Quantitative Finance For Dummies. Steve Bell

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СКАЧАТЬ style="font-size:15px;">      ❯❯ Strong efficiency goes a step further than semi-strong efficiency and says that prices can’t be predicted using both public and private information.

      Anomalies are systematically found in historical stock prices that violate even weak efficiency. For example, you find momentum in most stock prices: If the price has risen in the past few months, it will tend to rise further in the next few months. Likewise, if the price has fallen in the past few months, it will tend to continue falling in the next few months. This anomaly is quite persistent and is the basis for the trend following strategy of many hedge funds.

      Somehow, though, the EMH smells wrong. Even though you can find many vendors of market information, EMH has a cost. It’s no coincidence that some of these vendors are very wealthy indeed. Also, if you examine publicly available information, you soon find that such information is not perfect. Often the information is delayed, with the numbers published days or even weeks following the time period they apply to. Some exceptions exist and you can read about one of them in the sidebar, ‘The impact of US employment numbers’.

THE IMPACT OF US EMPLOYMENT NUMBERS

      One of the most widely anticipated numbers in finance is the so-called nonfarm payroll issued by the US Bureau of Labour Statistics. In fact, the nonfarm payroll isn’t just a number but a report with almost 40 pages. You can find the November 2015 report at www.bls.gov/news.release/pdf/empsit.pdf. Formally, this report is called the employment situation. Its headline figure is the nonfarm payroll employment and its companion figure is the unemployment rate, so it gives a picture of the employment situation in the United States.

      This number is hugely impactful globally and can move the value of currencies, stock markets and bond markets across the world within seconds of its release. In the US, though, the number is released one hour before the opening of the New York Stock Exchange so that traders get a chance to absorb the information before trading begins. Aside from the data being for the largest economy in the world, other factors make it influential:

      • The nonfarm payroll is timely. It’s issued on the first Friday in the month following the one it relates to. For example, the September 2015 report was issued on Friday 2 October 2015 at exactly 8:30 a.m. Eastern Daylight Time. This is no mean feat given the amount of information contained in it.

      • The nonfarm payroll is comprehensive. It has surveys including small business and the self-employed so the information is credible.

      • Although estimates and statistical models are used in some of the numbers, revisions are made to these numbers in subsequent months as more information becomes available. The existence of timely revisions based on a well-defined process supports market confidence in the numbers.

      Be warned: If you’re trading any instruments when the nonfarm payroll figures come out, you may be in for some significant turbulence!

      It’s far more likely that markets are not informationally efficient and that many participants for reasons of cost or availability are not perfectly informed. It’s also highly likely that most participants are not able to instantly work out in detail the consequences of the information presented to them. This working out may take some time.

      Indeed, if markets were informationally efficient, there would be no incentive to seek out information. The cost wouldn’t justify it. On the other hand, if everyone else is uninformed, it would be rewarding to become informed as you can trade successfully with those who know less than you.

      

The point that in an efficient market there’s no incentive to seek out information and so therefore no mechanism for it to become efficient is the Grossman-Stiglitz paradox, named after the American economists Sanford Grossman and Joseph Stiglitz. The implication is that markets will be efficient but certainly not perfectly efficient.

      Only with deep research into market data do markets have a chance of becoming efficient. That’s the norm in financial markets, but pockets of inefficiency are always left that market traders and savvy investors can attempt to exploit. Also, attempts to use the results of deep research drive the intense trading found in many markets. In Chapter 8, I talk about techniques for analysing historical price data for patterns.

      Recognising irrational exuberance

      Most markets are responding constantly to a flow of news on companies, economies, interest rates and commodities. They also react to changes in the supply and demand for the financial asset in question. If more fund managers decide to buy a stock than sell it, its price tends to rise. The greater the demand for loans from companies, the higher the interest rate lenders demand.

Markets don’t always behave in this sensible way, however. Sometimes, they defy gravity and keep on rising, which is called a bubble. Figure 1-2 shows an example of this in a chart for the share price of British Telecom, a fixed-line telecom operator. In September 1996, the Chairman of the US Federal Reserve Bank warned of irrational exuberance in markets. Unusual circumstances, especially low interest rates, were making markets overly excited. He was dead right. The Internet had just been invented so even traditional companies such as British Telecom saw their share price rocket upward. The market ignored Chairman Alan Greenspan when he made his warning, although the Japanese stock market respectfully dipped several per cent on the day of his speech. In a way, the market was right and farsighted: The Internet was going to be big, it was just that British Telecom wasn’t Google. After rising to a very sharp peak in early 2000, British Telecom shares crashed back down to earth and continued on in their usual way.

      © John Wiley & Sons, Ltd.

      FIGURE 1-2: Share price chart for British Telecom plc.

      

One thing for sure is that with crazy behaviour like this, the statistics of the price movements for shares don’t obey Gaussian statistics. In Chapter 2, I explain quantities such as kurtosis, a measure of how much statistical distributions deviate from the Gaussian distribution. A large positive value for the kurtosis means that the probability of extreme events is far more likely than you’d expect from a Gaussian distribution. This situation has come to be called a fat-tailed distribution. Statistics is the way of measuring and analysing the market price data used in quantitative finance, and I try to emphasise this throughout the book.

      Another possibility, of course, is that prices crash rapidly downwards far more often than you’d expect. The fear of prices crashing downwards is palpable. Market participants want to protect themselves against nasty events like that. To do that, you need financial instruments such as options and futures, which I explain in detail in Chapters 5 and 6, respectively. Options are a form of financial insurance. For example, if you think that the stock market is going to crash, then you buy an option that compensates you if that happens. If the market doesn’t crash, you’ve lost just the premium you paid for the option, just like an insurance contract.

      

George Soros, a billionaire hedge fund manager, attempted to explain these irrational market events with a concept he called reflexivity. He replaced the efficient market hypothesis view that the market is always right with something else:

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