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I have fond memories of this author – one of my favourites among the books I’ve read. I was hooked by his style of skeptical empiricism when I was introduced to his first book – ‘Fooled by Randomness’ – by serendipity. The author will be glad that it is through a series of black swan event that lead me to his first book, and consequently, to his second book.



Long time ago, my gf went to US for a conference on a paper she submitted. Man, in these conferences, there are people literally giving out books for free (to be fair, they did try to sell during the first few days of the conference, but towards the last day, book hell went loose). My gf, a typical free-must-grab Singaporean, grabbed a few of the books, regardless of race, language or religion (ok, I exaggerate). Among the horde of books, one of them is Nassim Nicolas Taleb’s ’Fooled by randomness’.

Talk about Black swan events (these are incalculable, low probabilistic and highly consequential events), my introduction to the author’s first book must fall squarely into such category. The author’s style is very refreshing, a logical salad mixed with lots of stories woven with facts, creating a potent mix of philosophical brouhaha.

Black swans are named as such because of this story. Imagine all throughout your life, you only saw swans which are white. Based on historical past, you can ‘extrapolate’ your data by induction that all swans are white. This is all jolly well and good until one day you saw your first black swan. This event totally tears away your hypothesis that all swans are white and is something that the past data can never predict. The probability of meeting a black swan is not calculable, though based on the past data it has a very low probability (which explains why you didn’t see one earlier) and has serious consequences. Here, ‘absence of evidence’ is misconstrued as ‘evidence of absence’.

Are we similarly fooled by such logic errors? I can think of a few:

1. From analyzing a company’s past earnings for say 10 years, I come to the conclusion that the earnings are stable and growing steadily. I treat the absence of evidence of ‘poor earning years’ as the evidence of absence of ‘poor earning years’, leading to my skewed over-bullishness of the company.

2. A man, having lived till a ripe age of 100 years old, declares that from his 100 years of non-dying, he is an immortal and thus will carry on living for a few 100 years more. He made a mistake of thinking that ‘no evidence of death’ is the same as the ‘evidence of no death’.

These are exaggerated examples to illustrate the points, but it blows my mind to think in this way.

‘The Black Swan’ carries with this style of writing, perhaps more brilliantly so. The new book talks about how we systemically and biologically ignore black swan events. In fact, he argued that the world is governed mainly by black swan events, not by regular and inconsequential events that are predictable. He goes as far as to say that what we do not know is far more important than what we know. Among the most important things I carried away from reading the book, is this notion of ‘silent evidence’ – how we are blinded by things that are not found in the sample size, hence we discounted them to the extent of skewing our perception of things.

Below is an example to illustrate this point:

Consider the world’s richest people – all of them exhibit traits of risk-taking, go-getter mindset, determined etc etc. Thus,

Rich people have a certain characteristics.
I have these characteristics.
Therefore, I’ll be a rich person.

This is logically flawed. Having a set of characteristics exhibited by rich people does not necessarily mean I’ll be a rich person. There are many examples of people with these characteristics but are not necessarily rich. Hence, these people who had these set of characteristics could be there based on pure luck.

I think these ideas ties in very much with the idea of causality. Correlation does not necessarily imply causality, though causality implies correlation. For example, almost 99.9% of cancer patients drink water (high correlation) but it does not mean that drinking water will cause cancer (no causality). We are often tricked into believing such logic errors, which tend to exacerbate the consequences of black swan events.

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The review is split into two parts because the books is exceedingly thick and I had to write down my thoughts halfway before it gets diluted and lost in transition.

This had to be the most the most relevant book I’ve read in my life. It’s like I’ve been born blind, then after a corrective surgery, I see the world as it is now but had been elusive to me before. I can never look at the world again because the book had permanently changed me. Don’t go out and grab the book because I said so…my experiences lead me to different perspectives when I read the book, which will be vastly different from another reader.

The second half of the books talks about more technical stuff. I confess I do not really understand the whole of it in the first reading. Perhaps, like Intelligent investor, the true gist of it will only be realized after subsequent readings. The author highlights the fallacy of treating everyday occurrences to fit the bell-curve (the Standard normal or Gaussian distribution). This fallacy is so rampant that it permeates most of the ‘scientific’ methods regarding a range of socio-economic disciplines like economics, sociology and finance. I’ve never believed in the Modern Portfolio Theory (MPT) and efficient market hypothesis (EMT), so this didn’t struck me hard enough to feel defensive about his ideas.

I guess the author wants us to follow a bottom-up approach rather than a top-down approach in everything. A bottom-up approach means using real, empirical data to look at the world. A top-down approach means learning ‘scientific’ theories and principles, then fitting data that follows them and ignoring or downplaying data that do not.

A simple example would be a stereotypical Western trained doctor who abhor using traditional chinese methods like acupuncture or herbs, judging it unscientific (though it’s very empirically based) because of top-down approach.

Am I also blinded by my own theoretical framework?

The author suggests a few ways to deal with the uncertain world:

1. Make a distinction between positive black swans and negative black swans. Black swans events can benefit or cripple. Once identified, expose yourself maximally from positive black swans, and limit exposure to negative ones

2. Invest in preparedness, not in prediction

3. Seize any opportunity, even those that looks like opportunity. This is the same as exposing one to positive black swans. Work hard, not in grunt work, but in maximizing one’s exposure to opportunities.

4. When caught between unknown probabilities of choices presented to you, focus on the (known) consequences of each choice and not on the probabilities. It’s also known as Pascal’s Wager.

I started this book being blind, and I finished this book knowing that I will never view the world the same again.

I watched a wonderful movie titled “Wanted” last weekend.


This movie had the philosophical depth of ‘Fight club’ (starring Brad Pitt and Edward Norton) and similarly, it had nothing to do with violence as any review of the movie would have you believe. The underlying theme of the movie is destiny and fate. Is one’s destiny in life determined by fate?

The main character is Wesley (it’s rare that I remembered the names of the characters). An accountant by professional, he suddenly found himself having the talents to be one of the greatest assassins in a brotherhood called the Fraternity. This brotherhood of assassin believes that the end justifies the mean – a theme explored by Niccolo Machavelli’s classic work “The Prince”. The assassins under the Fraternity will be issued a cloth, whose threads lay out a binary code which can be decoded to give the name of the person which had to be killed in order to preserve the fabric of life. Thus, there is a struggle within each assassin as they had to deal with the moral conflict of doing evil for a greater good.

To drive in the point of the main theme, the Fraternity also owns a yarn factory, where threads are woven into cloth. There is a special room where the encoded cloth is woven and decoded before given to the assassins to carry out the necessary. I half-expected to see three old hags inside, spinning threads into cloth. But no, they are not inside the movie, though the similarity in the Greek mythology of the old hags of Destiny and Fate – The Three Fates - is not lost on me. According to Greek legend, these old hags run a yarn operation. One of them spins the thread of life, the second allocates the length of the yarn and the third snips it off. Good and evil is all woven in one’s destiny and nobody can escape it, not even the Gods.

The story then goes on as the irony of it all unfolds. It’s not for me to reveal the ‘destiny’ of the plot in this review.

‘Wanted’ is a combination of the best of different movies – the philosophical depth of ‘Fight Club’, the awesomeness of John Woo’s slow-mo-matrix-like directing style and the hard thumping Ramstein-like heavy chugging soundtrack. In fact, the movie has the feel of ‘Nightwatch’ and ‘Daywatch’ that the main actor of these two movies even appeared in ‘Wanted’. As I was reading this review, I realized that both had the same director!

I’ll make a daring and bold statement – if you like watching ‘Daywatch’ and ‘Nightwatch’, you’ll love this movie even more. Watch it and think about what you’ve done lately to fulfill your destiny!

FY08 results analysis

This is just a brief analysis of popular. Time is too short to analyse too much on a company that I think does not constitute a good business. The report comes from here.



Turnover went up higher in FY08 than FY07, but gross profit doesn't follow through. In fact, gross margin fell from 17.1% in FY07 to 15.3% FY08. However, net profit margin increased slightly from 2.9% to 3.1%. They mentioned that the increase in turnover was mainly attributed to their retail and distribution business, due to more outlets opened in Singapore and M'sia.

Some important business news - they closed down their two english-learning schools and discontinued their school franchise business in Taiwan to minimise the risk and losses given the shrinking Taiwan economy and increasing credit risk in distribution industry. I did notice that their provision for doubtful debts increased from 155k to 370k in FY08, an increase of 1387%. They seem to do better in HK, where two of their pre-primary textbooks were adopted by schools, thus capturing a good market share to sustain their dominant leading position. More discounts were given (hinting you the strength of their pricing power) and more money spent on marketing their books. I think they should continue their pre-primary textbook business. I believe that based on Singapore's strong pre to primary textbook branding, they will do very good in this aspect.

Net margins, though improved slightly (might be just the usual business fluctuations, rather than real improvements), are still very low at around 3%. ROE improved to near 10% though. While current ratios seem healthy, their total debts to equity actually increased. We can see that their cash flow statement shows negative net cash from operating activities, negative investing activities but very positive cash flow coming from financing activities. Looking at it more carefully, there is an increase in long-term bank loan to the tune of $52,267,000 for their new property business. That alone constitutes a huge part of their cash/cash equivalent for the period.

Popular gave a dividend of $0.012 (tax exempt) in total this and last FY. Given last closing of $0.270, that's a dividend yield of 4.44%, constituting a payout ratio of 40%. PE (based on FY08) is 9x.

I’m in a rather pensive and introspective mood these days. Such days are usually my most creative days too. I remember doing some artwork when my mood is at the worst, perhaps I’ll share with my shareholders here one day.

I’m thinking a lot about the probability and chances since I’m in the midst of reading the book by Nassim Nicolas Taleb’s The Black Swan. Let me ask these 2 questions:

Question A: 90% of residents living in Tanah Merah are rich. I live in Tanah Merah, so what’s the probability of me being rich?

Question B: 90% of residents living in Tanah Merah are rich. I’m going to live in Tanah Merah, so what’s the probability of me being rich?

(Interesting note: I had to answer this question more often than I had to, so I thought I’ll be quite interesting to think harder about it when I was traveling on the bus today)

In question A, the probability of me being rich is 90%. Since I live in Tanah Merah and I’m considered a resident there, therefore I’m subjected to the sample which the probability of 90% is calculated. In 100 different alternate and parallel realities, I have (on average) 90 realities in which I’ll be rich.

In question B, the probability becomes unknown. If one didn’t think hard enough and gives it a fleeting thought only, it becomes quite tempting to think that the probability of me being rich is 90% too, based on ‘statistical data’. But did you notice that that there is not enough information to determine the probability of me being rich? This is quite different if I’m already living in Tanah Merah. In that case, my probability is 90%. Yet if I’m going to live in Tanah Merah, the probability cannot be determined.

Thus, the probability of past data will be changed when a new comer enters the data base. Yet, the past probability cannot be extended to the new comer.

Do we commit the same logical error when we’re chasing after historical results? Did we place too much faith on extrapolating past data to predict the future?

I can think of more such (hypothetical) questions:

1. A fund manager has 90% chance of making good returns, based on past data. If I’m already vested when the probability of 90% is calculated, I’ve 90% chances of good returns (I mean out of 10 alternate and parallel realities, 9 of the realities I had good returns). But if I’m thinking of investing with this fund manager, do I still have 90% chance of good returns?

2. 9 out of 10 adults developed cancer in their lifetime, or a 90% chance of getting cancer in one’s lifetime, based on historical data. Do I also have 90% chance of getting cancer?

3. From past statistics, 90% of people who cross a road get into accidents. I’m going to cross that road now, will I get a 90% chance of getting into an accident?

4. From my records, 90% of my students get A for mathematics after tutoring them. You’re going to be my student, will you also get 90% chance of getting A for mathematics?

5. From past data, 90% of traders fail to make money. I’m going to be a trader, does it mean I have 90% chance of failing to make money?

Don’t get me wrong, I’m not saying historical data are not important and one shouldn’t take a look at them when trying to predict the future. What I’m saying here is that one shouldn’t treat past data as sacred. The future remains as unpredictable with or without a good track record, hence it’s better to be on the safe side when making prediction. Always prepare yourself for the low probabilistic outlier event (a.k.a. black swans).