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Can Big Data Save Active Funds By Turning The Art Of Stock Picking Into A Science?

active-funds

“You may not get rich by using all the available information, but you surely will become poor if you don’t.” 

- Jack Treynor, former editor of the CFA Institute’s Financial Analysts Journal.

Traditional fund and portfolio managers seem to have lost their stock picking knack. They aren’t doing well in comparison with cheaper index-tracking alternatives and investors are now increasingly voting with their feet, and capital.

The actively managed investment sector is in arguably the stickiest moment of its history. But it is starting to fight back and looking at how to improve its recent record.

One of the great hopes for active investment management is to leverage the cutting edge technology of AI and big data-powered ‘quantamental investment’. Quant investors use algorithms to parse reams of traditional and alternative data to isolate patterns that inform their investment decisions.

Traditional fund and portfolio management companies are starting to attempt to combine quantitative investment data and approaches with their human-centric fundamental analysis. It is early days for this hybrid approach but many in the industry see it as the future of actively managed funds and portfolios.

Can adding big data and machine learning into the stock picking mix see actively managed funds start to outperform indices again? Or is the technology a fad fuelled by marketing that has little more substance than the kind of ‘guaranteed returns’ algorithmic trading software promoted to naïve wannabe day traders?

Traditional Stock Pickers And The Actively Managed Investment Sector Is In Trouble

Actively managed funds are seeing alarming outflows of capital as investors lose faith in traditional stock pickers. Scandals such as the closure of the Woodford Equity income fund certainly haven’t helped but the malaise runs deeper.

At the core of the sector’s woes is the stark reality that very few actively managed funds have consistently managed to beat their benchmark index. That’s a trend that has strengthened over the eleven years since the recovery from the international financial crisis kicked in.

Hedge funds

Source: Financial Times

There is an argument that cheap, index-tracking funds have benefitted greatly from the record-long equities bull market that has now rumbled on unabated for those 11 years. Indices have shown strong, consistent growth over the period, making it harder for stock pickers to beat them, especially when the higher fees of actively managed funds are taken into consideration.

But investors are not convinced that is the only reason why traditional stock pickers who focus on fundamental analysis of stock valuations are floundering. Index gains are the average of all of their constituent stocks, with the poorest performers dragging down the gains of the strongest performers. Why can’t stock pickers effectively filter out at least some of those poorer performers for a benchmark-beating return for their funds?

The fact that S&P data indicates a paltry 12% of actively managed US equity mutual funds have surpassed their benchmarks over the past decade, suggests there is something deeper broken in how fundamental analysis arrives at its conclusions. The performance of UK funds over the same period has not been materially better.

The amount of capital being lost from actively managed funds and portfolios to passive index-tracking alternatives is now putting serious pressure on the sector. Morningstar’s Ali Masarwah puts it succinctly with the statement:

“It has been a terrible year for active managers”.

Just how terrible is illustrated by the €15.8 billion investors withdrew from Standard Life Aberdeen, the UK’s largest-fund house measured by assets. Schroders, the country’s largest fund house by market capitalisation, lost out on €6.3 billion – the largest outflow from its Europe-based mutual funds in more than a decade. 

Plenty of other big names also saw investor capital taking off, included Invesco, M&G, which recently listed on the London Stock Exchange, BNY Mellon and Franklin Templeton, US managers with significant operations in London. Boutique houses were also not unscathed, with Merian, the £22bn manager co-founded by City veteran Richard Buxton, and £28bn house Artemis hit by large redemptions.

Had it not been for last year’s strong overall stock market returns boosting performance across funds, the situation for active managers across the UK and Europe would have been much worse. 

Morningstar data shows index trackers based in the UK attracted £19 billion in net inflows last year, while active funds had outflows of £32bn, their highest level on record. At the end of 2019, passive funds made up eight out of the 10 largest funds in the UK, compared to just three a year earlier. Funds run by BlackRock and Vanguard tracking the FTSE UK All Share displaced formerly popular active funds such as SLA’s Gars, M&G Optimal Income, BNY Mellon Real Return and Invesco High Income.

The fallout is an increasing number of companies known as traditional fundamentals-based stock pickers turn to quantitative investing technology in an effort to reclaim their edge.

What Exactly Is Quant Investing?

Quant investors use data mining techniques based on machine learning to inform investment decisions and is currently one of the most influential trends in investing. Quandl, the quantitative investment data firm owned by Nasdaq, confidently states on its home page:

“The biggest opportunity for investors in this decade comes from the signals buried in the data generated by the digital economy. Alternative data is the deepest, least utilized alpha source in the world”.

But what the quantamental approach to investing means in practice varies. It can range from automating back-office operations from record-keeping to compliance. Or, it can involve using software to improve risk management tools and portfolio analysis. However, quant investing is most commonly associated with mining alternative data sources to improve research and analysis.

‘Alternative data’ is also a hugely broad term. Quandl, for example, tracks private jet flight data and last April tipped off clients that a Gulfstream V jet belonging to Occidental Petroleum had arrived at Omaha airport. The company was, at the time, subject to a hostile takeover bid and had sent a delegation to Nebraska to talk ‘Sage of Omaha’ and Berkshire Hathaway CEO Warren Buffet into a rescue package.

Two days later, Occidental announced a $10 billion investment from Buffet, which allowed it to fend off the unwanted bid. Investors that had received the tip-off on the private jet’s landing in Omaha had advance warning something was in the works, providing them with a potential trading opportunity.

That is the kind of data that will only occasionally offer investors insight into something significant that might happen. But access to a wide range of alternative data points can mean regular clues that can help inform investment calls.

Other kinds of alternative data that quantamental investors use include scraped product reviews, social media noise and web traffic, to credit card purchase data, digital shipping records, email purchase receipts and even mobile phone locations and satellite imagery.

Ravit Mandell, who heads up the new data unit at JPMorgan’s $1.9 trillion asset management business says:

“Data is a big disrupter. Storage and computing power are really cheap now. So why not try to collect all the information available in all languages from around the world?”
 

JPMorgan Asset Management has invested in a natural language-processing dashboard. This continuously parses data from millions of documents and textual data sources, including investment bank research, social media, earnings call transcripts, online job boards, news stories, and regulatory filings. This data is then organised visually in a way that can offer its fund managers quick insights at a touch of their keyboards.

Do Quantamental Investment Techniques Actually Work?

The million, or billion, dollar question is how often mining a wide range of alternative data sources actually offers actionable information? With so much data, from so many sources, the hardest part is ordering it and combining the ‘noise’ in a way that is actually useful.

Some money managers are already privately commenting they have been left disappointed by the return on their investments in highly paid programmers and expensive alternative data set. And when they do discover something they can exploit amongst all that data, the advantage is often short lived as rivals cotton on. For example, data on credit card purchases is now so commonly used it is now less a competitive advantage than a ‘must’.

As Ms Mandell reflects:

“Everyone knows these data sets exist now. The challenge is taking it and creating a meaningful signal.”
 

Other data sets, such as satellite imagery and geolocation data from smartphones, are promising but expensive. It is often difficult to turn such information into actionable, profitable trades. There are also privacy concerns some investors are not comfortable with attached to exploiting such data. Other kinds of information, like the private jet data that revealed Occidental’s bid for an investment from Berkshire Hathaway, can be only occasionally useful.

Quandl CEO Tammer Kamel, warns that alternative data is in danger of being over hyped:

“It’s powerful, but it’s not as easy as people initially think it is. There are a few dozen firms that are having real and sustainable success with alternative data. But I don’t think the club is expanding very quickly.

Is The Answer Combining Quant and Fundamental Investing?
 

“For three decades the hedge fund industry largely took two different paths — fundamental and quantitative — and the two never really crossed. Now we’re seeing the two paths slowly coming together.”

The reflection on the gradual merging of fundamental and quantamental investment approaches was recently made to the Financial Times by Matthew Granade, a senior executive at Point72, a large US hedge fund.

It ties in with a Morgan Stanley poll of 400 big investment clients at a conference last year. 51% responded that machine learning was now either a component of or central to their investing process. In 2016, a similar poll in 2016 showed just 27% were leveraging machine learning in their investment processes.

Only 13% of the investors polled last year said machine learning-based quant investing techniques were not being researched. Three years earlier that figure stood at 44%.

alternative data

Source: Financial Times/Morgan Stanley

Almost everyone in the asset management industry expects to see increased reliance on quantitative techniques in the future. The question that remains is whether this will give stock pickers a genuine advantage or prove to be an expensive dead end.

There are, however, early adopters who appear to be making the hybrid approach work for them. In 2010, Man Group acquired GLG, a traditional fundamentals-based investment house manned by ‘discretionary’ traders.

Two years ago, GLG was moved from its Mayfair townhouse offices into Man Group’s modern offices overlooking the Thames. There were cost-cutting factors at play but the main motivation is said to have been the desire to ‘mesh’ GLG’s traditional approach with Man Group’s quant arm – AHL.

Now investment decisions taken by GLG traders are also informed by alternative data points provide by AHL. Teun Johnston, Man GLG’s chief executive

Teun Johnston, the chief executive of Man GLG comments on the development:

“I wholeheartedly believe that discretionary investing will continue to thrive, as there are many things that humans are much better at than machines. But I believe that quantamental investing will grow, and my job running a discretionary business is to ensure that it continues to thrive.”

The tricky part, as Point72’s Mr Granade reflects, is achieving a harmonious balance between traditional and quant investors working together:

“It’s much harder to do than most people realise, mostly for cultural reasons. Producing insight from data is not easy but the biggest challenge is figuring out how to drive collaboration and understanding between the portfolio managers and data scientists.”

Another company that has adopted the hybrid approach is Maverick Capital. Run by Lee Ainslee, who established a reputation as one of the City’s star stock pickers, Maverick hired its first quantitative analysts in 2006. Mr Ainslee admits he was initially sceptical but has been won over.

“Today quant plays a role in virtually every part of our investment process . . . It has helped us be better prepared for a much tougher environment.” 

He is convinced that, in time, investors that do not adopt a hybrid approach that leverages both fundamental and quantitative analysis, will not survive:

“We’re seeing some benefits, but we’re still in the early innings. While I don’t think we’re going to read that Warren Buffett has hired a quant team tomorrow, in the future if a fundamental investor doesn’t develop such capabilities I believe they will be at a competitive disadvantage.”

There will almost certainly be a lot of money thrown at pursuing what turn out to be expensive red herrings when it comes to active stock pickers attempting to harness the alternative data sources that quant investing makes use of. But data scientists will continue to learn which data points, and their multiple potential combinations, do yield valuable insights.

Like the traditional car makers today seeing their profits savaged by the investments they are making in pursuing the development of mass market EVs, many will feel that the expense that developing quant investment capabilities incurs is painful but necessary if they wish to survive and flourish in the future.

Not all will manage the transition successfully. Some will not even attempt it, betting on their scepticism that quant investing is more hype and marketing than anything else and will be found out the next time markets turn. There is no guarantee they won’t be proven right. But given the rapid gains being made in machine learning and data science, it’s a brave position and one late, grudging and non-adopters will live or die by.

Risk Warning:

This article is for information purposes only.

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