Information System

Application of Put Call Ratio to Market Interpretation and Trading Strategy

Abstract—The analysis of options volume on an asset may yield valuable information on investor sentiment. This information may then be extrapolated into a trading strategy, which will place trades with or against overall options sentiment. In this

paper, the authors explore what information, if any, options volume produces regarding future price activity. The authors then develop a basic system for applying this information to a trading strategy. The strategy holds long and short S&P 500

contracts.

Index Terms—Options Volume, Investor Sentiment, Sector Allocation, Portfolio Management, Trading Strategy

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1. INTRODUCTION HIS section will introduce the reader to a basic understanding of options and fundamental knowledge

of exchanges, equities, indices, Exchange Traded Products (ETPs) and the Volatility Index (VIX). Readers familiar with these products should skip to the dataset content section.

1.1. OPTIONS & THE PUT/CALL RATIO An option is a financial derivative that represents a

contract sold by one party (the option writer) to another party (the option holder). The contract offers the buyer the right, but not the obligation, to buy (call) or sell (put) a security or other financial asset at an agreed-upon price (the strike price) during a specified period or on a specific date (exercise date).

Options are publicly traded products which allow investors to generate income based on market direction. Options are divided into puts and calls. Calls are used by investors to generate income from market direction to the upside, while puts are used to generate income from downside activity. Investors primarily use options to hedge their positions. For example, if an investor owns 100 shares of Dunkin Donuts stock, they may buy a put to protect their downside. If their stock loses value, their put option will increase in value, thus offsetting their losses. If their stock increases in volume, their put option will eventually expire. When an option expires, the investor will only lose the amount they paid for the option. Typically, this amount is a minimal amount compared to the value of the stock they hold.

The put/call ratio is calculated as !"# %&'"() *+'' %&'"()

. The higher the ratio, the more puts are being purchased and the lower the ratio, the more calls are being purchased. This suggests that if the ratio is very high, investors are taking a bearish stance on the market and if it is low, investors are taking a bullish position on the market. A ratio of 1 would indicate a neutral market outlook. As our analysis will reveal, the interpretation of the put call ratio can be more complicated.

1.2. OPTION TYPES The options types analyzed include total exchange,

equities, indices, ETPs and the VIX. An exchange is a location where options are transacted. Thus, the total exchange options include options on equities, indices and ETPs. Note that options traded on exchanges do not include the VIX.

Options on equities are options on stocks such as Phillip Morris, Disney, Ford, IBM and Southwest Airlines. Investors typically trade these options to hedge positions they have on the stocks.

Indexes are averages of a number of different stocks. For example, the S&P 500 is a weighted average of 500 different stocks. Investors trade indices using contracts to diversify away risk. Options on indices allow investors to hedge positions they may have on the index itself or can be used as a tool to hedge overall portfolios.

ETPs are Exchange Traded Products which may contain several different assets and are traded in a similar manner to stocks. ETPs allow investors to gain exposure to various

T

INTEGRATION OF EXCHANGE, INDEX, EQUITY, ETP AND VIX PUT/CALL RATIOS INTO TRADING STRATEGY STOLARSKI, VARTANIAN 2

sectors and indexes without purchasing contracts or individual stocks. Options on ETPs allow investors to hedge their positions in ETPs or make directional trades on various sectors, commodities and indices.

The VIX is a volatility index whose value goes up during bearish market phases and down during bullish market phases. Purchasing options on the VIX allows investors to hedge their portfolio against volatility (by purchasing calls) or take a bullish directional stance on the market (by purchasing puts).

1.3. DATASET CONTENT The dataset contains data on put-call ratios from 11/01/2006 until 12/01/2017 for total exchange, equity, index, ETP and VIX options traded on the Chicago Board of Options Exchange. A 20-day exponential moving average was calculated to smooth erratic swings in the put-call ratio, thus providing a clearer picture of options volume activity. The CBOE has made no guarantee as to the accuracy of the data. The volume reflected in the dataset may be based upon preliminarily reported volume rather than cleared volume.

1.4. DETERMINING THE VALUE OF PCR The Put Call Ratio is traditionally used by traders as a contrarian indicator. That is, when the PCR is extremely low, traders consider the market to be irrationally exuberant and expect the market to drop. This is because a large volume of call options leads to a low PCR. Conversely, when the PCR is extremely high, traders consider the market to be over-pessimistic and a bullish, upwards, reversal to be fast approaching. This is because a high PCR indicates a large volume of put options.

Ultimately, there are three potential use-cases for the Put Call Ratio. The first is that it is a contrarian indicator. That is, it can be used to generate a profit by taking a bearish stance on the market when the PCR is below 1 and a bullish stance when the PCR is above 1. The second case is that the PCR can be used as a leading indicator. That is, when the PCR is above 1, a bearish stance is taken and when it is below 1, a bullish stance is taken. The final case is that the PCR does not contain any actionable information on future market direction. The final case would support the Efficient Market Hypothesis, which claims that historical data has no actionable information on future price movement. This paper will begin by performing exploratory data analysis on the PCR of total exchange, index, equity, ETP and VIX options. This phase will use various information visualizations to get an overall understanding of the dataset and analyze PCR versus market performance. The final stage will consist of building a simple long/short system to explore potential

uses of PCR.

2. VOLUME HEATMAP

2.1. TOOLS

The tools used for this section were Excel and Tableau. The data was formatted in Excel and each PCR was smoothed using a 20-day exponential moving average. The data was then loaded into Tableau and a heatmap was constructed. When interacting with the visualization, the user can see the total number of contracts traded for each option type. The darker the green, the more options are comparatively traded and the bluer, the fewer options are comparatively traded.

2.2. METHOD

This heatmap was constructed to create a general understanding of the dataset. To begin trying to understand which option type contains the most actionable information, we must understand where the most activity is.

2.3. RESULTS

Figure 2.1

2.4. INTERPRETATION

According to this heatmap, we can see that the most significant amount of volume occurs in equity options. This suggests that the most information about future price activity would be contained in equity options. This is because the more options are traded, the more information is being priced into put/call activity. This lays the groundwork for our long/short system as it suggests that the most valuable dataset may be the equity volume.

INTEGRATION OF EXCHANGE, INDEX, EQUITY, ETP AND VIX PUT/CALL RATIOS INTO TRADING STRATEGY STOLARSKI, VARTANIAN 3

3. VIX VERSUS S&P 500

3.1. TOOLS

The tools used for this section were Excel and Tableau. Data was formatted in the same manner as Section 2, with an Exponential Moving Average being used to smooth the PCR.

3.2. METHOD

This visualization was created to garner an overall understanding of how the PCR for various option types may correlate with the S&P 500. This section was also designed to give the reader a more thorough understanding of the VIX. To achieve this, the PCR for VIX options and the percentage change in the S&P 500 were loaded into Tableau. This data was then combined into a single visualization for comparison purposes.

3.3. RESULTS

Figure 3.1

3.4. INTERPRETATION

A general analysis of this visualization shows us that during months where the S&P 500 saw gains, the VIX PCR increased. This was because a large number of people were purchasing put options on the VIX, thus betting on a lower volatility environment.

4. PCR TIME SERIES CHART

4.1. TOOLS

In this section, Excel and Tableau were used to create a line chart. This line chart consisted of two panels, one showing the Put Call Ratios of various option types and the second panel showing the percentage change for the S&P 500. The scale applied is monthly.

4.2. METHOD

The purpose of this exploratory data analysis section

was to see how the Put Call Ratios of various option types worked together and to identify any potential correlations. The similarity between Put Call Ratio activity and the S&P 500’s change was also analyzed from a monthly perspective to determine possible uses.

4.3. RESULTS

Figure 4.1

4.4. INTERPRETATION

According to this visualization, we can observe that the PCRs of various option types generally move in tandem. This suggests that markets have become more interdependent during the past decade. This is confirmed by the tightening spread between different option PCRs. Furthermore, it can also be visually observed that during major market swings, as the PCR increases, the S&P 500 also rallies. This notion will be further investigated in section 6.

5. LIQUIDITY ANALYSIS

5.1. TOOLS

The tools used for this visualization were Excel and RAW. Excel served the aforementioned purpose of formatting the data while RAW processed the data and allowed for visualization creation.

5.2. METHOD

To further our understanding of the dataset, a boxplot was constructed for the PCR of each option type. This was done to explore the liquidity of each option type. The body of each box showed us the distribution of 50% of each type’s PCR and the upper and lower bounds showed us the extremes of each type.

INTEGRATION OF EXCHANGE, INDEX, EQUITY, ETP AND VIX PUT/CALL RATIOS INTO TRADING STRATEGY STOLARSKI, VARTANIAN 4

5.3. RESULTS

Figure 5.1

5.4. INTERPRETATION

It is clear from the visualization that liquidity is correlated with volume. This is because the more market participants are present, the more information is being priced into the market. As we saw in the heatmap, volume was highest in the equity option type. This corresponds to a tight PCR distribution in this visualization.

6. PCR SCATTERPLOTS

6.1. TOOLS

The tools used in this section were Excel and Tableau, in addition to a few statistical methods. The data was formatting in Excel in the manner previously described and loaded into Tableau. From there, a scatterplot was constructed for each option type. The output was analyzed using 𝑅-. The higher the 𝑅-, the more significant the correlation, the lower the 𝑅-, the less significant the correlation. A polynomial trendline was used to identify bullish and bearish PCR levels. A sample of the polynomial trendline output is provided below.

Scatterplots were not constructed for ETPs and Indices. This is because the S&P 500 is not highly correlated with these products in comparison to equities, total exchange options and the VIX.

6.2. METHOD

These scatterplots were created as a basis for the long/short system. To determine long/short levels, the polynomial equation was set to 0% and solved for PCR. 0% was selected because a value higher than 0% means

that the S&P 500 increased, whereas a value less than 0% means that the S&P 500 decreased. Valid crossover points were selected and then interpreted. A solution example is provided below.

In this example, only the 0.98 solution would be considered because PCR cannot be negative nor would it realistically reach 18.28. The 𝑅- value was then considered to select the optimal data to use for a long/short system. The dataset utilized 2791 PCR data points for a significant sample size.

6.3. RESULTS

SCATTERPLOTS

Figure 6.1

Figure 6.2

INTEGRATION OF EXCHANGE, INDEX, EQUITY, ETP AND VIX PUT/CALL RATIOS INTO TRADING STRATEGY STOLARSKI, VARTANIAN 5

Figure 6.3

EQUATIONS & CROSSOVER POINTS

Exchange Options

Significant Crossover Points: x = 0.98

VIX Options

Significant Crossover Points: x = 0.48, x = 3.87

Equity Options

Significant Crossover Points: x = 0.3, x = 0.67

6.4. INTERPRETATION

For total exchange, a PCR greater than 0.98 resulted in a decline in the S&P 500 whereas a PCR less than 0.98 resulted in a rise in the S&P 500. This suggests that for total exchange options, PCR is a leading indicator. When more calls are being purchased, the S&P rises and when more puts are being purchased, the S&P falls.

For the VIX, a PCR less than 0.48 resulted in a drop in the S&P, a PCR between 0.48 and 3.865 resulted in a rally in the S&P and a PCR above 3.87 resulted in a drop in the S&P. This suggests that for the VIX, PCR is a mixed indicator. When a large amount of VIX calls are being purchased, it is a leading indicator. That is, the S&P falls because purchasing calls on the VIX, which rises when the S&P falls, is bullish. When there is a small amount of calls or many puts being purchased, it is a contrarian and

leading indicator. Finally, when there is an outsize number of VIX puts being purchased, it is a contrarian indicator.

For equities, a PCR less than 0.3 resulted in a drop in the S&P. A PCR between 0.3 and 0.67 resulted in the S&P rallying and a PCR greater than 0.67 resulted in a drop in the S&P. This suggests that when a large amount of equity calls is being purchased, it is a contrarian indicator. When an above average amount of puts are being purchased, PCR is a leading indicator.

A comparison of 𝑅- values shows that overall, no polynomial function is highly accurate in accounting for variability in the dataset. This is natural, given the highly complex nature of financial markets and indeterminate mood of investors. However, we will utilize the significant crossover points for equity options, given that it has the highest 𝑅- , to develop a simple PCR system.

7. LONG/SHORT SYSTEM PERFORMANCE

7.1. TOOLS

The primary tool used in this section was Excel. The adjusted close for the S&P 500 was populated into a sheet and a system was built to make trades based on PCR. Once the system’s decisions were made, the data was loaded into Tableau for a clear visualization of profits and losses. A random distribution was also generated for comparison purposes.

7.2. METHOD

Our first step was to design a system which placed trades based on the previously identified crossover points. The crossover points from equities were utilized since its 𝑅- was the highest. That is, the system went short if the PCR was less than 0.3, long if the PCR was between 0.3 and 0.67 and short if the PCR was higher than 0.67. A visualization of this is presented in Figure 6.3 as a polynomial trendline. When the system went short, profit was generated when the market went down and when it went long, the system profited when the market went up. When the system reversed its position, profit and loss was booked. Each trade’s profit and loss was then taken and visualized as a distribution. Note that trading costs were not considered and no risk management strategies were integrated. Random numbers were then generated in excel with the same parameters as the trading system to allow for comparison.

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7.3. RESULTS

Figure 7.1

Figure 7.2

7.4. INTERPRETATION

Ultimately, the system was only marginally profitable, with a gain of 12.14% compared to the S&P 500’s 86.6%, over 11 years. The profit and loss diagram generated by the system approaches a high-kurtosis random distribution. This can be seen by comparing the random number distribution with the profit and loss distribution. This suggests that utilizing equity PCR for predicting the overall movement of the S&P 500 is unprofitable and that equity PCR does not yield information about future price movement that has not already been priced in.

8. DISCUSSION

Within the financial community, options volume is widely considered to be a contrarian indicator. Various media outlets such as CNBC regularly mention it in their coverage of the markets. The general theory is that when there is an outsize amount of puts or calls being purchased, the market will reverse.

To test this theory, we acquired archive data on options volume from the CBOE regarding the VIX, equities, ETPs and indices. Next, this data was preprocessed using a variety of different visualizations, to gain a basic understanding of the dataset. We observed that equities had the highest amount of options volume, followed by indices, ETPs and the VIX. This suggested that the equity option type dataset would have the most value since it contained the most significant priced-in information. Building on this, we developed a box plot, which showed the distribution of each option type’s PCR as an efficiency analysis. We observed that the higher the volume in each option type, the tighter, and thus more liquid, each option type.

Our preliminary analysis of whether the PCR had any actionable information about the direction of the S&P 500 began with a comparison between VIX PCR and the S&P 500. This visualization showed that during months where the S&P 500 saw a contraction, VIX PCR increased. Despite this, the results were scattered and did not seem to be significant. Our next step was to compare all option types to the S&P 500. This was done with the use of a monthly line chart. This visualization suggested that during months where the overall PCR spiked, the S&P 500 generally rallied. This suggested that the PCR typically works inverse to the S&P 500. Regardless, this did not provide any potentially actionable information.

After concluding our general analysis of the dataset and its potential uses, our attention turned to a more granular approach. The VIX, equities and total exchange options data was selected to be compared to the S&P 500’s activity. Scatterplots were generated for each option type’s PCR versus the percent change in the S&P 500. Next, polynomial trendlines were generated to show overall activity. A visual analysis was performed to see how the S&P 500 behaved at various PCR levels, followed by a quantitative approach where the crossover points were calculated. These crossover points allowed us to determine at which points PCR was considered bullish or bearish for the S&P 500.

Finally, we utilized the crossover points for equities, given that it had the most significant distribution, to create a system which placed S&P 500 trades based on the PCR. Despite the scatterplots suggesting there was no actionable information, we determined it was best to test whether this was the case with a real application.

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9. CONCLUSION

Ultimately, our analysis showed that the PCR does not contain actionable information about the future movement of the S&P 500. Despite finding that there was slight correlation between outsize PCR activity and reverse activity in the S&P 500, no metrics showed this correlation was significant.

Nonetheless, there is further room for research in this field, given hints that this method does have some value. Given more specific data, a similar approach could be taken on individual stocks and indices. With better data, the PCR for the S&P 500 could be compared to the S&P 500. A deeper understanding of the equity dataset’s composition would have aided our efforts in developing this strategy. Furthermore, various risk management strategies could be implemented to cut out losses from poor trades. Examples of risk management strategies include pyramiding and trailing stops. In addition to this, with more time, data could be further processed with improved smoothing techniques such as dynamic weighted moving averages.

Many regularly used tools, such as the PCR, are simply assumed to work because of their traditional use. As more data and analytical tools become available, it is imperative that members of the financial industry begin actively testing these long-held assumptions. A more data-driven industry will lead to more insights, less volatility and a clearer overall picture.

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