Which of the following statements agrees with you better?
- Stock markets are unpredictable.
- Stock markets are predictable some times.
First statement must be the one, most of you agree with. And for obvious reasons too. But, often times, or at least sometimes, we tell ourselves, backed by wisdom, ego, experience or whatever, that we can out think them all. We are captivated by the thought that, even though we believe that markets exist in the realm of unpredictability, we would like to tone down that belief, at least when it is our money that is on the line, willing the markets not to veer much away from our expectations. The idea of predictability and unpredictability coexisting is central to what drives investors to stock market.
But, if all investors believed that markets were unpredictable, then it is highly likely that the stock prices will display patterns. Now we know that patterns are recognizable, and recognisability reduces the unpredictability of stock markets. Hence, if investors are consistently expecting unpredictability, then the markets are likely to become more and more predictable. In other words, inconsistency in investor expectations is the key to markets staying truly unpredictable. Now, this has become a circular argument. But, this indeterminacy is not a unique problem. In philosophy, if one sets out to understand the meaning of things, by following the trail of words, it is likely that he ends up where he began.
However, we are not interested in stock markets’ philosophy, as much as we are interested in monetising it. How can we do that without attempting to understand where exactly the unpredictability resides? Is it stock market, or is it investor expectation, which is unpredictable? Can they be considered the same, given the fact that it is human behaviour that influences both? Rather than attempting to make sense of either, it might be easier to employ a simple measure of central tendency, which allows us sidestep the predictability question.
Central Tendency is a statistical measure that identifies a figure that represents the entire set of data. Mean, median and mode are the three such measures, with “average”, being the most widely used one, by beginners and experts alike.
The use of averages, however, does not answer all the questions, but it certainly seeks to convert the question of unpredictability to one of variation, thereby reducing the sticky problem that it was, to one that the human mind finds much easier to comprehend and act upon. Stock market studies usually employ “moving averages” in place of averages in order to incorporate the latest data as well. What does a moving average (MA) do? Primarily, it smooths the data, which in our case is price, allowing us to ignore the volatility of the previous days. For example, if the price of HDFC Bank is 2295 now, and its 10 day moving average is 2000, it throws us at least two inferences. Firstly, that it is on an uptrend, with respect to its last 10 days, and secondly, that it has gained quite strongly, given the percentage difference between the average and the price. In other words, not only do we get a sense of short term direction, but we also get to understand the strength of the same. Often times, in our rush to squeeze profit from stock market moves, we tend to give undue importance to calling the trend right. The concept of moving average allows us to take a binary approach to investment.
The binary approach:
Until it reverses, the trend is always up. This sounds a bit like Newton’s law of motion, which states that every object in a state of uniform motion will remain in that state of motion unless an external force acts on it. The advantage of taking a binary approach in stock price analyses is that it saves us the burden of prediction, having assumed already that the trend is always up. That leaves us with only one task: to identify when the uptrend stops. And that is where the moving average steps in.
Getting MAs to work.
Depending on the period used, MAs may be seen as either short term or long term averages. When you start to compare them that is when MAs begin to show their real worth. Several commonly used price indicators are either entirely built on, or based on Cross Overs. In the case of MACD (Moving Average Convergence Divergence), two lines are created using several moving averages, with the idea of using the cross overs of these lines in identifying trends. MACD line is the difference between the 12 day EMA and 26 day exponential moving average (EMA), while the signal line is the 9 day EMA of the MACD line. Bollinger band concept studies how price and volatility interact with each other, by having two price bands that follow two standard deviations away from a middle band which is a 20 day simple moving average (SMA). Moving averages not only help with the direction, but also indicates potential support and resistance levels, where price moves are likely to slow down. And when used in conjunction with other analyses, moving average becomes a very powerful weapon in the investor’s arsenal.
Golden Cross: A bullish crossover hinting uptrend, which occurs when the shorter moving average crosses above the longer moving average.
Dead Cross: A bearish crossover hinting reversal in uptrend, which occurs when the shorter moving average crosses below the longer moving average.
But, make no mistake; moving averages are no torch bearers of trend, in that they do not predict price direction. It is a lagging indicator, as they are based on past prices, and it can only confirm what has already unfolded. It is the price that leads the trend, while moving average is that step behind, where you rest your hind heel for assurance; that helps you lunge ahead. Often times, it is just what we require.
 A price crossover happens, when the price crosses from above or below a moving to signal a potential change in trend.
 Exponential moving averages (EMAs) reduce the lag by applying more weight to recent prices. The weighting applied to the most recent price depends on the number of periods in the moving average. EMAs differ from simple moving averages in that a given day’s EMA calculation depends on the EMA calculations for all the days prior to that day.