At Wellington, portfolio managers also code
It had been a long time since there had been a hard line between an active fundamental manager and a quantitative investor using advanced computer techniques to uncover ideas. Even though the line has blurred recently, Michael Masdea, head of Wellington Management’s Investment Science group, said his number one job was to preserve the art of investing while bringing in sophisticated IT capabilities to support managers. company assets.
âWe believe that the balance between art and science is essential. Machines can’t pick stocks, but they can help a lot in the stock picking process, âsaid Masdea, who is a former semiconductor analyst in Wellington as well as an equity portfolio manager.
The Investment Science Group focuses on applying scientific techniques, such as data analysis, to everything from how portfolio managers come up with new ideas and implement them to maximize returns, to development professional investors, which includes uncovering and mitigating the drawbacks of their behavioral biases. . The group has doubled in size in recent years to reach 68 people with expertise in data science, trading and quantitative investing, among other specialties.
Wellington’s model is to have scientists work alongside portfolio management teams on a temporary and permanent basis. The goal is to get scientists to understand how managers invest and to get investors to do the science, at least in some cases.
As an example, Wellington currently has 100 portfolio managers who code in Python, a relatively easy to learn and popular programming language for analyzing data, implementing machine learning algorithms, and performing other tasks. Masdea said the ability to use Python helps managers sift through the growing amount of data available. Managers, for example, can access credit card data to analyze consumer inventory, or data to see details of companies’ recruiting efforts globally. In the age of almost unlimited data, it pits Python against Excel. âWith Excel, you look at the data and hide the logic. With Python, you look at the logic and hide the data because there is too much data. It’s a change in mindset, “he said. But he added it wasn’t for everyone.” First of all, not all PMs should code in Python, I don’t think so. not that they need it, but they need to understand how it allows for much more in-depth research. ”
Peter Carpi, an equity portfolio manager in the company’s micro and small cap team, said he sees the tools created by the group as a natural outgrowth of similar applications he has developed for sift through the increasing availability of earnings call transcripts and other information over the years. âThere are some among us who are planning. More often than not, we work in partnership with people who are great programmers, great data scientists, âhe said. Carpi said investment science tools are especially valuable outside of idea generation. Traditional due diligence is unlikely to change at all. âBut when to buy and portfolio construction and mitigation and risk analysis – that aspect is being turbo-charged, as is behavioral analysis,â he said. Behavioral analysis examines the activities of portfolio managers, including buy and sell decisions, to assess trends.
âI don’t want to do anything out of passion. If I see information about how the market is going in the morning, I don’t want my response to be determined by whether I’ve already had coffee, âCarpi said.
As another example of helping professionals grow by analyzing behavior, Masdea said the group analyzed a portfolio manager’s behavioral biases and found that she had great ideas that generated a lot of alpha and had good timing to reduce positions. But it turned out that she should have sold the entire position, not just the cup. They then worked with her to change the behavior.
Adding scientists to the mix had a few bumps. Carpi said he would classify most investment professionals into three broad groups: long-time portfolio managers, “who are reluctant,” a few evangelists like him, and then new hires. âAt the margin, we take more people who know the data, who understand how to code. Usually new hires are all looking for a way to make an impact. And how you make an impact when you have a lot of senior executives who already know what they’re doing is doing what they’re not doing.
The reluctant are arriving, however.
âTwo years ago, the majority of people were like ‘why are we investing in this?’ The number of cries of âwhy are we doing thisâ has disappeared. Now that’s how can I embrace this, âCarpi said.