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on Market Microstructure |
| By: | Kentaro Iwatsubo (Graduate School of Economics, Kobe University); Clinton Watkins (Graduate School of Economics, Kobe University); Tao Xu (Graduate School of Economics, Kobe University) |
| Abstract: | We investigate intraday seasonality in, and relationships between, informational efficiency, volatility, volume and liquidity. Platinum and gold, both traded in overlapping sessions in Tokyo and New York, provide an interesting comparison because Tokyo is an internationally important trading venue for platinum but not for gold. Our analysis indicates that both platinum and gold markets in Tokyo are dominated by liquidity trading, while there is evidence supporting both liquidity and informed trading in New York. Separating global trading hours into Tokyo, London and New York day sessions, we also find that liquidity trading is more prevalent during the Tokyo day session while informed trading dominates the New York day session for both metals in both locations. This evidence suggests that futures markets for the same underlying commodity on different exchanges have different microstructure characteristics, while both informed and liquidity traders choose when to trade depending on market characteristics in different time zones. |
| Keywords: | intraday patterns, microstructure, efficiency, commodity futures |
| JEL: | G14 G15 Q02 |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:koe:wpaper:1715&r=mst |
| By: | Knoll, Julian; Stübinger, Johannes; Grottke, Michael |
| Abstract: | Over the past 15 years,there have been a number of studies using text mining for predicting stock market data. Two recent publications employed support vector machines and second-order Factorization Machines, respectively, to this end. However, these approaches either completely neglect interactions between the features extracted from the text, or they only account for second-order interactions. In thispaper, weapply higher-order Factorization Machines, for which efficient training algorithms have only been available since 2016. As Factorization Machines require hyperparameters to be specified, we also introduce the novel adaptive-order algorithm for automatically determining them. Our studyis the first one tomake use of social media data for predicting high-frequency stock returns, namely the ones of the S&P 500 stock constituents. We show that, unlike a trading strategy employing support vector machines, Factorization-Machine-based strategies attain positive returns after transactions costs for the years 2014 and 2015. Especially the approach applying thea daptive-order algorithm outperforms classical approaches with respect to a multitude of criteria, and it features very favorable characteristics. |
| Keywords: | finance,factorization machine,social media,statistical arbitrage,high-frequency data |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwqwdp:132017&r=mst |
| By: | Anatoliy Swishchuk |
| Abstract: | In this paper, we study various new Hawkes processes, namely, so-called general compound and regime-switching general compound Hawkes processes to model the price processes in the limit order books. We prove Law of Large Numbers (LLN) and Functional Central Limit Theorems (FCLT) for these processes. The latter two FCLTs are applied to limit order books where we use these asymptotic methods to study the link between price volatility and order flow in our two models by studying the diffusion limits of these price processes. The volatilities of price changes are expressed in terms of parameters describing the arrival rates and price changes. |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1706.07459&r=mst |
| By: | Ron Berndsen; Ronald Heijmans |
| Abstract: | This paper identifies quantitative risks in financial market infrastructures (FMIs), which are inspired by the Principles for Financial Market Infrastructures. We convert transaction level data into indicators that provide information on operational risk, changes in the network structure and interdependencies. As a proof of concept we use TARGET2 level data. The indicators are based on legislation, guidelines and their own history. Indicators that are based on their own history are corrected for cyclical patterns. We also define a method for setting the signaling threshold of relevant changes. For the signaling, we opt for a traffic light approach: a green, yellow or red light for a small, moderate or substantial change in the indicator, respectively. The indicators developed in this paper can be used by overseers and operators of FMIs and by financial stability experts. |
| Keywords: | risk indicator; central bank; granular data; TARGET2; oversight; financial stability; forecasting |
| JEL: | E42 E50 E58 E59 |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:557&r=mst |