offering.
He added that quant funds have come a long way since the days of Long Term Capital Management (LTCM) fund house – this was founded by one of the best investment brains in the industry as well as founders of modern finance theory.
“They used massive leverage to amplify small but consistent returns. This eventually ended badly when the market liquidity that was needed for the fund to operate disappeared during extreme market move. The collapse of LTCM created a major speed bump in global markets,”
“The lesson from this is that quant investing uses historical statistics to derive future returns - sometimes history neither rhymes nor repeats. This usually happens during extreme market situations. A kill switch is needed in such instances or the portfolio can suffer unaccountable losses,” he said.
Gan says that as traditional investing methods rely on human input or intuition, this can be extremely unreliable and hard to reproduce a consistent result.
“Quant investing takes away this emotional decision making process and tries to generate a consistent performance return. However, the result is only as good as the input (garbage in, garbage out), the money making model/algorithm, the ability of the algorithm to evolve with changing market conditions,” he says.
“For us, we believe that as long as businesses are run by people, the traditional form of investing where there is judgement needed for business viability, dynamism, integrity and prospects, this method will prevail over quant investing,” says Gan.
Rakuten Trade Research, vice- president Vincent Lau said that quant investing is another form of alternative investment strategy which is making a huge comeback globally with many hedge funds and global investment banks such as Goldman Sachs building up their quant teams. Hedge funds are also being set up based purely on quants.
“The benefits would include diversifying one’s portfolio to capture major events such as Brexit, and the Trump victory that traditional investing or stockpickers may not be able to emulate.
“However, there are cases of quant trading that has caused flash crash of stock, currencies and indexes such as the famous ‘Quant Quake’ in 2007, and Knight Capital’s famous US$440mil losses in 30 minutes in 2012 due to faulty trading algorithms,” said Lau.
Lau’s view is that quant creates volatility, which can be good for traditional investors.
“Nonetheless I feel that quant alone cannot outperform traditional investing. However a combination of both would perhaps be a better approach. For man and machine to work hand in hand to produce the best possible result,” said Lau.
In Malaysia, Lau is of the opinion that a lot of investors are still very much skewed towards traditional investing, although they are slowly warming up to idea of Quant investing and Robo advisors.
“Unless big pension funds such as KWAP and the EPF starts to allocate funds into Quant based funds, I feel many are still adopting the wait and see attitude,” said Lau.
He feels that quant investing lacks the emotional appeal for the typical retail investors who still prefer to read analyst and reports.
“A lot of stocks here are also very news driven, and the quant method may not be able to capture this sort of upside,” he said.
For now, quantitative stock selection are able to pick stocks based on the key parameters set by the system, and these parameters typically consist of profitability, valuation, cash flow, growth, capital allocation, price momentum, liquidity among others.
Nowadays with technology being so advanced, quant systems are even able to deduce transcripts of company meetings and include those findings in the stock selection process. Corporate governance factors are now also being incorpoated into quant methods.
“Quantitative management allows for objectivity and consistency. You can analyse 10,000 stocks at one go, whereas the traditional fund manager will only be able to have in depth info on 20-50 stocks at best,” said one fund manager.
Another difference between quant methods and active management is in terms of the information ratio. This measures a fund manager’s ability to generate excess returns.
A traditional and active stock picker fund manager would have a high information ratio because he is experienced and highly skilled at selecting individual outperforming stocks, hence being able to take concentrated bets.
The same cannot be said about quantitative methods. They do not have that same sort of skill as the active fund manager.
Therefore quant methods typically do not take on concentrated bets. They take on large bets on a huge number of stocks and therefore also diversifying away risk for the investor.
That is also why quantitative stock selection is more suited when the stock universe is very large.
This is especially apparent with more global small cap and emerging market strategies, quantitative methods are becoming increasingly important purely because of the massive resources that are needed if done using the traditional approach.
More money in quants
Venture capital funding of financial technology startups is driving the pursuit of ‘automated truth from data’ and also fostering the growth of the big-data ecosystem.
Big data is a disruptive force in financial investing due to the exponential growth in asset-price-relevant information.
Funds are investing heavily in technology and risk management tools, adding a ‘quant’ element to augment their existing investment process. These quantitative methods help with timing, sizing, and risk management.
Technology, and not how great the stocks pickers are, is the new reality of the investment world.
Thus, can traditional big fund managers such as Fidelity, Pimco, Franklin Templeton and Aberdeen continue to charge premium on the pretext that they are superior in outsmarting the broader market?
Over the last few years, more and more funds are abandoning expensive mutual funds for better performing funds that track various indexes at a fraction of the cost.
Last year, for example, US$423bil left actively managed stock funds and US$390bil poured into index funds, according to Morningstar. Of that amount, Vanguard captured US$277bil, nearly tripling the amount that went to its nearest rival, BlackRock.
According to data from Morningstar, only 11% of BlackRock’s actively managed equity funds have beaten their benchmarks since 2009. Since 2012, $27.5 billion has left BlackRock actively managed mutual funds, per Morningstar data.
Mark Wiseman, global head of active equities at the firm, was quoted as said: “Traditional methods of equity investing are being reshaped by massive advances in technology and data sciences. At the same time, client preferences are shifting, focusing not just on outcomes but on how both performance and fees impact value.
“Asset managers who simply use the same techniques and tools from the past will limit their ability to generate alpha and deliver on client expectations. The steps we are taking are an extension of the strategy we announced in 2016 to combine our quantitative and fundamental investment teams into a cohesive active equity investment platform that leverages the full scale and resources of BlackRock.”
A word of caution
It is widely believed that the primary reason quant funds stumbled badly beginning in mid-2007 was the correlation of stocks between managers, compounded by leverage.
Quants are often attracted to the same stocks, as the overlap values are high.
Common metrics of value, momentum and liquidity led quant fundss to hold similar stocks. Thus, when stocks began to sell off, many quant managers found themselves racing for the exits at the same time.
The use of leverage employed by some quant funds only made the problem worse.
Thus before 2009, nearly everyone in the quant industry were using the same factors. When these funds needed to unwind their position, there wasn’t anyone on the other side of the trade.
Thus when the financial crisis unfolded in 2008 and 2009, these same metrics used by quant managers caused them to be attracted to the same beaten down financial stocks - which also meant they were catching a falling knife.
Over the last few years, quant funds have improved their models to take into account the markets upward and downward trends.
Going forward, it is not all smooth sailing for quant managers. There are issues of correlating markets, style rotation, fundamental market shifts, and insufficient liquidity.
They also point to “too many people using similar models and the same data” as a critical issue facing the industry.
Innovation or the identification of new or unique model factors, will be the most important strategy for improving performance.
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