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.     

Tick size change on the Stock Exchange of Thailand

This paper explores the impact of an exogenous tick size reduction on bid-ask spreads, depths, and trading volume on the Stock Exchange of Thailand (SET). On November 5, 2001, the SET implemented a tick size reduction on stocks priced below THB 25. Even though trading on SET is largely dominated by retail investors, the tick reduction produces similar empirical results found in markets where institutional investors are more dominant. Tick reduction on the SET is associated with declines in spreads, and quoted and accumulated market depths. The study finds no significant change in trading volume due to the reduction.

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.     

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.     

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.     

Dynamic liquidation under market impact

The optimal liquidation problem with transaction costs, which includes a positive fixed cost, and market impact costs, is studied in this paper as a constrained stochastic optimal control problem. We assume that trading is instantaneous and the dynamics of the stock to be liquidated follows a geometric Brownian motion. The solution to the impulse control problem is computed at each time step by solving a linear partial differential equation and a maximization problem. In contrast to results obtained from the static formulation of Almgren and Chriss [J. Risk, 2000 Almgren, R and Chriss, N. 2000. Optimal execution of portfolio transactions. J. Risk, 3: 5–39. [Crossref] , [Google Scholar], 3, 5–39], when risk is not considered, the optimal liquidation strategy from our stochastic control formulation depends on temporary market impact cost and permanent market impact cost parameters. In addition, our computational results indicate the following properties of the optimal execution strategy from the stochastic control formulation. Due to the existence of a no-transaction region, it may not be optimal for some individuals to sell their assets on some trading dates. As the value of the permanent market impact parameter increases, the expected optimal amount liquidated at the terminal time increases. As the value of the quadratic temporary impact cost parameter increases, the expected optimal amount liquidated at trading times tends to be uniform, and the no-transaction region shrinks. In the presence of quadratic temporary market impact costs, in contrast to optimal strategies that result from fixed and/or proportional transaction costs alone, portfolios in the selling region are neither re-balanced into the no-transaction region nor into the sell and no-transaction interface.     

Who moves the stock market in an emerging country – Institutional or retail investors?

By analyzing the dynamic behavior of institutional and retail investors in the Indonesia Stock Exchange using their completed transactions (comprising over 250 million observations), this study highlights that their trading strategies and behavior, in which institutions play a more important role than individuals in the market, are indeed different. Specifically, past trading activities by individual (institutional) investors have significantly affected the current trading behaviors and strategies of individual investors (both investor types). Furthermore, retail (institutional) investors are most likely to perform contrarian (momentum) strategies and trade frequently (infrequently) with small (large) amounts of money and short (long) holding periods.     

Universal features of price formation in financial markets: perspectives from deep learning

Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary relation between order flow history and the direction of price moves. The universal price formation model exhibits a remarkably stable out-of-sample accuracy across a wide range of stocks and time periods. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific.

The universal model—trained on data from all stocks—outperforms asset-specific models trained on time series of any given stock. This weighs in favor of pooling together financial data from various stocks, rather than designing asset- or sector-specific models, as is currently commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations improves forecast accuracy, indicating that there is path-dependence in price dynamics.     

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.     

中国股票市场操纵对市场流动性的影响研究——基于收盘价操纵行为的识别与监测

基于收盘价操纵后股票价格的变动特征,本文构建了收盘价操纵行为的识别方法——尾市价格偏离模型,并利用中国股票市场的分时高频交易数据实现了可疑收盘价操纵行为的监测。进一步,本文采用面板数据回归实证分析了收盘价操纵影响市场流动性的方向、程度和机制。研究结果表明,收盘价操纵会导致股票交易成本上升和流动性下降,这种影响往往在股票市场处于震荡和下跌阶段时更为显著;同时,投资者报价策略趋于保守化是收盘价操纵对市场流动性产生影响的关键因素,而引发投资者调整报价策略的原因可能是股价波动加剧后订单非执行风险的降低。     

Hawkes model for price and trades high-frequency dynamics

We introduce a multivariate Hawkes process that accounts for the dynamics of market prices through the impact of market order arrivals at microstructural level. Our model is a point process mainly characterized by four kernels associated with, respectively, the trade arrival self-excitation, the price changes mean reversion, the impact of trade arrivals on price variations and the feedback of price changes on trading activity. It allows one to account for both stylized facts of market price microstructure (including random time arrival of price moves, discrete price grid, high-frequency mean reversion, correlation functions behaviour at various time scales) and the stylized facts of market impact (mainly the concave-square-root-like/relaxation characteristic shape of the market impact of a meta-order). Moreover, it allows one to estimate the entire market impact profile from anonymous market data. We show that these kernels can be empirically estimated from the empirical conditional mean intensities. We provide numerical examples, application to real data and comparisons to former approaches.