By David Garrard

The internet is an amazing resource; but it has enabled an information-based society that is often plagued with unwanted noise. In financial markets, we often experience that excess clutter such as information overload and a low informational signal-to-noise ratio.

The term information overload was first popularized in Alvin Tofflers book Future Shock. It refers to the detrimental effect on understanding and decision-making that too much information can have on individuals. Information overload is a form of sensory overload which is thought to cause disorientation. Toffler suggested that predictive accuracy falls off when individuals are confronted with rapidly and irregularly changing inputs and situations. In tests, decision-making is adversely affected due to our inability to make proper assessments due to the pace and volume of inputs. Its pretty obvious that if too much information can be toxic, then we, as individual investors, should seriously review how we are utilising information.

The internet provides a treasure trove of data that requires filtration and processing into information, which in turn needs to be cultivated into knowledge, leading to understanding and ultimately wisdom. For investors and traders, achieving that high level of understanding, and perhaps even wisdom, is a distinct challenge today due to the large quantity and unknown quality of the available data.

Psychological tests indicate that it is not just the overall quantity of information that is overwhelming. The frequency of data available in financial markets has an added toxicity due to a behavioural effect termed loss aversion. Humans experience a loss with a negative effect on us that is as much as 2.5 times the positive effect of a gain. Neuroscientists have theorized that the connections in our brains from the emotional to the cognitive are stronger than the reverse. So this multiplication effect can be dramatically detrimental to your confidence if you monitor your holdings too often. Even if you have a high probability of success say over 90% on a yearly basis, on shorter monitoring intervals the probability of experiencing a success diminishes, to just over 50% for a given minute. So that means, statistically, you are going to experience more disappointment if you monitor your holdings more often and with a 2.5 loss pain to pleasure ratio, you are asking for a whole lot more pain when you monitor your holdings excessively. In financial markets, the hard-core exercisers mantra No pain, no gain does not apply!

Another issue with the internet relates to quality of information. With so much information from so many disparate sources, it becomes more difficult to establish trust and reputation, so disinformation (The dissemination of intentionally false information to deliberately confuse or mislead) is also a danger. And then who can forget those internet get rich quick schemes that announce I made $6,457,000 in one year trading the XYZ method.

Signal to Noise Ratios

Noise is toxic. Tests have shown that our ideas are sticky. Once we come up with a hypothesis, right or wrong, we are slow to change our minds. If we base our early decision on some noise, and not valid information, that can lead to an early error in our judgment. Once we get moving down the wrong track, there are substantial costs to reverse since we are predisposed to not admit our errors and we will hang on to our poor decisions. Such belief perseverance is human nature we dont like to admit mistakes.

The adverse effect of a low signal to noise ratio on information is well documented. Since we are presented with news on a continuous basis, we sometimes will place too much emphasis on the noise, mistaking it for valuable information. To control this, you can read a news summary once a week instead of more often, which leads to a higher degree of noise filtration, and thus higher quality signal content. Tweeting, sound bite journalism and other modern social networking phenomena exhibit high degrees of noise content. The scary thing is that tests have proven that we are predisposed towards a higher degree of confidence if we are receiving more input, regardless of its informational quality. This hinges on our societies recent adoption of a materialistic more is better approach in all matters including information.

With so much information in our everyday lives or within our investment research activities, we should take a leaf from the software designers book. For information processing by computers, programmers create complex filters to discover the signal within all the noise. Once the signal is laid bare, events are more easily found using pattern recognition systems and delivered via event handlers.

Black Swans

A Black Swan is a statistical outlier that has extreme consequences. Although not anticipated, much study after the fact leads us to postulate explanations for the outlier attaching some predictability to it that would have been considered inconceivable in advance. The financial market crash in fall 2008 is considered a classic example of a Black Swan. Few saw it coming, but now we often see experts exhibiting severe hindsight or I knew that would happen bias.

One sure way to avoid information overload is to ignore all the noise and only listen to the signal. So where do you hope to find the signal? Perhaps you should rely on an expert, maybe even pay them a sizable commission for their expert advice. Before the internet era, we didnt really have an information overload problem. The experts had it all and didnt share it with the masses. The problem with relying on expert(s) is that they have proven to be as just as susceptible to Black Swans as the rest of us.

Experts versus Crowds

In his best-selling book Outliers, Malcolm Gladwell studies some of the human outliers in our midst. People such as highly successful sports players come to mind. Gladwell postulates that to become an expert, an individual must devote a minimum of 10,000 hours to their discipline. For those individuals that have been highly successful, often if we look back at their upbringing, we will find a best-case environment that allowed them to quickly get the 10,000 hours, setting them ahead of their peers earlier in life. That head-start can sometimes be overwhelming and unsurpassable.

But Nassim Taleb, the outspoken author of The Black Swan, states that experts, due to the time and focus required to achieve a level of expertise, will effectively tunnel. or evolve into narrowly-focused individuals of limited value in complex decision-making systems. In an interesting study, psychologist James Shanteau discovered that some disciplines have experts and others, due to their very fluid and transitory nature do not usually support experts for long. This suggests that experts can only survive in disciplines where Black Swans are inconsequential: think of chess masters, figure-skating judges and astronomers does anyone really get hurt in these fields? In finance, where a Black Swan might occur once or more within the 10,000 hour timeframe required to achieve expertise, and are extremely consequential to peoples savings, we rarely see experts surviving for long.

Taleb goes on to suggest that we need not always question the expertise delivered, but rather we need to question the error rate of experts. An overconfident expert, or one with a lack of appreciation of their own error rate, is a dangerous expert indeed.

In the 21 st century, a kind of anti-expert decision-making theory has evolved. The Wisdom of Crowds, a book by James Surowiecki, popularised the concept. Numerous attempts, some successful, others not, have been made to profit from the collective wisdom of the crowd. Complex text mining systems are trolling the internet as you read this, attempting to decipher news stories, blogs and other possible sources of investor sentiment for collective wisdom. Unfortunately, information overload and the challenges of signal-to-noise ratio lead to similar hurdles when attempting to derive wisdom from large quantities of crowd data using automated methods.

Actionable Intelligence and The Spies Amongst Us.

The intelligence community has adopted a different approach to information. For reasons of national security, intelligence gathering is all about averting Black Swans.

Unlike the financial markets that contain an abundance of structured data available for sale or free, the spying business is heavily dependent on unstructured information gathering. Both communities have at their disposal massive amounts of real-time data, with sophisticated information infrastructures feeding analytical and visualisation tools that improve human cognition. The goal in both the financial and intelligence communities is to generate actionable intelligence information that facilitates timely decision-making under conditions of uncertainty.

Spies have a concept called sense-making, or the ability to create situational awareness or make sense of an ambiguous or highly complex situation. When we evaluate opportunities for investment, we are conducting a sense-making exercise to improve our decisions.

Conclusion

Understanding information overload and its affect on us is important to investors and traders. We need to be more aware of some of the human weaknesses that affect our judgments. By using tools that control and filter data flow, we can expect to generate more actionable intelligence, heighten our tradable idea flow, and add some structure to our trading. If you begin by developing a systematic way to optimize idea flow, then that approach can be subsequently monitored for effectiveness.

If you are a beginner investor, you are well advised to commence tracking all your activities related to trading including the trades themselves in a trading diary. This is an important first step to develop a self-reliant, structured, monitorable and improvable trading methodology.

David Garrard is VP Asia Pacific for Recognia Inc. a provider of web-based financial analytics and research content for on-line stock brokers. David has been trading for close to 30 years, and has been instrumental in the development of the Recognia product suite, the underlying analytics and the extensive infrastructure required to deliver financial analytics to global clients. With a degree in Computer Science and Physics and access to Recognias extensive database of recognised patterns in stock prices, David has also developed back-testing and data mining systems that have been used in quantifying and optimising the performance of Recognias analytics. He can be contacted at dgarrard@recognia.com