Market transparency is not a field of recent literature. Papers such as the one by Garbade and Silber (1978) are considered to have opened up the interest in this field. They examined how technological advances have impacted on the integration of different markets. They found that technological advances such as the transatlantic cable (1866) reduced the price differentials between the markets. Since then a vast number of studies that employ theory, experimentation and empirical analysis, to study transparency have been published.
Madhavan (1992) is using two different market structures, quote driven and order driven, to address the key issue in pre-trade transparency: the degree to which the size and direction of order flow is visible to market participants. As discussed in the previous chapter, in dealership markets, dealers have the obligation to post prices at which they are willing to trade, while in an auction market orders are submitted and then trading prices are determined. Therefore, market participants have more information in an order driven market than in a batch auction market. Another, important issue in market transparency is how different degrees of transparency affect the distribution of gains among traders. Pagano and Roell (1993) examine how transparency can affect the trading costs (losses) of uninformed traders. Using a simple market structure that assumes one informed trader and many uninformed traders on Kyle’s model, they show that the expected trading costs of uninformed traders in the transparent market are always less than or equal to their expected trading cost in the dealer market. If
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however, there are as many informed traders as uninformed the origin of the trades can’t be determined and therefore, the trading costs in the two markets will be the same. Pagano and Roell analyse further the issue of transparency taking in to consideration the order size of uninformed traders. They find that uninformed traders are unlikely to trade large orders in a transparent market.
Flood, Huisman, Koedijk and Mahieu (1998a) investigate quote transparency in a setting in which trade information is never revealed, quote data may be available, and trading activity is dominated by interdealer trades. They find that quote transparency reduces opening bid-ask spreads and therefore, reduces the cost of asymmetric information. In Flood, Huisman, Koedijk and Mahieu (1998b) the authors find that trade transparency increases bid-ask spreads and increases market efficiency.
According to Anand and Weaver (2003), transparency has significant impact on market quality and the behaviour of traders. Their paper examines one type of pre-trade transparency – the ability to hide a portion of an order. The authors analyse the issue from the perspectives of market quality and trader behaviour. Examining confidential order data following the reintroduction of hidden limit orders reveals that total depth increases dramatically. They find support for their hypothesis that traders who actively monitor the market use hidden limit orders less often than other traders. They also find that while traders appear to use hidden size to reduce the option value of limit orders stocks of all activity levels, informed traders are more likely to use hidden limit orders if the risk of non- execution is small. In particular, while stocks at all activity levels exhibit an increase in hidden limit order usage, actively traded stocks experience the largest increase as well as the most aggressive order placement. Tuttle (2002) studies the use of hidden orders in the highly fragmented environment of the Super SOES system implementation in NASDAQ. She finds that hidden liquidity accounts for 22% of the inside depth for the NASDAQ 100 stocks, and mitigates adverse selection costs for limit order traders. She also documents an increase
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in depth around the implementation of the Super SOES system and attributes this increase to the ability to hide depth in the system. As the author notes, the NASDAQ market is highly fragmented and alternative systems such as ECNs allow a much higher degree of anonymity (for example, Island allows traders to hide all of their order size).
Scalia and Vacca (2001) discuss the influence of a decrease in transparency resulting from anonymous tradingon the Italian MTS electronic trading system, a dealer system. The study is in line with the theoretical evidence that a decrease in transparency makes liquidity traders worse off whereas large informed traders are favoured because they can hide their private information. Their work shows that lower transparency will increase market liquidity, reduce trading costs and price volatility and will increase market efficiency.
Madhavan, Porter and Weaver (2005) conduct an empirical study on the impact on market quality of a new rule on the Toronto Stock Exchange that allowed the dissemination of real-time information on the contents of the limit order book. They find that an increase in pre-trade transparency is associated with wider spreads and that higher transparency does not improve market quality. In particular, their analysis shows that transaction costs increased after the introduction of the rule change, even when controlling for other factors that may affect trading costs, such as volume, volatility, and price. Admati and Pfleiderer (1991) provide a model of sunshine trading where some liquidity traders can
preannounce the size of their orders while others cannot. They show that traders willing to provide information about their trading intentions before the trade, face narrower bid-ask spreads because market participants believe these investors are liquidity motivated and do not possess private information. However, the costs for liquidity traders who are unable to preannounce their trades rise. Therefore, any preannouncement is considered by market participants to be information free, but increases the adverse selection costs for other traders. Benveniste, Wilhelm, and Marcus (1992) describe a system where market
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makers can indentify liquidity driven and information driven traders. They show that the ability of dealers to identify the motive of the trader can lower spreads compared to an anonymous market. If liquidity traders are price sensitive, they trade more if their trading costs are lowered. Rindi (2003) studied the impact of pre-trade transparency on liquidity in an order-driven market with informed and uninformed risk-averse investors and liquidity traders. Her study concluded that the more transparent the market, the more liquid it is.
Section 3.9: Post-trade transparency and its influence on the trading process
The main issue under examination in research focusing on post-trade transparency is whether information about executed trades should be delayed or not. Support for the delayed publication of information is based on the argument that market-makers are willing to trade large blocks of shares because their identity and intentions are hidden until they explore the trading opportunity. On the other hand, the argument against delayed publication is that reduced market information will cause trading to move to other markets. Therefore, in examining this issue we have to consider who is going to benefit from post-trade information. In general, trade information delays will favour market makers and large trades.
Bloomfield and O’Hara (1999) show that “an increase in post-trade transparency leads to greater informational efficiency, to an increase in spreads and poorer execution for informed and uninformed traders to the benefit of market makers”. Bloomfield and O’Hara (2000) further analyse the issue of trade reporting from the perspective of competing dealers and find that low transparency dealers are typically more aggressive and more profitable than their high transparency counterparts. Gemmill (1996) examined the effect of block trades on the price of the 50 most active stocks on the LSE for one month in each of the six years 1987-1992. He found no evidence of a decrease in the speed of price response
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(and no effect on spreads) following the sharp reduction in transparency which came with the change in the publication regime in February 1989. Gemmill’s findings are similar those presented by Breedon (1993), who analysed a small sample of stocks in the two publication regimes in 1989 and 1991. Therefore, these studies show that a decrease in post-trade transparency has little effect on the price setting process. Gemmill also examined the link between volatility and post-trade transparency and found no relationship between the two. Board and Sutcliffe (1996) show the percentage of trades (by value) subject to delayed publication fell from 59.7 per cent, in the first half of 1995, to 27.7 per cent, in the first half of 1996, but this had no apparent effect on the size of their median bid- ask spreads.
Porter and Weaver (1998a) examine the role of transparency in the Toronto Stock Exchange (TSE) on April 12, 1990 when the TSE provided real-time public dissemination of the best bid and offer and associated depth (bid and ask size) as well as prices and sizes for up to four levels away from the inside market in both directions. They find that both effective spreads and the percentage bid-ask spread widened after the introduction of the system, suggesting a decrease in liquidity associated with transparency, even after controlling for other factors that may have affected spreads in this period, including volume, volatility, and price. That shows that limit order traders avoid markets with high transparency as their trading intentions (price at which they are willing to trade) will be observed by other traders. Chowdhry and Nanda (1991) suggest a model where dealers decide to disclose trade information to the public to discourage insider trading. At the same time uninformed traders feel safe to trade in this market as the risks of trading with an informed trader are reduced. In the long run, the market can develop a reputation of being “clean” and offer a platform for narrower spreads to liquidity traders.
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Concluding I can say that post-trade transparency as reflected in the speed of trade publication has little effect on the market characteristics such as bid-ask spreads, volume, volatility and the spread of price adjustment.