Multimarket trading and liquidity: a transaction data analysis of Canada–US interlistings

We use transaction data for Toronto Stock Exchange (TSE) listed stocks to examine the impact on trading costs of the decision to interlist on a US exchange. We measure trading costs using both ‘posted’ bid-ask spreads and ‘effective’ bid-ask spreads that measure actual transaction prices relative to standing bid-ask quotes. After controlling for price level, trade size and trading volume effects, we find that overall posted and effective spreads in the domestic (TSE) market decrease subsequent to the interlisting. However, the decrease in trading costs is concentrated in those TSE stocks that experience a significant shift of total trading volume (TSE and US) to the US exchange after listing. We interpret this result in the context of theories of multimarket trading as a competitive response by TSE market makers to the additional presence of US market makers.     

Brokered versus dealer markets: Impact of proprietary trading with transaction fees

In this study, we consider a one-period financial market with a dealer/broker and an infinite number of investors. While the dealer who trades on his own account (with proprietary trading) simultaneously sets both the transaction fee and the asset price, the broker who brings investors' orders to the market (with no proprietary trading) sets only the transaction fee, given that the price is determined according to the market-clearing condition among investors. We analyze the impact of proprietary trading on the asset price, transaction fee, trading volume, and the welfare of investors. We find that the bid and ask prices set by the dealer who can engage in proprietary trading are more favorable to average investors. As a result, both the trading volume and the transaction fee increase, and social welfare improves.     

Short‐ and Long‐Term Effects of Multimarket Trading

We analyze short‐ and long‐term effects of multimarket trading by examining the entries of multiple markets into transacting three ETFs, DIA, QQQ, and SPY. We find that large‐scale entries improve overall market quality, while small‐scale entries have ambiguous effects. Our results show that the competition effect dominates the fragmentation effect over a long horizon and that market fragmentation leads to a decline in trading costs. Further, we find that the order handling rules help mitigate the fragmentation effect and facilitate the competition effect. We do not find that multimarket trading harms price efficiency or increases price volatility.     

The Impact of Options Trading on the Market Quality of the Underlying Security: An Empirical Analysis

We find that option listings are associated with a decrease in the variance of the pricing error, a decrease in the adverse selection component of the spread, and an increase in the relative weight placed by the specialist on public information in revising prices for the underlying stocks. We also find that there is a decrease in the spread and increases in quoted depth, trading volume, trading frequency, and transaction size after option listings. Overall, our results suggest that option listings improve the market quality of the underlying stocks.     

Competition For Order Flow, Market Quality, And Price Discovery In The Nasdaq 100 Index Tracking Stock

We investigate competition for order flow, market quality, and price discovery in the Nasdaq 100 Index Tracking Stock (QQQ). The QQQ, an AMEX‐listed, exchange‐traded fund, is the most actively traded security in the U.S. equities market. On July 31, 2001, the NYSE began trading the QQQ, marking the first time it traded securities of companies it does not list. The greatest volume of trading takes place on electronic communication networks (ECNs), following by trading on the AMEX and the NYSE. Most of the block trades are executed on the AMEX, where the bid‐ask spreads are narrower. We find that ECNs contribute the most to the price‐discovery process. The spreads on all trading platforms have decreased and market quality and price discovery have improved since QQQ shares have traded on the NYSE.     

Increasing the Tick: Examining the Impact of the Tick Size Change on Maker‐Taker and Taker‐Maker Market Models

We investigate the effects of an increase in tick size on order and trading flow across market fee models. Using the pilot firms in the U.S. Securities and Exchange Commission's Tick Size Pilot Program, we document that trade and order volume declines on maker‐taker fee models after the tick size implementation. We find that the inverted fee models (taker‐maker) experience an increase in both trade and order volume. Additionally, we find that a tick size adjustment has a substantial influence on market participation in maker‐taker fee models. We also find that measures of both hidden and algorithmic trading decline with an increasing tick size, which is strongly moderated by the differences in the maker‐taker and taker‐maker fee models.     

THE EFFECT OF CROSS‐LISTING ON TRADING VOLUME: REDUCING SEGMENTATION VERSUS SIGNALING INVESTOR PROTECTION

We examine the relation between cross‐listing on the U.S. and UK regulated and unregulated exchanges and trading volume for a sample of 500 foreign firms from 34 countries. We find that the increase in trading volume is a function of both reducing segmentation and signaling investor protection. In addition, we find that home market trading volume, firm size, firm returns, and analyst forecast accuracy are the major determinants of a firm's trading volume. We also show that U.S. and UK investors trade foreign securities that originate from low‐investor‐protection countries more than they trade those from high‐investor‐protection countries, which is consistent with the bonding hypothesis.     

Crises,contagion and cross-listings

We investigate whether cross-listing shares in the form of depositary receipts in overseas markets benefits investors in emerging market countries during periods of local financial crisis from 1994 to 2002. We regress cumulative abnormal returns for three windows surrounding the crisis events on the cross-listing status while controlling for cross-sectional differences in firm age, trading volume, foreign exposure, disclosure quality and corporate governance. Further, we examine cross-listing effects in countries popularly thought to experience contagious effects of these crises. We find that cross-listed firms react significantly less negatively than non-cross-listed firms, particularly in the aftermath of the crisis. The results on contagious cross-listing effects are however mixed. Our findings are consistent with predictions based on theories of market segmentation as well as differential disclosure/governance between developed and emerging markets. We do not find evidence that foreign investors “panic” during a currency crisis.     

Capital gains tax,supply‐driven trading and ownership structure: direct evidence of the lock‐in effect

This study investigates the effect of differential capital gains tax rates on investor trading and share prices in a unique market setting that facilitates the resolution of conflicting prior evidence of holding period tax incentives. In particular, we examine whether the concessionary tax treatment of long‐term capital gains increases the supply of shares that qualify for long‐term status, thereby causing downward price pressure. We find evidence of abnormal seller‐initiated trading following the 12‐month anniversary of listing for IPO firms that appreciate in price (‘winners’) and report no such evidence for firms that decline in price (‘losers’). Consistent with the tax concessions being greater for individual than institutional investors, we report that abnormal seller‐initiated trading is mitigated by higher levels of ownership by institutional investors. We also report limited evidence, for winners, of declining share prices upon qualifying for long‐term tax status.     

Front‐Running Scalping Strategies and Market Manipulation: Why Does High‐Frequency Trading Need Stricter Regulation?

Regulators continue to debate whether high‐frequency trading (HFT) is beneficial to market quality. Using Strongly Typed Genetic Programming (STGP) trading algorithm, we develop several artificial stock markets populated with HFT scalpers and strategic informed traders. We simulate real‐life trading in the millisecond time frame by applying STGP to real‐time and historical data from Apple, Exxon Mobil, and Google. We observe that HFT scalpers front‐run the order flow, resulting in damage to market quality and long‐term investors. To mitigate these negative implications, we propose batch auctions every 30 milliseconds of trading.     

Semi‐transparency,dealership market,and foreign exchange market quality

This paper examines the effects of a semi-transparency event, the introduction of the electronic trading system (EBS), on the market quality of a typical dealership market – the FX market. We find that increasing transparency leads to smaller quote disagreement among dealers and higher trading volume, but the beneficial effects are bigger for uninformed dealers than informed dealers. We also find that information efficiency improves overall in the semi-transparent system; however, informed dealers are found to quote less aggressively in the more transparent market. We conclude that semi-transparency raises market quality in general, but that it is the uninformed dealers who benefit more from this increased quality.     

Wholesale Funding Dry‐Ups

We empirically explore the fragility of wholesale funding of banks, using transaction‐level data on short‐term, unsecured certificates of deposit in the European market. We do not observe a market‐wide freeze during the 2008 to 2014 period. Yet, many banks suddenly experience funding dry‐ups. Dry‐ups predict, but do not cause, future deterioration in bank performance. Furthermore, during periods of market stress, banks with high future performance tend to increase reliance on wholesale funding. We therefore fail to find evidence consistent with adverse selection models of funding market freezes. Our evidence is in line with theories highlighting heterogeneity between informed and uninformed lenders.     

The use of open source internet to analysis and predict stock market trading volume

The objective of this paper is to evaluate the impact of information demand and supply on stock market trading volume. Few studies have demonstrated the role of Google search data in analyzing trading volume activity. In this study, we employ a proxy for information demand which is derived from weekly internet search volume. The latest is from Google Trends database, for 25 of the largest stocks traded on CAC40 index, between April 2007 and March 2014. We use news headlines as a proxy for information supply. We use Garch model to analyze and predict trading volume.The empirical results present new evidences. First, information supply has an impact on trading volume but information demand's impact is much more important. Secondly, by applying MCA to results found, it could be concluded that the impact of public information on transaction volume is conditioned by two elements: the firm and market news disclosure and the second element relates to the characteristics of the market participants, more precisely their news interpretations and their risk aversion. Thirdly, we used Chow structural break test to verify the stability of our model. We found that for securities with structural changes, information demand is the responsible variable of the change in our model. Finally, we found that information variables have a predictive power on transaction volume.This paper contributes to existing literature by incorporating open source internet-based data into the analysis and prediction of transaction volume. Using internet information about the stock market, which has appeared recently as an interesting research for financial empiricists, computer scientists and practitioners, will have a very important utility because quantifying demand and supply of information becomes possible.     

Market or limit orders?

In this paper we investigate the problem of optimal order placement of an asset listed on an exchange using both market and limit orders in a simple model of market dynamics. We seek to understand under which settings it is optimal to place limit or market orders. Limit orders typically lower transaction costs but increase the risk of incomplete order execution, whereas market orders typically have higher transaction costs but are guaranteed to be executed. Rather than considering order book dynamics to determine if a limit order is executed we rely on price dynamics for this. We look at implementation shortfall in this setup with market impact of trading and propose a dynamic program to find the optimal placement of both market and limit orders for risk-neutral and risk-averse traders. With this we find a bound on the expected cost of trading and show that a trader who behaves optimally should always expect to pay less to trade less. We then solve the dynamic program numerically and examine optimal order placement strategies. We find that the decision between market and limit orders is sensitive to price volatility, risk aversion, and trading costs.     

Trading Volume and Transaction Costs in Specialist Markets

Prior work with competitive rational expectations equilibrium models indicates that there should be a positive relation between trading volume and differences in beliefs or information among traders. We show that this result is sensitive to whether and how transaction costs are modeled. In a specialist market with endogenous transaction costs we show that trading volume can be negatively related to the degree of informational asymmetry in the market. Our analysis highlights the dependence of volume on market structure, and our results suggest that the “volume effects” of corporate or macroeconomic events reflect a decrease, rather than an increase, in heterogeneity of beliefs or asymmetry of information.     

The determinants of debt maturity in Australian firms

We examine the determinants of debt maturity in the Australian capital market with the Top 400 firms listed on the Australian Securities Exchange for the period 1989–2006. We find that Australian firms not only exhibit a positive leverage–maturity relationship but also use short‐term debt to signal their high quality to the market. Our results are robust to different estimation methods that control for endogeneity and error‐dependence. We also find that ignoring the interaction between leverage and maturity can lead to erroneous conclusions about the support for the matching principle, the agency costs hypothesis and the transaction costs hypothesis.     

Adverse selection,brokerage coverage,and trading activity on the Tokyo Stock Exchange

Limit-order trading mechanisms, corporate ownership structure, and incentive structures in the Japanese brokerage industry differ from those in the US in several important ways. This paper exploits these differences to examine the joint and cross-sectional determinants of adverse selection costs, brokerage coverage, and trading activity for a large sample of Japanese firms traded on the Tokyo Stock Exchange. We find that adverse selection costs are associated with firm characteristics but not with ownership characteristics, which implies that adverse selection costs are affected by inside trading rather than inside holdings. We also find that while brokerage coverage reduces adverse selection costs, higher adverse selection costs lead more brokerage firms to enter the market because of the greater profit potential. Finally, we find that causality also runs both ways between brokerage coverage and trading volume: brokerage coverage increases trading volume and trading volume increases brokerage coverage.     

Volume autocorrelation,information, and investor trading

This study investigates whether the widely documented daily correlated trading volume of stocks is driven by individual investor trading, institutional trading, or both. We find that at least 95% of NYSE and AMEX stocks exhibit statistically significant, positive serial correlation. Volume autocorrelation decreases with the level of institutional ownership of a stock. We also show that the rate of arrivals of new information to the market contributes to the clustering of trades. When there is high information flow to the market, institutional trading generates a more pronounced effect on volume autocorrelation than individual investor trading. Our results are broadly consistent with the predictions of trading volume patterns suggested by most theoretical models of stock trading and by empirical research on investor trading.     

INTRADAY TRADING PATTERNS IN THE EQUITY OPTIONS MARKETS

Based on the analysis of data for over two million options transactions on the Chicago Board of Options Exchange, we find significant U-shaped intraday patterns in trading volume, transaction size, proportion of trades at the ask or bid, and other variables in the equity options market. These previously undocumented intraday temporal patterns are largely consistent with exogenous temporal influences on transactions volume in anticipation of and following nontrading periods. These patterns are also largely consistent with strategic trading endogenous to the market and are related to interactions between informed and liquidity or noise traders.     

An evolutionary multi‐objective optimization of trading rules in call markets

We evaluate an agent‐based model featuring near‐zero‐intelligence traders operating in a call market with a wide range of trading rules governing the determination of prices and which orders are executed, as well as a range of parameters regarding market intervention by market makers and the presence of informed traders. We optimize these trading rules using a multi‐objective population‐based incremental learning algorithm seeking to maximize the trading volume and minimize the bid–ask spread. Our results suggest that markets should choose a small tick size if concerns about the bid–ask spread are dominating and a large tick size if maximizing trading volume is the main aim. We also find that unless concerns about trading volume dominate, time priority is the optimal priority rule. Copyright © 2011 John Wiley & Sons, Ltd.