The Psychology of Trading

by Brett N Steenbarger

  What seems clear is that participants in the implicit learning studies are learning complex patterns: patterns too complex, Reber noted, to be learned in a single afternoon. In one study, participants learned to predict the production level of a sugar plant, given the underlying rule Production = 2w – (p + n), where w was the number of workers in the plant, p was the previous trial's output, and n was a noise factor. Reber asserted that knowledge acquired implicitly underlies performance that could be accounted for by conscious knowledge.

  What is the nature of this tacit knowledge? Evidence suggests that participants in implicit learning studies are acquiring knowledge of statistical regularities among the data. Reber noted that, if events El and E2 occur with probabilities .80 and .20, respectively, the guesses of participants over time will approximate .80 and .20 for the events. It is not necessarily the case that participants ever acquire the true rules of the grammar or sugar production task. Rather, they seem to build implicit representations of the probability of events determined by those rules.

  The key to implicit learning appears to be the large number of trials (generally one thousand or more), prompt and accurate feedback, and a high degree of focus and concentration on the part of the participants. If, for instance, the participants are distracted with other tasks during the implicit learning trials, the quality and the amount of learning will diminish significantly.

  Cleeremans proposed that human beings are processing the information during these studies much as a neural network processes data. They establish connections among events based on statistical regularities. When these connections are sufficiently strong, they reach the threshold of awareness and become explicit. What people know, therefore, exists on a continuum from tacit to fully explicit, with many gradations between. This sets up potential clashes between what people know—their verbal, explicit understanding—and what they sense. Indeed, this is what Reber found in his review of research. When participants are told the rules underlying the learning trials, or when they are prompted to search for rules and the rules are relatively simple, explicit thought appears to aid performance in the implicit learning experiments. But when the rules are so complex that participants cannot figure them out for themselves, efforts at explicit rule finding actually hinder performance. It appears that the tacit encoding of information in these studies is relatively independent of the accustomed, explicit learning processes.

  Summing up these studies in Implicit Learning and Tacit Knowledge, Reber concluded that participants can learn to utilize complex structural relationships in presented data in a completely nonreflective manner. I propose that traders acquire expertise in the markets in a very similar manner. They immerse themselves in complex, noisy stimuli (market data) and gradually acquire information about the regularities among these stimuli. Once they have achieved an extended degree of immersion—far before they can verbalize what they know about the markets—traders gain a sense for when markets are likely to rise and to fall. It is only after the patterns have been overlearned that they can be verbalized and captured in explicit rules and trading systems.

  If this is the case, then a key to developing trading expertise may be the degree of immersion with which one approaches the markets. By following markets day in and day out for a number of years and carefully following trades that are placed, traders may internalize rules of the markets much as children internalize the rules of spoken language. To the degree that traders approach their work on a part-time basis and/or lack immersion in the real-time patterns of the markets, their learning is apt to be impaired.

  This lack of immersion may also help to explain why so many people undertake trading and why so few actually succeed at making it their living. Quite simply, they cannot survive their learning curves. If the implicit learning of market patterns requires thousands of trials—as for the participants in Reber's and Cleeremans's studies—it would have to be a dedicated student—and perhaps also one with deep pockets—to undergo the inevitable frustration of the learning period. Then too, traders may not trade with sufficient frequency to obtain the exposure necessary to internalize market patterns. If a trader places only two orders per day, it would take over two years of trading to achieve the number of trials found in an implicit learning study. Even this, however, might not be sufficient to trigger tacit learning. The trials in a typical implicit learning study are very closely spaced, preventing any interference from intruding explicit thought and emotion. For the trader who is trading only twice daily, there is plenty of opportunity for such interference. It would be surprising if such distantly spaced learning trials could generate the learning necessary for internalized market expertise. Most traders may fail to learn market patterns not because of their emotional difficulties or self-defeating tendencies, but simply because of their lack of sufficient exposure to the markets.

  IMPLICATIONS OF THE IMPLICIT LEARNING RESEARCH

  This line of reasoning can be followed through to its possible conclusions. If implicit learning accounts for trading expertise, one would expect to see the best evidence of tacit knowledge among traders who have been immersed in trading, with frequent learning "trials." These would be floor traders, scalpers, and other very high frequency traders. In following the markets minute by minute, day after day, and in placing and following dozens, if not hundreds, of trades each day, the floor trader is a natural field study in implicit learning.

  It is interesting that among the traders Linda Raschke and I surveyed, several had floor experience or were very high frequency off-floor traders. It was clear to us that these traders were utilizing methods different from those used by the others. They were less likely to rely on elaborate research for their decision-making and much more likely to cite "instinct" in entering and exiting trades. The successful traders in this group did operate in a rule-governed way, but the rules were relatively simple heuristics that helped limit losses and that governed overall activity. For example, one of the traders automatically exited a position after it had moved against him by only several ticks. This strategy led him to enter and exit markets quite frequently, increasing the number of "learning trials" available to him.

  Such a rule helped reduce this trader's losses when he was wrong about the market, but, from his account, similar rules were not governing his entries. He had developed a feel for market momentum, the action of the strong and the weak players, and the patterning of the bids and asks. His knowledge was implicit, in that he knew enough to be successful on the floors but could not verbally duplicate his knowledge so that his listeners could replicate his performance.

  Moving from such scalpers to frequent but less frenetic traders, such as Linda, one finds that an increasing proportion of market knowledge—and market decision making—is explicit. Linda can indeed verbalize many of her rules for trading and illustrates these with examples each day for members of her trading chatroom. It is far from clear, however, that all of what Linda knows is subsumed in her rules. She does not take a trade every time a verbalized pattern occurs; rather, she seems to have an implicit sense for when these patterns might pay off and when they might not. Her trading is highly rule governed, but not mechanical. Implicit knowledge plays an important role in helping her apply her trading rules. Someone who simply read Linda's rules and mechanically applied them would be unlikely to obtain her trading results. No doubt this is because Linda has spent many years, day after day, immersed in the markets and in frequent trading.

  Moving further out the time line to swing traders and intermediate-term traders, who might be trading only one or twice a week or month, it becomes increasingly clear that learning trials occur too infrequently and with too much spacing to permit implicit learning. Unless the trader found some other means to generate the implicit learning trials—by faithfully and intensively reviewing charts, historical market data, and so on—little tacit knowledge could be expected. Successful longer-term traders could thus be expected to rely far more on explicit rules and systems as a ba
sis for their entries and exits. Good examples of such rule-based, longer-term trading are contained in Yale Hirsch's Stock Trader's Almanac and Jon Markman's Online Investing. Yale focuses on seasonal patterns for the broad market, such as the tendency of markets to perform much better during the latest and earliest months of the year, as compared to the middle months. Jon identifies model portfolios based on such screening criteria as momentum and growth. Other Markman portfolios draw on seasonality patterns among individual stocks and stock sectors. By utilizing the rules from such research as decision-making aids, longer-term traders who have not internalized an implicit feel for the markets can still give themselves an edge. After all, if a trader's hunches do not contain (tacit) information, eliminating those hunches from decision-making processes makes a lot of sense.

  If I am correct in surmising that the time frame of the trader plays a significant role in determining the relative importance of implicit, intuitive knowing versus explicit knowledge in trading, the psychological techniques reviewed in the previous chapters take on a different cast. Techniques for maximizing focus, for dampening intrusive emotional and cognitive reactions, and for becoming highly sensitive to personal and market markers would be especially helpful to the scalper. This is because all the scalper needs to know to make a successful trade is already present in his or her experience. The most successful psychological interventions will be those that remove interference from this tacit knowing.

  Longer-term traders have much more time than scalpers do to think about their positions and to act out personal patterns from other facets of their lives. They are most likely to benefit from techniques that undermine problem patterns by leaping to new states and by anchoring solution patterns in distinctive modes of experiencing. The longer-term trader, relying more on explicit rules than on tacit knowledge, will also benefit most from an immersion in formal research and from an increasing formulation and anchoring of trading rules.

  Trading failure could be expected among longer-term traders who attempt to trade intuitively and from scalpers who attempt to overanalyze their trading. The longer-term trader lacks the tacit knowledge to trade intuitively, due to insufficient learning trials. As a result, he or she is apt to fail in extracting the signal from the noise. The scalper, conversely, needs immediate access to his or her implicit knowledge and cannot afford to second-guess what is already known. Overanalyzing trading for the scalper would be analogous to a pitcher's becoming overly aware of his delivery, aiming the ball instead of delivering the pitch fluidly. The interference of explicit processing is apt to undermine the natural, tacit performance.

  Are some people more talented as implicit learners than as explicit ones? Do highly complex, noisy markets better lend themselves to implicit learning than to efforts at explicit formulation? There are many unanswered questions triggered by this research literature. It is quite conceivable that different people possess distinct information-processing strengths and weaknesses and that markets possess varying degrees of signal and noise. Finding the right fit between cognitive style, trading style, and trading vehicles may play a crucial role in determining the success or failure of trading.

  GREATNESS AND THE ACQUISITION OF EXPERTISE

  Studies of accomplished individuals in creative and scientific fields suggest that many years of practice, study, and apprenticeship typically precede the internalization of world-class skills. Reviewing research on genius, R. S. Albert pointed out that the key to extraordinary achievement is productivity, the ability to generate large volumes of varied contributions. Dean Keith Simonton, in his text Greatness: Who Makes History and Why, similarly noted that renowned individuals are driven by unusually strong motivational forces. Indeed, one of Simonton's more provocative findings was that eminent contributors to their fields produce the same proportion of unsuccessful works to successful ones as do lesser contributors. Their eminence is due to the fact that they produce so many more works than their peers that the odds of making a lasting contribution are greatly enhanced. But to sustain such productivity over a long span requires unusual dedication.

  This dedication is first manifested in the willingness to undergo a prolonged period of learning and practice. K. Anders Ericsson, in his edited volume The Road to Excellence, noted the "10-year rule," in which 10 years of intensive preparation are necessary for the cultivation of expertise in chess and in other domains. Ericsson and colleagues studied the acquisition of expertise by collecting diaries from skilled and amateur musicians. They found a direct correlation between the number of hours spent in deliberative practice and the degree of expertise of the musician. By the age of 20, the top group of violinists, for example, had spent over 10,000 hours on deliberative practice. Ericsson's review of studies further suggested that concentration is an essential ingredient of deliberate practice. The best-performing musicians practice with the greatest intensity, but they also take frequent naps to combat fatigue. They not only practice for more hours but also obtain more from each practice session due to the intensity of their focus.

  Francis Galton was correct when he indicated that great individuals seem to be urged by inherent stimuli to achieve their eminence. This can only happen among individuals who derive pleasure and fulfillment from the effortful pursuit of goals. This goes back to Mihalyi Csikszentmihalyi's notion of flow: the intrinsically rewarding state that creative individuals experience when they are immersed in their work. Work itself generates a state-shift for the creator and is anchored to a positive place on the creator's radio dial of consciousness. It is difficult imagining sustaining high-quality practice and skill development over 10 or more years in the absence of such inherent stimuli.

  And yet, there is something beyond the intrinsic pleasure of effort that appears to impel the high achiever. Without confidence, a deep belief in his or her success, no one could sustain years of effortful pursuit of life's goals. In his insightful autobiography, The Education of a Speculator, Victor Niederhoffer told the story of first meeting the tennis and squash coach at Harvard, Jack Barnaby. Before Niederhoffer had even started playing the game, he announced to Jack that he would be the best player ever. Sure enough, after 14 months of hard work, Niederhoffer was the National Junior Champion. The abiding belief in the self, yoked to a love of work and effort and aided by a depth of concentration, appears to be a potent combination in generating success in life—and in the markets.

  Niederhoffer's checkers coach, Tom Wiswell, left behind a legacy of proverbs about winning that his student shared in his book. "Success does not come all at once," Wiswell noted. "Even for masters it comes in stages, separated by years" (p. 168). But, Wiswell explained, "Only those with passion can become masters" (p. 168). The capacity to sustain passion over years makes for success in markets as well as in marriages.

  Could it be that greatness in trading follows the same patterns observed among artists, scientists, and squash players? If so, one would expect the expert trader to have spent a sustained period of time immersed in the markets, following market action, practicing trading, and maintaining a high level of focus and concentration. These are the very same conditions that generate optimal implicit learning. Expertise appears to be gained by maximizing the number of learning trials and by maximizing one's focus during these trials so as to extract the most learning possible. When this can be sustained over a lengthy period, the result is an internalized set of skills that, like the competence of the violinist, cannot be readily captured in verbal form.

  ETCHING THE BRAIN

  An important implication of the implicit learning research is that any technologies that could accelerate market exposure would be of immense benefit to the development of traders. These technologies would include, first, ones that simulate and/or play back market action for traders so that they can immerse themselves in market patterns even outside normal trading hours. It is in this context that my practice of creating flash cards from past market data makes particular sense. By printing out the best trades for each
day and what the markets were doing prior to those trades, I create a set of learning trials that can be rehearsed on demand. This is similar to Linda Raschke's exercise of posting charts for seminar attendees and asking them to predict the upcoming market action. In flashing one chart after another and providing ready feedback, she is, in essence, accelerating learning by stepping up the implicit learning trials.

  I believe there is much to be gained by such exemplar-based education. When traders experience a large number of examples of trading patterns in a compressed fashion, they may gain the ability to implicitly abstract essential features of markets that are ready to move. The key to this experience is generating enough examples and presenting them in a closely spaced manner. As I mentioned earlier, when I recently talked with Carl Swenlin, developer of the Decision Point web site (www.decisionpoint.com), he seemed to be sensitive to the learning needs of traders. He said that by organizing the charts both by indicator (looking at a given indicator across multiple markets) and by market (looking at a variety of indicators for a single market), the site provides "an easy way to quickly review different perspectives." One of my favorite exercises with the site is to rapidly click chart after chart of different sector indexes and market indicators, keeping my mind as loose and open as possible. Very often, a sense for a big picture will emerge that can provide the basis for more hard-nosed testing.