How trading analytics add value to equity index funds
Commentary by David Hsu, senior equity product specialist, and Robert Miller, execution consultant.
For many investors, the appeal of equity index strategies lies in their transparency, low cost and diversification. But while indexed exposures might be relatively simple for investors to understand, this belies the complexity of managing an index fund to accurately and cost-effectively replicate a broad benchmark.
One of the key ways that the managers of equity index funds add value for their investors is through performance-driven trading. And, as we will see, this is far from straightforward to implement.
The primary goal of index fund managers is to track their benchmark as tightly as possible. How well they do this is measured by a metric called tracking error, which is the consistency of the difference between a fund’s performance and its index over time.
In managing an index fund, the managers pick up some costs along the way, which can essentially be broken down into two types: explicit and implicit costs. Explicit, or “frictional”, costs include commissions, exchange levies and stamp taxes. These are incurred by all investors and most of this type of cost are largely beyond an investor’s control.
Then you have implicit costs. These are less easily measured than explicit costs and are sometimes considered market movements or “noise”. Nevertheless, they are very much real costs, impacting fund performance and increasing the tracking error.
The largest component of implicit costs is the trading impact cost, which is the cost of buying and selling securities in the prevailing liquidity conditions at the time. For example, if you need to buy 100 shares “at the market” (that is, at the prevailing market price at the time), but next two best “offers” (sell orders at prices at which sellers are willing to sell) are for 50 shares each, your buying those 100 shares will cause the market price to rise, incurring you higher costs.
However, index fund managers can mitigate impact costs by making use of their expertise and technology when trading, which is useful when it comes to index rebalancing and the daily management of cash flows.
Index providers regularly reconstitute and rebalance their indices to ensure that the benchmark composition accurately reflects their methodology and still meets their criteria. Certain stocks might be added to an index, some removed, while others might have their weightings adjusted.
Similarly, the managers of index funds which track these benchmarks must realign the weights of their funds so they match as nearly as possible the underlying benchmark, giving their investors a close representation of their desired exposure.
Some investors might assume that index fund managers simply rebalance their funds on the effective date of an addition, exit or change in weight of a constituent in the underlying index. However, in practice it’s not that simple, as owing to liquidity constraints and other market dynamics, this might not be possible or cost effective.
Instead, skilled index fund managers can handle rebalances in a way that minimises the market impact. They do this by executing trades at various points and sizes up to, on and after the effective date. Often this allows the managers of the fund to achieve a more favourable average price level than the effective benchmark price. And at Vanguard, the role of portfolio manager and trader is combined in our equity index fund teams, a unique structure which gives us the flexibility to execute these trades at advantageous points within a risk-controlled framework, with the aim of lowering the cost for our investors.
Classifying trade characteristics
There are a number of ways to classify a share. More traditional methods typically focus on factors such as market capitalisation, country, sector or average daily volume. However, these classifications offer little insight into how easy it is to actually execute a trade in those shares.
Liquidity, order book queue, spread, tick size and volatility, to name a few, are much more meaningful measures when it comes to trading. It is these factors that drive our machine-learning trading algorithms, allowing them to classify stocks in a way that helps us to improve trading outcomes. The algorithms identify patterns in executions by grouping data points that have similar properties or features.
For example, while spread size on its own might not provide particular insight into how to best trade a particular stock or group of stocks, when combined with tick size and other factors, it can give investors a better understanding of the overall characteristics of a trade.
Efficiently allocating trades
While the managers of Vanguard’s equity index funds manually direct part of our order flow, they also use a number of algorithms to execute orders and ultimately achieve better trading outcomes, based on the trading insights these profiles give us. But deciding how to allocate these trades among the different brokers in the market is highly complicated. To achieve this, they use tools called “algo wheels”.
An algo wheel randomly allocates each trade to an algorithm, which measures its impact in the market using data analytics techniques. The great value to us in using algo wheels is that they help remove trader biases which can distort the data and make it harder to properly assess the effectiveness of different trading options.
The type of algo wheel we use in each instance depends on the objective of the order we are handling at the time. We often have multiple wheels active at once when we want to achieve several trading objectives with the same stock; each algo wheel is aware of other active orders and can allocate trades based on this knowledge to help further reduce market impact and trading costs.
The “algo wheel” - driving better outcomes for investors
Algo wheels allow us to create a measurable environment free from bias in which we can statistically estimate the likely outcome of a trade, which in turn helps us to identify areas of over- and underperformance.
We term this approach performance-driven trading because, based on feedback gained from the algo wheels, we then tilt our allocation of trades to the best-performing broker algorithms, which ultimately drives better outcomes for our investors.
The value of trading analytics
Managing an index fund to keep tracking error and cost low is an extremely complex process, in part because of the market environment. In Europe, for instance, there are over 300 stock exchanges or trading venues. Telecommunications group Vodafone alone was traded on 27 different markets at the time of writing.
The market structure in Europe is constantly evolving, as the regulations dictating where you can buy and sell shares and the trading experience you get change. And that’s not to mention the trading innovation taking place, as new types of markets with new and different trading outcomes are developed all the time.
Executing trades efficiently within this environment is not straightforward. This is where data analytics comes to our aid. This helps us find evidence of optimal trading outcomes and guides the decision making that supports our trading desk and automation processes.
At Vanguard, we have hundreds of algorithms which we leverage to execute trades. And the knowledge we acquire through our trading, we put back to work in our systems to further improve our trading outcomes. This feedback loop constantly grinds out greater efficiencies to reduce the implicit trading costs.
A dynamic process
Trading analytics and performance-driven trading are key components in delivering some of the core characteristics that many investors seek in equity index exposures, be it close replication, low cost or diversification. It’s an exciting and evolving field. New developments are emerging all the time which can provide even greater insight into trading performance and opening up new opportunities for enhanced efficiency and lower cost.
But managing such a dynamic process takes an experienced, global team spread across regional desks, each with local expertise. Vanguard launched the first index fund in 1976. Our in-house team of quantitative research and data scientists provides analysis and builds trading analytics tools for all Vanguard trading desks. And our unique combined portfolio manager-trader model fosters deep sector specialism and expertise and allows us to better manage rebalances, corporate actions and cash flows than typical index fund team structures.
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