Switching to Continuous Trading and its Impact on Return Behavior and Volume of Trade

The study investigates changes that occurred following the transfer of 29 stocks in Israel from a single daily auction to a more continuous-like trading system. On average, the standard deviation of daily market model residuals decreased, and trading volumes increased. This suggests that continuous trading promotes end-of-day price efficiency and better suits investors' trading-mechanism preferences. An interesting phenomenon observed is that the noisiness of end-of-day prices decreases immediately after the transfer, while volume rises only later on. This constitutes evidence that adding rounds of trade contributes to price accuracy even when trading volumes do not increase.     

High frequency trading and the new market makers

This paper characterizes the trading strategy of a large high frequency trader (HFT). The HFT incurs a loss on its inventory but earns a profit on the bid–ask spread. Sharpe ratio calculations show that performance is very sensitive to cost of capital assumptions. The HFT employs a cross-market strategy as half of its trades materialize on the incumbent market and the other half on a small, high-growth entrant market. Its trade participation rate in these markets is 8.1% and 64.4%, respectively. In both markets, four out of five of its trades are passive i.e., its price quote was consumed by others.     

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.     

The diversity of high-frequency traders

The regulatory debate concerning high-frequency trading (HFT) emphasizes the importance of distinguishing different HFT strategies and their influence on market quality. Using data from NASDAQ-OMX Stockholm, we compare market-making HFTs to opportunistic HFTs. We find that market makers constitute the lion's share of HFT trading volume (63–72%) and limit order traffic (81–86%). Furthermore, market makers have higher order-to-trade ratios and lower latency than opportunistic HFTs. In a natural experiment based on tick size changes, we find that the activity of market-making HFTs mitigates intraday price volatility.     

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.     

Liquidity commonality and high frequency trading: Evidence from the French stock market

High frequency trading (HFT) depends on sophisticated algorithms to closely monitor price changes across securities. Theory predicts this technological advantage should translate into market-wide liquidity co-variation, by transmitting information-based liquidity shocks. Using a dataset of orders and trades from the French stock market, we investigate whether HFT algorithms constitute a source of systematic liquidity risk. We demonstrate that, across securities, the liquidity offered by high frequency traders is significantly less diverse than that of traditional traders; this finding is in line with the cross-asset learning hypothesis. The excessive co-movement in liquidity is also partly explained by common market making rules. In periods of increased market stress, we find HFT, designated market making, and order size to be important sources of liquidity commonality. Our results have policy implications for market regulators in Paris, suggesting the inclusion of maximum spread-limit rules in market making contracts will reduce the possibility of liquidity drying up when markets are in turmoil.     

Pick Your Poison—Fragmentation or Market Power? An Analysis of RegNMS,High Frequency Trading,and Securities Market Structure

The publication of Michael Lewis's has intensified an already contentious debate over high frequency trading (HFT). But the causes that have given rise to HFT are more complicated—and the general economic consequences far more positive—than at least the popular accounts (including Lewis's own) of the book would suggest. While directing much of its attention to the powerful computers and “predatory” potential of HFT, the “media” version of Lewis's book has all but ignored the fundamental driver of such activity: the implementation in 2007 of SEC Regulation NMS, which required all exchanges to direct their orders to the exchanges with the best prices. Before RegNMS, U.S. equity trading was largely dominated by NYSE and Nasdaq. The major exchanges' effective “ownership” of their order flow gave exchange specialists significant edges in trading. RegNMS has resulted in the proliferation of competing stock exchanges, which in turn has dramatically reduced both trading costs and the economic franchise value enjoyed by institutional traders associated with the previously dominant exchanges. The balance of evidence strongly indicates that the cost of trading has declined for retail traders and investors. The big losers have been the previously advantaged wholesale traders. HFT is simply one of the outcomes of the new, more competitive trading environment created by Regulation NMS.     

A note on the relationship between high-frequency trading and latency arbitrage

We develop three artificial stock markets populated with two types of market participants — HFT scalpers and aggressive high frequency traders (HFTrs). We simulate real-life trading at the millisecond interval by applying Strongly Typed Genetic Programming (STGP) to real-time data from Cisco Systems, Intel and Microsoft. We observe that HFT scalpers are able to calculate NASDAQ NBBO (National Best Bid and Offer) at least 1.5 ms ahead of the NASDAQ SIP (Security Information Processor), resulting in a large number of latency arbitrage opportunities. We also demonstrate that market efficiency is negatively affected by the latency arbitrage activity of HFT scalpers, with no countervailing benefit in volatility or any other measured variable. To improve market quality, and eliminate the socially wasteful arms race for speed, we propose batch auctions in every 70 ms of trading.     

Predictable Sports Sentiment and Local Trading

We examine whether predictable outcomes of the National Basketball Association playoff games can generate increased trading of firms headquartered in the geographic area of the participating teams. We find statistically significant increased trading before games and this effect is more pronounced and persistent for games with more predictable outcomes, for predictable losses more than wins, and for more critical games. We also find that this effect is more pronounced for firms that are more vulnerable to shifts in investor sentiment and we find weak evidence that trading leads to price effects. The results are consistent with the hypothesis that predictable sentiment can drive investor behavior.     

The fall of high-frequency trading: A survey of competition and profits

We investigate high-frequency trading (HFT) strategies, inventorying the strategies already studied in the literature and introducing innovative strategies detected by private institutional research. To this end, we expand the existing classification, and we offer names for new categories. In a complementary but original manner, we introduce counter reactions from professional traders in response to HFT predatory strategies. These human answers reverse the usual framework of competition between high-frequency traders (HFTs) and low frequency traders (LFTs) and also widen this cadre to HFTs algos (predators) versus execution algos.This survey notes that a continuous increase in competition, between high-speed trading algorithms themselves through predatory strategies and from professional human traders adapting and building adequate responses has made the business more difficult and has led to shrinking profits for HFT. In the end, we believe that excessive competition and a change in the current regulation (favorable to HFT) could kill the goose that laid the golden egg.     

The optimal bid-ask price strategies of high-frequency trading and the effect on market liquidity

We propose a model for determining the optimal bid-ask spread strategy by a high-frequency trader (HFT) who has an informational advantage and receives information about the true value of a security. We employ an information cost function that includes volatility and the volume of the asset. Subsequently, we characterize the optimal bid-ask price strategies and obtain a stable bid-ask spread. We assume that orders submitted by low-frequency traders (LFTs) and news events arrive at the market with Poisson processes. Additionally, our model supports the trading of the two-sided quote in one period. We find that more LFTs and a higher exchange latency both hurt market liquidity. The HFT prefers to choose a two-sided quote to gain more profits while cautiously chooses a one-sided quote during times of high volatility. The model generates some testable implications with supporting empirical evidence from the NASDAQ-OMX Nordic Market.     

Does high frequency algorithmic trading matter for non-AT investors?

The extant literature has typically measured the impact of high frequency algorithmic trading (HFT) on short term outcomes, in seconds or minutes. We focus on outcomes of concern for longer term non-algorithm investors. We find in some cases HFT increases volatility arising from news relating to fundamentals. Furthermore HFT is associated with the transmission of that volatility across industries, and that transmission is based on short term correlations. Finally, we find that the period since the introduction of algorithmic trading (AT) has seen increases in both the variances and covariances of return volatility in most industries. However increases in the variances has not been uniform in that it has fallen sharply in a few industries. The magnitudes are such that, overall, AT has coincided with reduced return volatility variance.     

Liquidity skewness in the London Stock Exchange

We study liquidity on the London Stock Exchange. We find that the average bid-ask spread declines, but that the skewness of the spread increases. These results are robust to firm size, trading volume and price level. Our findings hold when the bid-ask spread is estimated utilising high frequency data. We find that the bid-ask spread prior to earnings announcements dates is significantly higher than that of post earnings announcements, suggesting that asymmetric information has driven the increase in liquidity skewness. We also find that the effect of earnings announcements is more pronounced in the 2007 global financial crisis, consistent with the notion that extreme market downturns amplify asymmetric information. Our overall evidence also implies that increased competition and transparent trading environments limit market makers' abilities to cross-subsidize bid-ask spreads between periods of high and low levels of asymmetric information.     

Trading Halts and Price Discovery

Trading halts have their proponents and opponents. Recent literature has examined the benefits of halts, if any, by studying the consequences of halts on order flow and price volatility. This study complements existing literature by examining the consequences of trading halts on price discovery. Our results indicate that stock price adjustments surrounding trading halts are conditioned on the underlying event that caused the halt. The weighted price contributions of all subsamples are concentrated in the halt period and some subsamples show significant price contribution in the pre-halt period as well. We find minimal evidence of price discovery continuing after the halt is removed. In addition, cross-sectional analysis shows that price discovery in the pre-halt and halt periods are more pronounced for larger firms and for firms with specific news events.      

Price dispersion in OTC markets: A new measure of liquidity

In this paper, we model price dispersion effects in over-the-counter (OTC) markets to show that, in the presence of inventory risk for dealers and search costs for investors, traded prices may deviate from the expected market valuation of an asset. We interpret this deviation as a liquidity effect and develop a new liquidity measure quantifying the price dispersion in the context of the US corporate bond market. This market offers a unique opportunity to study liquidity effects since, from October 2004 onwards, all OTC transactions in this market have to be reported to a common database known as the Trade Reporting and Compliance Engine (TRACE). Furthermore, market-wide average price quotes are available from Markit Group Limited, a financial information provider. Thus, it is possible, for the first time, to directly observe deviations between transaction prices and the expected market valuation of securities. We quantify and analyze our new liquidity measure for this market and find significant price dispersion effects that cannot be simply captured by bid-ask spreads. We show that our new measure is indeed related to liquidity by regressing it on commonly-used liquidity proxies and find a strong relation between our proposed liquidity measure and bond characteristics, as well as trading activity variables. Furthermore, we evaluate the reliability of end-of-day marks that traders use to value their positions. Our evidence suggests that the price deviations from expected market valuations are significantly larger and more volatile than previously assumed. Overall, the results presented here improve our understanding of the drivers of liquidity and are important for many applications in OTC markets, in general.     

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 effect of liquidity and arbitrage on the price efficiency of Chinese ETFs

We study the potential factors that determine the large and persistent price deviations in Chinese equity exchange-traded funds (ETFs). Our results suggest that ETF liquidity and arbitrage activity are positively correlated with ETF price efficiency, and the relation is more pronounced with higher institutional ownership. We also evaluate the effect of two exogenous shocks in the Chinese market. Using a policy change that added market makers to ETFs on the Shenzhen Stock Exchange (SZSE) and Shanghai Stock Exchange (SSE), we find that market makers improve price efficiency and that the impact is stronger for ETFs with lower liquidity. We also exploit a change in trading rules on the SZSE and show that the relaxation of arbitrage restrictions improves price efficiency. Altogether, these findings provide evidence that lack of liquidity, due to the unique market structure and regulations of the Chinese market, contributes to price inefficiency of Chinese ETFs.     

News Trading and Speed

We compare the optimal trading strategy of an informed speculator when he can trade ahead of incoming news (is “fast”), versus when he cannot (is “slow”). We find that speed matters: the fast speculator's trades account for a larger fraction of trading volume, and are more correlated with short‐run price changes. Nevertheless, he realizes a large fraction of his profits from trading on long‐term price changes. The fast speculator's behavior matches evidence about high‐frequency traders. We predict that stocks with more informative news are more liquid even though they attract more activity from informed high‐frequency traders.     

High-frequency trading and market quality: The case of a “slightly exposed” market

Impacts of high-frequency trading (HFT) on market quality and various actors have been broadly studied. However, what happens when HFT is not a prominent figure in a market remains relatively unexplored. The paper seeks to answer this question focusing on 30 blue chip stocks in an emerging market, Borsa Istanbul, through Dec 2015 to Mar 2017. Despite a low share in the overall activity, HFT has observable effects, i.e. liquidity provision by non-HFT traders significantly reduces with HFT. Moreover, HFT generates profits on both positive and negative return days. Yet, HFT activity does not have an impact on volatility. These findings raise concerns regarding HFT and show potential externalities are not specific to the markets with HFT dominance.     

Transaction duration and asymmetric price impact of trades—Evidence from Australia

This paper investigates the asymmetric price impact of buyer and seller initiated trades and the informational role of the trade duration. Using trade data from the Australian Stock Exchange (ASX), our results indicate that buyer initiated trades increase the ask price more than the bid price, and seller initiated trades decrease the bid price more than the ask price. The transaction duration is modeled in a Box-Cox ACD framework. The unexpected portion of the duration is found to play a more significant role in causing price impact in both purchases and sales than the expected duration. A trade shortly after the previous trade results in higher price impact than one after a long period. We found evidence that increased trading activity is associated with larger price impact, therefore implying a higher degree of information-based trading.