Price follows volume, according to an old axiom of investing. When an unusually large number of shares of a stock are bought, it doesn't make sense for sellers to unload at the current price. The theory goes that, if investors are buying aggressively, the stock's desirability and scarcity ought to yield higher prices.

In some respects, this is nothing more than Economics 101 – examining supply and demand curves and the point at which they intersect. Since supply and demand are functions of quantity and price, it seems counterintuitive to notice fluctuations in one element without paying attention to the other element.

### MACD and the Chaikin Oscillator

So what is the best way to reconcile increasing or decreasing volume with price fluctuations? No one has a definitive answer, but Marc Chaikin has come close. This long-time stock trader and analyst has created dozens of indicators during his distinguished career, with many of them now staples of Wall Street technical analysis. His best known and most popular is the Chaikin Oscillator.

The Chaikin Oscillator is a third-derivative technical analysis indicator – an indicator of an indicator, the latter of which is derived from the stock price. The oscillator builds on the concept of moving average convergence divergence, or MACD. MACD is derived from the moving average, which is the mean price of an issue over a certain period.

For example, if stock XYZ closed at $22 two days ago, $23 yesterday and $24 today, the simple moving average (SMA) would be $23. The exponential moving average (EMA), its more complicated sibling, weighs recent prices more heavily than older prices. While the SMA in the above example is $23, the EMA would be higher due to the rise in price at the end of series.

In practice, the exponent is notated as a *smoothing factor*, i.e. a coefficient between 0 and 1, indicating the relative weight given to the most recent prices. A smoothing factor of 1 ignores all but the latest closing price, while a smoothing factor of 0 weighs all days equally (and the EMA would equal the SMA). Standard smoothing factors for calculating the exponential moving average are 0.05 or 0.1. Many analysts use both and take the average of those inputs.

MACD is constructed by subtracting a 26-day EMA from a 12-day EMA. MACD's purpose is to distinguish short-term from long-term trends, allowing educated guesses about future stock prices. (For more, see: *MACD: A Primer*.)

### Chaikin Oscillator Construction

The transition from MACD to Chaikin Oscillator requires several steps. The Chaikin Oscillator was created in reference to the *accumulation/distribution line (acc/dis line)*, another Chaikin brainchild. The acc/dis line builds on the *money flow multiplier*, which attempts to quantify the amount of money coming into the market and its impact on stock prices.

The multiplier formula is as follows: $f = \frac{[(\text{Close} - \text{ Low}) - (\text{High} - \text{ Close})]} {(\text{High} - \text{ Low})}$

Lets say the stock in the previous example peaked at $25 during the look back period and then fell to $21. A day later, it closed at $22. The money flow multiplier in this case would be $\frac{[(22 -21) - (25 - 22)]}{(25 - 21)} = -.5$

Multiply that number by the quantity of stock traded over the period to get *money flow volume*, while the running total generates the acc/dis line. The final step is to apply this output to MACD.

To summarize:

1. Determine a security's high, low and closing prices over a certain period.

2. Add, subtract and divide them as above, then multiply by volume over that period.

3. Do this for each day in the period, then measure the progress (or regress, as the case may be).

4. Subtract the 26-day EMA from the 12-day EMA.

5. Subtract the 3-day EMA from the 10-day EMA of #4 and plot.

The result is a value that, regardless of its starting point or the price of the underlying security, has an origin of 0. (A perfectly inert market = neither negative nor positive Chaikin Oscillator values.)

What the oscillator lacks in simplicity, it makes up for in authority. By measuring the momentum of the accumulation/distribution line using the MACD, the oscillator should anticipate when the line will change direction. By now, we're several levels removed from the stock price, but Chaikin devotees argue that the distance is needed to determine the importance of volume and price changes. Also, three- and 10-day values aren't locked in stone. For example, swapping in six- and 20-day EMAs will result in a Chaikin Oscillator that changes direction less abruptly.

### Chaikin Oscillator Example

The 3,10 Chaikin Oscillator plotted below Wal-Mart's three-year weekly price bars identifies three major turning points, ahead of or concurrent with price action. The indicator dropped to a yearly low and turned higher in October 2015. signaling the end of a multi-month decline while signaling a buying opportunity. A five-month pullback into 2017 set off a similar buying signal that preceded the strongest rally in several years. Finally, the indicator hit the highest high in many years in November 2017 and turned sharply lower while price rallied to an all-time high, generating a bearish divergence that preceded a major decline into the second quarter of 2018.

### The Bottom Line

The Chaikin Oscillator generates technical output that supports sound buy or sell decisions but is best used in conjunction with fundamentals and other indicators. (For additional reading, check out: *How to Use Volume to Improve Your Trading*.)