J.P. Morgan - the iconic financier of early 20th-century corporate America - was once asked what he thought the stock market was going to do. "It will fluctuate," he responded. Truer words have never been spoken. Volatility is an essential characteristic of dynamic financial markets. And while academic theories like the efficient market hypothesis might aim to rationalize wild price fluctuations, volatility remains a concrete reality for the common investor. But rest assured, this is a very good thing. The ebb and flow of daily (and even intraday) price movements give rise to the opportunities that make earning a return possible at all. After all, you can't "buy low and sell high" without market lows and highs, right? Studying a business's financial statements to determine its intrinsic value, by itself, won't help to explain the irrationally frequent changes in its market price. When is it a good time to enter (or exit) a position? Do current market prices reflect a fair value or an irrational euphoria that will soon be corrected? Something else must help us make sense of things. For that reason, many traders use technical analysis to get past the white noise of market fluctuations (and, potentially, maximize long-term performance). There's a sizable toolbox - available to almost all investors and traders - of technical indicators that can help us tune out the short-term noise of market behavior and recognize true market trends. Let's talk about one in particular. Developed by analyst John Bollinger, Bollinger Bands® are a unique way of setting reasonable boundaries for a stock's price movement over time. The method presents a high probability range for price activity based on historical performance. Below is an example of Bollinger Bands used on Pfizer's (NYSE: PFE) price chart... See those yellow and green lines hovering around the price? Those are the Bollinger Bands. And the dotted line is a 20-day moving average. The amount of space between each band and the moving average is based on what's called standard deviation: a statistical measure of variation within a set of numbers. In our case, the set of numbers being measured is the set of closing prices for the prior 20 days. The larger the standard deviation, the more prices are spread out from the average. The smaller the standard deviation, the closer prices are to the average. |
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