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Common Quantitive And Mental Mistakes Made By Investors

Summary

5 common quantitative and mental mistakes made by investors.

Experts opinion is not necessarily right.

Normal distribution is not so ubiquitous as it is known to be.

Investing rallying forward markets is not always the best option to choose.

There are certain common mistakes made not only by unexperienced but also by mature investors.Nowadays we have access to huge amount of information and sometimes it makes us confused about the real situation on the market. That is why it is always important to filter a noise from real information. Let's start out digest from informational flaws.

1. Don't trust all the financial analytic articles you read or analytical shows you watch.

There is an empirical research made by Jack Schwager that shows that after certain american investment advisory TV shows, the prices of stocks advised to be bought had a rise of 12% the night after the show. However,in the next 4 days,on average,there was a deep in their price in 10-30%.12% rise sounds logical because the fact that stocks are advised in a famous TV show makes a signal to the market that these stocks are the one to be trusted. But on the other hand,such demand is artificial and as numbers show(I repeat: on average) the real situation is different.

You should understand that every piece of information you get from financial analytic(independent) decreases the uniqueness of this information. The main question you can ask yourself is : "What independent analytic can get from this "free" advice?". Maybe your are manipulated right now by watching free advisory TV show?

2. Investing into mainstream,growing markets is not always the best idea

It is evident that IT sector is on rise now. But on the other hand,consider Chinese stocks 6-7 years ago. Almost everyone wanted to have them in their portfolio. However,when total Chinese rush ended,it appeared that these stocks were not the best thing to invest in. So,maybe next time you should make a good fundamental research of a market which does poorely now,but has a potential in the future? This will give you discoverer handicap.

3. Volatility as risk measure is right in terms of calculus,but not always right in terms of real life.

It is absolutely dogmatic that volatility became a synonym to risk. And I agree with it, but not completely. In terms of calculus it is absolutely right,however,lets make an example.

Consider a company X. On chart below we can see the price of its shares.

We can see a very good,in terms of dynamics and volatility,company. Its shares rise,volatility is relatively low. However,the next thing happened:

The probability of this event is extremely low,because the volatility is low.

Oh,sorry,I forgot to tell you one more thing about company X: it is based in country A and it had huge donations from government. But,in 70th month there was a revolution in country A(the one who lives there knows that it was coming,but not you,because you believe in calculus too much),so company X is no longer donated by government.

The key point in this story is this: company needs to have certain reasons for low-volatility stock price. If the there are no reasons for that(in fact our company was in extremely risky environment) statistics won't help you at all.However,if company has reasons for that and numbers prove it,you can use all statistics methods you have without any doubts. You should always take into account the background of each asset you invest in.

4. Ubiquity of normal distribution

Normal distribution is almost everywhere,but unfortunately it is not always true.

The main reason for normal distribution to be so popular and used on practice is Central Limit Theorem -en.wikipedia.org/wiki/Central_limit_theorem . Common mistake is that many people use it,but every theorem has certain assumptions which have to be checked. Unfortunately, people don't want to go too far and just say: Ā«OK,let's use normal distributionĀ». But,in real life these assumptions are not always true and as a result the normal distribution is not present in that situations.

To correctly define distribution you should make statistical tests(chi-square for example). In this case you can be pretty sure that the distribution is right.

Let me show you a plot taken from this article habrahabr.ru/post/191438/ :

It is evident that data is not distributed normally,but still author makes a normal distribution assumption. If you make chi-square distribution test in this case it will show that normal distribution doesn't fit the data.

The point is: if you use calculus or statistics try to make it correct and understand when and in which circumstances you can use theorems,but don't forget about the point shown in #3 mistake.

5. Attempt to get absolutely all the information provided

As it was said before the amount of information around is huge nowadays. Some investors try to collect almost every piece of information about particular asset or company,but the main skill is not the amount of information you have,but how relevant it is and how you use.

Let's think of chess players. The number of common strategies and chess-books is limited and every pro chess player knows approximately the same number of strategies.However,the main skill of good player is the way they use this information,but not the amount of it.

The same thing with investors: you should understand what piece of information you need for relevant analysis of portfolio,company or asset.

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.