*This article is an unpublished academic essay submitted for the National University of Singapore.
According to a Finnish study, some of the most intelligent people actively trade in the financial markets (Grinblatt et al. 344). However, history has shown time and again that these intelligent individuals have lost money in their endeavors or were unable to achieve above-market returns through active stock picking as compared to passively buying a fund that tracks the whole stock market index. For instance, Robert Merton, one of the most mathematically-gifted financial economists in his time and had won a Nobel Prize in 1997, lost a lot of money when his hedge fund LTCM famously blew up in 1998. More notably, research from S&P Dow Jones Indices showed that over 90% of active fund managers failed to beat the stock market over a 15-year horizon. Even Sir Issac Newton, one of the greatest scientific geniuses in history, also suffered huge losses investing in the South Sea Bubble of 1720, and famously declared, “I can calculate the movement of stars, but not the madness of men.”
This beckons the question: Why is there such a dialectical tension between being intelligent (as society conventionally defines it), and seemingly not being able to use that intelligence to pick out good investments (or ‘alphas’) in the market?
In my essay, I posit two reasons – namely, the underutilization of emotional intelligences when selecting investments, and the underutilization of critical thinking in understanding investments. I will also argue why the use of Artificial Intelligence (AI) tools in the financial market will not help with achieving sustainable investment returns given these humanistic flaws, and why, despite these flaws, human beings still have the upper-hand in potentially making good investment decisions.
In Gardner’s Theory of Multiple Intelligences, he proposes that the conventional “general” intelligence (GI) is simply one form of intelligence called “logical-mathematical” intelligence in a spectrum of other intelligences (135). Going by his theory and the findings from Grinblatt et al., it can be implied that the “smart” folks trading in the financial markets tend to focus on the use of their logical-mathematical intelligences to select investments and underplay their other intelligences. This is highly risky and could lead to impaired investment decisions. According to a study done on the investment club of Mensa (a non-profit group open only to high GI individuals), it showed that the investments they made over a 15-year period from 1986 to 2001 averaged 2.5% annual growth as compared to 15.3% of the US stock market (Laise). Laise attributed this poor return on what she calls a blind-spot bias in high IQ individuals. This bias allows people to recognize other’s poor investment choices, but not their own. The higher GI the individual, the greater the bias. Moreover, they are more likely to ignore the advice of experts or peers, or to seek help when required. This bias demonstrates the lack of intrapersonal skills (another type of MI) in high IQ individuals, where they seem unable to turn inwardly and understand their own decision-making impediments and come up with sound investments.
Intrapersonal intelligence is the precursor to what other scholars have called “Emotional Intelligence” (EI). I assert that especially in the financial markets, the ability to be self-aware, control or ignore one’s emotions and understand and interpret collective human behavior is of critical importance. This is because financial markets are driven chiefly by real human beings – where human emotions and group dynamic play a very big role in deciding market movements. According to Batnick, a research director at Ritholtz Wealth Management, “patience, discipline and perspective are all more closely-tied to EI than IQ… and these traits are far more important than IQ when it comes to investing success” (qtd. in Duggan). To support these claims, El-Chaarani’s paper on the impact of EI on portfolio performance showed strong positive correlation between the two factors (487). She explains that investors who have high EI tend to demonstrate high capacity to manage and control their emotions during market fluctuations, thus making sound and rational investment decisions that improve performance. To poignantly sum this part up, I feel a need to quote one of the world’s richest investors, Warren Buffett, “If you are in the investment business and have an IQ of 150, sell 30 points to someone else. You do have to have an emotional stability and an inner peace about your decisions.”
Another reason for significant disparity between intelligence and investment performance is the underutilization of critical thinking skills as opposed to using intelligence. Gardner’s MI theory does not address where “critical thinking” as a skillset fit into the spectrum of multiple intelligences. There is good reason for this. Research findings from Lee et al. show that there is a marked difference between critical thinking and multiple intelligences due to their low correlations (208). Moreover, another paper concurs with this separation by analyzing how MI can be used to improve critical thinking skills (Zobisch 74). The results show that as use of MI increases, critical thinking test scores also increase. This finding proves two points: (1) that critical thinking does not fall under the MI spectrum; and (2) that the use of MI (specifically intrapersonal intelligence) not only improves investment performance (El-Charaani 488), but also critical thinking – which in turn will also improve investment performance.
One of the key supporters of critical thinking as a key criterion to successful investing is David Einhorn, who runs a value hedge fund Greenlight Capital which has earned 16.1% annualized returns, significantly outperforming the US stock markets. During an interview, he mentions critical thinking in the context of investing as “the ability to look at a situation and see it for what it is, which is not necessarily what is presented. When something makes sense, figure out what. When something doesn’t make sense, question it, challenge it, look at it from a different way. In doing so, you may often come to the opposite conclusion.”
The ability to question assumptions about economic or financial data is entrenched in rationality. Some might argue that rationality is part of MI theory, developed through the logical-mathematical intelligence. However, Gardner himself has dispelled this by citing the works of Stanovich, saying that “he has assembled evidence that rational thinking is significantly different from the logical and mathematical capacities that are probed in IQ tests.” In fact, Stanovich terms these people as having “dysrationalia”, or the inability to think and behave rationally despite having high IQ (11). Although no studies have been done to show the correlation between critical thinking and investment results, we can refer to the recent works of Butler et al. who showed that critical thinking ability chiefly and strongly predicted the occurrence of negative life events, as compared to a relatively weaker prediction using intelligence (41). Hence, we might extrapolate the results to conclude that high critical thinking abilities can help an astute investor identify red flags in an investment and avoid negative outcomes. As history has proven with the stock market crashes during the 2000 dotcom crash and 2008 housing crisis, we can further infer that most investors do not fully utilize critical thinking in their investments as they fail to sidestep such negative outcomes. To further illustrate this point, we see a prominent example of herd mentality during the run-up to 2008. Investors saw their friends making money and acted on their logical-mathematical intelligences to capitalize on those bullish gains themselves (ie. logical investors equated market exuberance to strong growth in the underlying companies) but failed to exercise rationality and critical thinking in finding out what was indeed fueling the growth.
With the liminality of human decision-making processes, the move to Artificial Intelligence-based (AI-based) investment decision-making has been on the rise. AI-based investment systems promise an emotion-free, bias-free, systematic approach to investments which have drawn many investors over. However, I argue that such AI-based systems contain inherent flaws closely linked to the above reasons (ie. the more “humanistic” flaws) and thus cannot provide an investor with sustainable market-beating returns. To demonstrate this point, positive expectations for AI technology have been met with underwhelming results as an AI-based Exchange-Traded Fund (ETF) had only net a 3.1% gain, underperforming the US stock market which had a 5.1% gain in 2017 (De Aenlle).
The core argument against current AI-based systems is that these systems fundamentally assume that all market participants (ie. investors) make investment decisions rationally based on the information they have. This assumption echoes a widely-used conventional finance theory known as the Efficient Market Hypothesis (EMH). However, we have demonstrated above that in reality, investors are not always rational in their decision-making and barely use their critical thinking capabilities to make informed choices. Such disparity between reality and theory causes the AI-systems to predict false-positives or false-negatives and lead to foolish decision-making.
The applications of EMH by fund managers, investors and AI-based systems are not a result of “un-intelligent” thinking. Rather, these market participants have logically gained confidence in the theory because they have demonstrated epistemic authority (Eugene Fama, the influential economist who developed EMH, won a Nobel Prize in 2013) and provided students of the theory a “simple, neat and logical” framework (Harding) to understand financial markets. However, a study by Harvey and Liu found that “most of the empirical research in finance… is likely false” as they have not been rigorously tested against data (116). The findings proposed that some of this research may be the result of ‘data mining’, a scurried attempt to correlate datasets to get papers published, but they might not work in reality. As better data become more available and econometric systems become more sophisticated, we start to find inconsistencies in EMH that have failed to surface in the past (Jensen 100; Hoover 30-31).
By understanding this rationality assumption, we understand that AI-based systems “learn by studying past patterns they expect to continue” (qtd. In De Aenlle). Therefore, it seems apparent that AI cannot correctly predict and avoid sudden market crashes as it continues to forecast irrational optimism that the most recent data provides. For instance, the housing bubble of 2008 could have led AI-based algorithms to logically predict higher stock prices. However, only a humanistic observation of the increasingly loose due diligence in the issuance of mortgages, and the irrational optimisms surrounding financial markets which saw the rise in a lot of risky investments, will point us to potential red flags and rationally question why stocks are rallying so strongly during the 2007 period.
One might argue that AI-based systems could potentially process certain words or phrases from a whole databank of financial news reports and spit out a value on a gauge of investor sentiments. However, Arnott (qtd. in De Aenlle) argues that “there are too many moving parts in financial markets for A.I. to get its mechanical head around… and too many humans with flaws, motivations and unpredictable behavior doing the moving.” With this, he further asserts that the markets are far more complex than games like chess that AI has already mastered.
Even if the antithesis were true and that AI-based technologies can predict market-wide investor sentiments, Warren Buffett was quoted to have said, “forming macro opinions or listening to the macro or market predictions of others is a waste of time.” This is because even though AI-based systems have the advantage of digesting and interpreting macro data, it “may struggle with more qualitative judgements Buffett has excelled in, such as judging the character of a chief executive of the durability of a brand” (Johnson).
So, the question that faces us now is: “What should investors do?” Understanding the above shortcomings of human behavior (emotional) and AI-based systems (logical) in the financial markets, I posit that that investors can, and should, overcome these shortcomings by understanding stock picking through a ‘rational’ lens. This will give them the best chance of success in picking out alphas, potentially achieving higher returns as compared to the market, and give them a competitive edge over any AI-based technology.
Rationality encompasses and embraces the use of intrapersonal (or emotional) intelligences and critical thinking skills, as stated in my thesis. It gives human investors an upper hand as our brains can process more and diverse qualitative information and identify nuances or red flags within them to make an informed decision, as compared to the current AI models which excel at quantitative judgements. Stanovich approaches the degrees of rationality in individuals in what he terms as Rationality Quotient (RQ). Evidence of success using the rationality approach is seen through Warren Buffett’s stellar investment performance. He emphasized that it was RQ and not IQ that made the big difference: “How I got here is pretty simple in my case. It’s not IQ, I’m sure you’ll be glad to hear. The big thing is rationality.”
This paper has investigated the causes for the odd discrepancies between having high ‘g’ intelligences of professional stock-pickers and fund managers, and lackluster long-term stock-picking performances. The findings from this paper suggests it is due to the under-utilization of interpersonal intelligences (or EI) which led investors to give in to biases such as the herd mentality and being oblivious to blind-spots. The findings also suggest an under-utilization of critical thinking skills, which led investors to developing “dysrationalia” where they assume markets to be efficient and do not question if market optimism or pessimism is rationally justified. We also argued against the insufficiencies of AI due to their current inability to make accurate qualitative judgements – however, we do not rule out the possibility that this might be possible in the near future if we assume Moore’s Law to continue. These observations have led us to conclude that general intelligence (IQ) alone is insufficient and rationality (RQ) is also needed to achieve abnormal stock returns in the market. We hence propose using a ‘rational lens’ approach to analyze the stock markets. By doing so, we believe investors have the upper-hand against AI-based systems and other irrational human investors, and will come out the other end of the zero-sum game.
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