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.     

Is market impact a measure of the information value of trades? Market response to liquidity vs. informed metaorders

We examine a data-set of institutional trades where approximately one-fourth of the trades were labelled as having been created for cash flow purposes. We aggregate near-overlapping trades into metaorders and consider information, market impact and metaorder size. We find that during the execution, the functional form and scale of market impact are similar for cash flows and other metaorders. Differences arise in the price reversion following the end of a metaorder. For cash flows, presumed to have no true information content, the impact reverts almost completely on average in two to five days. For other metaorders, we find that reversion erases about one-third of the peak impact: for each size, price reverts to the average execution price, leaving no immediate profits after accounting for trading costs. Observed mark-to-market profits on metaorders that aggregate multiple portfolio manager orders, new metaorders and Nasdaq-listed stocks suggest that these metaorders are more informed than the average. Vice-versa, we find mark-to-market losses are more likely to occur on cash flows, metaorders in large-cap stocks, metaorders that follow momentum and additions to a prior position seeking to take advantage of an improved price. The complete price reversion for cash flows suggests that the mechanical permanent impact that is considered in no-quasi-arbitrage arguments would be much smaller than the information in typical institutional metaorders.     

Market impact as anticipation of the order flow imbalance

In this paper, we assume that the permanent market impact of metaorders is linear and that the price is a martingale. Those two hypotheses enable us to derive the evolution of the price from the dynamics of the flow of market orders. For example, if the market order flow is assumed to follow a nearly unstable Hawkes process, we retrieve the apparent long memory of the flow together with a power law impact function, which is consistent with the celebrated square root law. We also link the long memory exponent of the sign of market orders with the impact function exponent. One of the originalities of our approach is that our results are derived without assuming that market participants are able to detect the beginning of metaorders.     

The non-linear market impact of large trades: evidence from buy-side order flow

We perform an empirical study of a set of large institutional orders executed in the US equity market. Our results validate the hidden order arbitrage theory proposed by Farmer et al. [How efficiency shapes market impact, 2013] of the market impact of large institutional orders. We find that large trades are drawn from a distribution with tail exponent of roughly 3/2 and that market impact approximately increases as the square root of trade duration. We examine price reversion after the completion of a trade, finding that permanent impact is also a square root function of trade duration and that its ratio to the total impact observed at the last fill is roughly 2/3. Additionally, we confirm empirically that the post-trade price reverts to a level consistent with a fair pricing condition of Farmer et al. (2013 Farmer, D., Gerig, A., Lillo, F. and Waelbroeck, H., How efficiency shapes market impact, 2013. Available online at: http://arxiv.org/abs/1102.5457 [Google Scholar]). We study the relaxation dynamics of market impact and find that impact decay is a multi-regime process, approximated by a power law in the first few minutes after order completion and subsequently by exponential decay.     

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.     

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.     

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.     

On derivatives with illiquid underlying and market manipulation

In illiquid markets, option traders may have an incentive to increase their portfolio value by using their impact on the dynamics of the underlying. We provide a mathematical framework to construct optimal trading strategies under market impact in a multi-player framework by introducing strategic interactions into the model of Almgren [Appl. Math. Finance, 2003, 10(1), 1–18]. Specifically, we consider a financial market model with several strategically interacting players who hold European contingent claims and whose trading decisions have an impact on the price evolution of the underlying. We establish the existence and uniqueness of equilibrium results for risk-neutral and CARA investors and show that the equilibrium dynamics can be characterized in terms of a coupled system of possibly nonlinear PDEs. For the linear cost function used by Almgren, we obtain a (semi) closed-form solution. Analysing this solution, we show how market manipulation can be reduced.     

Cross-impact and no-dynamic-arbitrage

We extend the ‘No-dynamic-arbitrage and market impact’-framework of Gatheral [Quant. Finance, 2010, 10(7), 749–759] to the multi-dimensional case where trading in one asset has a cross-impact on the price of other assets. From the condition of absence of dynamical arbitrage we derive theoretical limits for the size and form of cross-impact that can be directly verified on data. For bounded decay kernels we find that cross-impact must be an odd and linear function of trading intensity and cross-impact from asset i to asset j must be equal to the one from j to i. To test these constraints we estimate cross-impact among sovereign bonds traded on the electronic platform MOT. While we find significant violations of the above symmetry condition of cross-impact, we show that these are not arbitrageable with simple strategies because of the presence of the bid-ask spread.     

Price impact and bursts in liquidity provision

This paper analyses brief episodes of high-intensity quote turnover and revision—‘bursts’ in quotes—in the US equity market. Such events occur very frequently, several hundred times a day for actively traded stocks. We find significant price impact associated with these market maker initiated events, about five times higher than during non-burst periods. Bursts in quotes are concurrent with short-lived structural breaks in the informational relationship between market makers and market takers. During bursts, market makers no longer passively impound information from order flow into quotes—a departure from the traditional market microstructure paradigm. Rather, market makers significantly impact prices during bursts in quotes. Further analysis shows that there is asymmetry in adverse selection between the bid and ask sides of the limit order book and only a sub-population of market makers enjoys an informational advantage during bursts. Market makers on the side opposite the burst suffer elevated adverse selection costs, while market makers on the side of the burst realize positive spread, irrespective of the order flow direction. Our results call attention to the need for a new microstructure perspective in understanding modern high-frequency limit order book markets and the quote manipulation strategies at the disposal of the fast market makers.     

Foreign Shocks on Chilean Financial Markets: Spillovers and Comovements Between Bond and Equity Markets

Abstract:

The domestic impact of external shocks will depend on the degree of coupling of domestic assets to foreign markets, but also on the spillovers among assets. The covariance between different types of assets could be affected by new information. Changes in the covariance, for example, could come from a stronger rebalancing between stocks and bonds. Therefore, we will analyze four different assets-government bonds, corporate bonds, money market instruments, and equities-and study the conditional correlation between them. We find that the corporate bond market tends to increase coupling in turbulent times, while the money market decreases coupling. We propose to test international spillovers taking into account a methodology for estimating the conditional mean, variance, and covariance on domestic bond and equity markets, while considering that shocks may have asymmetric effects depending on whether the news is good or bad.     

Have European Stocks become More Volatile? An Empirical Investigation of Idiosyncratic and Market Risk in the Euro Area

We examine the dynamics of idiosyncratic risk, market risk and return correlations in European equity markets using weekly observations from 3515 stocks listed in the 12 euro area stock markets over the period 1974–2004. Similarly to Campbell et al. (2001) , we find a rise in idiosyncratic volatility, implying that it now takes more stocks to diversify away idiosyncratic risk. Contrary to the US, however, market risk is trended upwards in Europe and correlations are not trended downwards. Both the volatility and correlation measures are pro‐cyclical, and they rise during times of low market returns. Market and average idiosyncratic volatility jointly predict market wide returns, and the latter impact upon both market and idiosyncratic volatility. This has asset pricing and risk management implications.     

Stochastic dividend yields and derivatives pricing in complete markets

When an underlying yields a stochastic dividend yield, derivatives with linear payoff at their maturities that are written on this underlying have the following properties: (i) they have a unique price only if markets are complete; (ii) the dynamic strategies that replicate these contingent claims contain hedging components against the state variables in the economy; (iii) the prices of these derivatives will depend upon the dynamics of the market prices of risk even when markets are complete. Within an affine framework, we explicitly price forward and futures contracts with stochastic dividends. We also show that the quantitative impact of assuming that dividends are deterministic when they are actually stochastic is significant. JEL Classification G12 · G13     

Myopic loss aversion,reference point,and money illusion

We use the portfolio selection model presented in He and Zhou [Manage. Sci., 2011, 57, 315–331] and the NYSE equity and US treasury bond returns for the period 1926–1990 to revisit Benartzi and Thaler’s myopic loss aversion theory. Through an extensive empirical study, we find that in addition to the agent’s loss aversion and evaluation period, his reference point also has a significant effect on optimal asset allocation. We demonstrate that the agent’s optimal allocation to equities is consistent with market observation when he has reasonable values of degree of loss aversion, evaluation period and reference point. We also find that the optimal allocation to equities is sensitive to these parameters. We then examine the implications of money illusion for asset allocation. Finally, we extend the model to a dynamic setting.     

The Informational Efficiency of the Equity Market As Compared to the Syndicated Bank Loan Market

The loan market is a hybrid between a public and a private market, comprised of financial institutions with access to private information about borrowing firms. We test whether this is reflected in informationally efficient price formation in the loan market vis-a-vis the equity markets, and reject this private information hypothesis. We also reject a liquidity hypothesis which suggests that equity markets always lead loan markets, despite bank lenders' access to private information, because of greater liquidity in equity markets. We further test, and reject, an asymmetric price reaction hypothesis that states that loan returns are more sensitive to negative information whereas equity returns respond symmetrically to both positive and negative information. We find evidence most consistent with an integrated markets hypothesis that suggests that both the equity and syndicated bank loan markets are highly integrated such that information flows freely across markets. This is particularly true when the equity market makers are also loan syndicate members.      

Macroeconomic Factors Do Influence Aggregate Stock Returns

Stock market returns are significantly correlated with inflationand money growth. The impact of real macroeconomic variableson aggregate equity returns has been difficult to establish,perhaps because their effects are neither linear nor time invariant.We estimate a GARCH model of daily equity returns, where realizedreturns and their conditional volatility depend on 17 macroseries' announcements. We find six candidates for priced factors:three nominal (CPI, PPI, and a Monetary Aggregate) and threereal (Balance of Trade, Employment Report, and Housing Starts).Popular measures of overall economic activity, such as IndustrialProduction or GNP are not represented.     

Is competition beneficial? The case of exchange traded funds

New ETF creation has surged in recent years, giving investors the option to choose from a wide range of similar ETFs within each group of competitors. We identify groups of ETFs that can be considered direct competitors and examine the impact of competition on their market quality. Results show improved market quality measures when competition increases. A change equivalent to going from a monopoly to a highly competitive market results in a 29% decrease in bid–ask spreads, a 72% decrease in illiquidity, and a 52% increase in turnover. However, we find that competition has a differential impact on ETFs according to their market depth. Market quality improves with competition for large or well-performing ETFs, while it worsens for small or under-performing ETFs. A case study on ETFs banned by the SEC in March 2010 further highlights our results in the artificially controlled competitive environment of the moratorium.     

台湾证券柜台交易市场结构及其混合交易模式研究

台湾证券柜台交易市场在促进台湾中小型高科技企业的快速成长和规范非上市公开发行公司的股权报价转让等方面发挥着不可替代的重要作用。本文详细考察了台湾证券柜台交易市场上柜股票和兴柜股票的交易模式,研究表明兴柜市场实行的以分散报价、集中成交的竞争性做市商交易模式具有内在的制度优势,是适宜个人投资者为主的柜台交易市场。台湾柜台交易中心取得成功的关键在于,以满足柜台交易市场交易性需求、流动性需求、波动性需求和透明度需求为基础,通过构建合理的市场结构创造性地引入包括竞价交易机制在内、与市场功能和交易对象的风险特性相适应的混合交易模式。