Bull or bear: Figuring out when to buy and when to sell

The Efficient Market Hypothesis refers to the condition whereby the current prices of the securities have already incorporated any news that arises from those securities without delay. In other words, the pricing of any security will be so efficient that there will not be any opportunity to arbitrage the price differential before and after the news is released.

Thus, even with the use of technical analysis using indicators and oscillators to TIME the market and fundamental analysis using company financial information and earnings to select undervalue stocks, the investor will still not be able to reap better than average returns on the market.

So, under normal conditions the current price of any security represents the best unbiased estimate value of the particular investment and any old information cannot be used to predict future price movement. In other words the Efficient Market Hypothesis has some relevance to the Random Walk Theory when it comes to predicting future price movement of any security.

The Random Walk Theory can be defined as the past movement or trend of a stock price that cannot be used to predict future prices. According to the theory, stock price movements are at best unpredictable and investors cannot consistently outperform the market. In other words, it is impossible to know whether the next move in the price will be up or down or by how much it will rise or fall.

It is best described with the following chart.

Weak, semi-strong and strong

The Efficient Market Hypothesis, was first brought to the limelight in 1965 by Eugene Fama in his PHD thesis. According to him, the Efficient Market Hypothesis takes three forms. which are weak, semi-strong and strong.

The weak form of EMH stipulates that the current price of the securities have already reflected any news that is available either on the company’s website, financial publication, newspaper, magazine and etc. This is because nobody will have any advantage over others in trading the markets by using this information because it has already been made publicly available and easily accessible.

The semi-strong form indicates that someone even with the knowledge that the company’s financial statements, announcements and other information related to the company will not be able to predict the future price movement and hence garner higher returns due to the speed the information incorporates into its price.

The strong form of EMH, states that even those armed with insider information will not be able to beat the speed in which the information is reflected into the price. In other words it also meant that company directors and managers are not able to take advantage given their knowledge in insider information to make gains in the movement of its shares prices.

Forces that cause prices to rise and fall

However, since the days of Eugene Fama, there has been a tremendous leap in technologies, techniques in studying the market, new studies in ‘behavioral finance’ and etc, that enables the investor to somewhat predict the future movement of stock price patterns. In others words, an investor will be able to reap better than average returns in the stock markets if he employs the right technical and fundamental techniques plus crystal-clear mindset when investing in the stock markets.

Before we intend to debunk the above hypothesis, we need to understand the inner workings of the financial markets first. We need to understand the forces that causes the price of stocks to rise and fall by the minutes, hours and days.

To begin with, we need to know how the market sets the stock prices? Stock prices are driven by the demand and supply factors initiated by a large amount of willing buyers and willing sellers. The buying and selling of a particular share or through the price discovery mechanism, will eventually settles on a price equilibrium.

To illustrate, say if you are considering to buy a stock that is going to declare a dividend of $3 per share in the next few months. Market analysts only project a growth of 4% for the company in the next year and the current term deposit rates pays 8%. So in order to compensate for the risk to purchase such a low growth stock, you need to have a higher rate of return than that offered by the term deposit which is 8%. Say you settle for a rate of return of 15% based on the risk involved in investing in a low growth company. This is the weak form of the EMH because the information is publicly available.

Another investor, Sam, has a different view on the market. He is confident that the dividend paid by the company will grow to 5% because he has received insider information from the management of the company and this only requires a 14% return on his investment due to the lower risk perceived. This represents the semi-strong form of the EMH because Sam has the information about the company through some insiders.

Andrew is the Director of the company, obviously he knows how much the next expected dividend payout is going to be (5%) and also the financial health of the company. He knows not only when the next dividend will be paid and but also how much. Armed with this information he does not mind having a lower return on his investment and in this case say 12%. Andrew’s case represents the strong form of the EMH.

So, based on the above information, how can we arrive to a price where it is considered optimum to you, Sam and Andrew?

According to Gordon Growth Model, the price of a security can be calculated based on the assumption of constant dividend growth. That is why most firms strive to increase their dividends at a constant rate every year. The formula for the calculation is as follows:

P = D/(K-G)*100 where,

P = price

D = recent dividend paid

K = the required return on a particular investment (stock in this case)

G = expected constant growth rate of the dividend

As for you, the price you should pay is :

P = 3/(15-4)*100 = $ 27.27

Sam will pay,

P = 3/(14-5)*100 = $ 33.33

And Andrew willing to pay.

P = 3/(12-5)*100 = $ 42.85

The Theory of Rational Expectation

In other words, the above case demonstrates how the market sets the price of the stock through different forms of strength in the EMH. It also tells us that people’s perception on the stock price evaluation is based on what they call ‘The Theory of Rational Expectation’.

Andrew is willing to pay more for the stock price because he expects this particular investment to be of lower risk, whereas you are willing to pay less for the stock price because you expect this investment to be more risky. This is because the market analysts only project a 1% increase in the dividend payout and also you do not have any information about the financial health of the company.

Accordingly, empirical evidence through history proves that stock price movement are predictable and non Random. In his book, a Non Random Walk Down Wall Street, Lo and MacKinlay, demonstrated that by using powerful computers and advance econometric modeling, the movement of stock prices indeed followed a non-random pattern and can be predicted.

Technical Analysis

In the publication of the Journal of Finance in 2000, Andrew Lo wrote an article titled : Foundations of technical Analysis, Computational Algorithms, Statistical Inference and Empirical Implementation. In his opening remark, it says :

"Technical analysis, also known as charting, has been part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis. The presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution conditioned on specific technical indicators, such as head-and-shoulders or double-bottoms, we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value." This paper can be found at www.nber.org

The origin of technical analysis can be traced back to the Dow Theory which dates back more than 100 years.The Dow Theory, is also proof that systems can outperform the market and reduces risk. The Dow Theory states that the market is in an uptrend if any of the Dow Transports and Dow Industrials averages advance above the previous high and vice versa.

Stephen Brown of New York and William Goetzmann of Yale University in their publication in the Journal Of Finance, demonstrated that when the Dow Theory tested using data from 1929 to 1998 manage to outperform the buy and hold approach by about 2% per year. It also proved that the portfolio carried significantly much less risk.

High Frequency Trading

The use of High Frequency Trading also proves that investors are able to make better than normal returns regardless of how efficiently the markets incorporate information into its prices. High frequency trading essentially refers to large and very fast execution of quote orders by computers programs which will create cascade-like buying and selling.

Trading cycles that used to be days and weeks now are being done in milliseconds and nanoseconds. In other words market has shifted from the traditional (fundamental and technical analysis) with long term holding for equity appreciation to short term trading that benefit only the speculators. The strategy of investing today refers to ‘here and now’ rather than ‘buy and hold’

The reason for the ‘race to zero’, is because it enables them to ‘front run’ their competitors. By front running it means it effectively put them at the front of the queue and have priority over other orders and help them react faster than others. According to some people in the know, Citadel which is one of the HFT heavyweights receives order flow from brokers like TOS. When they receive orders, they can decide whether or not to fill the order according to your price. If they fill you up, then they will know exactly what are the small players buying, their market sentiment and momentum. By totaling the retail orders they are able to judge market sentiment on a particular price and also the pain threshold of the weak holders.

Predatory HFT programs are designed to block retail investors from making successful trades against the house or Wall Street. An example in the following shows how the game of front running being played. Say stock ABC is being traded at the bid of $1.00 and ask $ 1.02. As a retail investor key in an order to buy at $1.02, normally it will get filled. But HFT programs which are ‘able to see’, will automatically raise the ask price to $ 1.03. So the next bid price will be higher and automatically set as $1.01, so that the HFT programs make the human investor to buy higher at $ 1.03 instead of $ 1.02.

Algorithmic Trading

Algorithmic Trading is another form of trading whereby investors use pre-programmed trading software to execute instructions given to it. A good example of when algorithmic trading is used is to look out for arbitrage opportunity that exists in inter market price differentials.

In financial lingo, arbitrage refers to the practice of taking advantage of the price differential of the securities traded in two or more markets.

If the market is so efficient then there wouldn’t be any differential in pricing that can be taken advantage of by stock arbitraging. So in essence, we can conclude that the Efficient Market Hypothesis and Random Walk Theory will not be applicable in today’s financial markets.

This is because due to the advancement in technologies, and given the right tools, we are able to more or less predict future price movements using the current available data.

Malaysia Chronicle