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By Noah Solomon
Special to the Financial Independence Hub
In a recent newspaper article, a Canadian investment executive described why he chose not to incorporate artificial intelligence (AI) into his firm’s portfolio management process. His reasoning was based on the distinctively human ability to “read a room” and gauge the sincerity of corporate management teams, which cannot be replicated by a machine or algorithm.
Even if you believe that investment professionals possess this “sixth sense,” the simple fact is that it has not enabled them to produce superior results. According to the latest SPIVA (S&P Index vs. Active) Canada report card, over the past 10 years:
- 91% of Canadian equity funds underperformed the TSX Composite Index
- 97% of U.S. equity funds underperformed the S&P 500 Index
- 100% of Canadian dividend-focused funds underperformed the TSX Dividend Aristocrats Index
Aside from the alleged ability to gauge the truthfulness of a person’s statements, there is another human characteristic that AI lacks. Unlike their human counterparts, AI algorithms do not have emotions or cognitive biases, which often lead to poor investment decisions.
We have met the enemy – and the Enemy is Us
The field of behavioural economics studies the effects of psychological, cognitive and emotional factors on the economic decisions of individuals and institutions. This field has produced countless studies that have conclusively demonstrated that when it comes to investment decisions, people harbour subconscious biases that result in suboptimal results. Moreover, these biases are not restricted to individual investors, but also permeate the decisions of professional managers and institutions.
A study called “Stock Repurchasing Bias of Mutual Funds” investigates whether the emotional association that managers have with stocks that they’ve either bought or sold in the past makes them any more likely to buy those stocks again. The authors concluded that selling a stock for a gain is associated with positive emotions such as pride and happiness, while selling a stock for a loss is associated with negative emotions such as regret and disappointment.
To repeat the positive emotional experience and avoid the negative one, mutual fund managers were more prone to repurchase stocks which they sold for a gain (i.e. a past “winner”) and were less likely to repurchase stocks that they sold for a loss (i.e. a past “loser”). The study concluded that this behaviour was associated with lower performance.
Another paper called “Fund Manager Overconfidence and Investment Performance: Evidence from Mutual Funds,” concluded that investment managers are “prone to overconfidence and behavioural biases,” and that “excessive overconfidence is associated, to a large extent, with diminished future investment returns.” In other words, outperformance tends to lead to overconfidence, which in turn tends to lead to underperformance.
Forget “Would be, Could be, Should Be” – Quantify what Is
Perhaps it is time to eschew intuition in favour of evidence-based investing. We should not rely on our “gut” or try to “read a room” to make our investment decisions. Applying sophisticated statistical analysis and machine learning techniques to large amounts of data to develop rules-based investment strategies will enable investors to achieve a favourable combination of upside participation in rising markets and downside protection in falling markets.
Noah Solomon is President and Chief Investment Officer of Outcome Wealth Management. Noah has 20 years of experience in institutional investing.From 2008 to 2016, Noah was CEO and CIO of GenFund Management Inc. (formerly Genuity Fund Management), where he designed and managed data-driven, statistically-based equity funds. Between 2002 and 2008, Noah was a proprietary trader in the equities division of Goldman Sachs, where he deployed the firm’s capital in several quantitatively-driven investment strategies. Prior to joining Goldman, Noah worked at Citibank and Lehman Brothers. Noah holds an MBA from the Wharton School of Business at the University of Pennsylvania, where he graduated as a Palmer Scholar (top 5% of graduating class). He also holds a BA from McGill University (magna cum laude).
