Volume-Weighted Average Price (VWAP)

What is VWAP

Volume weighted average price (VWAP) is a fundamental concept in the trading world. It gives the average price a security has traded at throughout the day based on both volume and price. It acts as a benchmark that many traders use to gauge whether they’re getting a good execution price.

How is VWAP Calculated?

VWAP is calculated by the total dollar amount traded in the security over a specific period, like for example trading day, is divided by the total volume traded in that same timeframe. This means that prices at which large volumes have been traded have a higher impact than those prices at which only smaller volumes have changed hands.

Formula of VWAP

How Volume-Weighted Average Price Works – Example

We’re looking at a two-hour window, which we have split into 15-minute intervals, to calculate the VWAP for that period. In the first step, we have to multiply each price that traded with the applicable volume. The simple sum of these products will give us the total dollar amount traded.

The second step is to divide the total dollar amount by the volume traded, and this gives us the VWAP of 81.7516.

The VWAP considers both price and volume, capturing a more comprehensive view of market activity throughout the day. This means that if a security has traded heavily at a particular price, that price gets more weight in the VWAP calculation. As a result, the VWAP gives a realistic average price at which the shares have traded over the day.

How is VWAP Used

VWAP is used in trading as a benchmark for evaluating execution prices. Traders compare their executed prices to VWAP to assess trading performance. VWAP provides a more comprehensive view of market activity throughout the day by considering both price and volume. It is commonly employed in algorithmic trading strategies, where algorithms are designed to execute trades in line with or outperform the VWAP benchmark. Additionally, VWAP is utilized in risk management, market analysis, and order execution strategies to optimize trading outcomes.

Traders use VWAP in various strategies. They can use it to confirm market trends and to establish trading guidelines. For example, if stocks are priced below VWAP are often seen as undervalued, while those above VWAP are considered overvalued. Traders might buy stocks that rise from below to above VWAP and sell or short stocks that drop from above to below VWAP.

Mutual funds investors can use VWAP to minimize their impact on the market when buying or selling large quantities of stock. They aim to purchase stocks at prices below VWAP and sell them at prices above VWAP. This helps to move the stock price towards the average rather than away from it.

Pros of using VWAP

• Comprehensive Representation: VWAP considers both price and volume, providing a more realistic view of market activity compared to TWAP, which only focuses on time.
• Better Benchmark: VWAP serves as a consistent benchmark for assessing trading performance, as it reflects true market conditions by accounting for trading intensity and depth.
• Accurate Assessment: Traders can evaluate their execution prices relative to VWAP to gauge how well they’ve performed compared to the majority of trading activity.
• Market Sensitivity: VWAP gives higher weight to prices where larger volumes were traded, making it sensitive to changes in trading intensity and volume distribution throughout the day.

Cons of using VWAP

• Algorithm Complexity: VWAP execution algorithms are more intricate compared to basic TWAP strategies due to their requirement for precise trading proportions aligned with market volumes.
• Dependency on Realized Volume: VWAP algorithms rely on realized volume over the entire relevant period, which is only available retrospectively, posing challenges for real-time execution.
• Execution Sensitivity: VWAP algorithms may underperform if realized volume distribution deviates significantly from the historical volume profile used for predictions, potentially leading to significant deviations from the desired VWAP benchmark.
  

  Pros	Cons
  Incorporates trading volume for a more accurate market picture	Necessitates extensive data for precision
  Identifies optimal buy and sell points by spotting market lows and highs	Ignores historical data, limiting its scope
  Indicates shifts in market sentiment, aiding in trend analysis	Effective primarily for intra-day analysis, with limitations over multiple days
  Assists in timing trades optimally considering liquidity and execution costs	Risks missing out on strong market trends if used in isolation without other indicators
  Beneficial for both individual and institutional investors	Can become less responsive or ‘laggy’ with excessive data input
  Serves as a critical component of various trading algorithms	Optimal performance observed in short-duration charts (1 or 5 minutes)
  Acts as dynamic support and resistance levels, offering strategic points
  Mitigates the impact of large transactions on market prices

Limitations of VWAP

While TWAP provides a consistent time-based benchmark, it doesn’t take into account the depth or intensity of trading, making the VWAP a more representative measure of true market conditions and a better consistent benchmark for assessing trading performance.

Given the fundamental nature of VWAP in the trading world, it’s no surprise that there are algorithms, or algos, designed around it. Those VWAP execution algorithms have been developed to help traders achieve or outperform the VWAP benchmark for a particular order. The basic TWAP strategy divides the order into uniform segments, executing consistently over the designated period. A VWAP algorithm, however, does not just demand regular execution, but also trading in precise proportions in line with fluctuating market volumes. These proportions depend on the realized volume over the entire relevant period, which unfortunately will only be known at the end of the period.

Application of VWAP in Excel

So how can this issue be addressed? A common way is to use historical volume profiles, which are given by averages of trading volumes observed in the past. Based on this approach, the basic mechanism of a VWAP algorithm works as follows. First, we have to determine the number of shares we want to buy or sell. The algorithm then uses historical data to predict the volume distribution throughout the day.

Based on the volume forecast, the algorithm slices the order into smaller chunks known as ‘child orders’ to be executed at different times. These child orders will then be automatically released at predetermined times.

Let’s look at an example. Let’s say we’re aiming to buy 10,000 shares at VWAP over a two-hour time period from 9:15 AM. Let’s assume that historically the security has traded in total volumes of 258,000 over the two-hour period with an average of 43,760 units at 9:15.

This corresponds to 16.96% of the total volume of the time period. So, the algorithm will trade 1,696 shares at the price available at 9:15.

The same methodology is applied to all other given points in time, and as a result, a total of 10,000 shares will have been purchased at the end of the two-hour period.

However, if we calculate the average price realized by the basic VWAP algorithm using the determined child order sizes, we get a result that differs from the actual VWAP. More specifically, the algo achieved an average price of 81.7495, whereas the VWAP for the period was 81.7516.

What is the reason for this difference? Remember, our basic VWAP algo predicted the volume distribution over the execution period using historical data and then statically executed the child orders.

The realized volume distribution during the period, however, was different. As we can see, the realized volume percentage was lower than the historical one for the first part of the period. This is also when the share price was relatively low. In the second half of the period, realized volumes were higher than the historical ones, and share prices were relatively high.

So the algorithm based on the historical volume distribution bought more shares at lower prices and fewer shares at higher prices than realized by the actual VWAP, which leads to the lower average price of the algorithm compared to the VWAP.

In this example, the algorithm beat the benchmark, but this example also shows the main issue of the VWAP program. If the realized volume distribution differs meaningfully from the historical volume profile, the average price achieved by the algorithm might differ significantly from the benchmark. While this could result in outperformance, it could of course lead to significant underperformance as well.

TWAP vs VWAP

TWAP and VWAP are both essential tools for traders, they serve different purposes and have distinct calculation methods. When traders compare their executed prices to the VWAP, they can assess how well they’ve performed relative to the majority of trading activity. In contrast, the TWAP (time-weighted average price) only considers the passage of time and ignores volume. This means it might give equal importance to periods of low trading activity as to periods of high activity.

At its core, TWAP is the average price of a security over a specified time. It is calculated by summing up prices of the asset observed at multiple points across a set period and dividing the sum of the total number of observation points.

So, if you are looking to calculate the TWAP of a 2 hour period which is sliced into 15 minute intervals, you sum up the 8 observed prices and divide this sum by 8, making the TWAP $81.7550.

Application of VWAP in Investment Banking

VWAP is a valuable tool for investment banks, enabling them to execute large trades efficiently, manage risk, evaluate performance, and serve their clients effectively. It plays a significant role in ensuring smooth and efficient market operations for various investment banking activities.

Block Trading

When investment banks execute large block trades, buying or selling a large number of shares for a client, they will aim to minimize market impact. VWAP helps achieve this by spreading the execution over time, targeting prices close to the average price throughout the day, and mitigating the potential for pushing the price up significantly when buying, or down when selling.

Algorithmic Trading

Investment banks use sophisticated algorithms for automated trading strategies. VWAP algorithms are frequently employed to ensure executions are aligned with the overall market trend throughout the day. This helps to achieve the desired average price for the client while staying within risk management parameters.

Performance Evaluation

When evaluating the performance of their trading desks, investment banks often compare execution prices to VWAP. This provides an objective benchmark for assessing whether trades were executed at a fair price relative to the prevailing market conditions.

Client Service

Investment banks can leverage VWAP to demonstrate their commitment to best execution for their clients. By aiming to achieve the VWAP or even outperform it through sophisticated algorithms, they can offer a higher level of service and transparency.

Market Analysis

Investment banks use VWAP to analyze overall market activity and identify potential trading opportunities. Deviations from the VWAP in specific sectors or securities can signal underlying market sentiment or potential price movements.

M&A Advisory

VWAP is also used by M&A advisors. When a potential bidder is evaluating what premium to pay to induce shareholders to sell they often consider not just the unaffected price before the announcement, but the Volume Weighted Average Price over a time period. The volume-weighted average price will indicate the recent price paid for the shares and a better sense of what premium a bidder will need to pay to secure the support of the selling shareholders.

Conclusion

VWAP considers both price and volume and it offers a clearer picture compared to simple averages. However, VWAP has limitations due to its use of historical data. Its accuracy greatly relies on whether predicted trading volumes match actual ones. Discrepancies between predicted and actual volume can lead to deviations from the desired VWAP, this impacts trading performance.

Get access our +50 Wall Street-recognized online finance courses, weekly live webinars, company and market data and more in one plan.

Additional Recourses

Sales and Trading Course

How to Get a Job in Sales and Trading

Global Economics Trade Advantages

Stock Exchanges