Fra-g-men-t-ed Ma-rke-ts

From VoxEu:

Financial innovation has brought about several new ways to trade equities: electronically, over-the-counter, through broker-dealer networks, and so on. But not all of the transactions are transparent, with many barely visible to outsiders – a practice known as ‘dark’ trades. This column finds that, in general, more ways to trade is a benefit, except when the trades are dark.

Sock (sic) markets are back in the news. The on-going falls have drawn comparisons with 2008, catching the attention of the media, politicians, and the wider public. They have also caught the eye of economists, who appreciate how much we still don’t know about how stock markets behave. Research in this area is as relevant now as it has ever been.

Fragmentation poses a challenge to the traditional stock markets, due to the entry of a variety of new trading venues – e.g., electronic communication networks (ECNs), broker-dealer crossing networks, dark pools, and over-the-counter markets (OTC). Trading nowadays has become dispersed over many trading systems –visible and dark– creating a fragmented market place. These changes in market structure are driven by innovations in financial regulation, such as the Regulation National Market System (Reg NMS) in the US and the Market in Financial Instruments Directive (MiFID) in Europe. An important question is how market quality is affected by the resulting fragmentation and the opacity of some of these entering trading venues.

Likely effects of fragmentation: Visible versus dark

The effects of fragmentation of visible order books and dark trading have captured widespread attention from researchers, regulators, investors, and trading institutions. 

First, there are opposing views about the effect of trading in multiple markets (i.e. fragmentation) on market quality. One view is that market quality is greatest in a consolidated market. A single exchange benefits from lower fixed costs and market monitoring costs compared with a fragmented market structure. Also, a single market that is already liquid will attract even more liquidity due to positive network externalities (e.g., Pagano 1989a, b; Admati, Amihud, and Pfleiderer 1991). Each additional trader reduces the stock’s execution risk for other potential traders, attracting more traders. But another view argues that different trading venues arise as they cater to the needs of a heterogeneous clientele. For example, investors differ in their preferences for trading speed, order sizes, anonymity and likelihood of execution (e.g., Harris 1993; Degryse et al 2009). Furthermore, competition fosters innovation and efficiency (Stoll 2003).

Technological improvements have brought these opposing views closer together. Indeed, the market is virtually unfragmented to traders with access to all venues (i.e. those employing Smart Order Routing Technology). Investors without access to this technology may then suffer from fragmentation.

A second question is how the degree of transparency (or opacity) of markets affects the impact of fragmentation on market quality. Visible fragmentation (i.e. fragmentation across markets with visible order books) may lead to improved liquidity because of the absence of time priority across trading venues. Specifically, it allows investors to jump ahead of the queue of limit orders in one market by submitting an equally priced limit order on a competing market. This “queue-jumping” effect reduces the rents of liquidity suppliers, which lowers trading costs for liquidity demanders (Foucault and Menkveld 2008). In addition, fragmentation enhances market quality as increased competition among liquidity suppliers forces them to improve their prices, narrowing the bid-ask spread (e.g. Biais et al 2000). The effect of dark trading, outside publicly displayed order books, is ambiguous, however (Hendershott and Mendelson 2000). On the one hand, dark trading may attract additional liquidity traders and therefore lead to improved market quality (i.e. lower spreads). On the other hand, market quality of the transparent market may decrease when investors systematically try to trade in the dark market first. Buti et al (2010) further argue that the initial level of liquidity at the visible markets determines the effect of the dark pool on quoted spreads.

In a recent empirical study, O’Hara and Ye (2011) find that fragmentation lowers transaction costs and increases execution speed for NYSE and Nasdaq stocks. They do not distinguish, however, between the differential impact on liquidity of fragmentation resulting from visible and dark trading venues. The main contribution from our recent paper (Degryse et al 2011) is to separate these impacts. In addition, we distinguish between consolidated liquidity – aggregated over all trading venues – and liquidity on the traditional market (i.e., the incumbent trading venue). Consolidated liquidity is available to investors using Smart Order Routing Technology (SORT), while non-SORT investors tap the traditional market only.

Our data

We use high-frequency data of 52 Dutch stocks, which form the large and midcap indices, from 2006 to 2009. The data on the visible-limit order markets stem from Thomson Reuters Tick History and include the seven most relevant European trading venues for the sample stocks: Euronext, Chi-X, Deutsche Boerse, Turquoise, Bats Europe, Nasdaq OMX and SIX Swiss exchange. For every trading venue and stock, we have all trades and the ten best quotes at both sides of the order book. We analyse data during the continuous auction time of Euronext Amsterdam, i.e., between 09.00 and 17.30 local time.

Figure 1 shows the trading volumes of the traditional exchange, visible competitors, and dark markets for our sample of Dutch stocks. These stocks, representative for European blue chips, show that visible fragmentation increased over time while dark trading remained relatively constant.

Figure 1.

Notes: Daily average trading volumes of the traditional exchange and dark and visible competitors. The data consist of 52 Dutch stocks forming the large and mid cap index, source: Own computations based on Thomson Reuters Tick History.

The dataset at hand allows us to compute different market quality metrics. We employ the depth (i.e., the number of shares available for a different set of basis points around the midquote), the quoted spread, realised spread, and effective spread, each time for the consolidated order book and the order book of the regulated market (Euronext Amsterdam, in our case).

The dataset also provides daily volume figures on “dark trades”, i.e. trades at dark pools, broker-dealer crossing networks, internalised, and OTC (including trades executed by telephone). These dark trades are taken from different venues reporting to the Thomson Reuters dataset.

We study the impact of visible fragmentation and dark trading on various measures of market quality using panel regressions. This data structure, with daily observations per firm, allows for an improved identification strategy of the parameters of interest.

• We introduce firm-quarter dummies implying that the impact of liquidity stems from variation within a firm-quarter, making the analysis robust to the issue that fragmentation tends to be higher for high volume and more liquid stocks.

• We use instrumental variables. Similar to O’Hara and Ye (2011), we use as instruments for visible fragmentation the average order size of the visible competitors, and also the number of limit orders to market orders on the visible competitors. Dark trading is instrumented by the average dark order size.

The main findings

Our main findings can be summarised as follows:

• The effect of visible fragmentation on market quality of the consolidated market is generally positive, while the effect of dark trading is negative.

An increase in dark trading of one standard deviation lowers global liquidity by 9%. The effect of visible fragmentation has an inverted U-shape, i.e. the effect is positive at low levels of fragmentation, but the marginal effect declines when fragmentation increases. Employing our most conservative estimates, the optimal degree of visible fragmentation improves global liquidity by approximately 32% compared with a completely concentrated market.

• The gains of visible fragmentation mainly hold for liquidity close to the midpoint, i.e. at relatively good price levels, but to a much lesser extent for liquidity deeper in the order book.

This result suggests that newly entering trading venues with visible order books primarily improve liquidity close to the midpoint.

• While the consolidated market quality benefits from fragmentation, we find that the market depth at good prices at the regulated market decreases.

As such, investors without access to SORT are worse off in a fragmented market, especially for relatively small orders.

Conclusions

This column shows that the impact of fragmentation on market quality depends on the type of fragmentation. We find that fragmentation in visible order books improves market quality for investors who access all trading venues, whereas dark trading has a detrimental effect on the market quality of visible markets. Small retail investors without access to the new markets are worse off.

References

Adamati, A, Y Amihud, and P Pfleiderer (1991), “Sunshine trading and financial market equilibrium”, Review of Financial Studies, 438, 443-481.
Biais, B, D Martimort, and J Rochet (2000), “Competing Mechanisms in a Common Value Environment”, Econometrica, 68(4), 799-837.
Buti, S, B Rindi, and I Werner (2010), “Dynamic Dark Pool Trading Strategies in Limit Order Markets”, Working Paper Fisher College of Business, Ohio State University.
Degryse, H, F De Jong, and V Van Kervel (2011), “The Impact of Dark Trading and Visible Fragmentation on Market Quality”, CEPR Discussion Paper 8630.
Degryse, H, M Van Achter, and G Wuyts (2009), “Dynamic order submission strategies with competition between a dealer market and a crossing network”, Journal of Financial Economics, 91:319-338.
Foucault, T, and A Menkveld (2008), “Competition for order flow and smart order routing systems”, Journal of Finance, 63(1):119-158.
Harris, L (1993), “Consolidation, Fragmentation, Segmentation and Regulation”, Financial Markets, Institutions & Instruments, 5:1-28.
Hendershott, T and H Mendelson (2000), “Crossing networks and dealer markets: competition and performance”, Journal of Finance, 55(5):2071-2115.
O’Hara, M and M Ye (2011), “Is Market Fragmentation Harming Market Quality?”, Journal of Financial Economics, forthcoming.
Pagano, M (1989a), “Endogenous Market Thinness and Stock Price Volatility”, Review of Economic Studies, 56:269-287.
Pagano, M. (1989b), “Trading volume and asset liquidity”, Quarterly Journal of Economics, 104:25-74.