The Evolution of Price Discovery in US Equity and Derivatives Markets

The Evolution of Price Discovery in US Equity and Derivatives Markets Damien Wallace, Petko S. Kalev and Guanghua (Andy) Lian Centre for Applied Financial Studies, School of Commerce, UniSA Business School, University of South Australia January 15, 2015 ABSTRACT The paper considers the evolution of price discovery in the U.S. equity and derivatives markets. For the period January 2002 through December 2013, we investigate the price discovery for two popular securities based on the S&P 500 index, namely the S&P 500 E-mini futures and the SPDR Exchange Traded Fund. We observe a significant change in the price discovery of these two securities over this period. The E-mini futures are dominant for price discovery though on a steady decline until January 2006 when the contribution of both price series is approximately the same. During the Global Financial Crisis, however, the E-mini dominates the price discovery process. From the end of 2009 onward the SPY ETF dominates the price discovery process. We also find that changes in relative costs are significant determinants of price discovery. Using the VIX index as a measure of expected volatility, we observe that in times of high expected volatility the price discovery process is dominated by the futures contract. We also provide some evidence that when the trade size of the SPY decreases relative to the E-Mini futures, the information share of the SPY increases, results that are consistent with expectations of informed trading. JEL classification: G14 Keywords: Information share; Price discovery; Price volatility; Market liquidity; Stock index futures; Stock Exchange Traded Funds Corresponding Author: Centre for Applied Financial Studies, School of Commerce, University of South Australia Business School, University of South Australia, GPO Box 2471, Adelaide SA 5001, Australia. E-mail: Petko.Kalev@unisa.edu.au. 1

1. Introduction This paper investigates the evolution of price discovery in the US equity and derivative markets for two important and actively traded securities based on the S&P 500 index, the State Street Global Advisors Exchange Traded Fund (SPY) and the E-mini futures contract. Price Discovery is commonly accepted as being the process by which markets incorporate information into market prices. Where there is a single asset that is traded in multiple venues or where multiple price series have a common element, the price discovery process may be discovered in each market. Price discovery leadership is considered to be the security that is the first to reflect new information about the price of the security on average. An ETF is commonly structured as an open ended mutual fund except that ETF units are priced and traded continuously through the day. The electronically traded S&P 500 E-mini futures are sized at one-fifth of the size of a regular sized contract and are therefore better suited and accessible to smaller investors. Research of price discovery in the US markets has produced results that are misleading from a number of perspectives. While we do not contest these studies validity we point out a number of limitations. First, the majority of previous studies investigating price discovery in these two markets are, in general, over periods that are relatively short. For instance Chu et al. (1999) uses a period of 59 months, while Hasbrouck (2003) and Tse et al. (2006) utilise a period of just 3 months for their analyses. One problem that is evident when investigating price discovery over these time periods is that a snapshot is observed based on the prevailing market conditions prevalent at that particular time. This may be beneficial when investigating a specific event or conditions but this approach does not give an overall view to price discovery. Second, in addition to these relatively short periods of 1

time there are some restrictions in these studies, whereby the sample period occurs prior to significant structural changes in the markets. Both the Chu et al. (1999) and Hasbrouck (2003) studies have sample periods prior to the change from 1/16 th to penny pricing. Electronic Communications Networks (ECNs) were also not prevalent during the period of these two aforementioned studies. The prevalence of algorithmic trading (AT) was not a significant factor in previous studies as the equity trading volume generated by AT was considerably lower in the period prior to 2001 compared to the approximately 75% in 2010 (McGowan, 2011). Third, the most recent study of these two markets was for the period of May through July 2004 by Tse et al. (2006) which gives a long period of time where market conditions and innovations may have occurred. 1 In terms of the findings from these applicable papers, Chu et al. (1999) investigate the S&P 500 index, the SPY ETF, and the E-mini futures contract and find that the E-mini futures dominate the price discovery process. Hasbrouck (2003) also investigates price discovery, but includes floor traded futures rather than the index in addition to the ETF and the E-mini futures of the S&P 500 index and finds that the E-mini futures contribute 90% of the price discovery process. In contrast, Tse et al. (2006) find results showing the ETF contributing 49% of the price discovery process. The lack of a definitive indication of the price discovery for these related assets raises a number of questions. First, this paper aims to address the conflicting results in the prior literature and identify the price discovery contributions of two of the securities that track the S&P 500 index, the E-mini future and the SPDR ETF using the Hasbrouck (1995) common factor model. In contrast to the other studies in this area, we use a significantly 1 Innovations in the market may include technological improvements to exchanges. It is well accepted that exchanges around the world have heavily invested in order to reduce the time it takes to send and receive information to customers. These innovations are likely to have had a significant effect on the market participants in these markets including allowing high frequency traders to transact with ease. 2

longer period of time than previously. Consequently, we use a twelve year period from 2002 till 2013, allowing us to identify the evolution of the price discovery process in the S&P 500 related securities. While this period does not include the studies prior to the change from 1/16 th to penny pricing it can be used to confirm the findings of Tse et al. (2006). In using this significantly longer time period this study will be able to identify the general trend of price discovery in these markets and may discover changes in market participants behaviour during periods where there have been abnormal market conditions occurring. Second, we investigate what the factors affect how much price discovery occurs in these related markets. In answering these questions, this paper attempts to extend and enhance our knowledge and understanding of the information process of the S&P 500 markets and build a larger understanding of the price discovery in these markets which will complement the current literature. These index securities are naturally expected to be affected by the same underlying set of economic fundamentals and are likely to move in very similar ways, however, any differences in the prices of these securities may reflect differences in the speed at which they incorporate information into the price. An examination of the price discovery process for these two highly liquid and well know securities may have implications into the price discovery process and speed at which other similar integrated securities incorporate information. Control over and management of hedging strategies and trading applications are some of the practical implications that this paper addresses. This paper also has ramifications for policy and regulatory setters to ensure that the market for these securities is fair and equitable. Securities that track the Standard and Poor 500 index (S&P 500) have been chosen in this study due to relevance and importance of the index. In light of this the two securities 3

that are utilised are the S&P 500 E-mini and the S&P 500 tracker fund, provided by State Street Global Advisors, and commonly referred to as the Spider. 2 The tracker fund is an Exchange Traded Fund where its performance aims to track that of the S&P 500 index. The significant growth and trading volume of ETFs over the past two decades is evidence to their popularity and ability to be easily transacted. ETFs cover a diverse range of markets, investment styles and sectors. The number of ETFs has increased from just 280 to over 3300 in 2012 and Assets Under Management have increased from US$142 billion to over US$1.6 trillion for the same time period. More specifically, the S&P 500 tracker fund (SPY) is very highly traded with over 100 million units exchanged per day on average in the US markets. It is also the largest ETF in the world with over $150 billion in Assets under Management (AUM), which is almost three times the next largest equity ETF. The E-mini futures contract on the S&P 500 is also highly traded with typical daily traded volumes of over 1 million contracts transacted per day. The US market is one of the most recognisable and most developed in the world, with the indices that track the market being some of the most quoted and recognisable in the world. The liquidity of this market makes the securities that track it very desirable as they are easily bought and sold at low costs. While studies have been conducted on this market, the US market is still of great importance. The size of the US market, the openness of trading and lack of restrictions make the US market an excellent candidate in which to study this phenomenon. This study utilises data for the E-mini futures contract and the SPDR ETF over the 12 year period from Jan 2, 2002 through Dec 31, 2013. Using the Hasbrouck (1995) information 2 The regular sized S&P 500 futures contract could also be considered, however after the introduction of the E- mini contract it has been observed that the liquidity in the regular sized contract has decreased while the smaller contract s liquidity has increased. In addition, the price discovery of the regular sized contract was significantly lower than the smaller futures contract after February 2000 (Ates & Wang, 2005). 4

share metric we find that the portion of information share for the futures and the ETF differs dramatically over the period of this study. We find that, consistent with Tse et al. (2006) that the information share of the ETF has increased from below 10 per cent in 2003 to dominate the price discovery process for the period 2009 onwards where this price discovery is approximately 70 per cent. We find evidence to suggest that the reduction of costs in the ETF market relative to the futures market is a leading determinant of the increase in the price discovery of the ETF. Furthermore, we use VIX as a measure of expected volatility in the future and find that in periods where the level of the VIX index is high the price discovery of the E-mini futures is higher. This results indicates that in periods of high expected volatility market participants may be moving to the security that they perceive as being the safest which appears to the the futures contract. There is also evidence that algorithmic traders have some influence on the price discovery process. We find that as the trades size and time between trades decrease for the ETF relative to the E- mini futures, we observe an increase in the price discovery for the ETF which we interpret as being an increase in either AT s or other informed traders entering the ETF market. The paper proceeds as follows. Section 2 documents the relevant literature; Section 3 describes the data sources and data content; Section 4 details the methodology utilised and Section 5 contains the findings. Section 6 concludes. 2. Background Literature 2.1 Price Discovery While the term price discovery is not new, some of the methods to measure this tern have changed since the early research in this area. This early research focused on the lead 5

lag relation between price series. Kawaller et al. (1987) document that the futures contract lead the index market using this lead lag relationship by up to 45 minutes, with a later study by Stoll and Whaley (1990) indicating that this lead may only be 5 minutes on average. Chan (1992) investigates this relation using component stocks rather than the index and finds that the majority of the information is impounded into prices in the futures contract. However, this methodology became outdated after a move toward a metric that measures permanent price changes caused by information and utilised by informed traders. Hasbrouck (1995) put forward his metric for the measurement of the contribution of a price series to price discovery, and is considered to be the price that is first to reflect (and change price) to reflect this new information. 3 This method has been used examined over a number of years for various asset classes. One of the most common areas of study in the Price Discovery area involves the comparison of an index and instruments that follow the index such as futures, options and Exchange Traded Funds. The Hasbrouck (1995) paper investigated the price discovery of stocks that are listed on multiple different markets, with subsequent papers investigating futures, the index, options and ETFs in various configurations. For instance, Tse (1999) investigates the Dow Jones Industrial Average (DJIA) and the index and finds that the DJIA contributes 88.3% of the information share within this market. Chu, Hsieh and Tse (1999) also investigate the US market whilst including the Spider ETF and its counterparts, the S&P 500 E-mini and the S&P 500 index and find that, for the period Jan 29, 1993 through Dec 31 1997, that the futures were the dominant security for the price discovery process. The authors find that the reason the futures contract is the price leader over the ETF and the index is mainly due to the leveraged effect hypothesis, 3 The Gonzalo & Granger (1995) is a competing methodology that is concerned with only the error correction process that involve only permanent shocks that result in disequilibrium. This disequilibrium is modelled as the securities incorporation of news at different rates. For a detailed commentary on the differences between the Hasbrouck (1995) and the Gonzalo & Granger (1995) models please see Baillie et al. (2002). 6

implying that securities with a higher level of leverage provide more return on investment than low leveraged instruments with the same amount of capital available. Considering that futures contract on the S&P 500 is a leveraged product, the authors suggest that it is this primary reason as to why futures lead in terms of price discovery. This hypothesis, the leverage hypothesis, states that the security that provides superior return using the same amount of capital will provide higher price discovery. That is the security with the highest leverage will result in being the security that dominates price discovery as for investors with private information will want to utilise this information to obtain the highest profits and will be expected to trade with the security that has the highest leverage. The E-mini futures contracts provide a higher level of leverage than the SPDR ETF and as such under this hypothesis the E-mini price will move first in the price discovery process. These early papers consistently found that the price discovery process originated within the futures market as opposed to the spot or the ETF market which is hardly surprising as it was substantially easier to purchase the exposure to the market through the futures (or an ETF) than to individually purchase shares to fulfil a representative sample of the index. In addition, the ETF market in this period was significantly smaller, and smaller relative to the futures market and in addition was not as well known as it is at present, therefore the results showing that the price discovery occurs in the futures market was to be expected. In more recent papers, however, there is some divergence in the results obtained for the price leadership between the futures and the ETF. Hasbrouck (2003) investigates the price discovery of US equity indices, exchange traded funds and small denomination future contracts (E-mini) as well as the S&P 500 sector ETF's. His data covers the period 1 March 2000 through 31 May 2000 and shows that index futures generally lead the S&P 500 stock index. He does show that the contribution to price discovery for exchange traded funds is 7

statistically significant however small for this period. For the S&P 400 index however he shows that the applicable sector ETF is the dominant security for price discovery. Chou and Chung (2006) use intraday quote and transaction data to investigate price discovery in the US market using three broad indices and corresponding ETFs and find that index futures still assume a dominant role in information discovery however the information share of these ETF's have increased over the period of their study from October 2000 till April 2001. It should be noted here that this paper not only addresses price discovery in a general sense but also price discovery across a period where the US markets moved to decimalisation. The findings in this paper find that the spread and depth of ETF's decline significantly after decimalisation, and trading activities of ETF's and index futures generally increase. They also find that the adverse selection component for ETF's increases after decimalisation implying that inform traders seem to trade ETF's more intensively. One of the major reasons for this increase in price discovery for ETF's is the reduction in trading cost and information efficiencies hence the authors in this paper attempt to test the trading cost hypothesis. Tse et al. (2006) use the Hasbrouck (1995) model to explore the price discovery process for the Dow Jones Industrial Average and its applicable ETF, the Diamond, as well as the S&P 500 and its ETF counterpart, the Spider, for the period May through July 2004 in the NYSE s Transactions and Quotes database. Some of the findings of this study are contrary to the findings proposed by Hasbrouck (2003) and show that the ETFs contribute significantly to the price discovery process. When considering the DJIA and Diamond ETF, the futures provide approximately 69% to the price discovery, with the ETF contributing 28.6%. When the authors duplicate the study on the S&P 500 E-mini and Spider, the results show that the 8

Spider ETF contributes 49.3% to the price discovery process while the S&P 500 E-mini future contributes only 35%. 4 While documenting the amount of contribution to price discovery, previous authors in this area did not, in general, comment as to the reasons for the differences in price discovery, leaving future research open to identify the reasons for past levels and in the change in price. There are a number of studies that investigate the influence of costs on trading behaviour with Fleming et al. (1996) finding that the market that has the lowest trading costs are most likely to react the quickest to any new information as short term investors are seeking to utilise their information to obtain the largest profit. While Kyle (1985) theoretically shows that an informed trader will trade in a market with the highest liquidity in an attempt to hide the impact of their trades. In a paper that specifically investigates trading cost, Kim et al. (1999) find that the S&P 500 futures index leads other similar futures contracts which they attribute to lower costs. Thiessen (2002) investigates price discovery on the Frankfurt Stock Exchange and finds that, for 42 trading days in 1997, the relation between the relative sizes of the bid-ask spread and the contribution to price discovery is quite weak. In terms of a change in the market tick size, Chou and Chung (2006) investigate price discovery pre and post the penny pricing change in the US and find that after the change to decimalisation there is an increase in the price discovery of ETFs as their relative minimum tick size had decreased due to this change. As such the trading cost hypothesis predicts that the market with the lowest overall trading costs will react the most 4 Note that this study s main objective was to identify the differences in using electronic trading platforms as opposed to the trading pit transactions that was used in the analysis of Hasbrouck (2003). Hence this analysis investigated in which market price discovery occurred, but when restricting the analysis to the Arca Exchange and investigating the price discovery for SPY trades and quotes, and both sized S&P futures contracts were the above results achieved. 9

quickly to new information. 5 Trading in the index market would be very costly as many of the constituent stocks would have significant spreads as well as brokerage whereas the futures and the ETF are generally quoted in narrow spreads. Minimum tick size is also very important as this represents the minimum price variation allowed, and is usually defined by exchange regulations. There are however factors that are implied in the literature to influence price discovery. Chakravarty et al. (2004), using options and stock price series and Hasbrouck (1995) methodology, find that trading volume and, to a lesser extent, volatility are drivers of price discovery. In a sense, volatility can be seen as a proxy for information flow. As new information gets impounded into prices the flow of money causes prices to become relatively more volatile as investors with superior information trade quickly to take advantage of this information. Where two or more markets are concerned, volatility can be used to identify where information is being impounded into prices first. Indeed there may be volatility aspects that spillover from one market to another such that, in a price discovery process, innovations in one market may spillover into the other market implying significant informational roles for both markets. Chan et al. (1991) show that innovations in either the S&P 500 futures market and spot market will spillover through volatility to the other market, however Koutmos and Tucker (1996) show, for the same securities, that the volatility spillovers originate in the futures market and run to the spot market. Finally Tse (1999) test for volatility spillovers in the DJIA index and futures markets and find bidirectional spillovers, but found that spillovers from the futures to the spot market are more significant than the reverse direction. Under this expectation it may be expected that 5 We do not explicitly attempt to test the trading cost hypothesis. 10

innovations in either a futures or ETF market would influence the other market through volatility spillovers. 2.2 What factors affect the relative information share levels? The information share measures for both the E-mini and the SPY are used in a panel regression model framework to explore the importance of various determinants on these information share values. Specifically we investigate how the change in the ratio of the information share of the E-mini to the SPY is affected by relative liquidity as the ratio of relative effective spread, daily dollar volume, order imbalance, time between trades, the total number of trades and volatility. 2.2.1 Relative Effective Spread Trading costs are an important and critical aspect in the trading process. It is expected that in a frictionless market news would be impounded into price simultaneously. In a multi-market aspect, this news would be impounded simultaneously into both markets. However, markets contain frictions and are not rational at all times therefore prices are impounded at different rates. Fleming, Ostdiek and Whaley (1996) indicate that the market that incorporates news into prices first is generally the market with the lowest costs. They also show that commissions and the price impact of trades, along with the bid ask spread are main components of total costs. Bid ask spreads are the most observable component of total trading costs, hence we would expect that this component would be a main determinant on changes of the information share of each security. We measure the effect of spread costs as the average of the relative effective spread of each security on a daily basis. Relative effective spread is calculated as a round trip transaction of the transacted price 11

minus the midpoint relative to the midpoint multiplied by the trade direction. 6 We take the ratio of each of the securities relative effective spread for an indication of the movement of the spreads relative to their price. Based on Fleming, Ostdiek and Whaley (1996) it is expected that as the relative spread increases in one market, the information share portion in that market will decrease as informed traders will transact in the market that has the lowest costs. Hypothesis 1 The higher (lower) the costs of transaction as a portion of the price of the asset, the lower (higher) the information share of the asset. The costs in this aspect refer to the spread of the asset from the perspective of an investor that aims to both purchase and sell either of the securities. With this in mind, the investor would have to transact across the spread twice hence a measure of relative effective spread. 2.2.2 Volatility Volatility is of great importance in financial markets, and in particular in this area of interest as the advent of news that may influence the markets, and therefore the price discovery metric, is also likely to affect the volatility of any asset that is associated with this news. While it may be expected that the volatility change in two assets that are close substitutes would be similar it is difficult to imply that the volatility changes would be the same as they are similar, not exact substitutes. Intuition would suggest that the security with the lowest level of volatility is likely to be the security that is transacted upon. However, as these securities do follow a common trend it is likely that any volatility in the 6 The trade direction is calculated using the Lee and Ready (1991) algorithm where a buyer initiated trade is assigned a value of 1 and a seller initiated trade is assigned a value of -1. 12

S&P 500 market would affect the price discovery metric. The S&P 500 volatility index (VIX) is used for this purpose. As this index is not a measure for either the E-mini future or the ETF it is impossible to identify the ex-ante influence of the VIX index on the price discovery metric. 7 However, the idea in using the VIX metric is to identify any association with volatility and the ratio of information share as this will show the security that traders would rather transact in periods of increased volatility. 2.2.3 Order Imbalance Trading activity is linked to volume (Stoll, 1978), however order imbalances have been found to have a significant relation with daily changes in liquidity and with contemporaneous market returns (Chordia et al., 2002) which implies that investigating volume from a standalone perspective may not give a true indication of the overall influence of volume. Order imbalances in one market may therefore induce a trader to place their trades in another due to the increase in liquidity risk which in turn may affect the price discovery process within the two markets. The order imbalance metric used in this study is based on the Chordia et al. (2002) paper whereby the order imbalance is given as the buyerinitiated dollars paid less the seller-initiated dollars received on day t, where each transaction is designated as buyer or seller initiated by the Lee and Ready (1991) algorithm. To test for we use a dummy variable that takes the value of one when the order imbalance of the SPY and E-mini are both positive or both negative, and a value of zero otherwise. If there is news in the market that causes a daily order imbalance in both markets to be skewed to either the buy or sell side, the dummy used here will be able to identify if any 7 Other methods for measuring volatility could be used instead of the VIX measure such as realised volatility, but as the SPY ETF and the E-mini future are co-integrated the realised volatility of both of them would be very similar and would therefore have the same effect as using one measure of volatility. 13

market participants would prefer to transact in one or the other markets in this situation. We do not have any intuition to imply which market would be expected to impound prices first. 2.2.4 Volume We put forward several metrics within this general area of volume as indicators that have an interaction or influence the information share metric. These metrics are the ratio of total dollar E-mini futures volume to SPY volume, the ratio of the average E-mini trade size to SPY trade size, the ratio of the average time between trades of the E-mini to SPY and the ratio of the total number of E-mini trades to SPY trades, all calculated on a daily basis. Stoll (1978), as previously identified, suggests that trading activity is linked to volume. In this modern era of high frequency trading it is not enough to simply investigate total volume, instead metrics designed to identify the influence of the HFTs on the market structure or makeup. The ratio of dollar volumes in the E-mini market compared to the ETF market is used a first and simple method to identify if changes in the level of this ratio has any influence of the information share ratio. It would be expected that the market that had higher relative traded volume would be the market that would impound new information into prices the first. The intuition is relatively simple; liquidity in a given market allows participants to transact with ease, and the market with the higher portion (or an increasing portion) may have the benefit that traders will obtain the best price that updates the quickest to new information. Hence we would expect a contemporaneous relation between the information share and the relative volumes. We propose the following hypothesis: 14

Hypothesis 2a: Higher dollar volumes in the E-mini market (SPY market) result in higher levels of price discovery in the E-mini market (SPY market). The increasing popularity in ETFs has not seemed to have had an extensive influence in the relative growth in percentage volume traded as an average compared to the E-mini futures. The growth in the average daily volume for the E-mini future, on a yearly basis, is 283 per cent compared to the 259 per cent for the ETF over the 2002 till 2013 period. This shows that both of these assets seem to be growing in transacted volume at the same rate. For a given length of time in the day, we would expect that the time between trades would be growing at a similar rate also. The time between trades is used as a control variable because we believe that it gives an indication into the determination of the information share from the perspective of the market participants. A ratio of the time between trades for the E-mini compared to the SPY naturally gives an indication of the relative changes in the average time between trades. Decreasing time between trades may be seen as an indication that informed investors are trying to hide their trades by breaking them into small portions; alternatively any decrease in the time between trades could also be seen as a greater portion of HFT entering the market. The market in which the informed traders are breaking up their trades, or the market where the HFTs are trading is therefore likely to be the market where news is being impounded into prices relatively more quickly and therefore we would expect to have a negative relationship between the market that has lower time between trades and the information share metric. In a similar vein, a decrease in the relative average trade size may also be due to either informed traders or HFTs trading in a specific market, therefore a negative relation between the relative trade size and the information share metric. Hence we propose the following hypotheses: 15

Hypothesis 2b: The lower the time between trades in the E-mini market (SPY market) the higher the information share portion of the E-mini (SPY) Hypothesis 2c: The smaller the average trade size on average of the E-mini (SPY) the higher the information share of the E-mini (SPY) The growth in the number of trades in a given day for both securities would be expected to grow at approximately the same rate over the sample timeframe, on average, therefore as before we can identify the influence of changes in daily average ratios of the total number of trades of the E-mini to the SPY has on the price discovery metric. Using a similar starting point as the previous hypotheses, we would expect that informed traders would attempt to break their trades into smaller portions to try to hide them and therefore we would expect to see a positive association with the number of trades and the information share of the particular security. Alternatively it may be that HFTs have chosen over a period of time to trade in one particular market to a greater degree than the other. The following hypothesis aims to test this: Hypothesis 2d: The higher the total number of trades on a daily basis in the E-mini market (SPY market) the higher the information share portion contributed by the E-mini contract (SPY). 2.3 Surprises in Scheduled Announcements It has long been suspected that macroeconomic developments have a significant effect on equity returns. These macroeconomic variables are expected to vary the future investment opportunity set and should therefore be able to be valued through pricing models. Flannery and Protopapadakis (2002) show that a number of these macroeconomic 16

variables can be used for pricing factors and indicate that these factors have a real influence on either returns or volatility. Further research follows along this path, but investigates the effect of surprise announcements on the pricing of assets. Balduzzi et al. (2001) investigate the effects of surprises in scheduled macroeconomic announcements on prices, trading volume and bid-ask spreads of treasury securities. They find that several of the announcements influence price, price volatility and bid-ask spreads. A number of papers investigate specific announcements on stock prices. For a broad selection of securities including stocks, bonds and foreign exchange, Anderson et al. (2007) find that announcement surprises in scheduled U.S. macroeconomic news produce condition jump means implying that these fundamental announcements are linked to these securities fundamentals. Boyd et al. (2005) investigate unemployment rates while Chulia et al, (2010) investigate the influence of federal funds target rate changes on returns and volatilities of the S&P 100 index. Both find a significant influence of these macroeconomic variables on returns. The implication of these papers is that surprise announcements have an impact on the market in terms of returns and volatilities and hence days where there is a surprise announcement may have an impact on the market that traders transact in, and therefore the information share of that market. The link here is that unexpected changes through these announcements can be seen as a risk and that risk may not be uniform across the futures and ETF markets. On days where there is this increased level of risk it may be likely that traders will move from one market into the safer market. We do not have any prior expectations on the market in which investors would move into in this situation. 17

3. Data This study examines the evolution of price discovery over time for the S&P 500 ETF (SPY) and the S&P 500 E-Mini futures during a 12 year period from Jan 2, 2002 through Dec 31, 2013. We obtain intraday trade and quote data for both of these securities from the Thomson Reuters Tick History (TRTH) database. Normal trading hours for the E-mini futures on the Globex exchange is almost 24 hours a day, Monday to Friday from 17:00 the previous day till 16:15 which includes a trading halt from 15:15 to 15:30. The SPY ETF trades on the New Your Stock Exchange s ACRA platform with the normal trading hours of 9:30 till 16:00, Eastern Standard Time. There is after hours trading on this exchange, however the liquidity in after-hours trading is thin compared to the normal trading hours. For the price discovery model to estimate correctly identical time series are needed to be included, hence the trade and quote data is restricted to those trades and quotes that occur during the trading hours of 9:30 to 15:15. Following Engle and Lange (2001), the first five minutes in each trading session are removed. The E-Mini contract size is US$50 per index point. The minimum tick size is 0.25 index points with the contract months are the three nearest months of the cycle March, June, September and December. The last trade date occurs up to 8:30 a.m. on the 3 rd Friday of the contract month. The S&P 500 E-mini futures contract in continuous form is developed by rolling over to the next contract upon maturity. TRTH provide price series that are constructed in this manner, with the lead month price series being constructed as the lead month contract being rolled to the second month on the last day of the lead month. A contract becomes nearby at the beginning of the previous contract s expiration month. Rolling the contracts in this manner includes the last few days in the lead month which 18

results in decreased observed volumes and open interest in this contract as investors roll their contracts to the second month contract. To alleviate this problem, the last seven trading days at the end of the lead month contract are removed. It is expected that the more actively a contract is traded the more information contained in its price. 4. Methodology 4.1 Price Discovery Where a single security is able to be traded in two (or more) separate markets it is assumed that the arbitrage process will keep both prices from diverging to a large degree. This implies that the prices are co-integrated and the price series share at least one common factor. The Hasbrouck (1995) model defines price discovery in terms of innovations to this common factor 8. Given that this security can be traded in two markets the linkage of prices in the markets are given as: p 1,t = p 1,t 1 + ω 1,t, (1) p 2,t = p 1,t 2 + ω 2,t, where p 1 and p 2 are the actual prices either quoted prices or transacted prices for the security. It is assumed that the price of the security will follow a random walk, and the second price is expected to track the first price lagged to some degree. This secondary price also reflects some random error in the tracking of the first price. As these two prices are 8 Alternate methodologies include the Gonzalo and Granger (1995) model, commonly called the component shares (CS) which identifies the permanent (as opposed to the transitory) shocks that result in a disequilibrium. While the Hasbrouck (1995) and Gonzalo and Granger (1995) models are quite different in there mechanics, both provide similar results. See Baillie et al, (2002) for an extensive description. The other model that can be used here is the Yan-Zivot-Putnins information leadership share (ILS) metric which includes different levels of volatility in the price series (Putnins, 2013). 19

prices in different markets for the one security it is expected that they are highly integrated and as such the prices for the securities should never differ by a large margin. Based on this premise the assumption that there is one cointegrating relation between these prices, which can be represented as the cointegrating vector A = [1, -1]. It can be assumed that the error correction description of the prices can be written in the form (for a multi-price model): k Y t = αβ Y t 1 + j=1 A j Y t j + ε t, (2) where Y t is a vector of cointegrated prices, α is the error correction vector, β is a matrix of co-integrating vectors, and ε t is a zero mean vector of serially uncorrelated innovations. Following the Hasbrouck (1995) methodology the above VECM is transformed into an integrated form of a vector moving average and is described as: t Y t = ψj τ=1 e τ + (L)e t, (3) where J is a column vector of ones and is a matrix of polynomials in the lag operator, L. Hasbrouck (1995) states that the component of price change that is permanently impounded into the security price is the increment of ψe t, which is presumably due to new information. Not included in this measure are the impacts of temporary or transient effects that may be the result of many different market factors like bid-ask bounce (Baillie et al, 2 2002). The variance of the permanent component of price change, ψe t, is represented as σ f = ψωψ, where Ω is the covariance matrix of the residuals e t. For the information share to be correctly identified, the above covariance matrix of the residuals needs to show that there are no contemporaneous correlations between the 20

residuals, i.e. the matrix needs to be diagonal which is done through a Cholesky factorisation with the information share therefore being identified as: S i = ψm i 2 / σ f 2, (4) where (ψm) i is the ith element of the row matrix ψm. The Cholesky factorisation will result in an upper (and lower) bound to be identified, but will differ based on the ordering of the securities. It will therefore be important to test this with robustness across different orderings of the securities. 4.2.3 Regression Model for the Determinants of Price Discovery This section tests whether changes in the relative information shares of the E-mini and the SPY can be predicted by liquidity, volatility and volume metrics. Thus, the ratio of Information Shares is regressed against the ratio of relative effective spreads, the open value of the VIX index, a dummy for aggregate order imbalances and we consider four different proxies for volume, namely the ratio of the number of trades, the ratio of dollar traded volume, the ratio of average trade size and the ratio of the time duration between trades. The regression equation to examine which factors affect the ratio of information share is given as: IS t = β 0 + β 1 Spread t + β 2 Vola t + β 3 OI t + β 4 Volume t + ε t, (5) where IS t is the natural logarithm of the ratio of the information share of the E-mini to the information share of the SPY at time t, Spread is the ratio of the relative effective spreads, Vola is the open value of the VIX index, OI is a dummy variable that takes the value of one where the dollar order imbalances for the E-mini and SPY are either positive or negative, and zero otherwise and Volume, whose four proxies are the turnover ratio, the trade size 21

ratio, the number of trade s ratio and the time between trades ratio. These volume variables are calculated as the ratio of dollar weighted volume, the ratio of the average trade size, the ratio of the total trades and the ratio of the time between trades respectively where the ratio is always given as the E-mini value divided by the SPY value. All metrics are computed on a daily basis. 5. Results 5.1 Descriptive Statistics Table 1 presents the descriptive statistics of the S&P 500 E-mini futures contracts (Panel A) and for the SPDR ETF (Panel B). Note that there are differences in the unit price of the ETF compared to the futures contract. In general the E-mini contract is 10 times as large as the price of an ETF. There is little increase in volume of the ETF relative to the E-mini. In terms of the range in prices and actual price we observe that there is little difference over the entire period between the range and the price after considering the size difference. < Table 1 here > Distinct changes in the price, volume and range can be observed over the GFC period where the standard deviation as a measure of volatility in these basic statistics is significantly elevated. It is difficult to anticipate the impact of these kinds of changes in the market on the price discovery process. Increases in volume during this period are, in themselves, not a good indicator of an increase in liquidity, and indeed in this period this increased volume may coincide with a lack of liquidity. The range also does not imply any ex-ante expectations on the direction of the price discovery of a security as range is simply 22

the average variation in the prices per day, but may lead to implications regarding other volatility metrics such as VIX or realised volatility. 5.2 Price Discovery Table 2 presents the descriptive statistics for the information shares of the E-mini and SPY ETF in Panel A and B respectively on a yearly basis. Of some interest is the standard deviation of the information share estimates on a daily basis. Again there is some observed increase in the standard deviation of these estimates around the GFC period. This is not unexpected as the information share metric for each security impounds price movements from any source and those movements may not be rational. Given that the GFC was partially fear driven and these securities are based on an underlying index some of the movements could be due to arbitrage activities or rebalancing of portfolios, hence the volatility in the estimates information share is not surprising but should be investigated. As implied above, instruments that explain future or (possibly) current volatility such as VIX may be useful in predicting future changes in the information share of these securities. < Table 2 here > Figure 1 documents the evolution of the price discovery for the E-mini futures and the Exchange Traded Fund SPY. We observe that the security that dominates price discovery changes from the early part of the sample period through to the later period. Specifically the E-mini dominates the price discovery until Mid-Jan 2007 where the ETF becomes dominant in the price discovery process. Note that the period around the Global Financial Crisis reverses the general trend and the E-mini is the dominant security for price discovery through this period. The ETF becomes the dominant security from 2009 onwards, but the E- 23

mini still contributes significantly to the price discovery process and averages between 20% and 40% to the price discovery process for this period based on the average monthly price discovery for each security. < Figure 1 here > Overall, the change in price discovery over the period is initially in line with previous studies of Hasbrouck (2003) and Chou and Chung (2006) whereby the E-mini future contract is the major contributor to the price discovery process. Both Chou and Chung (2006) and Tse et al. (2006) document an increase in the price discovery of the ETF in their studies with the former study indicating that after decimalisation ETF spreads have decreased and the portion of price discovery has increased relative to the period prior to decimalisation. This suggests that trading costs are a significant factor in determining price discovery of a security. Fleming et al. (1996) find that the market that incurs the lowest costs is likely to lead price discovery. The minimum tick size for the E-mini futures contract has been at a constant $0.25 for the entire 2002 till 2013 period of this study, and as indicated by Kurov (2008) the bid ask spreads of this contract rarely exceed the minimum tick sizes. In contrast, the minimum bid ask spread for the ETF is $0.01 for the period, but unlike the E- mini, the spread was, at the start of the period, relatively high compared to the minimum tick size. By comparing the spread of each security to its price gives a measure of the relative spread. It can be observed that the relative spread of the ETF was very volatile at the start of the sample period, but decreased dramatically through late 2002 till late 2004 to a value of approximately 0.01% of the value of the ETF. The E-mini contract s relative spread decreased over the same approximate period but is still significantly higher than the ETF s spread. Relative spreads were also volatile over the GFC period. 24

< Figure 2 here > From these initial results it would appear that the switch in the price discovery leadership from the E-mini to the ETF is as a result of the reduction of trading costs given as the relative effective spread. This result indicates that costs have a large influence in the choice of market in which traders transact, and suggests that traders look more to the most costless market rather than the market that can provide them with the most leverage or other factors. 5.3 Determinants of price discovery in derivatives and equity markets Table 3 documents the summary statistics for the average daily number of transactions, the average daily trade s size and the average daily time between trades which have been grouped under the broad heading of volume. Note that these (including the dollar weighted volume) have high correlation, and the volume statistics have been presented in table 1. < Table 3 here > The average number of trades is what we would expect as the markets for both of these securities gets larger over time and then decreases slightly with increasing competition. It is not surprising that the number of trades have decreased more for the SPY owing some of that to a larger increase in competition that the E-mini futures contract. Also note an abnormal level of trades during the GFC period. The average trade size for both securities has been decreasing over the entire period, with the SPY ETF having a larger decrease than the E-mini contract. This decrease is in line with the increasing influence that HFT s exert in both of these markets. The larger decline in the average trade size in the ETF 25

market comparatively to the E-mini market may be due to an increase in the number of HFTs in the market which would have the effect of reducing trade size. Table 4 shows the correlation of both the determinant and independent variables. Notice that the correlation between the turnover ratio, trade size ratio, time between trade ratio and the number of trade ratio are all quite high implying that we should not put all of these variables into a single regression. Indeed multicollinearity may become an issue whereby a regressor has a nearly linear combination of other regressors in the model. < Table 4 here > Table 5 shows the results of the regression model to describe the influence of the chosen factors on the change in the information share metric. We present four model specifications and all regression variables are expressed in ratios with the exception of the open value of the VIX index and dummy variables. < Table 5 here > For the analysis of the determinants of information share we regress market characteristics on the log of the ratio of information shares calculated using the Hasbrouck (1995) method. We find a negative relation between the relative effective spread and information shares. The result can be interpreted that as costs decrease in the ETF market relative to the E-mini futures market there will be a corresponding increase in the portion of information share in the ETF market. This result is hardly surprising in that Fleming et al, (1996) and Kim et al, (1999) indicate that the market that the market with the lowest costs is likely to be the market where informed traders will place their orders. 26

We also document a significant and positive relationship between the VIX index and information shares. This implies that, on average, as expected volatility using the VIX index increases we would expect that either the information share of the E-mini futures contract will increase or the information share of the SPY would decrease. Note that during the Global Financial Crisis there was a reversion in the general trend of the information share values from a period where the ETF contributed significantly, and based on the trend would have been expected to dominate the price discovery process, the E-mini futures were the dominant force by a significant margin. The finding that an increase in volatility given by the VIX fear index corresponds with an increase in the relative information share of the E-Mini implies that during periods where there are increased levels of volatility there will be anticipation for traders to position their trades in the futures market rather than in the ETF market. The implication of this finding is that in periods of high expected volatility traders will move to the futures market which leads us to believe that traders believe that the futures contract is a safer security, and move their trades to the futures market and hence price discovery in these times. The negative association between the information share metric and the time between trades may imply that high frequency traders are entering the market (becoming a larger portion of the market), possibly due to lower costs, but possibly also due to the change in the information share. High frequency traders may be trading in the ETF market as costs are decreasing or may be some of the cause of the reduction of costs and the increase in the contribution to price discovery. Irrespective of the interaction between high frequency traders and the change in the information share, we take this negative relation between the duration between trades and this information shares as being due to the ETF market being the market where high frequency traders predominately trade in (or have a 27

higher level of representation in this market). The increase in trading activity over the period due to this proposed increase in HFTs has the effect of decreasing the average trade size in the market that they are trading. We also document a negative association between the average size of each of the securities on a dollar basis and the information share ratio. This result indicates that, on average, lower relative trade size in the ETF market is associated with a higher level of information share in the ETF market. This may be due to a number of factors, and therefore propose two different implications to explain this result. One explanation is that informed investors who have information that they are trying to protect are likely to break up their orders to hide this information. The market where investors are willing to trade will therefore be likely to be the market that has smaller relative trade sizes. This reduction in relative trade sizes is observed in the ETF market. If this is the case then it is likely that informed traders are entering the ETF market to trade which is causing a change in the information share portions over time. An alternate explanation of the lowering of the ETF trade size relative to the trade size in the futures market may again be due to the increase in the number of high frequency traders in the market. As we know HFT s trade smaller amounts more regularly. Therefore this reduction in trade size of the ETF relative to the futures contract may be due to HFTs moving from the futures market to the ETF market due to the fact that information is being incorporated into the prices relatively more quickly in the ETF market, and therefore we would observe a reduction in the trade size. There is also a positive association for both the number of trades and the dollar turnover and the information share metric. This indicates that when there are a larger number of trades or dollar volume of trades in the E-mini futures market then there is a higher level of contribution of the E-mini to the price discovery process. This is in line with expectations 28

that indicate that the market with the higher level of trades and dollar volume is associated with higher levels of information share. 6. Conclusions This study investigates the evolution of price discovery for the S&P 500 E-mini futures and for the S&P 500 Exchange Traded Fund (SPY). The study utilises a significantly longer period of time that previous studies, from Jan 1, 2002 till Dec 31, 2013 gives a continuous time period of 12 years. Using the Hasbrouck (1995) metric for price discovery we document that the price discovery has moved from the S&P 500 E-mini futures contract to the S&P 500 ETF. Indeed the shift is quite significant, with the contribution to price discovery for the ETF in 2002 to 2003 being less than 5 per cent, and by 2008 the ETF contribution was above 60 per cent, and indicates that traders are likely to prefer trading the Exchange Traded Fund over the E-mini futures contract. The information share of these two securities has similar results as compared to previous studies up until 2004 where data finished from the most recent applicable study. The E-mini futures still make a meaningful contribution to price discovery. The change in the information share was investigated through a panel regression model to identify the determinants that have caused this change in the portions of information share. The findings show that spread measured as the ratio of relative effective spreads has a significant relation with the information share metric. An increase in the costs of either security relative to the other will result in an expected decrease in the information share of that same security such that as relative costs increase, information share decreases. This 29

finding is in line with the cost hypothesis that states that investors will likely transact in the market that has the lowest transaction costs. We also find that volatility has a significant influence of the future values of information share for the two securities. As the level of expected volatility increases we observe that the price discovery moves from the ETF to the E-mini futures market and would therefore expect that traders would be more likely to transact in the E-mini market. We also document that volume related metrics such as the amount of trades on a daily basis, the dollar weighted amount of trades, the average size of trades and the time between trades all have a significant influence on the information share metric. Order imbalance was not found to be significant and may be due to order imbalances in these markets being fleeting and not observed in daily order imbalances. 30

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Table 1 Descriptive Statistics This table reports the descriptive statistics for the E-mini futures and the SPDR ETF between Jan 1 2002 and Dec 31 2013. Volume is the average daily traded volume in units of 100,000, Range is the average daily range where the range is the daily high price minus the daily low price and Price is the average daily price. Panel A : E-Mini Futures Year Volume Range Price Mean Median Std Dev Mean Median Std Dev Mean Median Std Dev 2002 4.12 3.60 2.32 22.590 20.625 9.524 994.68 973.50 113.96 2003 5.62 6.16 2.27 15.071 14.500 5.753 963.88 985.38 78.37 2004 5.86 6.15 2.39 12.165 11.500 4.698 1130.16 1126.00 31.69 2005 7.26 7.32 2.98 11.913 11.000 4.673 1209.59 1205.75 29.78 2006 8.90 9.00 3.63 12.627 12.000 5.260 1314.91 1300.13 51.51 2007 14.34 12.63 7.63 19.777 16.500 11.669 1482.59 1479.50 45.43 2008 21.65 20.37 9.72 35.948 30.125 21.441 1218.61 1304.75 200.93 2009 19.55 20.04 7.21 21.776 20.125 9.626 945.19 934.56 114.99 2010 19.69 19.94 8.47 18.380 16.500 10.523 1137.27 1135.38 55.65 2011 21.48 20.94 9.34 24.243 21.625 13.644 1264.42 1278.75 62.06 2012 16.45 16.92 5.74 17.480 16.750 7.544 1375.90 1375.88 46.26 2013 15.78 15.81 5.71 16.677 14.750 7.681 1639.32 1646.69 99.53 Panel B : SPY Year Volume Range Price Mean Median Std Dev Mean Median Std Dev Mean Median Std Dev 2002 338.82 296.05 183.08 2.048 1.910 0.850 99.81 97.36 11.388 2003 411.09 396.55 119.41 1.405 1.335 0.522 96.91 99.03 7.901 2004 429.65 410.30 128.02 1.114 1.050 0.442 113.46 113.04 3.068 2005 616.13 576.37 191.39 1.122 1.040 0.437 120.87 120.67 2.997 2006 700.97 650.78 257.44 1.160 1.060 0.495 131.14 129.83 5.108 2007 1569.98 1338.83 898.89 1.816 1.490 1.080 147.81 147.92 4.414 2008 3002.67 2568.46 1422.05 3.247 2.640 2.019 122.17 129.16 19.080 2009 2470.52 2285.00 982.49 1.905 1.680 1.057 94.96 93.92 11.546 2010 2102.32 1933.09 866.64 1.518 1.365 0.956 114.19 113.95 5.543 2011 2180.72 2013.89 1015.88 1.926 1.623 1.169 126.94 128.30 6.193 2012 1433.12 1377.03 399.30 1.380 1.255 0.595 138.12 138.30 4.696 2013 1216.20 1114.80 410.64 1.365 1.200 0.662 164.43 165.07 9.983 34

Table 2 Information Share Statistics This table reports statistics for the E-mini and SPY ETF. The price discovery is calculated using the Hasbrouck (1995) information share model. Panel A presents the findings across years for the E-mini futures contract, while Panel B presents the findings for the SPY ETF. The Mean, Median and St Dev are all based on daily estimates of the information share metric. Panel A: E-Mini Futures Information Share Descriptive Statistics Upper Bound Lower Bound Year Mean Median St Dev Mean Median St Dev 2002 0.978 0.99 0.064 0.969 0.98 0.070 2003 0.949 0.96 0.036 0.928 0.94 0.042 2004 0.812 0.83 0.101 0.749 0.76 0.115 2005 0.714 0.70 0.099 0.619 0.61 0.096 2006 0.657 0.65 0.109 0.546 0.54 0.104 2007 0.677 0.65 0.169 0.495 0.46 0.161 2008 0.667 0.64 0.187 0.281 0.18 0.248 2009 0.464 0.46 0.106 0.149 0.13 0.101 2010 0.404 0.38 0.121 0.144 0.13 0.092 2011 0.438 0.40 0.155 0.134 0.12 0.091 2012 0.370 0.36 0.081 0.142 0.14 0.059 2013 0.387 0.36 0.128 0.163 0.14 0.121 Panel B: SPY Information Share Descriptive Statistics Upper Bound Lower Bound Year Mean Median St Dev Mean Median St Dev 2002 0.031 0.02 0.070 0.022 0.01 0.064 2003 0.072 0.06 0.042 0.051 0.04 0.036 2004 0.251 0.25 0.115 0.188 0.17 0.101 2005 0.381 0.39 0.096 0.286 0.30 0.099 2006 0.454 0.46 0.104 0.343 0.35 0.109 2007 0.505 0.54 0.161 0.323 0.35 0.169 2008 0.719 0.82 0.248 0.333 0.36 0.187 2009 0.852 0.87 0.101 0.536 0.54 0.106 2010 0.857 0.87 0.092 0.596 0.62 0.121 2011 0.866 0.88 0.091 0.562 0.60 0.155 2012 0.858 0.86 0.059 0.630 0.64 0.081 2013 0.837 0.86 0.121 0.613 0.64 0.128 35

Table 3 Volume metrics descriptive statistics This table reports the mean, median and standard deviation of three out of the four volume metrics. The metrics are computed on a daily basis, and are averaged for the entire year. Trades relate to the number of trades transacted on a day, and give an indication of how the market has grown. Trade size is the average trade size in contracts or units of the E-mini or ETF respectively. Time between trades is the time in seconds from one trade to the following trade. Panel A : E-Mini Futures Year Trades Trade size Time between trades Average Median Std Dev Average Median Std Dev Average Median Std Dev 2002 35057 30602 16446 7.403 7.515 0.978 1.842 0.656 8.752 2003 35815 43320 19174 9.434 10.093 2.786 8.394 1.776 15.188 2004 32954 37855 18011 11.748 12.820 3.474 8.875 2.076 15.948 2005 35163 36937 20365 14.572 15.788 4.202 10.230 2.040 18.631 2006 31784 33977 18497 18.770 19.465 5.529 9.099 2.217 15.525 2007 51448 45391 36322 16.918 18.089 5.042 6.259 1.611 12.588 2008 109467 98473 75564 11.429 12.057 4.574 2.524 0.791 4.879 2009 88651 98655 47772 11.918 12.750 3.080 1.855 0.793 2.385 2010 129872 133995 85442 4.719 4.729 1.006 1.523 0.582 2.266 2011 123390 123976 74576 4.532 4.532 1.009 1.258 0.626 1.761 2012 98787 109715 53760 4.134 4.302 0.699 1.441 0.715 1.653 2013 89956 97260 51946 3.840 3.862 0.611 1.682 0.809 2.088 Panel B : SPY Year Trades Trade size Time between trades Average Median Std Dev Average Median Std Dev Average Median Std Dev 2002 9395 8474 3812 1908 1870 455 2.441 2.319 0.873 2003 13861 14059 3014 1889 1856 298 2.695 2.648 0.496 2004 18968 18658 4432 1316 1288 306 1.930 1.926 0.437 2005 28606 28631 6583 1114 1077 218 1.360 1.305 0.294 2006 26638 25655 7967 1219 1196 266 1.535 1.474 0.385 2007 45765 39093 20046 1019 978 204 1.089 1.082 0.349 2008 202299 130472 147995 620 643 149 0.365 0.356 0.191 2009 187570 176072 68194 511 495 83 0.310 0.293 0.125 2010 190306 175342 87591 471 459 65 0.321 0.301 0.130 2011 185455 172639 67712 487 475 76 0.321 0.310 0.104 2012 137842 131552 34140 502 491 61 0.404 0.397 0.092 2013 121004 111472 38293 478 469 66 0.488 0.490 0.127 36

Table 4 Correlation Matrix: Determinants of information shares Presented in this table is the correlation matrix for the important determinants in the regression. Info Share is the ratio of the information share attributable to both the E-mini and SPY. Spread is the ratio of relative effective spread, VIX open is the open value of the VIX volatility index, Turnover is the ratio of the dollar value of the traded daily volume, Tradesize is the ratio of the average tradesize, T2T is the ratio of the time between trades and Num trades is the ratio of total trades. All ratios are presented as the E-mini per SPY unit and all variables are calculated on a daily basis. Info Turn Trade Num Spread VIX open $OI T2T Share over size Trades Info Share 1 Spread -0.868 1 VIX open 0.066-0.087 1 $OI 0.012-0.005-0.014 1 Turnover 0.420-0.315-0.365 0.020 1 Tradesize -0.414 0.193 0.098-0.043-0.363 1 T2T -0.709 0.603 0.315-0.041-0.541 0.597 1 Num Trades 0.878-0.764-0.052 0.017 0.568-0.534-0.813 1 37

Table 5 Determinants of information shares This table reports the coefficient estimates of the determinants of price discovery from the following regression of day observations: IS t = β 0 + β 1 Spread t + β 2 Vola t + β 3 OI t + β 4 Volume t + ε t, where Spread is the ratio of the relative effective spreads calculated as the ratio of the transacted price minus the mid-price divided by the mid-price multiplied by the direction of the trade, Vola is the open value of the VIX index, OI is a dummy variable that takes on a value of 1 if the daily dollar order imbalance of both the E-mini and SPY are both positive or negative and takes the value of 0 otherwise. We consider four different proxies for Volume, namely the turnover ratio, the trade size ratio, the number of trade s ratio and the time between trades ratio. These variables are calculated as the ratio of dollar weighted volume, the ratio of the average trade size, the ratio of the total trades and the ratio of the time between trades respectively where the ratio is always given as the E- mini value divided by the SPY value. Year and day of week indicate that dummies are used to control for yearly fixed effects and day of the week effects. All metrics are computed on a daily basis. P- values are reported in parenthesis and ***, ** and * are used to indicate significance at the 1%, 5% and 10% respectively. Intercept 1.4988 1.8956 2.0517 1.3389 (<.0001)*** (<.0001)*** (<.0001)*** (<.0001)*** Spread -1.1221-1.1078-1.091-1.0888 (<.0001)*** (<.0001)*** (<.0001)*** (<.0001)*** Vola 0.0153 0.0116 0.014 0.0122 (<.0001)*** (0.0015)*** (<.0001)*** (0.0001)*** OI -0.004258 0.001863 0.000266-0.000285 (0.8583) (0.9377) (0.9911) (0.9904) Turn 1.6342 (0.0027)*** Tradesize -18.9751 (0.0002)*** T2T -0.2346 (<.0001)*** NumTrade 0.3743 (<.0001)*** SurpDum 0.007319 0.004031 0.007819 0.004283 (0.7826) (0.8791) (0.7684) (0.8712) Year Yes Yes Yes Yes Day of Week Yes Yes Yes Yes N 2670 2670 2670 2670 Adjusted R 2 (%) 49.76 52.82 56.55 64.00 38

Jan-02 Aug-02 Mar-03 Oct-03 May-04 Dec-04 Jul-05 Feb-06 Sep-06 Apr-07 Nov-07 Jun-08 Jan-09 Aug-09 Mar-10 Oct-10 May-11 Dec-11 Jul-12 Feb-13 Sep-13 Figure 1 Evolution of Price Discovery This figure depicts the evolution of price discovery for the S&P E-mini futures and the SPDR (SPY) ETF between Jan 1, 2002 and Dec 31, 2013. 100.00% 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% MIN SPY 30.00% 20.00% 10.00% 0.00% 39

Figure 2 Relative spreads for the SPDR ETF and the E-mini futures contract This figure shows the relative spread change over our sample period. The relative spread is calculated as quoted spread divided by price for each price update and averaged over the trading day. 0.0005 Spread relative to price for the E-Mini and SPY 0.0004 0.0003 0.0002 MIN SPY 0.0001 0 40