Are financial markets predictable and if so by what means ? Let us wade into the heated discussion that has raged for well over 100 years and see if we can find some answers or even just some insights! Part 1 of this article takes a critical look at those who claim the markets are totally unpredictable.
Our story begins with a French stock broker Jules Regnault who in 1863 described the stock marked as a random walk. In 1900, a French mathematician, Louis Bachelier, expanded his idea further in his PhD thesis, “The Theory of Speculation.”
Bachelier was undoubtedly looking for a mathematical principle or formula that “scientifically” described the movements of markets in a similar vein to Newton’s laws of gravity and motion.
Given the times and the relative difficulty that he would have had in accessing and processing large amounts of financial data, his theory was not all that unreasonable.
You can construct a random walk quite simply by using two coloured dice. A green one for up and red one for down. Toss the two dice together and subtract the thrown value of the red dice from the thrown value of the green one. You should get a final number between 5 and -5. As an example let us assume you throw a red 5 and a green 2.
2 – 5 = – 3 so plot this negative value on the Y axis of a graph, in the left most position of the x axis. Now throw the dice again. Add your result to the previous value and plot this value on the graph in the next position on the X -axis. Keep repeating this until you have filled the chart.
We know, of course, that each throw of the dice is random. Every random walk will have a different pattern which can take on the appearance of trends and counter trends. The size and direction of any following moves, by virtue of their randomness, are always unpredictable even though in hindsight they may look very real and profitable!
Let us create a bin for each value of our dice pair throws. We will need 11 bins, one for each possible value, labelled -5,-4,-3,-2,-1, 0, 1, 2, 3, 4, 5.
If we record the results of each throw on a piece of paper and put each one into the corresponding bin we will find that we have many more pieces of paper in the zero bin than in either the -5 or +5 bins. The probability of throwing a zero or low number is higher than the probability of throwing a high number due to the different possible combinations of numbers that the two dice can make.
These bins can be represented by a bar graph with the height of each bar indicating the number of throws that we got for each value. This graph is known as a histogram and illustrates the way in which the results of a number of throws are distributed. If we plot one of these graphs we will get the well-known and familiar bell curve which is more formally known as a normal distribution. To get a nice smooth graph like the one shown above we need a large number of throws.
Now have a look at a histogram showing the distribution of the daily price moves for the US S&P500 stock index. It does look rather like a normal distribution, doesn’t it?
In any case, that was Bachelier’s conclusion and being a mathematician he also knew that random walks and normal distributions have a number of important mathematical properties. One of these is that if a distribution is truly random or normal, then all individual moves must also be random, which means that the movements are not predictable. In his own words “the mathematical expectation of the speculator is zero.”
With that proclamation, Bachelier dismissed the entire profession and practice of financial market analysis claiming that anyone trying to beat the market was wasting their time. This remains a strongly held view by many people even to this day.
His work, however, was largely ignored until the 1950s when it started finding its way into the world of financial modelling. The experts moved from trying to predict price movements to trying to predict risk. They have been somewhat more successful at doing this but not entirely and while the mathematics they used have become considerably more complex, it is still based on normal distributions and random walks.
The next important figure to look at is Eugene Fama, a professor of finance at the University of Chicago. He extended Bachelier’s work with his Efficient Market Hypothesis (EMH) theory.
Like Bachelier, his Ph.D. thesis published in 1965 concluded that short-term stock price movements approximate a random walk and were therefore unpredictable.
Fama argued that stocks always trade at their fair value, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. According to Fama, it is impossible to “beat the market” because stock market efficiency always causes existing share prices to fully reflect all known information so that even an uninformed investor buying a diversified portfolio will receive the same return as an expert.
Later he modified his theories to try and take into account obvious anomalies but the basic premise that markets are unpredictable remains. Fama received the Nobel Memorial Prize in Economic Sciences in 2013.
Burton Malkiel, a professor of economics at Princeton University is another influential individual who published a book entitled A Random Walk Down Wall Street in 1973. In his book, Malkiel claimed that a blindfolded chimpanzee throwing darts at the Wall Street Journal could select a portfolio that would do as well as the experts. This book is well known and has had many reprints over the years. It has also probably helped convince many that markets are unpredictable.
We have had a brief look at some of the history and theories surrounding financial market prediction. These views, based on random walks and efficient markets, are still widely held by many people and are taught, even today, in most universities. No doubt the view that financial markets are unpredictable has been supported by the many retail traders and investors as well as a considerable number of professionals who have failed to profit from the markets.
These views, however, are not without their dissenters. Numerous individuals, some just as eminent and well qualified as those we have already mentioned, have challenged these theories and not without good reason.
We will examine some of these ideas in the next article and hopefully, by looking carefully through the cracks, will be able to discover some of the elusive truth about predicting financial markets.