Charles River Investment Management Solution is helping traders to systematize their accumulated knowledge and leverage it to increase efficiency, improve execution quality and better quantify the “whys” of trade decisions.
by Kyle Pedrotty and Jay Hinton.
In academia and research, there’s a word for the kind of evidence that’s collected from observation and reputable sources, but doesn’t come from experiments: “anecdata,” a combination of ‘anecdote’ and ‘data.’ It’s a useful concept to apply in areas like capital markets, where there are so many variables that experiments with isolated variables are difficult and often extremely expensive to perform. We mention this because, historically, the ability to collect and effectively use anecdata was what separated good traders from bad ones, as well the rookies from the veterans. Or, as a mentor of ours was fond of saying “There are old traders, and there are bold traders. There are no old, bold traders.”
The anecdata we’re talking about for trading is the combination of many elements, including information about the fund, the portfolio manager, color on stock behavior, intelligence on who might be buying and execution history. Another way of thinking about this is to say that effective traders do particularly well what human brains do best: take in a lot of unstructured data to identify patterns, respond iteratively, and free-associate to create new behaviors when the data changes. More succinctly, they develop intuition, or a “gut” feeling. In the best case scenarios, they can also articulate after the fact why certain choices were made.
The reality that most of this knowledge is captured solely in the heads of traders presents a challenge as we move towards automating manual and low-value processes so that traders have more time to focus on complex orders. One of the first tasks is to take all the unstructured data that traders use and begin to systematize, or structure it in a way that is complementary to, the structured data that already exists in their trade blotter, portfolio management system, and security master.
This process in and of itself must be carefully considered. The old adage “what gets measured gets managed” is true here. Ultimately, the goal is to help traders leverage their accumulated knowledge to increase efficiency, improve execution quality, and better quantify the “whys” of trade decisions. This is the foundation of Charles River Development’s trade automation efforts.
The first step begins with what we call “Order Classification.” This process allows us to identify orders uniquely based on attributes of the underlying funds, the trader, the portfolio manager, market conditions, security specifics, projected execution costs, historical performance, and potentially hundreds of other data points.
This type of structured and persistent classification has two uses. First, we can write systematic handling and execution rules. Second, it provides data that can be used to analyze whether our decisions were correct, and uncover additional opportunities to improve handling and execution. The first aspect has the most immediate impact on trader workflows, as it supports improving their day-to-day efficiency. The second becomes more important in the long term, as it allows desks to analyze the quality of what they’re doing today, and build a positive feedback loop to improve not just the “how,” but also the “why” in a transparent, reportable fashion.
In future Insights, we’ll delve deeper into additional aspects of Charles River’s trade automation offerings. This, though, is the foundation—our ability to help you better systematize what you already know, synthesize the data you have available, and offer new insights and efficiencies to improve the entire trade lifecycle.
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