Order Flows and Stock Returns: Compensation for Market Makers with Inventory Concerns

We investigate whether market makers with inventory concerns are compensated with subsequent monthly returns in the cross‐section. We find a significant negative relation between order flows and monthly returns, “the order flow effect,” suggesting that market makers lower prices for stocks with sell order flows and demand a reward in the form of higher expected returns. Further, the order flow effect is stronger for high‐volatility or high‐volume stocks for which market makers have serious inventory concerns. Funding liquidity of market makers also affects the order flow effect. Finally, our finding is independent of existing regularities and robust to the decimalization.     

Limit order books and trade informativeness

In the microstructure literature, information asymmetry is an important determinant of market liquidity. The classic setting is that uninformed dedicated liquidity suppliers charge price concessions when incoming market orders are likely to be informationally motivated. In limit order book (LOB) markets, however, this relationship is less clear, as market participants can switch roles, and freely choose to immediately demand or patiently supply liquidity by submitting either market or limit orders. We study the importance of information asymmetry in LOBs based on a recent sample of 30 German Deutscher Aktienindex (DAX) stocks. We find that Hasbrouck's (1991) measure of trade informativeness Granger causes book liquidity, in particular that required to fill large market orders. Picking-off risk due to public news-induced volatility is more important for top-of-the book liquidity supply. In our multivariate analysis, we control for volatility, trading volume, trading intensity and order imbalance to isolate the effect of trade informativeness on book liquidity.     

Time Variation in Liquidity: The Role of Market-Maker Inventories and Revenues

We show that market-maker balance sheet and income statement variables explain time variation in liquidity, suggesting liquidity-supplier financing constraints matter. Using 11 years of NYSE specialist inventory positions and trading revenues, we find that aggregate market-level and specialist firm-level spreads widen when specialists have large positions or lose money. The effects are nonlinear and most prominent when inventories are big or trading results have been particularly poor. These sensitivities are smaller after specialist firm mergers, consistent with deep pockets easing financing constraints. Finally, compared to low volatility stocks, the liquidity of high volatility stocks is more sensitive to inventories and losses.     

Trading imbalances,predictable reversals,and cross-stock price pressure

We test the implications of a multi-asset equilibrium model in which a finite number of risk-averse liquidity providers accommodate non-informational trading imbalances. These imbalances generate predictable reversals in stock returns. An imbalance in one stock also affects the prices of other stocks. The magnitude of the cross-stock price pressure depends on the correlations of the stocks’ underlying cash flows. The model implies that non-informational trading increases the volatility of stock returns. We confirm the model's implications using data from the Taiwan Stock Exchange.     

Liquidity and risk sharing benefits from opening an ETF market with liquidity providers: Evidence from the CAC 40 index

This article examines how the introduction of an ETF replicating a stock index impacts on the liquidity of the underlying stocks when the ETF market involves liquidity providers (LPs). We find that index stock spreads decline, relative to those of non-index stocks, after the introduction of the ETF but this liquidity improvement is not driven by changes in adverse selection costs or recognition effects. By contrast, we show that it is mainly explained by a decrease in order processing and order imbalance costs. This most probably results from additional risk sharing capacities provided by increased cross-market trading and LPs' liquidity provision in low-liquidity times.     

Stock repurchases and treasury share sales: Do they stabilize price and enhance liquidity?

Can companies reduce the volatility and increase the liquidity of their stocks by trading them? In the context of the Italian stock market, where companies have far more leeway to sell as well as buy their own stocks than in the U.S., the answer is yes. We examine the effects of trading (open-market share repurchases and treasury shares sales) on liquidity (bid-ask spread) and volatility (return variance). Further, we examine the impact of shareholder approvals of repurchase programs on liquidity and volatility. We find clear evidence that trading increases liquidity and reduces volatility. These results are consistent with our analysis of the motives Italian companies give for making share repurchases.     

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.     

Stock returns and trading at the close

This paper analyzes stock returns at the close across the stocks of the Russell 1000 using (a) Transaction-level data for the period June 1997–July 1998, and (b) The complete record of all market-on-close (MOC) order imbalance indications. The last 5 min of the trading day explains a disproportionate fraction of the variation in daily returns, consistent with the hypothesis that institutional trading interest induces a common component to stock returns at the end of the day. This phenomenon reflects a higher demand for immediacy in the closing period. We find systematic return reversals following order imbalance publications consistent with temporary price pressure related to liquidity trading.     

STOCK MARKET OPENNESS AND MARKET QUALITY: EVIDENCE FROM THE SHANGHAI–HONG KONG STOCK CONNECT PROGRAM

We study the impact of capital market openness on high-frequency market quality in China. The Shanghai–Hong Kong Stock Connect program (SHHKConnect) opens China's stock market to foreign investors and offers a natural experiment to investigate this question. Using a difference-in-differences approach, we find that market liberalization leads to lower quoted spread, lower effective spread, lower market depth, and higher short-term volatility. Our findings imply that opening the markets to more sophisticated foreign investors is associated with higher competition and more cross-market arbitrage activities, narrowing the spread and reducing liquidity providers’ profits, but increasing the price impact and short-term volatility of connected stocks.     

Do Proprietary Algorithmic Traders Withdraw Liquidity during Market Stress?

We investigate the role of proprietary algorithmic traders in facilitating liquidity in a limit order market. Using order‐level data from the National Stock Exchange of India, we find that proprietary algorithmic traders increase limit order supply following periods of both high short‐term stock‐specific volatility and extreme stock price movement. Even following periods of high marketwide volatility, they do not decrease their supply of liquidity. We define orders from high‐frequency traders as a subclass of orders from proprietary algorithmic traders that are revised in less than three milliseconds. The behavior of high‐frequency trading mimics the behavior of its parent class. This is inconsistent with the theory that fast traders leave the market when stress situations arise, although their limit‐order‐supplying behavior becomes weaker when the increase in short‐term volatility is more informational than transitory. Agency algorithmic traders and nonalgorithmic traders behave opposite to proprietary algorithmic traders by reducing the supply of liquidity during stress situations. The presence of faster traders in the market possibly instills the fear of adverse selection in them. We document that the order imbalance of agency algorithmic traders is positively related to future short‐term returns, whereas the order imbalance of proprietary algorithmic traders is negatively related to future short‐term returns.     

Does high-frequency trading actually improve market liquidity? A comparative study for selected models and measures

The increasing volume of messages sent to the exchange by algorithmic traders stimulates a fierce debate among academics and practitioners on the impacts of high-frequency trading (HFT) on capital markets. By comparing a variety of regression models that associate various measures of market liquidity with measures of high-frequency activity on the same dataset, we find that for some models the increase in high-frequency activity improves market liquidity, but for others, we get the opposite effect. We indicate that this ambiguity does not depend only on the stock market or the data period, but also on the used HFT measure: the increase of high-frequency orders leads to lower market liquidity whereas the increase in high-frequency trades improves liquidity. We hypothesize that the observed decrease in market liquidity associated with an increasing level of high-frequency orders is caused by a rise in quote volatility.     

The Cross-Section of Volatility and Expected Returns

We examine the pricing of aggregate volatility risk in the cross‐section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book‐to‐market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility.     

Stock price synchronicity and liquidity

We argue and provide evidence that stock price synchronicity affects stock liquidity. Under the relative synchronicity hypothesis, higher return co-movement (i.e., higher systematic volatility relative to total volatility) improves liquidity. Under the absolute synchronicity hypothesis, stocks with higher systematic volatility or beta are more liquid. Our results support both hypotheses. We find all three illiquidity measures (effective proportional bid-ask spread, price impact measure, and Amihud's illiquidity measure) are negatively related to stock return co-movement and systematic volatility. Our analysis also shows that larger industry-wide component in returns improves liquidity. We find that improvement in liquidity following additions to the S&P 500 Index is related to the stock's increase in return co-movement.     

The trades of NYSE floor brokers

This paper studies the contribution of NYSE floor brokers to the Exchange's agency auction market. Floor brokers represent 44%, specialists 11%, and system orders 45% of the value of all executed orders in our sample. We analyze how the cross-sectional distribution of floor broker trading depends on liquidity, block volume, on- and off-exchange competition, volatility, and order flow internalization. Floor brokers participate in large trades, primarily in liquid stocks, and they trade more when volatility is high. They provide two-sided liquidity to the market and often provide liquidity that would otherwise have been supplied by NYSE specialists.     

The impact of the French Tobin tax

We analyze the impact of the introduction of the French Tobin tax on the turnover and measures of the liquidity and volatility of the affected stocks with nonparametric tests on individual stocks, difference-in-difference tests and other robustness checks controlling for simultaneous month-of-the-year and size effects. Our findings indicate that the tax produces a significant reduction in turnover and volatility (measured in terms of stock price volatility and the high–low price range) and inconclusive effects on liquidity when the latter is evaluated under the two dimensions of the estimated bid–ask spread and the Amihud (2002) price impact ratio.     

Order-to-trade ratios and market liquidity

We study the impact on market liquidity of the introduction of a penalty for high order-to-trade ratios (OTRs), implemented by the Italian Stock Exchange to curtail high-frequency quote submission. We find that the fee is associated with a collapse in the quoted depth of the stocks that make up the bulk of trading in Italian equities and with an increase in price impacts of trading across the treated stocks. Spreads do not change, however. Stocks from a pan-European control sample show no such liquidity changes. Thus, the Italian OTR fee had the effect of making Italian stocks markets more shallow and less resilient. Large stocks are more severely affected than midcaps. We also find evidence of a limited decrease in turnover. Consolidated liquidity, constructed by aggregating across all electronic trading venues for these stocks, decreases just like that on the main exchange. Thus, liquidity was not simply diverted from the main exchange, it was reduced in aggregate.     

Time to Slow Down for High-Frequency Trading? Lessons from Artificial Markets

In this paper, we focus on the French cancel order tax implemented on 1 August 2012. We question the effectiveness of the modified tax with no exemptions and we analyze its impact on market quality, measured by liquidity, volatility and efficiency. Additionally, this paper raises the question whether this tax leads to a reduction of high-frequency trading (HFT) activities and a decline in trading volume. Based on our findings we report that introduction of cancel order tax only slightly reduces HFT activities, but it significantly affects market liquidity, increases market volatility and leads to deteriorating market efficiency. We conclude that it is difficult to dissuade investors from entering into unproductive trades and eliminate negative outputs of HFT (such as price manipulations) through tax, without altering the benefits of HFT like liquidity provision and efficient price discovery.     

Quote-based competition and trade execution costs in NYSE-listed stocks

This study examines quotations, order routing, and trade execution costs for seven markets that compete for orders in large-capitalization NYSE-listed stocks. The competitiveness of quote updates from each market varies with measures of the profitability of attracting additional order and with volatility and inventory measures. The probability of a trade executing on each market increases when the market posts competitive quotes. Execution costs for non-NYSE trades when the local market posts competitive (non-competitive) quotes are virtually the same (substantially exceed) costs for matched NYSE trades. Collectively, these results imply a significant degree of quote-based competition for order flow and are consistent with off-NYSE liquidity providers using competitive quotations to signal when they are prepared to give better-than-normal trade executions.     

Return Volatility,Trading Imbalance and the Information Content of Volume

In this paper, we examine the relationship between volume and return volatility using the transaction data. We introduce transaction and volume imbalance measures to capture the information content of trades. These two information measures are shown to have a strong explanatory power for return volatility and contain incremental information about the asset values over and above that conveyed by the size and frequency of trades. Also, return volatility is significantly correlated with the percentage of trading volume taking place at NYSE. This result suggests that NYSE trades are more informative and contribute more to price discovery. There is evidence that price discovery concentrates in more heavily traded stocks, particularly the Dow Jones Stocks. Finally, return volatility is found to be persistent at the intraday level. The persistence level is higher for less frequently traded stocks. Return volatility also exhibits temporal variations. In particular, return volatility is significantly higher in the opening half-hour for less frequently traded stocks. Thus, stocks with different frequencies of trades may follow different volatility processes.     

Option market liquidity: Commonality and other characteristics

This study examines option market liquidity using Ivy DB's OptionMetrics data. We establish convincing evidence of commonality for various liquidity measures based on the bid–ask spread, volumes, and price impact. The commonality remains strong even after controlling for the underlying stock market's liquidity and other liquidity determinants such as volatility. Smaller firms and firms with a higher volatility exhibit stronger commonalities in option liquidity. Aside from commonality, we also uncover several other important properties of the option market's liquidity. First, information asymmetry plays a much more dominant role than inventory risk as a fundamental driving force of liquidity. Second, the market-wide option liquidity is closely linked to the underlying stock market's movements. Specifically, the options liquidity responds asymmetrically to upward and downward market movements, with calls reacting more in up markets and puts reacting more in down markets.