Large institutions are the primary source of the trading volume and the driver of market direction through reversals and profit taking. Further, larger institutions have the capacity to anticipate post-earnings announcement drifts (PEADs) and surprise earnings. Therefore, surveillance of large institutional trading through volume analysis is a viable, informed trading strategy for individual investors. (Grief, 1992) asserted that based upon historical institutional analyses, the comprehension of the actual determinations of trade require an analysis of the institution. (Matras, 2013) supported that an optimal strategy for identifying progressive stock movement is the screening for stocks that are significantly increasing in volume; as the stock movement due to growth in volume is an outcome of institutional buying. This paper will present an exploratory overview of the theory, strategy, and potential benefits of following large institutional trading with volume analysis.




The history of  large institution trading within the financial markets has been linked to the Commercial Revolution between the 11th and 14th centuries in which long distance European trades emerged after a significantly long period of minimal activity. Since then, the magnitude of large institutional trade volume has expanded tremendously, and remains the predominating force and a predictive power in the financial markets. (Lakonishok, Shleifer &Vishny, 1992) estimated that approximately 50% of the United States based equities were held by institutional investors; and that the institutional investor trading accounted for approximately 70% of the New York Stock Exchange trading volume. Daily and weekly volume data from stocks listed on the exchanges include useful information in regard to the stocks with the highest, most recent accumulations, and the stocks that are most heavily bought and sold relative to the large trades of institutional buyers. This information is of great importance to stock exchange officials, financial and economic policy makers, and researchers as well as the individual investor community.




Popular theory of large institutional trading address the relationship between the large institutional investor and the market; particularly in regard to price movements and volume. The volume indicator for large trades may assist in financial analyses of trending stocks; breakouts; and volume spikes. Institutional trades less than $2000 and trades above $30,000 produce valuable information in regard to institutional activity (Campbell, Ramadorai & Schwartz, 2005). Large equity or bond transactions, or block trades, are tools of institutional investors and hedge fund managers that are significant predictors of stock price movement. 


From the onset of institutional trading, the large institution has represented stable, self-reinforcing systems that were not bound to any responsiveness to opportunities of welfare enhancement. Some theorize that the large institutions destabilize the stock prices; pushing the prices away from the fundamental values and increasing the long run volatility of the prices. Others focus upon liquid stock volumes, within which the concentration of large institutional trades are made. Interestingly, even large institutional investors investigate the impact of their own trades on the financial markets.


Large Institutional Investors and Trading Volume


The volume analysis is an approach to assessments of stock trend health that is based upon the volume activity that is generated by buying and selling in the capital markets. The “explanations” for trading volume have been described in terms of liquidity trading, tax-driven trading, speculation and portfolio re-balancing. The outcomes of volume that are extracted from models that are based upon feasible expectations do not compare simply to models based upon speculative markets due to the fact that such models include liquidity, speculative trades and portfolio balancing and are driven by exogenous shocks. In this light, the large institution trade is distinguished from other trades based upon comparisons of the volume-weighted average for a stock on the date of trade; the transaction price to the stock’s 3 intra-day price averages; the simple average for the transaction costs of same day stock returns; and the volume weight average from excluded trades. 


(Chemmanur & He, 2016) analyzed the institutional investor role in information production for corporate spin-offs by an analysis of a sample of institutional trading data on the transaction level from 1999 to 2004. The objective of the study was to measure the extent to which corporate spin-offs impact information production by institutional investors; and ultimately, whether the spin-offs benefit the institutional investors. (Chemmanur & He, 2016) concluded that the spin-offs increase the trading welfare of the institutional investors by relaxation of trading constraints that were in place prior to the spin-off event. 

(Campbell, Ramadorai & Schwartz, 2005) investigated institutional ownership changes based upon a diverse sample of equity transactions in order to gain a more in-depth understanding of institutional trading patterns. The methodology consisted of an analysis of the daily flow of institutional trades. (Campbell, Ramadorai & Schwartz, 2005) supported that across all institutional trades of various trade sizes, the volume as classifiable as the buys are predictors of increases; while the volume of sells are predictors of decline in the institutional ownership. However, (Lakonishok, Shleifer & Vishny,1992) supported that the pursuit of trading strategies by large institutional investors is “generally benign” in that irrespective of the generation of substantial trade volumes, the institutional investors do not destabilize stock prices. 


(Harris & Raviv, 1993) concluded that trading volume may be characterized as positively autocorrelated; and that upon the market opening, volume tends to be larger. (Lakonishok, Shleifer &Vishny, 1992) supported that, on the average, larger institutional investors do not follow positive or negative feedback trading strategies; but rather pursue a diversity of trading styles that offset each other. (Cochran, 2013) supported that in the absence of the noise that is associated with trading; orders of a specific size will control and drive the markets and presented a method of tracking the volume of institutional trades. The size of the block trades are indicators as triggers of price movements relative to imperfect substitution; the effects of information; and short run costs of liquidity. The volume-by-price indicator reflects volume for specific price ranges, based upon the closing price of the asset. Figure 1 shows the tracking method using daily charts that are filtered for institutional order size: 

Figure 1. Institutional Trade Volume Tracking by Order Size (Cochran, 2013)

The low was set one bar prior to the first purchases that were made by the institution, at 143-20. The institution remained on the buy side and added position. The high of the day was at 144-06, at which the institution began to profit. The integration of volume and the closing price defines higher volume price ranges relative to resistance or support. 

(Cochran, 2013) pointed out that trading by following a specific institutional volume size would produce higher profits than the outcomes from the typical retail account arbitrary exit. Figure 2 shows the institutional trades filtered in order to track the retail accounts trades: 


Figure 2. Institutional Trade Volume Tracking Retail Trade Accounts (Cochran, 2013)


Here, the retail accounts followed the short covers and sells of the institution until the market reached 144-00 and traded to 143-25. The retail accounts exited the market prematurely; however, the institutional investors stayed long and continued to add to their positions. The average volume is also significant in tracking the large institution trades. (Matras, 2013) proposed screening parameters that highlight price increases within periods of 1 to 3 weeks; and volume increases from 1 to 3 weeks. The volume-by-price for 3 years would be based upon the weekly closing data for 3 years. Further, the Average 20-day Volume in the chart should be set to a minimum of 100,000 shares for tracking.


Large Volume Trades and the Bid-Ask Spread

(Abhyankar, 1997) explored the intraday variations in bid-ask spreads, return volatility and trading volume based upon a sample of 835 stocks that were traded on the London Stock Exchange in the 1990s. The objective of the study included evidence-based outcomes of intraday behaviors based upon the trading volume per transaction; spreads and volumes across portfolios of diverse variations in liquidity; and the number of transactions that occur per 15 minute intervals. The intraday behaviors were observed based upon the assumptions that the volume would be concentrated over the course of the day; that negative correlations are reflected between volume and depth; and that positive correlations are reflected between volatility and volume. The outcomes of the study indicated that the average bid-ask spread reflects a U-shape during trading hours and that the spread is at its peak at the market open. 

(Wang & Yau, 2000) estimated the elasticity and parameters of the bid-ask spread, price volatility, and trading volume in a structural, 3 equation model and found a positive correlation between price volatility and trading volume and an inverse correlation between the bid-ask spread and trading volume. The relationship between price volatility and trading volume is based upon the theories of the mixture of distributions hypothesis (MDH) and the sequential information model (SIM). The outcomes of the study also confirmed that the bid-ask spread, price volatility, and trading volume are endogenously determined. However, in regard to the Deutsche mark futures and the S& P 500, only the bid-ask spread is found to be the endogenous variable in cases where the dependent variable is the trading volume.


Market Delta Footprint Charts

The analytical complexity of institutional trading summons a diversity of methods for financial data analysis that provide visual revelations based upon high-level market theory. The footprint chart was made possible by rapid advancements in computer technology that prompted electronic trading and the development of sophisticated software programs that could contain and process a large magnitude of required financial data (Market Delta, 2016). The footprint chart leverages the financial data within highly structured presentations that improve insight and expand the potential of both large and small volume trading. 

Common footprint charts are created from stock exchange information and provide the location of volume across a series of prices; the order flow; and the price data discrete volume (Market Delta, 2016). The footprint chart has been developed based upon Bid/Ask; Total Volume; Delta Profile; Visibility; Volume (Delta Color) Footprint; Bid/Ask Volume and several other types. The darker the shading in the footprint chart, the more the aggressive the trading. According to Market Data, the footprint chart “is a dissection of the bar” and “quantifies and displays the exertion of energy by the buyers and sellers”. 

The cumulative delta volume analysis allows the investor to visualize the auction process that occurs within the market. Momentum shifts and divergencies are observed between the buyers and sellers in order to pinpoint potential entries into market trends or to counter trend trades. Figure 3 shows a Delta Profile footprint chart, which provides a DELTA distribution as opposed to volume: 


Figure 3. Delta Profile Footprint Chart (Market Delta, 2016)


The absolute delta for each stock price are displayed along with absolute values. The blue deltas are positive values and the red deltas are negative. The larger the absolute delta measure, the larger the footprint. (Harris & Raviv, 1993) supported that a positive correlation exists between volume and absolute price changes as well as between volume and absolute changes in the average projections of final payouts. Thus, the benefits of the Delta Profile include the visual clustering of the negative and positive delta relative to price on a price by price basis; and thus, if the trends are becoming stronger or weaker. 


The behaviors of the bid-ask spread, trading volume price volatility are often researched in terms of market reaction and interactions that may be used to predict the prices of futures. The trading volume is depicted by a U-shape; but rather is represented by a double hump with highs at approximately 9:30am and 4pm (Abhyankar, 1997). Figure 4 shows a Volume (Delta Color) footprint chart, which is a depiction of the volume percentage from the trading dominant side: 

Figure 4. Volume (Delta Color) Footprint Chart (Market Delta, 2016)


The chart shows a ratio of the ask traded volume and bid traded volume from the dominant side of the trade. (Abhyankar, 1997) supported that the average bid-ask spread reflects a U-shape during trading hours; peaks at the market open; rapidly declines to constant levels; and modestly widens at market close. (Wang & Yau, 2000) confirmed that bid-ask spread was negatively correlated with lagged trading volume. Here, the ask percent traded volume is represented in blue; while the bid percent traded volume is represented in red. Further, the delta’s absolute size in comparison to the other deltas is used to adjust the shading. The Volume (Delta Color) chart is commonly used by investors that require visualizations of the volume measurements and the positive or negative state of each bar.




Persistent institutional trading has the potential to predict long horizon cross sections of returns after the occurrence of controlling prior returns. Griffin, Harris & Topaloglu (2003) presented that institutional trades constitute approximately 86% of all block trades; but that institutional trades only constitute approximately 18% of small trades. The costs of trading are a significant issue for institutional traders which results in trading behaviors that aim to control or reduce the transaction costs. Service firms are required to bear the burden of costs that must be met from the firm’s capital along with management fees. Several large institutions pay commissions through the clients, financial analysts, and the portfolio managers. Thus, institutional investors exhibit tendencies of herding; and such behaviors have a tendency to stabilize market prices. 


The impact of large institutional trading on the stock price provides an opportunity to measure the significance of the outcomes upon the supply schedules and flow demand of the assets. Institutional buys are predicated upon differential information from its sells and significantly impact capital flow as it enters and exits the market. (Campbell, Ramadorai & Schwartz, 2005) found that daily trades by larger institutions are “highly persistent and positively respond to recent daily returns”; but that the trades “respond negatively to past, long-term daily returns”. They also submitted that recent interest in institutional trading has shifted from the quarterly and lagged institutional flows to the holdings. (Cochran, 2013) concluded that the individual trader reduces market research time and improves profitability by following the market entrance and exits of the institutional traders. Overall, the assets and price zones that reflect a high magnitude of trading volume are indicators of large institutional trader interest that may significantly impact the supply and demand for the asset in the future.




Abhyankar, A. Ghosh, D. Levin, E. Limmack, R. Bid-Ask Spreads, Trading Volume and Volatility: Intraday Evidence from the London Stock Exchange. Journal of Business Finance & Accounting, 24(3) & (4), 1997.


Campbell, J. Ramadorai, T. Schwartz, A. Caught on Tape: Institutional Trading, Stock Returns, and Earnings Announcements, Journal of Financial Economics, 2005.


Chan, L. Lakonishok, J. Institutional Trades and Intra-Day Stock Price Behavior. BEBR, Working Paper 91-0167, 1991.


Cochran, C. 2013. How to Track Institutional Trading During Day Sessions. Trader Kingdom. Retrieved from


Chemmanur, T. He, S. Institutional trading, information production, and corporate spin-offs. Journal of Corporate Finance, 38, 54-76, 2016.


Greif, A. Institutions and International Trade: Lessons from the Commercial Revolution. The Economic Review, 82(2), 128-133, 1992.


Griffin, J. Harris, J. Topaloglu, S. The Dynamics of Institutional and Individual Trading. The Journal of Finance, LVIII(6), 2003.


Harris, M. Raviv, A. Differences in Opinion Make a Horse Race. Review of Financial Studies, 6(3), 473-506, 1993.


Lakonishok, J. Shleifer, A. Vishny, R. The impact of institutional trading on stock prices. Journal of Financial Economics, 32, 23-43, 1992.


Market Delta. 2016. Anatomy of a Footprint. Retrieved from


Matras, K. 2013. How to Uncover Institutional Buying. Zacks. Retrieved from


Wang, G. Yau, J. Trading volume, bid-ask spread, and price volatility in futures markets. Journal of Futures Markets, 20(10), 943-970, 2000.

This website is intended for educational purposes only. Nothing is a solicitation or recommendation to buy or sell securities. You are responsible for your own financial decisions.

© 2018 Hani Gittens, LLC

  • Black Facebook Icon
  • Black Twitter Icon
  • Black YouTube Icon
  • Black Instagram Icon
  • Black LinkedIn Icon
For business inquiries, collaborations, and advertising please use the contact page or our chat to reach us.