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on Market Microstructure |
| By: | Giancarlo Corsetti (University of Cambridge; Centre for Macroeconomics (CFM)); Romain Lafarguette (International Monetary Fund); Arnaud Mehl (European Central Bank) |
| Abstract: | Focusing on the foreign exchange reaction to macroeconomic announcements, we show that fast trading is positively and significantly correlated with the entropy of the distribution of quoted prices in reaction to news: a larger share of fast trading increases the degree of diversity of quotes in the order book, for given liquidity, order book depth and size of order flows. Exploiting the WM Reuters’ reform of the fixing methodology in February 2015 as a natural experiment, we provide evidence that fast trading raises entropy, rather than reacting to it. While more entropy in quoted prices means noisier information and arguably complicates price discovery from an individual trader’s perspective, we show that, in the aggregate, more entropy actually brings traded prices closer to the random walk hypothesis, and improves indicators of market efficiency and quality of trade execution. We estimate that a 10 percent increase in entropy reduces the negative impact of macro news by over 60% for effective spreads, against over 40% for realized spreads and price impacts. Our findings suggest that the main mechanism by which fast trading may have desirable effects on market performance specifically hinges on enhanced heterogeneity in trading patterns, best captured by entropy. |
| Keywords: | High-Frequency Quoting, Asset Pricing, Macroeconomic News, Market Efficiency, Random Walk, Quality of Trade Execution |
| JEL: | F31 G14 G15 |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:cfm:wpaper:1914&r=all |
| By: | \'Alvaro Cartea; Sebastian Jaimungal; Leandro S\'anchez-Betancourt |
| Abstract: | Latency (i.e., time delay) in electronic markets affects the efficacy of liquidity taking strategies. During the time liquidity takers process information and send marketable limit orders (MLOs) to the exchange, the limit order book (LOB) might undergo updates, so there is no guarantee that MLOs are filled. We develop a latency-optimal trading strategy that improves the marksmanship of liquidity takers. The interaction between the LOB and MLOs is modelled as a marked point process. Each MLO specifies a price limit so the order can receive worse prices and quantities than those the liquidity taker targets if the updates in the LOB are against the interest of the trader. In our model, the liquidity taker balances the tradeoff between missing trades and the costs of walking the book. We employ techniques of variational analysis to obtain the optimal price limit of each MLO the agent sends. The price limit of a MLO is characterized as the solution to a new class of forward-backward stochastic differential equations (FBSDEs) driven by random measures. We prove the existence and uniqueness of the solution to the FBSDE and numerically solve it to illustrate the performance of the latency-optimal strategies. |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1908.03281&r=all |
| By: | Adamantios Ntakaris; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis |
| Abstract: | Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price movement prediction. We focus on a wrapper feature selection method using entropy, least-mean squares, and linear discriminant analysis. We also build a new quantitative feature based on adaptive logistic regression for online learning, which is constantly selected first among the majority of the proposed feature selection methods. This study examines the best combination of features using high frequency limit order book data from Nasdaq Nordic. Our results suggest that sorting methods and classifiers can be used in such a way that one can reach the best performance with a combination of only very few advanced hand-crafted features. |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1907.09452&r=all |
| By: | Marco Di Maggio; Mark L. Egan; Francesco Franzoni |
| Abstract: | Brokers continue to play a critical role in intermediating institutional stock market transactions. More than half of all institutional investor order flow is still executed by high-touch (non-electronic) brokers. Despite the continued importance of brokers, we have limited information on what drives investors' choices among them. We develop and estimate an empirical model of broker choice that allows us to quantitatively examine each investor's responsiveness to execution costs and access to research and order flow information. Studying over 300 million institutional trades, we find that investor demand is relatively inelastic with respect to commissions and that investors are willing to pay a premium for access to top research analysts and order-flow information. There is substantial heterogeneity across investors. Relative to other investors, hedge funds tend to be more price insensitive, place less value on sell-side research, and place more value on order-flow information. Furthermore, using trader-level data, we find that investors are more likely to trade with traders who are located physically closer and are less likely to trade with traders that have misbehaved in the past. Lastly, we use our empirical model to investigate the unbundling of equity research and execution services related to the MiFID II regulations. While under-reporting for the average firm is relatively small (4%), we find that the bundling of execution and research allows some institutional investors to under-report management fees by up to 15%. |
| JEL: | G14 G2 G23 G24 G28 L14 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26147&r=all |
| By: | Mallaburn, David (Bank of England); Roberts-Sklar, Matt (Bank of England); Silvestri, Laura (Bank of England) |
| Abstract: | We study the network structure and resilience of the sterling investment-grade and high-yield corporate bond markets. Using proprietary, transaction-level data, first we analyse the key properties of the trading networks in these markets. We find that the trading networks exhibit a core-periphery structure where a large number of non-dealers trade with a small number of dealers. Consistent with dealer behaviour in the primary market, we find that trading activity is particularly concentrated for newly issued bonds, where the top three dealers account for 45% of trading volume. Second, we test the resilience of these markets to the failure or paralysis of a key dealer, or to bond rating downgrades. We find that whilst the network structure has been broadly stable and the market broadly resilient around bond downgrades over our 2012–2017 sample period, the reliance on a small number of participants makes the trading network somewhat fragile to the withdrawal of a few key dealers from the market. |
| Keywords: | Corporate bond market; financial networks |
| JEL: | G10 G20 |
| Date: | 2019–08–02 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0813&r=all |
| By: | Chung-Han Hsieh; B. Ross Barmish; John A. Gubner |
| Abstract: | Stock trading based on Kelly's celebrated Expected Logarithmic Growth (ELG) criterion, a well-known prescription for optimal resource allocation, has received considerable attention in the literature. Using ELG as the performance metric, we compare the impact of trade execution delay on the relative performance of high-frequency trading versus buy and hold. While it is intuitively obvious and straightforward to prove that in the presence of sufficiently high transaction costs, buy and hold is the better strategy, is it possible that with no transaction costs, buy and hold can still be the better strategy? When there is no delay in trade execution, we prove a theorem saying that the answer is ``no.'' However, when there is delay in trade execution, we present simulation results using a binary lattice stock model to show that the answer can be ``yes.'' This is seen to be true whether self-financing is imposed or not. |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1907.08771&r=all |
| By: | Camilleri, Silvio John; Galea, Francelle |
| Abstract: | Purpose: The main objective of this study is to obtain new empirical evidence about the connections between equity trading activity and five possible liquidity determinants: market capitalisation, dividend yield, earnings yield, company growth, and the distinction between recently-listed firms as opposed to more established ones. Design / Methodology / Approach: We use a sample of 172 stocks from four European markets and estimate models using the entire sample data and different sub-samples to check the relative importance of the above determinants. We also conduct a factor analysis to re-classify the variables into a more succinct framework. Findings: The evidence suggests that market capitalisation is the most important trading activity determinant, and the number of years listed ranks thereafter. Research limitations / implications: The positive relation between trading activity and market capitalisation is in line with prior literature, while the findings relating to the other determinants offer further empirical evidence which is a worthy addition in view of the contradictory results in prior research. Practical implications: This study is of relevance to practitioners who would like to understand the cross-sectional variation in stock liquidity at a more detailed level. Originality / value: The originality of the paper rests on two important grounds: (a) we focus on trading turnover rather than on other liquidity proxies, since the former is accepted as an important determinant of the liquidity generation process, and (b) we adopt a rigorous approach towards checking the robustness of the results by considering various sub-sample configurations. |
| Keywords: | Dividend yield, European equity markets, Factor analysis, Liquidity, Liquidity determinants, Market capitalisation, Newly established firms, Securities markets, Trading activity. |
| JEL: | G10 G12 G15 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95298&r=all |