Tick Size, Market Liquidity and Trading Volume: Evidence ...

Tick Size, Market Liquidity and Trading Volume: Evidence from the Stockholm Stock Exchange*

Jonas Niemeyer** Patrik Sand?s***

JEL Classification Codes: G10, G12, G14 Key Words: Discreteness, Market Liquidity, Trading Volume

First Version: April, 1993 This Version: December 14, 1994

* We wish to thank the Stockholm Stock Exchange and Stockholms Fondb?rs Jubileumsfond for providing the data set. We also wish to thank seminar participants at the Stockholm School of Economics and the Swedish School of Economics and Business Administration in Helsinki as well as participants at the European Finance Association Meetings in Copenhagen, August 1993 and at the First Annual Conference on Multinational Financial Issues in Atlantic City, June 1994 for helpful comments. We are especially indebted to Kaj Hedvall, Ragnar Lindgren, Atulya Sarin and Anders Warne, for their comments on earlier drafts. For all remaining errors, we absorb full culpability.

** Department of Finance, Stockholm School of Economics, P.O. Box 6501, S-113 83 Stockholm, SWEDEN. Phone: +46-8-736 90 00, Fax: +46-8-31 23 27, Internet address: "finjn@hhs.se". Jonas Niemeyer gratefully acknowledges financial support from Bankforskningsinstitutet.

*** Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, PA 15213, USA. Fax: +1-412-268 7064. Internet address: "ps4n+@andrew.cmu.edu".

Abstract The regulated tick size at a securities exchange puts a lower bound on the bid/ask spread. We use cross-sectional and cross-daily data from the Stockholm Stock Exchange to assess if this lower bound is economically important and if it has any direct effect on market depth and traded volume. We find a) strong support that the tick size is positively related to the bid/ask spread (market width) and b) support that it is positively correlated to market depth and c) some support that it is negatively related to traded volume. We identify different groups of agents to whom a lower tick size would be beneficial and to whom it would be detrimental.

1. Introduction and Definitions

Apart from the tick size, this paper also deals with the market liquidity and we therefore need a definition of that concept. In principle, liquidity refers to how quickly and how cheaply investors can trade an asset when they want to. However, liquidity is a complex term. There are, at least, four highly interrelated dimensions to market liquidity: width, depth, immediacy and resiliency. Harris defines these in the following manner.1

"Width refers to the bid/ask spread (and to brokerage commissions and other fees per share) for a given number of shares ... Depth refers to the number of shares that can be traded at given bid and ask quotes. Immediacy refers to how quickly trades of a given size can be done at a given cost. Resiliency refers to how quickly prices revert to former levels after they change in response to large order flow imbalances initiated by uninformed traders." When discussing liquidity in this paper, we primarily refer to width or depth.2

When discussing the impact of the tick size, it is important to recognize that even when agents are free to choose their prices, discrete price schemes will emerge. For the trading in many assets, the discreteness is not regulated but the result of different customs. Why are real estate prices on odd dollars rarely found? There must be some positive effect of clustering prices to discrete values. In fact, there are several effects. The costs of negotiating may be lowered and a deal be struck faster when discrete prices are used. Furthermore, the risk of ex-post misunderstandings of the actual trading price will be lower using discrete prices.3 The degree of discreteness depends on the assets' characteristics. When economic agents have similar reservation prices and available information sets, a fine price grid is likely to emerge.

Even on exchanges with a regulated tick size, prices of financial assets tend to cluster on round numbers. Harris notes that NYSE stock prices cluster on round fractions. "Integers are more common than halves; halves are more common than odd quarters; odd quarters are more common than odd eighths."4 Recently, there has been increased interest in the reasons and consequences of clustering on a discrete set of rounded prices.

It should be noted that even if prices tend to cluster by themselves, a superimposed tick size may have an economically important effect. The purpose of this paper is to shed some light on the impact of the tick size on market liquidity, in the form of both width

1 Harris (1990b) p. 3 2 There is some confusion of terminology in the literature. Hasbrouck and Schwartz (1988) define depth

essentially as the bid/ask spread (i.e. our width) and breath as the order volume (i.e. our depth). 3 See Harris (1991) p. 390. 4 Harris (1991) p. 389.

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and depth, and on the volume of trading. Data from the Stockholm Stock Exchange is highly suitable since there are two price ranges with different nominal tick sizes for normally priced stocks. The paper extends the existing literature by examining some stocks where the nominal tick size has changed, or put differently where the price of the stock has moved from one tick size range to another.

In a related paper using data from the NYSE and AMEX, Harris (1994) concludes that a lower tick size would reduce both the bid/ask spread and the quoted volume while it would increase traded volume. One purpose of our paper is to investigate whether these results can be generalized into another trading mechanism. We use data from the Stockholm Stock Exchange, a continuous auction market based on a consolidated electronic open limit order book with a high and symmetric transparency, without any specialist, and where strict price, display and time priorities are imposed.5 The least obvious effect in Harris (1994) is the effect of the tick size on market depth. Harris explains his relationship between tick size and quoted volume with the quote matcher argument. It is possible to argue that this effect would be less pronounced at the Stockholm Stock Exchange since there is no designated specialist and all traders have similar information and strategy sets. The trading environment is symmetric. This symmetry combined with the high transparency of the limit order book might reduce the quote matcher problem. Interestingly, our results are similar to those of Harris (1994), despite the considerable differences in trading structure. Our results are also interesting since the completely electronic trading structure at the SSE does not facilitate combined trades to overcome the negative effect of the tick size.

The remainder of the paper is organized as follows. In Section 2, we present our data set. Section 3 gives some background to the problem and discusses earlier work. Section 4 contains our empirical results, using the cross-sectional sample, of the impact of the tick size on the bid/ask spread or width, on the quoted volume or depth and on trading volume. In section 5, we report our empirical findings using daily averages on some stocks which moved from one tick size regime during the period studied. The summary and conclusion are found in section 6.

2 The Data Set

Our data set consists of transactions and order book data from a number of stocks traded at the Stockholm Stock Exchange. The data include all quotes, quote revisions and transactions (excluding after hours trading) on some of Sweden's most traded stocks.

5 For a detailed description of the trading structure of the Stockholm Stock Exchange, see Niemeyer and Sand?s (1993).

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The data set is divided into three samples. The first two samples are purely crosssectional. The first cross-sectional sample, which is used for specification of the regression models, includes 52 stocks and the variables are simple averages using data for the time period between December 3, 1991 and January 17, 1992. The second crosssectional sample, used to control for the model specifications includes 69 stocks and the variables are simple averages using data from January 20, 1992 to March 2, 1992. The third sample includes five stocks which, during the time period, moved from one tick size regime to another. Here, the variables are daily averages and we ran our regressions across days for all five stocks. For clarification, we want to stress that all our data are averages over time for each specific stock. We are therefore not able to estimate possible differences in trading costs, bid/ask spreads, etc. during the trading day.

The stock transaction data set contains the time, price and the number of stocks traded. The set from the electronic limit order book consists of the five best bid and ask prices, the associated quantities, and the number of orders at each bid and ask level in the electronic open limit order book. Stocks with fewer than 50 transactions were excluded from the samples. Using this criterion, three and nine stocks respectively were found to be too inactively traded to be included. The first sample thus included 49 and the second sample 60 stocks. The data from the second sample is included in Appendix 1. Only results from the second and third samples are reported below. In order to avoid some econometric problems, we also ran our test on some reduced versions of the second sample (see section 4).

It should be noted that different trading systems record transactions in a different manner. An example may clarify this issue. In a market maker based trading system, if investor A buys 3000 shares, one transaction between the market maker and investor A will be recorded. When the market maker later unwinds his position against investors B, C and D, there will be an additional three transactions recorded. In total, the transaction tape will include four transactions. In an order book driven trading system, investor A's 3000 shares will be matched directly with the standing limit orders of investor B, C and D. The total transaction record will therefore only contain three transactions. All variables averaged over the number of transactions will naturally be influenced by this phenomenon.6

In this study, we use order book data. These will be influenced by a similar phenomenon. When investor A's 3000 shares are matched against the three limit orders

6 This raises the question of the differences between the definitions of a trade and a transaction. The question is whether we should consider investor A's purchase as one trade or three transactions. In this paper we view it as three transactions.

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