What Is On-Balance Volume (OBV)?

On-balance volume (OBV) is a technical trading momentum indicator that uses volume flow to predict changes in stock price. Joseph Granville first developed the OBV metric in the 1963 book Granville’s New Key to Stock Market Profits.

Granville believed that volume was the key force behind markets and designed OBV to project when major moves in the markets would occur based on volume changes. In his book, he described the predictions generated by OBV as “a spring being wound tightly.” He believed that when volume increases sharply without a significant change in the stock’s price, the price will eventually jump upward or fall downward.

The Formula for OBV Is


$\begin{aligned} &\text{OBV} = \text{OBV}_{prev} + \begin{cases} \text{volume,} & \text{if close} > \text{close}_{prev} \\ \text{0,} & \text{if close} = \text{close}_{prev} \\ -\text{volume,} & \text{if close} < \text{close}_{prev} \\ \end{cases} \\ &\textbf{where:} \\ &\text{OBV} = \text{Current on-balance volume level} \\ &\text{OBV}_{prev} = \text{Previous on-balance volume level} \\ &\text{volume} = \text{Latest trading volume amount} \\ \end{aligned}$​OBV=OBVprev​+⎩⎪⎨⎪⎧​volume,0,−volume,​if close>closeprev​if close=closeprev​if close<closeprev​​where:OBV=Current on-balance volume levelOBVprev​=Previous on-balance volume levelvolume=Latest trading volume amount​

Calculating OBV

On-balance volume provides a running total of an asset’s trading volume and indicates whether this volume is flowing in or out of a given security or currency pair. The OBV is a cumulative total of volume (positive and negative). There are three rules implemented when calculating the OBV. They are:

1. If today’s closing price is higher than yesterday’s closing price, then: Current OBV = Previous OBV + today’s volume

2. If today’s closing price is lower than yesterday’s closing price, then: Current OBV = Previous OBV – today’s volume

3. If today’s closing price equals yesterday’s closing price, then: Current OBV = Previous OBV

What Does On-Balance Volume Tell You?

The theory behind OBV is based on the distinction between smart money – namely, institutional investors – and less sophisticated retail investors. As mutual funds and pension funds begin to buy into an issue that retail investors are selling, volume may increase even as the price remains relatively level. Eventually, volume drives the price upward. At that point, larger investors begin to sell, and smaller investors begin buying.

Despite being plotted on a price chart and measured numerically, the actual individual quantitative value of OBV is not relevant. The indicator itself is cumulative, while the time interval remains fixed by a dedicated starting point, meaning the real number value of OBV arbitrarily depends on the start date. Instead, traders and analysts look to the nature of OBV movements over time; the slope of the OBV line carries all of the weight of analysis.

Analysts look to volume numbers on the OBV to track large, institutional investors. They treat divergences between volume and price as a synonym of the relationship between “smart money” and the disparate masses, hoping to showcase opportunities for buying against incorrect prevailing trends. For example, institutional money may drive up the price of an asset, then sell after other investors jump on the bandwagon.

Example of How to Use On-Balance Volume

Below is a list of 10 days’ worth of a hypothetical stock’s closing price and volume:

1. Day one: closing price equals $10, volume equals 25,200 shares
2. Day two: closing price equals $10.15, volume equals 30,000 shares
3. Day three: closing price equals $10.17, volume equals 25,600 shares
4. Day four: closing price equals $10.13, volume equals 32,000 shares
5. Day five: closing price equals $10.11, volume equals 23,000 shares
6. Day six: closing price equals $10.15, volume equals 40,000 shares
7. Day seven: closing price equals $10.20, volume equals 36,000 shares
8. Day eight: closing price equals $10.20, volume equals 20,500 shares
9. Day nine: closing price equals $10.22, volume equals 23,000 shares
10. Day 10: closing price equals $10.21, volume equals 27,500 shares

As can be seen, days two, three, six, seven and nine are up days, so these trading volumes are added to the OBV. Days four, five and 10 are down days, so these trading volumes are subtracted from the OBV. On day eight, no changes are made to the OBV since the closing price did not change. Given the days, the OBV for each of the 10 days is:

1. Day one OBV = 0
2. Day two OBV = 0 + 30,000 = 30,000
3. Day three OBV = 30,000 + 25,600 = 55,600
4. Day four OBV = 55,600 – 32,000 = 23,600
5. Day five OBV = 23,600 – 23,000 = 600
6. Day six OBV = 600 + 40,000 = 40,600
7. Day seven OBV = 40,600 + 36,000 = 76,600
8. Day eight OBV = 76,600
9. Day nine OBV = 76,600 + 23,000 = 99,600
10. Day 10 OBV = 99,600 – 27,500 = 72,100

The Difference Between OBV and Accumulation/Distribution

On-balance volume and the accumulation/distribution line are similar in that they are both momentum indicators that use volume to predict the movement of “smart money”. However, this is where the similarities end. In the case of on-balance volume, it is calculated by summing the volume on an up-day and subtracting the volume on a down-day.

The formula used to create the accumulation/distribution (Acc/Dist) line is quite different than the OBV shown above. The formula for the Acc/Dist, without getting too complicated, is that it uses the position of the current price relative to its recent trading range and multiplies it by that period’s volume.

Limitations of OBV

One limitation of OBV is that it is a leading indicator, meaning that it may produce predictions, but there is little it can say about what has actually happened in terms of the signals it produces. Because of this, it is prone to produce false signals. It can therefore be balanced by lagging indicators. Add a moving average line to the OBV to look for OBV line breakouts; you can confirm a breakout in the price if the OBV indicator makes a concurrent breakout.

Another note of caution in using the OBV is that a large spike in volume on a single day can throw off the indicator for quite a while. For instance, a surprise earnings announcement, being added or removed from an index, or massive institutional block trades can cause the indicator to spike or plummet, but the spike in volume may not be indicative of a trend.

What Is a Moving Average (MA)?

In finance, a moving average (MA) is a stock indicator commonly used in technical analysis. The reason for calculating the moving average of a stock is to help smooth out the price data by creating a constantly updated average price.

By calculating the moving average, the impacts of random, short-term fluctuations on the price of a stock over a specified time frame are mitigated. Simple moving averages (SMAs) use a simple arithmetic average of prices over some timespan, while exponential moving averages (EMAs) place greater weight on more recent prices than older ones over the time period.

Understanding a Moving Average (MA)

Moving averages are calculated to identify the trend direction of a stock or to determine its support and resistance levels. It is a trend-following or lagging, indicator because it is based on past prices.

The longer the period for the moving average, the greater the lag. A 200-day moving average will have a much greater degree of lag than a 20-day MA because it contains prices for the past 200 days. 50-day and 200-day moving average figures are widely followed by investors and traders and are considered to be important trading signals.

Investors may choose different periods of varying lengths to calculate moving averages based on their trading objectives. Shorter moving averages are typically used for short-term trading, while longer-term moving averages are more suited for long-term investors.

While it is impossible to predict the future movement of a specific stock, using technical analysis and research can help make better predictions. A rising moving average indicates that the security is in an uptrend, while a declining moving average indicates that it is in a downtrend.

Similarly, upward momentum is confirmed with a bullish crossover, which occurs when a short-term moving average crosses above a longer-term moving average. Conversely, downward momentum is confirmed with a bearish crossover, which occurs when a short-term moving average crosses below a longer-term moving average.

Types of Moving Averages

Simple Moving Average

A simple moving average (SMA), is calculated by taking the arithmetic mean of a given set of values over a specified period. A set of numbers, or prices of stocks, are added together and then divided by the number of prices in the set. The formula for calculating the simple moving average of a security is as follows:

$\begin{aligned} &SMA = \frac{ A_1 + A_2 + \dotso + A_n }{ n } \\ &\textbf{where:} \\ &A = \text{Average in period } n \\ &n = \text{Number of time periods} \\ \end{aligned}$​SMA=nA1​+A2​+…+An​​where:A=Average in period nn=Number of time periods​

Charting stock prices over 50 days using a simple moving average may look like this:

Exponential Moving Average (EMA)

The exponential moving average gives more weight to recent prices in an attempt to make them more responsive to new information. To calculate an EMA, the simple moving average (SMA) over a particular period is calculated first.

Then calculate the multiplier for weighting the EMA, known as the “smoothing factor,” which typically follows the formula: [2/(selected time period + 1)]. 

For a 20-day moving average, the multiplier would be [2/(20+1)]= 0.0952. The smoothing factor is combined with the previous EMA to arrive at the current value. The EMA thus gives a higher weighting to recent prices, while the SMA assigns an equal weighting to all values.

$\begin{aligned} &EMA_t = \left [ V_t \times \left ( \frac{ s }{ 1 + d } \right ) \right ] + EMA_y \times \left [ 1 – \left ( \frac { s }{ 1 + d} \right ) \right ] \\ &\textbf{where:}\\ &EMA_t = \text{EMA today} \\ &V_t = \text{Value today} \\ &EMA_y = \text{EMA yesterday} \\ &s = \text{Smoothing} \\ &d = \text{Number of days} \\ \end{aligned}$​EMAt​=[Vt​×(1+ds​)]+EMAy​×[1−(1+ds​)]where:EMAt​=EMA todayVt​=Value todayEMAy​=EMA yesterdays=Smoothingd=Number of days​

Simple Moving Average (SMA) vs. Exponential Moving Average (EMA)

The calculation for EMA puts more emphasis on the recent data points. Because of this, EMA is considered a weighted average calculation.

In the figure below, the number of periods used in each average is 15, but the EMA responds more quickly to the changing prices than the SMA. The EMA has a higher value when the price is rising than the SMA and it falls faster than the SMA when the price is declining. This responsiveness to price changes is the main reason why some traders prefer to use the EMA over the SMA.

Example of a Moving Average

The moving average is calculated differently depending on the type: SMA or EMA. Below, we look at a simple moving average (SMA) of a security with the following closing prices over 15 days:

• Week 1 (5 days): 20, 22, 24, 25, 23
• Week 2 (5 days): 26, 28, 26, 29, 27
• Week 3 (5 days): 28, 30, 27, 29, 28

A 10-day moving average would average out the closing prices for the first 10 days as the first data point. The next data point would drop the earliest price, add the price on day 11 and take the average.

Example of a Moving Average Indicator

A Bollinger Band® technical indicator has bands generally placed two standard deviations away from a simple moving average. In general, a move toward the upper band suggests the asset is becoming overbought, while a move close to the lower band suggests the asset is becoming oversold. Since standard deviation is used as a statistical measure of volatility, this indicator adjusts itself to market conditions.

The Bottom Line

A moving average (MA) is a stock indicator commonly used in technical analysis, used to help smooth out price data by creating a constantly updated average price. A rising moving average indicates that the security is in an uptrend, while a declining moving average indicates a downtrend. The exponential moving average is generally preferred to a simple moving average as it gives more weight to recent prices and shows a clearer response to new information and trends.