A change in market microstructure: the switch to electronic screen trading on the New Zealand stock exchange

The New Zealand Stock Exchange (NZSE) switched from open outcry trading to an electronic screen trading system on June 24, 1991. The change was made by the members of the exchange to improve the trading system and to reduce costs. This paper investigates empirically whether improvement was achieved through a reduction in transaction costs. The tests and results focus on order-flow migration to the exchange from alternative execution locations and changes in bid-ask spreads. On balance, we conclude that transaction costs have declined.     

Tick size, the compass rose and market nanostructure

Economists have begun using methods borrowed from the physical sciences to search for non-linearities in economic and financial data. The so-called phase portrait from chaos theory, in which the values of a time-series are plotted against their delayed values, is one of the techniques employed for this purpose. It has recently been shown, however, that when returns on traded assets are plotted in this manner in two- or three-dimensional space, surprising patterns arise which seriously distort the conclusions that can be drawn about the underlying data. These patterns – which resemble a compass rose – reflect the microstructure of the market or, more specifically, the finite size of the “ticks” by which prices can change in a market.The present paper clarifies the reason for this phenomenon. It is shown that within the microstructure there exists nanostructure that becomes visible only when the computations are performed without approximation. High frequency data from a foreign exchange market are used to illustrate this phenomenon.     

Order price clustering,size clustering,and stock price movements: Evidence from the Taiwan Stock Exchange

This paper examines order price clustering, size clustering, and stock price movements in an active emerging country’s equities market, the Taiwan Stock Exchange (TWSE). We first explore the relationships between investor types and order price/size clustering. Next, we investigate the joint determinants of the round-price and round-size orders based on daily and intraday data analyses. Finally, we look at the relationships among investor types, round prices/sizes, and stock price movements. The findings reveal that all investor types exhibit price and size clustering phenomena. After controlling for other factors, institutional investors have a relatively lower level of size clustering when compared with individuals. Our results confirm the price resolution hypothesis, whereby the levels of daily price and size clustering increase with firm risk, and the probability of a round-price or round-size order increases as transitory volatility rises. Partially consistent with the negotiation hypothesis, the probability of a round-price or round-size order increases when order competition turns fiercer. Mutual funds exhibit stronger quarter-end and session-end effects than do other investors. We also detect strategic trading behaviors, showing that the probability of an order with a tail price of one (nine) increases when buy (sell) order competition is fiercer. Lastly, stocks with mutual funds’ round-price or round-size buy (sell) orders experience rising (falling) future stock returns.     

网络关注度对我国股票市场信息不对称程度的影响

以2013-2016年391支股票的360搜索指数中的投资者关注度和媒体关注度的指数作为网络关注度度量指标,同时基于股票市场交易数据采用多种信息不对称计算方法构建了信息不对称性度量指标,并进一步构造了信息不对称主成分综合指标。通过建立横截面回归模型,探究投资者和媒体关注度对我国股票市场的信息不对称程度的影响作用。实证分析及稳健性检验结果表明：投资者关注度的增加会减少知情交易及信息不透明程度,从而减少了股票市场的信息不对称程度,提高了股票市场的流动性;媒体关注度对不同的信息不对称性度量指标的影响存在着不一致性。本研究通过探索投资者关注度及媒体关注度在新兴市场中的应用,对于我国证券市场监管层制定政策以及对于普通投资者优化投资策略都具有重要的参考意义。     

上海证券交易所开设“高增长板”问题探讨

科技型中小企业对上海的经济发展贡献率不断增加,设立上海“高增长板”可以解决科技型中小企业（特别是长三角地区）的融资问题以及风险投资的退出渠道问题,完善上海资本市场,有助于实施将上海建设成国际金融中心的国家战略。本文通过分析科技型中小企业面临融资难的困境,研究了上海建立“高增长板”的必要性,并初步探讨了上海“高增长板”与深圳创业板、香港创业板的定位和关系处理问题。     

The effect of stock splits on clientele: Is tick size relevant?

We explore whether the relation between stock splits and clientele is driven by binding tick sizes. We find little evidence that firms adjusted prices to maintain similarly binding tick sizes as the NYSE reduced tick sizes. Furthermore, though splits that increase the extent to which tick sizes are binding are associated with greater increases in spreads, these splits experience similar changes in measures related to clientele, including trade size, breadth of individual and institutional ownership, and analyst following. We find little evidence supporting theories, such as spread-induced sponsorship, that rely on binding tick sizes to link splits and clientele.     

Performance-weighted ensembles of random forests for predicting price impact

For any large player in financial markets, the impact of their trading activity represents a substantial proportion of transaction costs. This paper proposes a novel machine learning algorithm for predicting the price impact of order book events. Specifically, we introduce a prediction system based on ensembles of random forests (RFs). The system is trained and tested on depth-of-book data from the BATS and Chi-X exchanges and performance is benchmarked using ensembles of other popular regression algorithms including: linear regression, neural networks and support vector regression. The results show that recency-weighted ensembles of RFs produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks. Feature importance ranking is used to explore the significance of various market features on the price impact, finding them to be highly variable through time. Finally, a novel procedure for extracting the directional effects of features is proposed and used to explore the features most dominant in the price formation process.     

Do Japanese candlesticks help solve the trader’s dilemma?

In this paper, we investigate whether Japanese candlesticks can help traders to find the best trade-off between market timing and market impact costs. Based on fixed-effect panel regressions on a sample of 81 European stocks, we show that implicit transaction costs are better characterized by using specific Japanese candlesticks patterns. Although market timing costs are not lower when Hammer-like and Doji configurations occur, market impact costs are significantly lower when and after a Doji structure occurs. We further check the potential gains through order submission simulations and find that submission strategies based on the occurrence of Doji result in significantly lower market impact cost than random submission strategies. These findings are of great interest for investors who look for occasional liquidity pools to execute their orders inexpensively such as institutional traders or hedgers.     

Real-time market microstructure analysis: online transaction cost analysis

Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of causes that lie behind poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. The common acronym for investigating the causes of bad and good performance of trading is transaction cost analysis Rosenthal [Performance Metrics for Algorithmic Traders, 2009]). Automated algorithms take care of most of the traded flows on electronic markets (more than 70% in the US, 45% in Europe and 35% in Japan in 2012). Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance (like in Almgren and Chriss [J. Risk, 2000, 3(2), 5–39]), a stochastic control (e.g. Guéant et al. [Math. Financ. Econ., 2013, 7(4), 477–507]), an impulse control (see Bouchard et al. [SIAM J. Financ. Math., 2011, 2(1), 404–438]) or a statistical learning (as used in Laruelle et al. [Math. Financ. Econ., 2013, 7(3), 359–403]) viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions that influenced its efficiency or inefficiency. For a given set of characteristics describing the market context, selected by a practitioner, we first show how a set of additional derived explanatory factors, called anomaly detectors, can be created for each market order (following for instance Cristianini and Shawe-Taylor [An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 2000]). We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts (i.e. have influence, in the sense of predictive power or information defined in Basseville and Nikiforov [Detection of Abrupt Changes: Theory and Application, 1993], Shannon [Bell Syst. Tech. J., 1948, 27, 379–423] and Alkoot and Kittler [Pattern Recogn. Lett., 1999, 20(11), 1361–1369]) which of the orders are underperforming while calculating the predictive power of this explanatory factor set. Armed with this information, which we call influence analysis, we intend to empower the order monitoring user to take appropriate action on any affected orders by re-calibrating the trading algorithms working the order through new parameters, pausing their execution or taking over more direct trading control. Also we intend that use of this method can be taken advantage of to automatically adjust their trading action in the post trade analysis of algorithms.     

论证券交易所补充责任制度的创设——以投资者民事权利保护为基点

证券交易所补充责任制度的创设既防止了对证券交易所的过度归责倾向,又保护了投资者的合法权利.目前,我国学术界、实务界和司法界对该制度很少论及.本文对证券交易所补允责任的含义及构成要件、性质、归责原则、效力以及因果关系和举证责任等问题进行论述和探讨,以期完善我国投资者民事权利保护的内容.     

The Impact of the Reduction in Tick Increments in Major U.S. Markets on Spreads,Depth, and Volatility

This study presents an analysis of the impact of the introduction of quotes in sixteenths of a dollar on the AMEX, Nasdaq, and NYSE in mid-1997 on select market characteristics such as spreads, effective spreads, quoted depth, and volume. The findings of the study document reductions in the bid-ask spread, effective spread, and a statistically significant increase in the number of quotes. Interestingly, we find that liquidity, as measured by the total depth at the bid and ask, declines significantly on the AMEX and NYSE, but increases on the Nasdaq. Trading volume increases on the NYSE, but remains unchanged for the AMEX and Nasdaq. We also find that the proportion of even-increment quotes is a relevant factor affecting percentage spreads for Nasdaq both before and after and for the NYSE only after the change in quoting increments.     

Liquidating large security positions strategically: a pragmatic and empirical approach

This article investigates static liquidation strategies for large security positions in illiquid markets. Under the assumption that the liquidation horizon is given exogenously, a discretionary liquidity trader solves for the optimal sales trajectory so as to maximize an objective function that considers the expected liquidation revenues and their standard deviation. Although existing literature tends to focus on theoretical aspects with the intention of deriving closed-form solutions for special types of market impact functions, this article considers a framework that is able to capture important empirical phenomena in the stock market, such as the intraday U-shaped pattern of price impact and the resiliency of the order book. The new model is very flexible since it allows for liquidation intervals of varying length and foregoes the assumption of constant speed of trading. Examples with real-world order book data demonstrate how the setup can be implemented numerically and provide deeper insight into relevant properties of the model.     

The Toronto Stock Exchange preopening session

This paper documents order submission strategies during the Toronto Stock Exchange preopening session. I find that the registered trader (RT) actively participates in the market opening, even though he cannot set the opening price directly, and has no apparent informational advantage. RT opening trades are profitable, moderate overnight price changes, and appear to be motivated, in part, by inventory adjustment concerns. I examine interlisted stocks that simultaneously open for trading under two different mechanisms and show how the comparative levels of pre-trade market transparency of each exchange impacts RT profits and participation.     

Commonality in liquidity: Evidence from the Stock Exchange of Thailand

This study examines commonality in liquidity of the Stock Exchange of Thailand (SET) using a limited order book data from 1996 to 2003. Strong evidence is found for market-wide commonality in liquidity, which prevails across several liquidity measurements. Industry-wide commonality is found to be stronger than market-wide commonality in liquidity. However, we do not find a market-wide correlated liquidity supply imbalance. There is evidence that indicates a fall in individual liquidity on Monday and after a day with a positive return.     

Price Movers on the Stock Exchange of Thailand: Evidence from a Fully Automated Order‐Driven Market

This study examines which trade sizes move stock prices on the Stock Exchange of Thailand (SET), a pure limit order market, over two distinct market conditions of bull and bear. Using intraday data, the study finds that large‐sized trades (i.e., those larger than the 75th percentile) account for a disproportionately large impact on changes in traded and quoted prices. The finding remains even after it has been subjected to a battery of robustness checks. In contrast, the results of studies conducted in the United States show that informed traders employ trade sizes falling between the 40th and 95th percentiles ( Barclay and Warner, 1993 ; Chakravarty, 2001 ). Our results support the hypothesis that informed traders in a pure limit order market, such as the SET, where there are no market makers, also use larger‐size trades than those employed by informed traders in the United States.     

Market microstructure: A review from down under

The early automation of the Australian and New Zealand financial markets provided researchers with access to high‐frequency data to undertake extensive empirical market microstructure research. We use this anniversary edition of Accounting and Finance to review some of this research and to discuss the development of the Australian and New Zealand markets since their automation. We identify issues currently facing the markets and highlight potential areas for future research. The paper also provides a review of market microstructure theory on inventory control models and asymmetric information models.     

Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange

This paper investigates the use of tick-by-tick data for intraday market risk measurement. We propose a method to compute an Intraday Value at Risk based on irregularly spaced high-frequency data and an intraday Monte Carlo simulation. A log-ACD–ARMA–EGARCH model is used to specify the joint density of the marked point process of durations and high-frequency returns. We apply our methodology to transaction data for three stocks actively traded on the Toronto Stock Exchange. Compared to traditional techniques applied to intraday data, our methodology has two main advantages. First, our risk measure has a higher informational content as it takes into account all observations. On the total risk measure, our method allows for distinguishing the effect of random trade durations from the effect of random returns, and for analyzing the interaction between these factors. Thus, we find that the information contained in the time between transactions is relevant to risk analysis, which is consistent with predictions from asymmetric-information models in the market microstructure literature. Second, once the model has been estimated, the IVaR can be computed by any trader for any time horizon based on the same information and with no need of sampling the data and estimating the model again when the horizon changes. Backtesting results show that our approach constitutes reliable means of measuring intraday risk for traders who are very active in the market.     

How efficiency shapes market impact

We develop a theory for the market impact of large trading orders, which we call metaorders because they are typically split into small pieces and executed incrementally. Market impact is empirically observed to be a concave function of metaorder size, i.e. the impact per share of large metaorders is smaller than that of small metaorders. We formulate a stylized model of an algorithmic execution service and derive a fair pricing condition, which says that the average transaction price of the metaorder is equal to the price after trading is completed. We show that at equilibrium the distribution of trading volume adjusts to reflect information, and dictates the shape of the impact function. The resulting theory makes empirically testable predictions for the functional form of both the temporary and permanent components of market impact. Based on the commonly observed asymptotic distribution for the volume of large trades, it says that market impact should increase asymptotically roughly as the square root of metaorder size, with average permanent impact relaxing to about two-thirds of peak impact.     

Determinants of bid and ask quotes and implications for the cost of trading

Financial transaction costs are time varying. This paper proposes a model that relates transaction cost to characteristics of order flow. We obtain qualitatively consistent model results for different stocks and across different time periods. We find that an unusual excess of buyers (sellers) relative to sellers (buyers) tends to increase the ask (bid) price. Hence, the ask and bid components of spread change asymmetrically about the efficient price. For a fixed order imbalance surprise these effects are muted when unanticipated total volume is high. Unexpected high volatility in the transaction price process tends to widen the spread symmetrically about the efficient price. Our findings are consistent with predications from market microstructure theory that the cost of market making should depend on both the risk of trading with better-informed traders and inventory risk. We also find that order flow surprises have a significant impact on the efficient price and can also explain a substantial amount of persistence in the volatility of the efficient price. This dependence does not violate the efficient market hypothesis since the surprises, by definition, are not predictable.     

The effect of tick size on managerial learning from stock prices

We investigate the effect of tick size, a key feature of market microstructure, on managerial learning from stock prices. Using a randomized controlled tick-size experiment, the 2016 Tick Size Pilot Program, we find that a larger tick size increases a firm's investment sensitivity to stock prices, suggesting that managers glean more new information from stock prices to guide their investment decisions as the tick size increases. Consistently, we also find that changes in managerial beliefs, as reflected in adjustments of forecasted capital expenditures, respond more strongly to market feedback under a larger tick size. Additional evidence suggests the following mechanism through which tick size affects managerial learning: a larger tick size reduces algorithmic trading, in turn encouraging fundamental information acquisition. Increased fundamental information acquisition generates incremental information about growth opportunities, macroeconomic factors, and industry factors, with respect to which the market has a comparative information advantage over management.