The ever-changing investing environment requires portfolio managers to constantly adjust. Government regulations, macroeconomic considerations, and changing market trends can all have an impact on how investors develop their strategy.
However, a few things never change: Risk must be reduced by investment professionals, and bets must be hedged by hedge fund managers. This is particularly crucial in troubling times. Investment experts will be trying to do just that as they deal with a bear market and the worst inflation in 40 years.
But what if the majority of financial professionals aren’t actually hedging their portfolios? What if technology could help investors better protect themselves from the risks they have in their portfolios and the ones that lie ahead?
What Today’s Most Hedging Looks Like
Let’s start by looking at the current situation, in which an investor can view shorting as applying a bandage to the larger issue of portfolio risk. For instance, they might short the entire S&P 500 index or the entire iShares Russell 1000 ETF. As an alternative, they can ask a third party for a “custom” index to short in an effort to modify their hedge basket.
These techniques can reduce total market exposure, which is the basic objective, but they can also introduce a number of traps that can make or break a portfolio’s performance.
The main issue with a hedge like shorting an index or ETF is that it typically won’t match the amount of attention to detail, care, and accuracy that a manager will put into their longs. While a status quo hedge can lessen overall market exposure, it might not fully protect the portfolio against certain factor risks. Why would an active manager devote so much time, energy, and study to honing their long picks if they were only going to use a generic hedge?
Although “custom” hedging baskets appear to offer a more focused hedge, the truth is that it also has a number of drawbacks. These “special” baskets are highly susceptible to crowding in practise because many of the same investors may use them. Due to the increased likelihood of shorting the same stocks as everyone else, the risk of short squeezes is also increased.
What Today’s Hedging Should Look Like
Understanding the elements influencing your portfolio in-depth can help you build a basket of unique, ideal, and occasionally counterintuitive hedges.
Let’s first examine how machine learning can be used to identify portfolio hazards.
In other words, machine learning enables investors to recognise and manage unnamed risk variables in their holdings. Machine learning can uncover baskets of stocks that are trading together for reasons other than the conventional ones like value, growth, and momentum, and it can do this at a pace and complexity that no human analyst can. This is a perfect example of how artificial intelligence thrives in big data contexts.
A more individualised strategy is preferred over an off-the-shelf one, according to the majority of active investment managers. But how do you start altering the current situation, and what should you focus on first? According to my experience, it’s crucial to remember the following three points:
1. Ensure that every aspect of your portfolio is based on the best data possible. Machine learning won’t function correctly without high-quality data at the beginning, whether that means looking for the best alternative dataset to limit unforeseen risk or onboarding a speciality dataset to ensure that your hedges are actionable.
2. Test repeatedly. and do so by using technology. Backtesting is a crucial part in any hedging process and enables you to comprehend the numerous variables and causes affecting your portfolio, however it can only go so far. You may give your staff more time to work on higher-level tasks by relying on technology to automate hundreds of potential outcomes.
3. Having said that, don’t ignore the importance of people. A machine can aid in testing, position customization, and the discovery of illogical connections, but it cannot take the place of your entire procedure. The most knowledgeable investors in the world are finding that the financial markets are best served by combining machine learning with human intelligence.
Increasing Profits While Reducing Risk
When seeking outsized returns, investors will always assume some level of risk, but that doesn’t mean they shouldn’t make every effort to keep that risk to a minimum. Explainable machine learning can assist investors in hedging against outsized risk by using these cutting-edge tools to uncover it.
At the end of the day, it is up to the human investor to correctly hedge their portfolio and adopt (or not implement) a machine’s recommendations. But before doing so, they should be able to take into account all accessible information, including concealed ones.
