The Problem With Intuitive Investing

“Hundreds of studies have shown that wherever we have sufficient information to build a model, it will perform better than most people.” – Daniel Kahneman

Ray Dalio and Bill Ackman are arguably two of the greatest hedge fund managers in the business today. Last week they shared the stage at an investment conference to discuss their respective investment processes. Dalio gave Ackman some interesting insights into his quantitative process at Bridgewater Associates during their discussion (courtesy of some notes from ValueWalk):

Dalio continued with the advice that you should write down your method so it can be back-tested, he feels that everything can be analyzed and quantified.

99% of the time he agrees with his quantitative strategy, the 1% of the time when he disagrees with the machine he realizes in retrospect that  the machine was right 66% of the time.

Dalio is one of the most brilliant investors alive, but he’s wrong 2 out of every 3 times he tries to override his quantitative investing model. I think this is a great lesson for those investors that plan on using their gut instinct to make investment moves. The majority of the time following a rule-based approach is superior to making ad-hoc decisions.

In Thinking, Fast and Slow Daniel Kahneman discusses how study after study have proven that simple algorithms used for making decisions tend to beat experts that try to use their intuition in a wide variety of fields – medicine, business, investing and more. Experts are overconfident in their abilities, while models are not. Models are disciplined while the experts tend to let their biases blind them to potential errors in their line of thinking.

Tobias Carlisle and Wes Gray share an interesting case study on the problems that come about from making changes to a simple model in their book Quantitative Value. They looked into Joel Greenblatt’s Magic Formula stock-picking system. The Magic Formula is a simple screen that looks for quality stocks trading at cheap valuations, something Greenblatt and others have proven to be a winning combination.

Over a two year period from 2009 to 2011, Greenblatt’s firm created what turned out to be a very telling experiment created by the options presented to their investors. They offered clients two choices: a “self-managed” account or a “professionally-managed” account. The self-managed accounts would choose their own stocks using the Magic Formula list generated by Greenblatt’s screen. In the professionally managed account the stocks would be chosen automatically for them by following the model.

Greenblatt found the self-managed accounts slightly underperformed the market (59.4% vs. 62.7% for the S&P 500) while those accounts that were automated returned 84.1% after all expenses, handily outpacing the market. Adding a discretionary component to the model hurt the performance of the self-managed accounts for a few reasons. First, these investors avoided the best performing stocks. The best performers tended to be the most depressed stocks, but investors generally know why stocks are depressed so they avoid them at the time. Second, investors tended to sell stocks after periods of poor performance and buy stocks after periods of good performance, the opposite of a value strategy. Interestingly enough, it was the discretionary account that made no changes that performed the best:

Perhaps Greenblatt’s most interesting data point comes from the best-performing self-managed account. It didn’t do anything. After the initial account was opened, the client bought stocks from the list and didn’t trade again for the entire two year period. It seems even doing nothing outperformed all the other active self-managed accounts.

For most people it’s hard to believe that an automated system could make better decisions than their own gut instincts. But having a rule-based approach doesn’t mean investors have to come up with a complex black box trading system. Automation in the investment process is all about making level-headed decisions ahead of time. If you’re able to create if/then rules to guide your actions you can systematically keep overconfidence and overreactions out of your investment decisions during difficult market environments.

Obviously, investors still have to make some decisions along the way even when implementing a model or a system. Life isn’t always formulaic as circumstances change and updates have to be made to your investment plan. The point is that a rules-based, systematic approach relieves you from having to make decisions under stressful situations, something the majority of investors are terrible at.

Taking emotions out of the decision-making process will almost always be the right move because most of the time it’s difficult to see our own behavioral biases.

Ackman and Dalio Share the Stage at Harbor Investment Conference (ValueWalk)
Quantitative Value

Further Reading:
The Curious Case of John Hussman



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  1. MarketFox commented on Feb 16

    Nice post. Sticking to your investment process is easy to say hard to do.

    Two great example of investors gut instincts working against them are Morningstar’s annual “Buy the Unloved” survey and the story of the CGM focus fund.

    You can find out more about the “buy the unloved” strategy here:

    CGM focus delivered 18% annually from 2000-2009, while the average investor in the fund lost -11%!

    Checklists can be a great way to avoid making errors in judgment. This article form the New Yorker goes through examples from medicine and aviation to show how checklists can help with complex decision making.

  2. Dinero commented on Feb 17

    Did the automated system switch between allocations on the list during the trial, or stay with the first pick from the list.

    • Ben commented on Feb 17

      Rebalanced one a year per the Greenblatt rules.

  3. Irondoor commented on Feb 18

    If you follow a good systematic value-stock-picking approach and overlay it with a trend-following system such as the 10 week EMA crossover of the 40 week EMA on the SPY, you will then have a method that keeps you in the market and allows for the capture of majority of the gains. Hold 10 stocks only. Don’t use trailing stops, but do use a maximum-loss-from-purchase stop (say 15-20%).

    Sell all stocks and then sell short (or go to cash if you don’t like shorting) when the 10/40 cross turns down. Turn your systematic stock-picking rules on its head; in other words re-write the algorithm to find the worst (most overpriced) stocks and short them. Again, use stops.

    Combine these two rules (for buys and shorts) into one complete system and you’ll always know what to do. This combined system will be long approximately 70% of the time and will likely get whip-sawed often when short due to Fed intervention.

    FYI, the last 10/40 cross on the SPY was Jan 3, 2012. This system would have been long since then and shows no sign of topping out.

    • Ben commented on Feb 18

      Thanks. Agree that if you’re going to try to change exposure that a rule-based trend following rule is the best approach. Volatile days in the markets tend to cluster and the back-tests show this strategy reduces exposure to those days.

      • Irondoor commented on Feb 18

        Volatility is just a fact that comes with being in the market. Being able to stick to your system when the fit hits the shan is basically the key to long-term success. Volatility is what everyone is afraid of and the reason most people don’t trust the stock market. Accept it and use it as a friend, since it is what creates those “value” stocks.

  4. connecting.the.dots commented on Feb 18

    […] and overreactions out of your investment decisions during difficult market environments…Taking emotions out of the decision-making process will almost always be the right move because most of the time it’s difficult to see our own behavioral biases.” […]

  5. Lucas commented on Mar 15

    A lot of different qualitative methods have come down the pike over the years, but most outlive their usefulness after a while. They work great until they don’t, and you can automatically lose a lot of money when a method stops working. Where is Magic Formula in the method lifecycle?