Insights for Investors and Hedge Fund Managers
Thought Leadership Forum 2017 | icon 1:39
In its eighth year, J.P. Morgan’s recent Thought Leadership Forum in New York drew more than 300 institutional investors representing $815 billion in direct hedge fund investments.
During the day-long session, attendees heard panels on the impact of technology on investing, transformative inroads being made by artificial intelligence and how hedge funds will continue to evolve in step with the markets and their clients.
Despite the changes taking place in the industry, hedge fund managers generally agreed that success comes from uncovering opportunities the old-fashioned way – mining the markets for companies with strong earnings. One manager explained that his firm concentrates on getting into stocks and industries where it can see earnings compounding at 20% to 30% over two or three years. Leverage can boost gains, he said, but leverage can also be a negative when events need to be addressed by rotation in the portfolio.
Always re-evaluate your facts and your approach. Even if the strategy continues to be successful, and the facts embedded in it have proven to be accurate, that may not be true forever. Most models look back at a relatively recent data fact set, extrapolate forward and then presume it will continue going forward. Of course, it will continue until it doesn’t. Fund managers need to be open to fine tuning how they invest and adjusting how much risk they’re taking.
Quants tend to focus on mining inefficiencies that either people don’t see, or the inefficiencies are slight but spread out over a large quantity of stocks. People look for ways to make sense of the data, make sense of the models and make sense of the portfolios. So when building models, be really rigorous in assembling models that have the persistence to last for the next 10 years. Then, implement the models using a technology and an architecture that allows for risk management.
Anyone investing with a particular quant manager should ask how frequently the model required revisions. While some degree of revision is necessary and positive, numerous changes and adjustments may signal weaknesses in the underlying approach. Managers should be open to making adjustments, but frequent shifts may signal uncertainties in their approach. Investors should also ask how the fund would react to changes in prevailing market conditions. For example, how would a fund built for a low volatility market environment react if volatility were to increase? Investors should also explore what accounts for wide disparities between how a fund is expected to perform based on the algorithms driving it, and its actual market performance, as large differences could underscore shortcomings in how the fund is structured.
Dogma can lock a quant fund into being resistant to change, especially if the fund has performed well. A firm that has been in business and successful for a while is going to build up a resistance to change and be unprepared to adapt when necessary. Such funds continue to be guided by their past experience when they need to push against the dogma to move forward unimpeded. That’s difficult to do because a successful quant is very dogmatic by its nature.
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