SINGAPORE (Dec 9): Traditionally conservative institutional investors such as pension funds are now exploring allocating a larger portion of their assets to alternative investments such as hedge funds, private equity, venture capital, real estate and infrastructure.
There has been tremendous growth in this asset class, with the hedge fund industry alone having total assets under management of US$3.15 trillion ($4.31 trillion) by mid-2019, more than double its 2011 year-end value.
This is not just a US story; it is a global phenomenon. In Asia, Japan’s Government Pension Investment Fund entered into non-traditional asset classes in 2017, and has targeted 3% of its US$1.4 trillion in AUM for alternative investments over the next three years.
Malaysia’s Employees Provident Fund (EPF), which traditionally has been an investor in private equity, real estate and infrastructure, now plans to place up to 10% in real estate and infrastructure alone. The EPF’s investments in real assets include the Guoco Tower in Singapore, the redevelopment of Battersea Power Station in London and toll roads in Malaysia.
Some may argue that the current spate of institutional allocations to alternative investments, particularly in private equity, is just “hot money” chasing the asset class. In some cases, it has even been at the expense of underperforming hedge funds. Whatever the case, the alternative investments asset class is growing rapidly.
Fortunately, many finance practitioners are also believers in contrarian and behavioural strategies. In the popular factor investing space, the valuation factor (aka “the Warren Buffet factor”), which measures the cheapness of a security relative to its industry median, has not been performing well over the last few years. It is a bread-and-butter factor in many quantitative equity funds.
Yet, academic studies and empirical evidence so far indicate that, in the long run, the contrary is true. Perhaps now is the time to be a contrarian and invest in beaten-up quan- titative equity funds that bet on valuation?
Similarly, long/short hedge funds have been getting the rap for being expensive underperformers. Adding to this, hedge fund strategies using high-frequency, algorithmic or statistical trading techniques have been accused of being black boxes, hence lacking transparency.
However, algorithmic trading-based hedge funds created by those with combined training in financial economics, investments and computer science, do merit a closer look.
Most long-term investors express a preference for investment managers who not only have low correlations to traditional asset classes, but also stability in investment strategy, risk-adjusted performance and human capital.
So, could an algo-driven hedge fund, which successfully marries skills in financial investments and computer science, along with big data, be the answer to the institutional investor’s quest for low correlations and stability in both returns and human capital?
An algo fund is attractive for several reasons. Program-driven strategies are systematic and disciplined. Those within scope are strategies based on sound academic research and science and that have been backtested and stress-tested over multiple market cycles to yield stable risk-adjusted returns or alpha.
Such strategies help avoid the “key man” risk issue, where the loss of a vital person could affect confidence in — and hence result in the outflow of assets from — the hedge fund. All that matters for the firm’s business continuity is that a team of well-trained financial economists, computer scientists and big data analysts can trans- late investment research into a set of profitable hedge fund trading strategies using reams of well-commented computer programs.
With this in place, there will also be consistent and systematic application of the algorithmic process globally, irrespective of investment universe, region, industry and/or employee location.
While it may sound like something from science fiction, the chief investment officer of such a hedge fund is also an algo. This “robo-CIO” dispassionately and scientifically selects optimal risk-adjusted strategies, constructs portfolios, controls leverage, creates the lowest-cost execution and trading strategies using state-of-the-art order management systems, and, finally, manages portfolio risk. Human intervention is only a last resort.
With the robo-CIO at work, humans are freed up to test new research ideas and strategies, refine and update the process, ensure compliance with the regulatory authorities, and perform client-facing activities that require the human touch.
An attendant benefit of algos is that fund subscriptions and redemptions — and therefore, fund openings and closures — are driven by systematic, process-driven programs and algorithms. An algo fund, therefore, will never become “too large”, nor take on opportunities unless the risk-adjusted alpha still exists — that is, it will not be a consequence of capital in- flows.
Finally, the customisation of portfolios to an investor’s desires and needs is made simpler and faster using algos. This includes incorporating client-specific considerations concerning sustainable and responsible investing, taxes, compliance with Syariah law in the case of Islamic bonds and so on.
So, how does all this digitalisation of the investment management process benefit pension funds?
For owners of long-term capital looking for hedge fund strategies that are systematic and disciplined, that can be customised for lower volatility and leverage, and are automatically screened for quality, sustainability, “greening” and other criteria. The algo-driven hedge funds’ time has probably arrived.
Joseph Cherian is practice professor of finance at NUS Business School. Manish Sansi is co-founder of Xen Capital and founder of Algante, a quant AI research firm. The opinions expressed are those of the writers and do not represent the views and opinions of NUS.
