Strategic trading and learning about liquidity

Many practitioners point out that the speculative profits of institutional traders are eroded by the difficulty in gauging the price impact of their trades. In this paper, we develop a model of strategic trading where speculators face such a dilemma because of incomplete information about time-varying market liquidity. Unlike the competitive market makers that they trade against, informed traders do not know the distribution of liquidity (“noise”) trades. Instead, they have to learn about liquidity from past prices and trading volume. This learning implies that strategic trades and market statistics such as informational efficiency are path-dependent on past market outcomes. Our paper also has normative implications for practitioners.     

Very fast money: High-frequency trading on the NASDAQ

This paper provides evidence regarding high-frequency trader (HFT) trading performance, trading costs, and effects on market efficiency using a sample of NASDAQ trades and quotes that directly identifies HFT participation. I find that HFTs engage in successful intra-day market timing, spreads are wider when HFTs provide liquidity and tighter when HFTs take liquidity, and prices incorporate information from order flow and market-wide returns more efficiently on days when HFT participation is high.     

The role of trades in price convergence: A study of dual-listed Canadian stocks

We examine the role of trades in restoring price parity for equities trading in multiple markets. Using a sample of stocks trading on the Toronto Stock Exchange and on the NYSE, AMEX or NASDAQ, we contrast price convergence when market makers (a) observe only lagged quotes from both markets and (b) also observe local order flow. Traditional error correction model estimates show that prices in the two markets adjust towards parity in response to quoted price discrepancies, meaning that observation of the cross-market quote helps restore parity. Including order flow in an augmented error correction model, we find that incremental price convergence occurs when trades are routed to the market with the better price, and the importance of quotes in the price convergence process is reduced. Cross-sectional analysis reveals that the importance of order flow in each market is decreasing in firm size and increasing in measures of liquidity. Our findings point to an important, and hitherto unexamined, role for trades in promoting inter-market price convergence.     

Are tightened trading rules always bad? Evidence from the Chinese index futures market

This paper investigates the impact of tightened trading rules on the market efficiency and price discovery function of the Chinese stock index futures in 2015. The market efficiency and the price discovery of Chinese stock index futures do not deteriorate after these rule changes. Using variance ratio and spectral shape tests, we find that the Chinese index futures market becomes even more efficient after the tightened rules came into effect. Furthermore, by employing Schwarz and Szakmary [J. Futures Markets, 1994, 14(2), 147–167] and Hasbrouck [J. Finance, 1995, 50(4), 1175–1199] price discovery measures, we find that the price discovery function, to some extent, becomes better. This finding is consistent with Stein [J. Finance, 2009, 64(4), 1517–1548], who documents that regulations on leverage can be helpful in a bad market state, and Zhu [Rev. Financ. Stud., 2014, 27(3), 747–789.], who finds that price discovery can be improved with reduced liquidity. It also suggests that the new rules may effectively regulate the manipulation behaviour of the Chinese stock index futures market during a bad market state, and then positively affect its market efficiency and price discovery function.     

Transparency and Liquidity: A Study of Block Trades on the London Stock Exchange under Different Publication Rules

This article examines whether reducing a market's transparency, by delaying the publication of prices for block trades, has any impact on liquidity. The analysis uses a sample of 5987 blocks from the London Stock Exchange that cover three different publication regimes: immediate (1987/88), 90 minutes (1991/92), and 24 hours (1989/90). Delaying publication does not affect the time taken by prices to reach a new level, which is rapid under all regimes. Spreads differ across years, but their size relates more closely to market volatility than to speed of publication. There is therefore no gain in liquidity from delayed publication.     

LIQUIDITY COMMONALITY BEYOND BEST PRICES

Previous market microstructure research focuses on commonality in liquidity at the inside spread. However, liquidity at the inside spread only determines the systematic liquidity risk of small and medium trades. We study commonality in displayed liquidity beyond best prices, which determines the systematic liquidity risk of large trades. We show that it is much larger than commonality at the inside spread. The deeper we look into the order book, the higher is the level of commonality. In addition, it rises in the morning and when markets fall.     

Intraday asymmetric liquidity and asymmetric volatility in FTSE-100 futures market

In this study, we use both quote and trade data for the FTSE-100 futures for 2001–2004 in order to examine asymmetric volatility in the context of extreme sells. We define extreme sells as ask quotes that involve large percentages of total depth, selling orders executed at prices much closer to bids than to asking prices, and consecutive sell-initiated trades. Sell trades tend to demand higher liquidity than buys, while extreme trading conditions demand more liquidity than non-extreme ones. In extreme sells, liquidity demand surpasses supply. We show that asymmetric liquidity (quote demand vs. supply) better explains the asymmetric volatility observed in high-frequency data than trade information does. Ask-depth share plays a dominant role in asymmetric volatility, while order flow (sell-initiated volume share) makes a far smaller contribution.     

Time-Varying Arrival Rates of Informed and Uninformed Trades

We propose a dynamic econometric microstructure model of trading,and we investigate how the dynamics of trades and trade compositioninteract with the evolution of market liquidity, market depth,and order flow. We estimate a bivariate generalized autoregressiveintensity process for the arrival rates of informed and uninformedtrades for 16 actively traded stocks over 15 years of transactiondata. Our results show that both informed and uninformed tradesare highly persistent, but that the uninformed arrival forecastsrespond negatively to past forecasts of the informed intensity.Our estimation generates daily conditional arrival rates ofinformed and uninformed trades, which we use to construct forecastsof the probability of information-based trade (PIN). These forecastsare used in turn to forecast market liquidity as measured bybid-ask spreads and the price impact of orders. We observe thatPINs vary across assets and over time, and most importantlythat they are correlated across assets. Our analysis shows thatone principal component explains much of the daily variationin PINs and that this systemic liquidity factor may be importantfor asset pricing. We also find that PINs tend to rise beforeearnings announcement days and decline afterwards.     

Relation between bid–ask spread,impact and volatility in order-driven markets

We show that the cost of market orders and the profit of infinitesimal market-making or -taking strategies can be expressed in terms of directly observable quantities, namely the spread and the lag-dependent impact function. Imposing that any market taking or liquidity providing strategies is at best marginally profitable, we obtain a linear relation between the bid–ask spread and the instantaneous impact of market orders, in good agreement with our empirical observations on electronic markets. We then use this relation to justify a strong, and hitherto unnoticed, empirical correlation between the spread and the volatility per trade, with R ²s exceeding 0.9. This correlation suggests both that the main determinant of the bid–ask spread is adverse selection, and that most of the volatility comes from trade impact. We argue that the role of the time-horizon appearing in the definition of costs is crucial and that long-range correlations in the order flow, overlooked in previous studies, must be carefully factored in. We find that the spread is significantly larger on the NYSE, a liquid market with specialists, where monopoly rents appear to be present.     

Bid ask spread in a competitive market with institutions and order size

Large orders, particularly from institutions, are quite common these days and hence there is interest to know if institutional trading has any bearing on the price effect associated with large trades. Recent empirical studies contradict earlier evidence of negative price effect on selling large blocks and find no price effect associated with large trades. Existing theoretical framework suggests a monotonic and increasing adverse price effect for large trades, where the motivation for a large trade is private information. We model a trading system where pure information, information-liquidity, and pure liquidity traders trade small and large sizes. The pure information traders strategically choose an order size. Institutions trade only large sizes because of their low execution costs for large trades; they are information-liquidity traders whose ability to use an information signal to determine their trades is subject to a binding liquidity constraint. We show that in such a market a separating equilibrium where trade size is informative does not exist and hence there is no price effect for large trades. Trade size may be revealing only if there is a buy sell asymmetry (large buy size is not equal to large sell size) or the corresponding price effect is asymmetric (price effect due to a large buy is not equal to that of a large sell). Further for a pooling equilibrium to exist, where trade size is not informative, the width of the market denoted by the ratio of order size (large size/small size) needs to be small, while the shallowness (inverse depth) of the market denoted by the ratio between pure information and institutional trades and the information signal needs to be stronger (higher). Our results on bid and ask prices and spread confirm recent empirical evidence on price effect of large and institutional trades found in the literature.

Applying hedging strategies to estimate model risk and provision calculation

We present an empirical study of the intertwined behaviour of members in a financial market. Exploiting a database where the broker that initiates an order book event can be identified, we decompose the correlation and response functions into contributions coming from different market participants and study how their behaviour is interconnected. We find evidence for the following. (1) Brokers are very heterogeneous in liquidity provision—some appear to be primarily liquidity providers while others are primarily liquidity takers. (2) The behaviour of brokers is strongly conditioned on the actions of other brokers. In contrast, brokers are only weakly influenced by the impact of their own previous orders. (3) The total impact of market orders is the result of a subtle compensation between the same broker pushing the price in one direction and the liquidity provision of other brokers pushing it in the opposite direction. These results enforce the picture of market dynamics being the result of the competition between heterogeneous participants, interacting to form a complex market ecology.     

Liquidity, analysts, and institutional ownership

In this paper, we investigate the empirical relationship between institutional ownership, number of analysts following and stock market liquidity. We find that firms with larger number of financial analysts following have wider spreads, lower market quality index, and larger price impact of trades. However, we find that firms with higher institutional ownership have narrower spreads, higher market quality index, and smaller price impact of trades. In addition, we show that changes in our liquidity measures are significantly related to changes in institutional ownership over time. These results suggest that firms may alleviate information asymmetry and improve stock market liquidity by increasing institutional ownership. Our results are remarkably robust to different measures of liquidity and measures of information asymmetry.     

Momentum and Foreign Investors: Evidence from the Korean Stock Market

Abstract:

We examine whether the price impact of foreign investors on the Korean stock market from December 2000 to February 2007 generated a momentum phenomenon. In our empirical results, foreigners seem to have exerted a significantly positive impact on prices in “up” markets (periods of positive stock returns), but have had little impact on prices in “down” markets (periods of negative returns). We document that the impact of foreigners’ trades is concentrated in large companies. Most importantly, when the market is in the up state, the returns of stocks of large companies that were positively affected by foreign investors in the previous six-month period continue to increase in the subsequent six-month period. As a result, the subsequent six-month return on a past “winner” stock portfolio is significantly higher than that on a past “loser” stock portfolio. This brings to mind a momentum phenomenon that has been reported not to exist in the Korean stock market.     

The impact of aggressive orders in an order-driven market: a simulation approach

This article investigates resiliency in an order-driven market. On basis of a vector autoregressive model capturing various dimensions of liquidity and their interactions, I simulate the effect of a large liquidity shock, measured by a very aggressive market order. I show that, despite the absence of market makers, the market is resilient. All dimensions of liquidity (spread, depth at the best prices and order book imbalances) revert to their steady-state values within 15 orders after the shock. For prices, a long run effect is found. Furthermore, different dimensions of liquidity interact. Immediately after a liquidity shock, the spread becomes wider than in the steady state, implying that one dimension of liquidity deteriorates, while at the same time, depth at the best prices increases, meaning an improvement of another liquidity dimension. In subsequent periods, the spread reverts back to the steady-state level but also depth decreases. Also, I find evidence for asymmetries in the impact of shocks on the ask and bid side. Shocks on the ask side have a stronger impact than shocks on the bid side. Finally, resiliency is higher for less-frequently traded stocks and stocks with a larger relative tick size.     

Prices, liquidity, and the information content of trades

We investigate the effect of asymmetric information on pricesand liquidity by analyzing trades, quotes, spreads, and depths.Information content should increase with trade size and theinformation asymmetry of the trading period. Results show thatprice and liquidity effects are significantly associated withinformation content as measured by both trade size and timingrelative to information events. Results are stronger for purchasesthan sales. Quoted prices are better measures of informationeffects than transaction prices, because they control for bid-askbounce. Finally, trades that a priori contain no informationhave no impact on prices and liquidity, despite their largesize.     

Fluctuations and response in financial markets: the subtle nature of ‘random’ price changes

Using trades and quotes data from the Paris stock market, we show that the random walk nature of traded prices results from a very delilcated interplay between two opposite tendencies: long-range correlated market orders that lead to super-diffusion (or persistence), and mean revrting limit orders that lead to sub-diffusion (or anti-persistence). We define and study a model where the price, at any instant, is the result of the impact of all past trades, mediated by a non-constant ‘propagator’ in time that describes the response of the market to a single trade. Within this model, the market is shown to be, in a precise sense, at a critical point, where the price is purely diffusive and the average response function almost constant. We find empirically, and discuss theoretically, a fluctuation-response relation. We also discuss the fraction of truly informed market orders, that correctly anticipate short-term moves, and find that it is quite small.     

The determinants of the price impact of block trades: further evidence

This article extends previous literature which examines the determinants of the price impact of block trades on the Australian Stock Exchange. As previous literature suggests that liquidity exhibits intraday patterns, we introduce time of day dummy variables to explore time dependencies in price impact. Following theoretical developments in previous literature, the explanatory power of the bid–ask spread, a lagged cumulative stock return variable and a refined measure of market returns are also examined. The model estimated explains approximately 29 per cent of the variation in price impact. Block trades executed in the first hour of trading experience the greatest price impact, while market conditions, lagged stock returns and bid–ask spreads are positively related to price impact. The bid–ask spread provides most of the explanatory power. This suggests that liquidity is the main driver of price impact.     

Time and the Price Impact of a Trade

We use Hasbrouck's (1991) vector autoregressive model for prices and trades to empirically test and assess the role played by the waiting time between consecutive transactions in the process of price formation. We find that as the time duration between transactions decreases, the price impact of trades, the speed of price adjustment to trade-related information, and the positive autocorrelation of signed trades all increase. This suggests that times when markets are most active are times when there is an increased presence of informed traders; we interpret such markets as having reduced liquidity.