It seems, however, that seamless communication between PMs and traders is lacking here. The RMS and the OMS aren’t really linked in any way. The portfolio construction process is comprised of a series of inputs and assumptions that generate investment decisions. The execution process is comprised of a completely separate set of inputs and assumptions that support the implementation decision. Without using the same data, it is likely that trading strategies are not always an accurate extension of investment strategies. They may, in fact, be at odds.
Take the example of how PMs and traders might use the transactions costs data in a different way. While spreads, average daily volume, volatility and risk factors might be inputs to the portfolio construction models, they are a subset of a much larger series of data points that go into the choice of securities. Fundamentals and quantitative metrics such as historical price ranges, price and earnings ratios, growth and margin trends, cost and revenue comparisons, corporate announcement dates and pending corporate actions are just a small set of inputs that the PM may take into consideration. Transaction cost data can be integrated with these input variables to develop timing, portfolio weighting or even stock substitution decisions. The data would be utilized to evaluate the optimal risk vs. return or opportunity vs. cost ratio as well as the relative size and weighting of individual securities within the portfolio.
The trader, on the other hand, could take TCA data and apply it in a completely disassociated manner. Say a trader is using a benchmark such as arrival price or VWAP, and he receives two baskets of orders from two different PMs at the start of the trading day. The order sizes as a percentage of average daily volume and real-time pricing and volatility data might lead that trader to conclude that the same equity algorithm would be equally effective for a list of buy orders in the small cap growth portfolio and another basket for the small-cap value portfolio. Using historical trade data and share sizes, the two portfolios of securities may appear to have a similar profile, and, in a quiet market, a participation strategy might appear to be the appropriate algorithm for both.
Advancements in portfolio trading algorithms support the ability to maintain the integrity of the overall basket during the transaction. There is functionality to protect individual stock executions from proceeding to quickly or too slowly relative to the rest of the securities in the basket. Portfolio-level algos can prevent individual orders from being completed while others are only partially executed, for example. Or they can alert to the fact that executions in stocks in a particular sector or industry are executing faster than others. These capabilities allow the buy-side trader to avoid inadvertent tilt or risk when prices and liquidity behave in an uneven fashion. But they don’t necessarily produce the optimal trading strategy to align perfectly with the underlying stock selection process.
Clearly the growth portfolio and the value portfolio were constructed based upon different expectations of stock price behavior. They are comprised of securities with very different alpha characteristics, covariance and risk factors. If they are traded through the same algorithm, some securities may very well be executed sub-optimally relative to portfolio construction expectations. Value stock executions may be crossing the spread too frequently while growth stocks with momentum may not be traded aggressively enough. The result is a significantly different market impact and opportunity cost for both baskets than would have been expected given the TCA data and alpha expectations built into the portfolio construction models. The right choice of algorithms with the right set of order parameters would take into consideration differences in the expected behavior of securities beyond similar percentages of trading volume and execution results vs. the benchmark. And would do it in real-time.
Even within each of these portfolios, every individual small-cap stock has its unique characteristics with reference to liquidity, expected alpha, volatility and risk. And when combined together into an investment portfolio, their individual stock characteristics have blended to achieve a unique investment profile. If a trader then takes this basket of blended behaviors and executes them using one algorithm, one set of order parameters and one execution time frame, it can be expected that execution results would not optimize the investment decision. The result would likely be differential alpha preservation and loss within that basket trade due to a lack of differentiated execution. While these differences were an integral component of the investment decision process, they are not now an integral part of the implementation strategy.
Next generation tools will allow for a higher degree of integration and coordination between the investment mandate and the trading mandate. If the PM has utilized particular trade cost data in his portfolio investment process, those assumptions become part of the analytic used by the trader to put investment analytics and trading analytics in sync with one another. Best execution, in this instance, will be the ability to establish trade parameters that coincide precisely with the assumptions used in portfolio construction as both the investment model and the trading strategy are optimized in real-time. Today there is no effective means for the PM to communicate his trade cost input variables or his risk / return ratios to the trader, and there is no practical means for the trader to apply them in his execution strategy.
The role of the buy-side trader is to implement his PM’s investment decisions in an optimal fashion so as to minimize transactions costs and preserve alpha. The best way to do that would be to have tools at his disposal that would extend those parameters from the investment decision right into the trading room. Bringing the PM and the trader closer in concert would engender execution strategies that are truly Best Ex. No more bubble gum and scotch tape.