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Portfolio – The future is algorithm trading and big data analytics whether it’s a bull or bear market in 2019

Mon 31 Dec 2018

Algorithm trading is likely to remain at the forefront of the investment industry worldwide and it has even been blamed for some two weeks of wild volatility in US stocks in the last two weeks of December 2018.

But this is missing the point. Some of the biggest investment funds are sticking to algorithm trading and upgrading them (with improvement in technology, machine learning, artificial intelligence, etc.) and are steadfast that this strategy will continue to be their trading edge for clients for the future.

And it doesn’t matter whether it will be a bear market in 2019 or a bull market resumption.

According to Pensions & Investments, an international periodical of money management and Hedge Fund Research, an established global leader in the indexation, analysis and research of the global hedge fund industry, the total hedge fund industry capital globally have increased by USD20.6 billion to a new record of USD3.235 trillion in 2Q2018.

And despite the general weak markets in 2018 and especially after 2Q2018, leading to investor outflows, hedge fund performance still drove the total industry capital to a small net increase of $8.4 billion, ending the 3Q2018 quarter at a record $3.240 trillion, according to the latest HFR Global Hedge Fund Industry Report released on 18 Oct, 2018.

And among these, hedge funds known as “quant”, which stands for quantitative strategies, adopts algorithm trading strategies and they continue to rule the hedge fund world.


Institutional Investor’s (an international finance publisher) 17th-annual Hedge Fund 100 ranking of the 100 largest hedge fund firms in the world as of year-end 2017 finds that the four biggest hedge fund firms — and five of the six biggest — on its ranking rely largely or fully on quantitative strategies using computers to make their investment decisions and have continued to attract assets.

The value of assets managed by the quants alone passed USD1 trillion in 2018.

In a recent article by Bloomberg, Man Group Plc, the world’s largest publicly traded hedge fund says it is increasingly relying on winning a technological arms race that’s roiling the entire business it is in.

The public hedge fund manages US$114.1bn for its global clients, with institutional investors contributing 82% of the group’s funds under management.

The article opines that hedge-fund investment managers are in agreement that alpha is becoming more elusive as both the data and the techniques used to sort through the numbers become more widely available.

The business is undergoing a Darwinian thinning of the herd, with a decline in both the number of new firms opening and the number of shops closing their doors.

The surge in money flowing to passive investment strategies suggests investors have a newfound awareness that they’ve been paying elevated fees to funds that are closet index-trackers.

A proper bifurcation between cheaper products that only pledge to deliver beta and more expensive offerings that maintain they can generate true alpha should kill off the pretenders. And that seems to be happening.


And a key element of Man Group Plc’s strategy is to harness advances in machine learning, artificial intelligence and other computing technologies to gain an edge in both trading and execution.

According to Luke Ellis, the CEO of Man Group Plc, the firm wants to use the above strategy in its alpha generation, in its trade execution, in its portfolio construction, in the way it interacts with clients, in the way it back-office processes work, and in the way its risk management processes work.

He says that while one of the elements is to build smarter robots and writing better algorithms to free the computers to choose what investments to make, its key requirement is to employ overall technology to improve the performance of the firm’s humans in delivering better returns for clients.

He adds that while maintaining balance across the portfolio’s strategy might require corresponding sales of some holdings “and while computers are perfectly suited to calculating and flagging the need for such cross-portfolio tweaks, humans are still better at deciding which stocks to offload” at least for now.

To repeat its strong growth rate in the next half-decade, Ellis says he needs to oversee a harmonious integration of people and technology. If he gets it wrong, a whole industry will be watching.

Quant and discretionary strategies actually share common objectives and thinking. According to global asset manager AQR, quant strategies and their fundamental counterparts actually rely on similar investment thinking.

AQR says that it’s time for investors to finally bury the idea that quantitative funds are a black box whose strategies are mysterious and highly correlated to one another — and that they are dramatically different from fundamental strategies.

It argues that both systematic and discretionary managers rely on similar fundamental — and economically intuitive — investment strategies, such as buying cheap stocks. It adds that systematic strategies are less correlated to each other than many believe and are no more correlated than their fundamental brethren.

There are some differences between systematic and discretionary strategies, but saying that they are opposites is plain wrong.

For example, portfolio managers that use fundamental research to identify investments might target companies that they feel are undervalued after poring through financial information.

Quants instead program algorithms to sweep the markets for cheap companies based on specific measures.

But both strategies are essentially looking for the value factor, or undervalued stocks.

It’s just that quant managers that rely on computer models can cover more territory and construct more diversified portfolios, according to AQR.

Algorithm strategies can go for scale and breadth in portfolios while fundamental managers can kick the tires and go deeper into a particular company for obvious reason.

Computers help quants cover more territory and AQR says that “diversification means that if you can apply the same idea in many places, as opposed to doing it in one place, you will get smoother returns.”

The value of having a computerised and repeatable process allows the quant to filter an idea across a huge universe of asset classes and geographies.

AQR say that available quant strategies aren’t any more similar than the ones used by fundamental manager.

“Importantly, historical correlations among systematic investors are also low, as low as they are among discretionary investors, suggesting that the notion that ‘all quants trade on the same signals’ is misplaced,” it states.

According to AQR, systematic managers represent 14 percent of mutual fund assets, rising from 9 percent in 1999. It says that twenty-six percent of hedge funds and about a quarter of institutional equity funds are now quantitative.

With volatility likely rising in 2019 after a long bull market in the global markets and the pullback of the effects of quantitative easing, investors, traders and fund managers are likely to find the challenges of maintaining returns rising not to mention the preservation of capital.

Those who focus strictly on fundamentals and those who focus strictly on quantitative strategies may well find that a blend of both could easily navigate the choppy waters.

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