Identifying a small number of underperforming shares can help index investors increase their gains. But how to do that? A high securities lending fee is one signal, and avoiding such shares would have led to outperformance over the past seven years, says Simon Colvin of Markit.
Asset management has gone full circle in recent years. Passive index tracking is now increasingly favoured over active management, which industry analysts have accused of being little more than “closet indexing”.
The last couple of years of macroeconomic turmoil have not helped. With the markets moving in lockstep, driven by events outside the remit of established asset management, there has been little scope for managers to beat runaway index swings. Cheaper tracker funds with lower turnover and fewer overheads have won out over their more active peers.
Yet this race to the bottom, in terms of cost, looks to be swinging back. There is now an ever-increasing set of products aimed at investors dissatisfied with the “one size fits all” approach to investment.
To this end, Markit’s ETP business has seen nearly $30 billion of inflows into “optimised strategy” exchange-traded products, which aim to expose investors to such strategies as growth, value or dividend income. These inflows represent 21.4% of the assets managed at the start of the year, outpacing the 14.7% gain seen in equity exchange traded products.
The world of quantitative investing has grown in leaps and bounds since its genesis in the 60s as the industry has grown to cover just about every asset class. While there is plenty of appetite for the more erudite multi asset, long/short, high speed algorithmic strategies, asset managers will no doubt find it hard to pass such a strategy by many investment committees which have grown wary of “black box” approaches.
The new frontier in quantitative investment today lies in simplicity. A strategy which lays out a clear, proven investment thesis and leverages a quantitative approach to identify potential investments from a universe is now the challenge faced by today’s quant innovators. Such a strategy goes a long way towards bridging the gap between high touch discretionary investing and passive index tracking by leveraging new analytics which provide previously inaccessible insights into the factors driving share price returns.
The final piece of the quant investment puzzle is infrastructure which enables asset managers to incorporate a quant angle to their investment decision. These platforms need to enable quant users without the need to establish and maintain databases which power the quant selection model.
They also need to incorporate new and innovative datasets which allow investors to draw on new signals – or sources of alpha – without need ing to gather, clean and maintain datapoints first-hand.
The optimal meeting of quant investing and passive index tracking is not a marriage of equals. The type of quant-based products that have gained traction with investors look largely similar to the original index and only seek to increase the exposure to certain specific characteristics, growth and value being point and case. For example, the iShares S&P 500 Value and Growth ETFs have 354 and 300 of the original 500 index components respectively.
Optimisation is the name of the game and outperformance is achieved not through laser sharp stock picking, but by sifting out the minority of shares which underperform. The onus will now be on fund managers to find and exploit these sources of underperformance which drag index returns.
IMPLIED LOAN RATE SIGNAL
One factor which identifies a proportion of shares that consistently underperform is the implied loan rate, according to our research. The factor ranks shares based on the fee paid to borrow them in the securities lending market. The strategy is to avoid the shares which cost the most to borrow as this is a sign of heavy demand, and a strong negative conviction from short sellers who are willing to pay a high fee to cover the cost of their short positions.
From a performance point of view, the 10% of shares with the highest stock loan fees in Europe have underperformed their peers by 75% since the genesis of the dataset, making them the worst performing tenth decile group of any of the 300 factors we analysed in the developed Europe universe.
Current shares to avoid, according to the factor, include Stobart Group in the UK, French telecommunication firm Alcatel-Lucent and Spanish stainless steel company Acerinox.
This factor also scores very highly in consistency, another key performance metric. Overall, the bottom decile of the factor has underperformed over the last seven years and is on track to do so again this year. On a monthly basis, shares with the highest stock loan fees in Europe have underperformed the market two out of every three months.
Overall, an investor who would have stayed clear of expensive to borrow shares (which fell by an average of 30 basis points a month over the last seven years) would have seen a 9 basis point uptick in average monthly return. This uptick in returns rolls up to 1.1% over a year.
While these numbers might not seem spectacular, this type of consistent outperformance is what investors are after, rather than volatile alpha. This is achieved by leveraging a speciality dataset with a systematic quant approach to single out shares which consistently fail to match the performance of their better-scoring peers.
For managers looking to leverage factors closer to their established investment toolbox, there are five fundamentals-based factors which have seen their worst ranked tenth decile group underperform the market by an average of 0.6% or more over the past 10 years.
An example is the Long Term Debt to Cash Flow factor, which ranks the bottom decile on shares which have the largest proportion of long-term debt to cash flow. While the fact that companies with unsustainable debt load underperform is fairly intuitive to anyone with a modicum of financial acumen, a quant process validates this fact and provides a performance figure to the gut feeling, 82% in this case.
The platform also processes, verifies and ranks the information from company filings, a cumbersome process given the different reporting requirements and currencies to consider in Europe.
Simplicity is the name of the game for quant investing. Finding consistent sources of underperformance and adjusting index based asset allocation to take these into account allows investment managers the ability to outperform a broader index. This process, if confined to a few factors, provides the simplicity that investors have begun to demand in the wake of passive indexing.
Simon Colvin is an analyst at Markit
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