Spencer Li

Self-Attribution Bias – Don’t Confuse Brains With a Bull Market!

Self- attribution bias (or self-serving attributional bias) refers to the tendency of individuals to ascribe their successes to innate aspects, such as talent or foresight, while more often blaming failures on outside influences, such as bad luck. There are actually two kinds of self-attribution bias, namely self-enhancing bias and self-protecting bias.

Self-enhancing bias represents people’s propensity to claim an irrational degree of credit for their successes, for example, if people intend to succeed, then outcomes in accordance with this intention will be perceived as the results of them acting to achieve the intention, regardless of whether the actions indeed played a crucial role.

 

Self-Attribution Bias

 

Self-protecting bias represents the corollary effect – the irrational denial of responsibility of failure, for example people trying to maintain their self-esteem by protecting themselves psychologically as they attempt to comprehend their failures.

Irrationally attributing successes and failures can impair traders in two ways. First, people who aren’t able to perceive the mistakes they’ve made are, consequently, unable to learn from those mistakes. Second, traders who disproportionately credit themselves when desirable outcomes do arise can become detrimentally overconfident in their own market savvy, leading to overconfidence bias.

When trades turn out well, people like to think that their method or analysis was fantastic, and that they are good traders. When trades do not turn out well, people will blame their broker, their platform, the news – basically anything but themselves. As you can see, over time, this leads traders to think that they are much better than they actually are.

What is the best solution for this?

One way to overcome this bias is to treat both winning and losing trades as objectively as possible, tabulating and recording them to obtain a running record. It also helps to do an objective post-trade analysis, reviewing your records to learn from past mistakes. With sufficient data, one can then objectively analyse the consistency of the methods and returns, and as they say – the numbers do not lie.

“Don’t confuse brains with a bull market.”

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Spencer Li

Simple chart-reading can tell you all you need to know

Leonardo Da Vinci once said that simplicity is the ultimate sophistication. Let’s take a moment to ponder that. This applies to research analysis as well. When you hear people talking about some sophisticated trading system or some flashy indicators or some complex wave projections, think again. It is more likely to be smoke and mirrors. All these tell you nothing new if you know how to read the bare charts. It’s as simple as that. Simple, and yet sophisticated. Looking back at my last few stock picks, I found that it is possible to read and understand what is happening on the charts. This makes it possible to pinpoint the low risk entry points, as seen in some of my previous posts.

https://synapsetrading.com/dbs-are-the-banks-leading-the-decline/
https://synapsetrading.com/noble-group-evening-star-signals-turn-to-the-downside/

Compare that with indicators. If you see a green arrow, do you know why it is a buy? Maybe it worked the past 3 times, but will it work this time? Maybe. Or maybe not. You won’t know. In fact, you won’t have any idea why there is a green arrow. You won’t know what is happening in the market. You won’t know what the smart money is doing. That is why chart-reading is an important skill everyone should master. Banks, funds and proprietary trading firms use it as their main tool. Maybe you should consider it too.

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Spencer Li

Representativeness Bias – The Dangers of a Small Sample Size

In order to derive meaning from life experiences, people have developed an innate propensity for classifying objects and thoughts. When they confront a new phenomenon that is inconsistent with any of their preconstructed classifications, they subject it to those classifications anyway, relying on a rough best-fit approximation.

 

Representativeness Bias

 

There are two main types of representativeness bias, namely (i) base-rate neglect and (ii) sample-size neglect. We will focus on the latter, since it occurs more frequently in trading.

In sample-size neglect, traders, when judging the likelihood of a particular trade outcome, often fail to accurately consider the sample size of the data from which they base their judgments. They incorrectly assume that small sample sizes are representative of populations. This is also known as “the law of small numbers”.

This problem is observed when traders try to backtest systems by using small sample sizes of data, and extrapolate their favourable results. However, these results are most likely not representative of the effectiveness of the system. This is a common tactic applied in marketing gimmicks.

Another common phenomenon has to do with hot tips. For example, you might hear someone say “my broker gave me three great stock picks over the past month, and each stock is up by over 10%”. While this is enough to sway most people, thinking that the broker is a genius, this assessment is based on a very small sample size.

What is the best solution for this?

If you want to evaluate the effectiveness of system or the stock-picking skills of a person, make sure you do it over a large sample size, and count both the hits and misses. This will give you a more complete representation of reality.

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Spencer Li

Cognitive Dissonance Bias – This Can’t Be True!

When newly acquired information conflicts with preexisting understandings, people often experience mental discomfort – a psychological phenomenon known as cognitive dissonance. Cognitions, in psychology, represents attitudes, emotions, beliefs, or values; and cognitive dissonance is a state of imbalance that occurs when contradictory cognitions intersect.

 

Cognitive Dissonance Bias

 

This term encompasses the response that arises as people struggle to harmonize cognitions and thereby relieve their mental discomfort. For example, a trader might take a long position in s stock thinking that the trend is up, however when a new cognition that favours a downtrend is introduced, representing an imbalance, cognitive dissonance then occurs in an attempt to relieve the discomfort with the notion that perhaps the trader did not make the right decision.

People will go to great lengths to convince themselves that the decision they made was the right one, to avoid the mental discomfort associated with their wrong decision.

Psychologists hence conclude that people often perform far-reaching rationalizations in order to synchronise their cognitions and maintain psychological stability. There are actually two kinds of cognitive dissonance bias – (i) selective perception, where people only register information that appears to affirm a chosen course, and (ii) selective decision making, where people rationalise actions in order to stick to an original course.

The dangers are obvious. Traders who are not bias-free cannot read the markets objectively, and will not be able to adapt fast enough to changing market conditions. Selective decision making could also lead to a resistance to cutting losses, and coming up with various excuses to avoid admitting their initial entry was indeed erroneous.

What is the best solution for this?

The key to overcoming this is to immediately admit that a faulty cognition has occurred, address feelings of unease and take appropriate rational action. If you think you have made a bad trading decision, analyse the decision; if the fears prove correct, confront the problem head-on and rectify the problem.

This above all: to thine own self be true,
And it must follow, as the day the night.
– Polonius to Laertes, in Shakespeare’s Hamlet

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Spencer Li

Market Seasonality and Patterns – When is the Best Month to Buy?

One aspect of market analysis is statistical analysis, which is using statistics to find correlations and patterns, where opportunities of skewed probabilities may lurk, giving you an edge over the market in the long run. For investors, this lets you know the best month to start building your portfolio, or to rebalance/adjust your portfolio allocation.

Market Seasonality and Patterns - When is the Best Month to Buy?

Market Seasonality and Patterns – When is the Best Month to Buy?

Seasonality is a characteristic of a time series in which the data experiences regular and predictable changes which recur every calendar year. Any predictable change or pattern in a time series that recurs or repeats over a one-year period can be said to be seasonal.

This is different from cyclical effects, as seasonal cycles are contained within one calendar year, while cyclical effects (such as boosted sales due to low unemployment rates) can span time periods shorter or longer than one calendar year.

For the Singapore stock market, I have done a seasonality study, showing which months are more bullish and bearish. Contrary to popular belief, October is actually a rather bullish month. Every month has its unique characteristics, which skews the probability. As a trader,anything that tilts the probability in our favour is considered an edge.

Here are the results of my research:

Singapore stock market

Some key points to note: the best months for being LONG are April, November and December, while the best months for being SHORT are June, August and September.

There are many other patterns (some less obvious) which could have a significant impact on the stock market. Although your trading decisions should not be based solely on these, they can act as a powerful confirming indicator, or help you adjust your position-aggressiveness.

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