Abstract

Before Facebook, Twitter, and Snapchat, there was the New York Stock Exchange. The 20th Century NYSE trading floor was one of the world’s most important early social networks. Social networks and social media-based investing have become commonplace today. Many investment strategies even seek to use investor sentiment from these platforms to determine the future direction of a security’s price. While a generation of investors thinks that this is new, investors have been actively managing money using market sentiment for decades. This paper introduces a unique and innovative indicator that measures the depth of sentiment of today’s virtual crowd of traders and investors for a security using readily available trading volume. It is called Normalized Relative Volume (NRV) and will help active managers generate alpha through security selection and portfolio weighting.

The 20th Century NYSE floor was structured as an auction market that brought buyers and sellers together to determine the fair value for a security. Interested auction participants formed crowds that ebbed and flowed based on market moving information, as investors evaluated their portfolios and sought to own the best performing securities. Astute traders learned that crowd size was an important indicator. Being early to a growing crowd meant opportunity to profit from increasing price momentum. Alternatively, a small crowd of participants might mean that an ignored stock was mispriced.

During the last twenty years, electronic trading and competition from new exchanges have caused traders to move upstairs. As a result, the crowds that made up each auction on the NYSE floor are no longer viable as an indication of investor sentiment. However, today’s virtual crowd is as valuable as ever, for those who know how to read it.

Academic and practitioner researchers have found that volume should be normalized to effectively provide investment signals. Normalizing volume is typically done by adjusting it to a moving average or to shares outstanding and makes volume more easily comparable to its own history or to other securities. However, researchers find an important shortcoming with volume: the most and least active stocks rarely move into other deciles. One group of researchers uses this to their advantage, determining that liquidity could be considered an investment style, akin to value and growth investing. They find that low liquidity securities perform best.

This paper builds upon that research, as well as the historical relationships between traders on the floor, and introduces Normalized Relative Volume (NRV). This indicator solves the problems of other liquidity measures. First, all stocks undergo periods of high and low volume, relative to their own history. NRV makes this actionable so that every stock offers tactical opportunity. Second, NRV uses volume data that is available freely to any investor. Last, it is the only measure that can be effectively used at any data frequency, from intraday, to daily, weekly, or monthly. As a result, NRV is an effective proxy for the size of the virtual crowd trading a security and is an important new investment factor.

This paper shows the value of NRV through three tests across stocks and ETFs. First, it compares 170 observations of high NRV-ranked stocks to 170 observations of stocks ranked by simple trading volume and finds that the high NRV ranking is better at finding event-driven trading opportunities. High NRV stocksare more volatile, and exhibit greater instances of corporate events as well as abnormally high volume levels. By comparison, the stocks on the most active list tend to have significant daily overlap—there were only 36 unique stocks across the 170 observations—and many stocks failed to achieve volume that was two times their daily average. It is believed that High NRV stocks are undergoing revaluation by a larger than normal group of investors and that these stocks will underperform in subsequent periods.

Next, the paper tests S&P 500 constituents from 2000-2013 and determines that stocks with a high NRV in the first month tend to underperform other stocks in the second month. However, stocks with low NRV in the first month tend to outperform other stocks in the second month. This indicates that stocks which trade on low NRV are being priced less efficiently by the market and will readjust in the second month. While many investors focus on high NRV stocks to find trading ideas, they would do better to look for opportunity among the stocks with smaller virtual crowds and lower NRV. Finally, this paper offers a method for weighting a portfolio using NRV. The results show that a low NRV weighted index outperforms high NRV, equal weighted, and S&P 500 indexes over both bull and bear markets and generates alpha. It is likely that an active manager could use this weighting methodology on a well selected portfolio of stocks to generate additional outperformance. This paper’s three tests confirm the ability of NRV to provide information about securities pricing. As a new indicator, it can be used by active investment managers either on a discretionary or quantitative basis.

Fear and greed drive the sentiment for investors, especially among crowds of traders. This paper proves that larger crowds of virtual traders are more effective at pricing securities than smaller crowds. Although the physical crowd is gone, NRV provides a way to measure the modern-day virtual crowd and increase portfolio returns.

Part 1: Introduction

Before Facebook, Twitter, and Snapchat, there was the New York Stock Exchange. The 20th Century NYSE trading floor was the original social network. Unlike Facebook, this social network connected investors who interacted to determine the fair value of a company. On the floor of the NYSE, there were boundless opportunities for traders skilled at reading the depth and direction of crowd sentiment. As markets became electronic, the crowd moved off the floor and was no longer seen as an effective sentiment indicator. However, today’s virtual crowd is as valuable as ever, for those who know how to read it.

This paper presents a unique and innovative indicator called Normalized Relative Volume (NRV). NRV uses trading volume to model the size of the virtual crowd as well as the depth, or commitment, of crowd sentiment. The results will show that the virtual crowd remains an effective indicator for traders. Most importantly, this paper adds to the body of investment knowledge by proving that securities with low Normalized Relative Volume are mispriced and present opportunity to generate alpha.

History of the Crowd

From its beginnings until the early 21st Century, trading on the New York Stock Exchange (NYSE) was a continuous auction system that brought human traders face-to-face in a centralized location. Trading in each stock was managed by a specialist tasked with maintaining a fair and orderly market. Brokers gathered and formed crowds of interested buyers and sellers. Crowd size became a measure of sentiment. Larger crowds were considered to have greater depth of sentiment. With more traders vying for a piece of the action, a deeper crowd was more committed.

With the rise of electronic trading and new exchanges, markets are decentralized, and the crowd of floor traders is largely disbanded. Today’s crowd is virtual and unmeasured. However, the crowd remains as valuable today as it was during the heyday of the NYSE floor. What has changed is the ability to measure it. While it is impossible to know the number of traders in today’s virtual crowd, their trading volume is an effective proxy for their level of interest in a security.

Volume is the quantity of an item that changed hands during a period. For a stock, it is the number of shares that moved from one owner to another. Volume measures money flow and is a proxy for crowd size and depth of sentiment.

Price, on the other hand, measures the sentiment of the quality of the company. If investors believe that earnings will grow and outpace those of rivals, their demand for shares will cause an increase in the share price. Alternatively, the price may fall when investor sentiment is negative towards a company’s earnings quality. Price sentiment is either bullish, bearish, or neutral. Together, price and volume make up the two components of supply and demand and paint a complete picture of investor sentiment and the crowd of traders.

Traditional Uses of Volume

Review of Prior Research

Academic researchers and practitioners have long studied volume as an important factor driving returns. The CMT Association’s Charles H. Dow Award has been granted for two papers related to volume. Buff Dormeier, CMT won for his work on the Volume Price Confirmation Indicator. It monitors supply and demand to see if volume supports the price trend. George Schade, CMT provided a history of On Balance Volume and the showed that volume has been an important investment factor for many years. The concepts in both papers combine reported volume with price changes to create supply and demand indicators.

Steve Woods developed the concept of float turnover (volume/shares in float) to measure supply and demand for individual stocks. He, too, defined buy and sell patterns. Woods' methodology normalizes volume in a way that is consistent with the turnover metric used by academic researchers. Andrew Lo and Jiang Wang published “Stock Market Trading Volume” in 2001. They describe ten measures of volume often used in academic literature. (Appendix A) These include shares traded, turnover (shares traded/shares outstanding), dollar turnover (dollar volume/market capitalization), number of trades, and even number of trading days per year. One of their conclusions is that, “if the focus is on the relation between volume and equilibrium models of asset markets, turnover yields the sharpest empirical implications and is the most natural measure.”

Lo and Wang also find that “there is some persistence in turnover deciles from week to week—the largest—and smallest-turnover stocks in one week are often the largest-and smallest-turnover stocks, respectively, the next week.”

In 1994, Stickel and Verrecchia published “Evidence that Trading Volume Sustains Stock Price Changes.” They also use turnover as a measure of volume and break the market into two types of traders. Informed traders act on research and uninformed traders trade on liquidity. They find that, “as volume increases, the probability that the price change is information driven increases.”

Jian Wang published “A Model of Competitive Stock Trading” in 1994 that also uses turnover to measure informed vs. uninformed trading. Informed traders are event driven and trade based on valuation. Uninformed traders are asset allocators rebalancing a portfolio. In his review of several papers, Wang finds that “a high return accompanied by high volume implies high future returns if the first component (informed traders) dominates and low future returns of the second component (uninformed traders) dominates.”

In “Trading Volume and Stock Investments”, Brown, Crocker, and Foerster find that volume is correlated with returns. They conclude, “portfolios of S&P 500 Index and large-capitalization stocks sorted on higher trading volume and turnover tend to have higher subsequent returns (holding periods of 1-12 months) than those with lower trading volume.”

In their review of prior research, they also find:

• Volatility of liquidity is inversely correlated with returns
• Historical volume is predictive of future price momentum
• Short-term mean reversion occurs after large price changes on high volume
• Unusual volume often leads to price increases
• Stocks with traditionally high volume tend to overreact to news events whereas stocks with low volume tend to underreact

Yale professor and CIO of Zebra Capital Management, Roger Ibbotson, along with academic and practitioner researchers Chen, Kim, and Hu, find that liquidity is an investment style, akin to size, value, and momentum. In “Liquidity as an Investment Style” they define turnover as “the sum of 12 monthly volumes divided by each month’s shares outstanding” and find that a liquidity factor “added significant alpha to all the Fama-French factors when expressed either as a factor or as a low-liquidity long portfolio.” As with other research, they find that liquidity is stable and that “69.23% of the stocks stayed in the same quartile.”

Research shows that volume is a useful factor for predicting future returns of securities, especially when normalized. Additionally, there is some persistence, especially in higher volume stocks. Last, price changes that occur on higher normalized volume tend to be persistent.

Problems with Volume

Volume has varied dramatically over time due to systemic changes and investor emotions. Data from the World Bank clearly show that aggregate turnover (shares traded as a percent of market cap) is not stable, rising into the 2008 Financial Crisis, before falling to a 20-year low (see Chart 1). Changes to the rules for NASDAQ dealer trade counting and the rise of high frequency market making both impacted market microstructure and trade volume. Trader emotions were also a factor, pushing volume to new highs during the Dotcom bust and Financial Crisis.

Chart 1: Stocks trades, turnover ratio of domestic shares (%), (1984-2017)

Volume patterns fluctuate with seasonality. Volume is low during holidays and the summer vacation season. It is high during quarterly earnings periods, as new information drives asset repricing. Volume cannot be compared across securities. As Lo and Wang reported, stocks in the top and bottom deciles rarely moved to other groups.

Finally, daily volume is cumulative and, using traditional methods, can only be analyzed at the end of the trading day.

As these issues highlight, raw volume is neither comparable across time nor across a universe of securities. Therefore, volume analysis should be performed on normalized volume.

Introduction to Normalized Relative Volume

As discussed above, researchers normalize volume to compare it across time and across securities. Two popular methods include turnover and calculating a ratio of volume to its average. Both methods can be compared across stocks as well as historically. However, they cannot be calculated using intraday data.

Volume may also be compared to a volume benchmark. The benchmark is typically the total volume on an exchange or sum of volume across the constituents of an index. This indicates the percentage of the volume for that universe which is attributable to a single stock. It can also be thought of as a measure of the virtual crowd of investors interested in this stock.

Normalized Relative Volume (NRV) combines these two methodologies into a single, unique market indicator that measures the size and change of the virtual crowd. Importantly, NRV can be calculated using intraday data for high frequency studies or for longer term investing using daily, weekly, or monthly data.

Normalized Relative Volume is calculated as:

Where:

• Security’s Volume is the number of shares traded during the period
• Benchmark Volume is the number of shares traded on an exchange or the sum of volume for index constituents
• Security’s Average Volume is the average volume for the selected period
• Benchmark Average Volume is the average volume for that benchmark

This can be thought of as:

Normalized Relative Volume quantifies the virtual crowd of traders. It measures activity relative to benchmark volume as well as historically, in the same way that a broker on the NYSE floor would monitor crowd sizes for opportunity.

For example, there are ten traders on a fictitious exchange that trade two stocks. On an average day, stock ABC trades 100 shares and stock XYZ trades 400 shares. The ten traders would normally split based on volume with an average of two traders in the crowd for ABC and eight traders in the crowd for XYZ. This morning, ABC announced earnings and opened on abnormally high volume of 100 shares, equal to its average daily volume. XYZ has no news today and opens on its normal opening volume of 100 shares.

Normalized Relative Volume for each stock is:

Based on the equal volume in the two stocks, the ten floor traders have split so that each crowd includes five traders. However, ABC’s crowd is 2.5 times larger than usual while XYZ’s crowd is smaller.

ABC, with its earnings release, is attracting greater interest than usual as traders update growth targets for the company’s earnings and decide whether to liquidate or add to holdings.

This dynamic occurred every day on the historic NYSE floor. Today, that activity happens electronically, where the crowd is neither seen nor heard.

Normalized Relative Volume is a simple but sophisticated algorithm that helps investors measure the depth of crowd sentiment. The rest of this paper will share three ways that Normalized Relative Volume can be used on its own as well as the advantages of Normalized Relative Volume over reported volume.

Part 2: Short-Term Idea Generation Using Normalized Relative Volume

Most news websites publish daily lists of the most actively traded securities. Investors use the lists to find interesting investment ideas. However, Normalized Relative Volume, as a proxy for crowd size, is better at this task.

To test the value of NRV and its ability to generate better ideas than the most actively traded stocks, two lists were created from S&P 500 constituents. One was a most actives list that ranked stocks by volume. The second ranked stocks by NRV. The observation period included 17 trading days, from October 31, 2017 through November 22, 2017. The top 10 stocks from each list were compared. Nearly every stock in the most actives list would be recognizable to readers of the financial press. It included Apple, General Electric, and Bank of America.

Summary statistics for the most actives show that, of the 170 observations, there was significant daily overlap. Only 36 unique securities appeared during the observation period and 70% of the securities from one day would appear on the next day’s list. In fact, four stocks, AMD, BAC, GE, and T, appeared in all 17 days. Although these stocks were the most actively traded, few exhibited abnormal volume. Just 55% of observations were above their 50-day average volume and only 24% exhibited a volume spike of at least 2 times the average. Only 40% of observations occurred with market-moving news, like earnings results, management changes, mergers, or corporate actions. These stocks were actively traded, but not very interesting.

NRV was more effective at finding interesting stocks. Using a 50-day average for both the stock’s and market’s volume, 100 unique stocks appeared during the 17 trading days. Of those, only 26% would appear on the next day’s list and only three, CBS, HSIC, and TWX, appeared more than five times total. Moreover, those three stocks released significant news: a reorganization (CBS), disappointing earnings (HSIC), and merger talks (TWX). They weren’t the only stocks with interesting news. 73% included news like mergers, reorganizations, earnings disappointments, and corporate actions. Last, every single security chosen for its high Normalized Relative Volume offered reported volume for that day which was at least as great as the 50-day average volume and 91% of observations were at least two times the 50-day average.

NRV can be used by investors to find a diversified list of stocks with higher than normal intraday trading activity and a larger potential to coincide with market moving news. As a result, the stocks tend to attract larger than normal crowds.

Comparing the returns from both lists, high NRV stocks tended to be more volatile on the event day, with a higher standard deviation of returns. Returns at the 80th and 20th percentiles show a spread of 8.23 percentage points vs. 3.16 percentage points for the Most Actives list. The absolute return is 1.72 percentage points higher for High NRV stocks, too.

Table 1

Long-term investors, acting on fundamentals, along with event-driven traders swell the virtual crowds of high NRV stocks, quickly adjusting the valuations for these stocks. The next section will show that high NRV stocks tend to underperform lower NRV stocks over the following month.

Part 3: Analysis of Monthly NRV

Introduction

To test the idea that high NRV stocks underperform low NRV stocks, monthly frequency data is used to measure forward month returns for stocks in five different NRV buckets.

Data

This test was run using the Optuma software platform and the historic S&P 500 constituent database. This database accounts for survivorship bias to test how the strategy would have performed in real-time. The test was run on monthly frequency data from 9/1/2000 through 2/1/2013 and included two bull and bear markets. The statistics exclude the top and bottom 0.5% which contained unrealistic outliers and potential data errors.

Methodology

Normalized Relative Volume was calculated for each stock vs. the S&P 500 constituent total volume. Stocks were sorted into five buckets representing lowest to highest NRV and returns were measured over the following month.

Results

Table 2 (colors represent ranking, green highest to red lowest)

The results support the hypothesis that high NRV stocks have already priced in their prior month events. They tend to have weaker mean and median returns and distributions that are less skewed. This is also observed at the 80th percentile. Volatility rises for outliers at both the lowest and highest NRV observations.

This test built upon the analysis in part 2, which demonstrated a consistent tendency for high NRV stocks to be more volatile than a similar list of most actively traded stocks. Traders and investors reacted to important events, buying and selling shares based on new growth assumptions. Part 3 proved that in the month following a high NRV event, those stocks tended to underperform other stocks that did not have high NRV. Low NRV stocks offered higher returns but also the highest volatility, indicating that investors and traders adjusted their growth assumptions for these stocks in the following month.

Part 4: Normalized Relative Volume Indices

In the study by Ibbotson, Chen, Kim, and Hu, they find that volume is an investable factor which investors can take advantage of by volume weighting a portfolio’s constituents. This section will show that volume weighting outperforms buy and hold of the S&P 500 and that low relative volume weighting even beats an equal weighted portfolio of sector ETFs and generates alpha.

Data

The nine original State Street Global Advisors’ Select Sector SPDR ETFs were used to create three indexes. The selected ETFs track Global Industry Classification Standard (GICS) sectors and began trading in 1998. These ETFs were selected because of their long history, their high level of trading volume, and their effectiveness at tracking their chosen benchmarks. Together, they offer a broad look at the market and can be easily compared to the S&P 500. Although the portfolios underlying these funds are market cap weighted, it is unlikely that this impacted the results. Price data came from Yahoo! Finance and was adjusted for distributions. The State Street Global Advisors’ SPDR S&P 500 ETF Trust (SPY) ETF was used as a benchmark in order to remain consistent with ETF usage in the other indexes and because it is investable.

Data frequency was weekly, spanning July 4, 2005 through June 18, 2018, in order to include the Financial Crisis in 2008, market declines in 2010, 2011, and 2015, as well as all bull markets during those periods.

Data was segmented into 26-week bull and bear market periods in order to analyze performance in different market environments. See appendix for a table of bull and bear market periods including returns.

Methodology

Index descriptions:

• Equal Weighted Index (EW Index): weekly returns were weighted equally across all 9 ETFs
• High Normalized Relative Volume Index (Hi NRV): Weekly returns were weighted by prior week NRV (Higher NRV = Higher Weight)

• Low Normalized Relative Volume Index (Lo NRV Index): Weekly returns were weighted using inverted prior week NRV (Lower NRV = Higher weight)

• S&P 500 Index (SPY Index): Like the others, it was indexed to 100 on the start date

Results

Each constituent in an index or portfolio using NRV weighting will have approximately equal weight over long periods of time, although the weight for any one period will fluctuate. For example, in the Lo NRV index, each of the nine constituents displayed an average weekly weighting of 11.11%. At the 80th percentile, weekly weights rose to 13.48% and fell to 8.18% at the 20th percentile. (See appendix B for table.) This is an improvement over turnover, where stocks tend to stay in the same quartile. NRV, through tactical weighting, offers additional potential to generate alpha. Both the Hi NRV Index and Lo NRV Index are strongly correlated with the SPY Index and the EW Index. This is expected as the underlying ETFs can be aggregated to closely approximate the S&P 500 Index’s returns and implies that all differences in returns are due to portfolio construction.

Table 3

The Lo NRV Index generates a positive alpha against both the EW Index and SPY Index, whereas the Hi NRV Index generates a positive alpha against the SPY Index but a negative alpha against the EW Index (table 3). Alpha is a statistical measure of risk adjusted performance that measures excess return beyond expectations set within the Capital Asset Pricing Model. The positive alpha generated by the Lo NRV Index prove that this methodology successfully finds mispriced securities.

Chart 2: Index Price History

Returns bear this out. During the test period, the Lo NRV index rose by 243.13% (9.93% annualized), easily beating the Hi NRV index, the EW Index, and the SPY Index. The Hi NRV Index gained 201.17%, beating only the SPY Index.

Table 4

Chart 3: Drawdown Comparison

Drawdown was comparable across all three indices, however, the Lo NRV index spent fewer weeks in drawdown. It was later to decline and, in the case of the financial crisis, quicker to rebound and set a new all-time high.

Bull and Bear Markets

In addition to the drawdown analysis, it is important to compare performance from the indices in bull and bear markets to see if Lo NRV outperforms Hi NRV in both market environments. During the measurement period, there was only one bear market lasting more than one calendar year. However, there were nine non-overlapping twenty-six-week periods where the S&P 500 was down. Therefore, a bear market was defined as any 26-week period that the S&P 500 fell and a bull market was defined as any 26-week period that the S&P 500 rose. (See appendix C for Bull and Bear market dates and index returns)

Table 5

The Lo NRV Index performed best, averaging 5.43% during all 26-week periods. However, it had the highest standard deviation and the weakest single period. The 27.48% decline occurred between June 30, 2008 and December 29, 2008. The magnitude of this decline, relative to the others, can be partially explained by the Lo NRV’s relatively strong performance prior to this period. In the 52 weeks before June 30, 2008, the Lo NRV index had dropped just 8%, versus the Hi NRV index’s decline of nearly 15%. The Lo NRV stocks fell further and caught up to the market, possibly due to selling from margin calls. Removing the 2008 financial crisis from the test shows that the Lo NRV index offered the smallest 6-month drawdown and lowest volatility.

Bull Market Performance

Table 6

Low Normalized Relative Volume stocks outperform in bull markets, although with a narrow lead. The spread in 26-week returns between the four indices falls to just 0.41%, with the Lo NRV Index performing best, gaining 10.90%, and the Hi NRV Index returning 10.49%. Lo NRV, Hi NRV, and EW Indexes offered similar volatility that was almost 10% above that of the SPY Index.

Bear Market Performance

Table 7

During the observation period, there were nine bear markets. During these 26-week periods where the SPY Index fell, Low Normalized Relative Volume stocks outperformed. They tended to lose the least, falling an average of 4.90% and offered positive returns during two periods.

Volatility was highest for the Lo NRV Index. However, excluding 2008, the Lo NRV Index wins in every category. Standard deviation improves to 3.28% from 9.12% and the worst percentage decline easily beats the other indexes at -6.24%.

The Lo NRV Index outperforms because it is weighted towards securities that are less efficiently priced. The Hi NRV stocks attract a large virtual crowd of event driven traders who use new data to reprice securities. Those trades become crowded. The Lo NRV securities’ virtual crowds are smaller than normal, resulting in mispricing of the securities. All stocks will have periods of high NRV and low NRV, because NRV is normalized to the market and historical relationships. Therefore, NRV is superior to reported volume and can be used to measure crowd activity and the depth of investor sentiment.

Conclusion

This paper introduces a unique and innovative proxy for today’s virtual trading crowd called Normalized Relative Volume and proves that volume measures the depth of investor sentiment. NRV normalizes volume across time and across an aggregate benchmark and improves upon other liquidity tools because it uses easily available volume data and can be calculated intraday.

This paper’s three tests confirm the ability of NRV to provide information about securities pricing. First, the paper shows that NRV-ranked stocks are better at finding event driven trading opportunities than most active stocks lists. Second, stocks with high NRV are proven to be more accurately priced at the time of measurement and tend to have lower returns in the following month. Stocks with low NRV offer higher forward returns. Third, weighting a portfolio towards low NRV securities generates alpha and suggests that investors could use this methodology to generate excess returns on their own portfolios.

Fear and greed drive the sentiment for investors, especially among crowds of traders. This paper proves that larger crowds of virtual traders are more effective at pricing securities than smaller crowds. Although the physical crowd is gone, NRV provides a way to measure the modern-day virtual crowd and increase portfolio returns.

Appendix A: Volume measures presented by Lo and Wang, 2001.

Appendix B: NRV Weighted Index Statistics

Hi NRV Index Weights

Lo NRV Index Weights

Appendix C

Bull and Bear Markets are defined by whether the SPY Index was up or down during a 26-week period. Bear markets are highlighted in red.

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