Pub Date : 2020-06-19DOI: 10.1142/s2382626620500057
Bastien Baldacci, J. Benveniste
We solve explicitly the Almgren-Chriss optimal liquidation problem where the stock price process follows a geometric Brownian motion. Our technique is to work in terms of cash and to use functional analysis tools. We show that this framework extends readily to the case of a stochastic drift for the price process and the liquidation of a portfolio.
{"title":"A note on Almgren-Chriss optimal execution problem with geometric Brownian motion","authors":"Bastien Baldacci, J. Benveniste","doi":"10.1142/s2382626620500057","DOIUrl":"https://doi.org/10.1142/s2382626620500057","url":null,"abstract":"We solve explicitly the Almgren-Chriss optimal liquidation problem where the stock price process follows a geometric Brownian motion. Our technique is to work in terms of cash and to use functional analysis tools. We show that this framework extends readily to the case of a stochastic drift for the price process and the liquidation of a portfolio.","PeriodicalId":8509,"journal":{"name":"arXiv: Trading and Market Microstructure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82776153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-13DOI: 10.1142/s0129183121500200
Maximilian Beikirch, T. Trimborn
The Levy-Levy-Solomon model (A microscopic model of the stock market: cycles, booms, and crashes, Economic Letters 45 (1))is one of the most influential agent-based economic market models. In several publications this model has been discussed and analyzed. Especially Lux and Zschischang (Some new results on the Levy, Levy and Solomon microscopic stock market model, Physica A, 291(1-4)) have shown that the model exhibits finite-size effects. In this study we extend existing work in several directions. First, we show simulations which reveal finite-size effects of the model. Secondly, we shed light on the origin of these finite-size effects. Furthermore, we demonstrate the sensitivity of the Levy-Levy-Solomon model with respect to random numbers. Especially, we can conclude that a low-quality pseudo random number generator has a huge impact on the simulation results. Finally, we study the impact of the stopping criteria in the market clearance mechanism of the Levy-Levy-Solomon model.
列维-列维-所罗门模型(股票市场的微观模型:周期，繁荣和崩溃，经济快报45(1))是最有影响力的基于主体的经济市场模型之一。在一些出版物中，对该模型进行了讨论和分析。特别是Lux和Zschischang(关于Levy, Levy和Solomon微观股票市场模型的一些新结果，Physica A, 291(1-4))表明该模型具有有限尺寸效应。在这项研究中，我们在几个方向上扩展了现有的工作。首先，我们展示了揭示模型的有限尺寸效应的模拟。其次，我们阐明了这些有限尺寸效应的起源。此外，我们证明了Levy-Levy-Solomon模型对随机数的敏感性。特别是，我们可以得出结论，低质量的伪随机数生成器对仿真结果有很大的影响。最后，我们研究了停止标准在Levy-Levy-Solomon模型的市场出清机制中的影响。
{"title":"Novel insights in the Levy–Levy–Solomon agent-based economic market model","authors":"Maximilian Beikirch, T. Trimborn","doi":"10.1142/s0129183121500200","DOIUrl":"https://doi.org/10.1142/s0129183121500200","url":null,"abstract":"The Levy-Levy-Solomon model (A microscopic model of the stock market: cycles, booms, and crashes, Economic Letters 45 (1))is one of the most influential agent-based economic market models. In several publications this model has been discussed and analyzed. Especially Lux and Zschischang (Some new results on the Levy, Levy and Solomon microscopic stock market model, Physica A, 291(1-4)) have shown that the model exhibits finite-size effects. In this study we extend existing work in several directions. First, we show simulations which reveal finite-size effects of the model. Secondly, we shed light on the origin of these finite-size effects. Furthermore, we demonstrate the sensitivity of the Levy-Levy-Solomon model with respect to random numbers. Especially, we can conclude that a low-quality pseudo random number generator has a huge impact on the simulation results. Finally, we study the impact of the stopping criteria in the market clearance mechanism of the Levy-Levy-Solomon model.","PeriodicalId":8509,"journal":{"name":"arXiv: Trading and Market Microstructure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74629482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The competitiveness of the IPO underwriting market suffers from the concentration of restricted mandates in the hands of a limited number of underwriters with good reputations. Academic research has focused extensively on the relationship between underpricing and the influence of an individual underwriter. However, we have only scant research on the effect of IPO syndication on underpricing. Nevertheless, most IPOs are managed by underwriters operating as an IPO syndicate. Here, we contribute to an analysis of underwriting syndicates by examining 329 IPOs issued in the Indian IPO market in the period 2000-10 that were made subject to either a discretionary or a proportionate allocation regime. We find that the underwriting market in India is highly concentrated and is dominated by a few large and reputable underwriters who have ongoing relationships amongst themselves to manage IPOs. This highlights a potential entry barrier for new underwriters. We find that underwriters form large syndicates when the issue size is large. Also, we do not find any evidence that the motivation for underwriters to form a syndicate is due to market risk sharing or price manipulation. We conclude that underwriters syndicate to share the inventory risk of an IPO. When allocation discretion is regulated, and the risk of managing an IPO is high, we observe that the size of an underwriting syndicate is smaller. However, the results support the role of institutional subscription acting as a mediating factor for reputable underwriters to syndicate. Able to share the risk in this way, the syndicate partnership benefits the issuer with lower underpricing. Overall, we conclude that regulatory intervention is positive for market welfare due to lower underpricing. We also find that in the absence of allocation discretion, syndication by reputable underwriters acts as an effective medium of discretion for higher information and risk sharing.
{"title":"Allocation Discretion, Information Sharing and Underwriter Syndication","authors":"N. Parikh, V. Marisetty, M. Tan","doi":"10.2139/ssrn.3217770","DOIUrl":"https://doi.org/10.2139/ssrn.3217770","url":null,"abstract":"The competitiveness of the IPO underwriting market suffers from the concentration of restricted mandates in the hands of a limited number of underwriters with good reputations. Academic research has focused extensively on the relationship between underpricing and the influence of an individual underwriter. However, we have only scant research on the effect of IPO syndication on underpricing. Nevertheless, most IPOs are managed by underwriters operating as an IPO syndicate. Here, we contribute to an analysis of underwriting syndicates by examining 329 IPOs issued in the Indian IPO market in the period 2000-10 that were made subject to either a discretionary or a proportionate allocation regime. We find that the underwriting market in India is highly concentrated and is dominated by a few large and reputable underwriters who have ongoing relationships amongst themselves to manage IPOs. This highlights a potential entry barrier for new underwriters. We find that underwriters form large syndicates when the issue size is large. Also, we do not find any evidence that the motivation for underwriters to form a syndicate is due to market risk sharing or price manipulation. We conclude that underwriters syndicate to share the inventory risk of an IPO. When allocation discretion is regulated, and the risk of managing an IPO is high, we observe that the size of an underwriting syndicate is smaller. However, the results support the role of institutional subscription acting as a mediating factor for reputable underwriters to syndicate. Able to share the risk in this way, the syndicate partnership benefits the issuer with lower underpricing. Overall, we conclude that regulatory intervention is positive for market welfare due to lower underpricing. We also find that in the absence of allocation discretion, syndication by reputable underwriters acts as an effective medium of discretion for higher information and risk sharing.","PeriodicalId":8509,"journal":{"name":"arXiv: Trading and Market Microstructure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81964070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1142/S2382626619500035
Xiaofei Lu, F. Abergel
Market making is one of the most important aspects of algorithmic trading, and it has been studied quite extensively from a theoretical point of view. The practical implementation of so-called "optimal strategies" however suffers from the failure of most order book models to faithfully reproduce the behaviour of real market participants. This paper is twofold. First, some important statistical properties of order driven markets are identified, advocating against the use of purely Markovian order book models. Then, market making strategies are designed and their performances are compared, based on simulation as well as backtesting. We find that incorporating some simple non-Markovian features in the limit order book greatly improves the performances of market making strategies in a realistic context.
{"title":"Order-book modelling and market making strategies","authors":"Xiaofei Lu, F. Abergel","doi":"10.1142/S2382626619500035","DOIUrl":"https://doi.org/10.1142/S2382626619500035","url":null,"abstract":"Market making is one of the most important aspects of algorithmic trading, and it has been studied quite extensively from a theoretical point of view. The practical implementation of so-called \"optimal strategies\" however suffers from the failure of most order book models to faithfully reproduce the behaviour of real market participants. \u0000This paper is twofold. First, some important statistical properties of order driven markets are identified, advocating against the use of purely Markovian order book models. Then, market making strategies are designed and their performances are compared, based on simulation as well as backtesting. We find that incorporating some simple non-Markovian features in the limit order book greatly improves the performances of market making strategies in a realistic context.","PeriodicalId":8509,"journal":{"name":"arXiv: Trading and Market Microstructure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91225551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-02-01DOI: 10.1142/S2382626619500011
D. Challet, Nikita Gourianov
We first investigate static properties of opening and closing auctions such as typical auction volume relative to daily volume and order value distributions. We then show that the indicative match price is strongly mean-reverting because the imbalance is, which we link to strategic behavior. Finally, we investigate how the final auction price reacts to order placement, especially conditional on imbalance improving or worsening events and find a large difference between the opening and closing auctions, emphasizing the role of liquidity and simultaneous trading in the pre-open or open-market order book.
{"title":"Dynamical regularities of US equities opening and closing auctions.","authors":"D. Challet, Nikita Gourianov","doi":"10.1142/S2382626619500011","DOIUrl":"https://doi.org/10.1142/S2382626619500011","url":null,"abstract":"We first investigate static properties of opening and closing auctions such as typical auction volume relative to daily volume and order value distributions. We then show that the indicative match price is strongly mean-reverting because the imbalance is, which we link to strategic behavior. Finally, we investigate how the final auction price reacts to order placement, especially conditional on imbalance improving or worsening events and find a large difference between the opening and closing auctions, emphasizing the role of liquidity and simultaneous trading in the pre-open or open-market order book.","PeriodicalId":8509,"journal":{"name":"arXiv: Trading and Market Microstructure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89461231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Milla Siikanen, K. Baltakys, H. Kärkkäinen, J. Jussila, Ravikiran Vatrapu, R. Mukkamala, Abid Hussain, J. Kanniainen
Recent studies combining social media data with data from capital markets have mainly focused on the relationship between returns and activity on social media. In contrast, we study how behavior of different investor categories are associated to Facebook posts and other activity on a company's Facebook wall. We provide evidence that the decisions of less sophisticated investors---namely, households and nonprofit organizations---to increase or decrease their shareholdings are associated with Facebook data, while the decisions of more sophisticated investors---financial and insurance institutions---are not associated with Facebook data.
{"title":"How Facebook Drives Investor Behavior","authors":"Milla Siikanen, K. Baltakys, H. Kärkkäinen, J. Jussila, Ravikiran Vatrapu, R. Mukkamala, Abid Hussain, J. Kanniainen","doi":"10.2139/SSRN.3040621","DOIUrl":"https://doi.org/10.2139/SSRN.3040621","url":null,"abstract":"Recent studies combining social media data with data from capital markets have mainly focused on the relationship between returns and activity on social media. In contrast, we study how behavior of different investor categories are associated to Facebook posts and other activity on a company's Facebook wall. We provide evidence that the decisions of less sophisticated investors---namely, households and nonprofit organizations---to increase or decrease their shareholdings are associated with Facebook data, while the decisions of more sophisticated investors---financial and insurance institutions---are not associated with Facebook data.","PeriodicalId":8509,"journal":{"name":"arXiv: Trading and Market Microstructure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75847888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-08DOI: 10.1007/978-3-319-49872-0_3
P. Barucca, F. Lillo
{"title":"Behind the Price: On the Role of Agent’s Reflexivity in Financial Market Microstructure","authors":"P. Barucca, F. Lillo","doi":"10.1007/978-3-319-49872-0_3","DOIUrl":"https://doi.org/10.1007/978-3-319-49872-0_3","url":null,"abstract":"","PeriodicalId":8509,"journal":{"name":"arXiv: Trading and Market Microstructure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91113006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1142/S2382626618500065
Kyle Bechler, M. Ludkovski
We investigate the behavior of limit order books on the meso-scale motivated by order execution scheduling algorithms. To do so we carry out empirical analysis of the order flows from market and limit order submissions, aggregated from tick-by-tick data via volume-based bucketing, as well as various LOB depth and shape metrics. We document a nonlinear relationship between trade imbalance and price change, which however can be converted into a linear link by considering a weighted average of market and limit order flows. We also document a hockey-stick dependence between trade imbalance and one-sided limit order flows, highlighting numerous asymmetric effects between the active and passive sides of the LOB. To address the phenomenological features of price formation, book resilience, and scarce liquidity we apply a variety of statistical models to test for predictive power of different predictors. We show that on the meso-scale the limit order flows (as well as the relative addition/cancellation rates) carry the most predictive power. Another finding is that the deeper LOB shape, rather than just the book imbalance, is more relevant on this timescale. The empirical results are based on analysis of six large-tick assets from Nasdaq.
{"title":"Order Flows and Limit Order Book Resiliency on the Meso-Scale","authors":"Kyle Bechler, M. Ludkovski","doi":"10.1142/S2382626618500065","DOIUrl":"https://doi.org/10.1142/S2382626618500065","url":null,"abstract":"We investigate the behavior of limit order books on the meso-scale motivated by order execution scheduling algorithms. To do so we carry out empirical analysis of the order flows from market and limit order submissions, aggregated from tick-by-tick data via volume-based bucketing, as well as various LOB depth and shape metrics. We document a nonlinear relationship between trade imbalance and price change, which however can be converted into a linear link by considering a weighted average of market and limit order flows. We also document a hockey-stick dependence between trade imbalance and one-sided limit order flows, highlighting numerous asymmetric effects between the active and passive sides of the LOB. To address the phenomenological features of price formation, book resilience, and scarce liquidity we apply a variety of statistical models to test for predictive power of different predictors. We show that on the meso-scale the limit order flows (as well as the relative addition/cancellation rates) carry the most predictive power. Another finding is that the deeper LOB shape, rather than just the book imbalance, is more relevant on this timescale. The empirical results are based on analysis of six large-tick assets from Nasdaq.","PeriodicalId":8509,"journal":{"name":"arXiv: Trading and Market Microstructure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84102175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1142/S2382626618500016
Federico Gonźalez, M. Schervish
We propose a limit order book (LOB) model with dynamics that account for both the impact of the most recent order and the shape of the LOB. We present an empirical analysis showing that the type of the last order significantly alters the submission rate of immediate future orders, even after accounting for the state of the LOB. To model these effects jointly we introduce a discrete Markov chain model. Then on these improved LOB dynamics, we find the policy for optimal order choice and placement in the share purchasing problem by framing it as a Markov decision process. The optimal policy derived numerically uses limit orders, cancellations and market orders. It looks to exploit the state of the LOB summarized by the volume at the bid/ask and the type of the most recent order to obtain the best execution price, avoiding non-execution and adverse selection risk simultaneously. Market orders are used aggressively when the mid-price is expected to move adversely. Limit orders are placed under favorable LOB conditions and canceled when non-execution or adverse selection probability is high. Using ultra high-frequency data from the NASDAQ stock exchange we compare our optimal policy with other submission strategies that use a subset of all available order types and show that ours significantly outperforms them.
{"title":"Instantaneous order impact and high-frequency strategy optimization in limit order books","authors":"Federico Gonźalez, M. Schervish","doi":"10.1142/S2382626618500016","DOIUrl":"https://doi.org/10.1142/S2382626618500016","url":null,"abstract":"We propose a limit order book (LOB) model with dynamics that account for both the impact of the most recent order and the shape of the LOB. We present an empirical analysis showing that the type of the last order significantly alters the submission rate of immediate future orders, even after accounting for the state of the LOB. To model these effects jointly we introduce a discrete Markov chain model. Then on these improved LOB dynamics, we find the policy for optimal order choice and placement in the share purchasing problem by framing it as a Markov decision process. The optimal policy derived numerically uses limit orders, cancellations and market orders. It looks to exploit the state of the LOB summarized by the volume at the bid/ask and the type of the most recent order to obtain the best execution price, avoiding non-execution and adverse selection risk simultaneously. Market orders are used aggressively when the mid-price is expected to move adversely. Limit orders are placed under favorable LOB conditions and canceled when non-execution or adverse selection probability is high. Using ultra high-frequency data from the NASDAQ stock exchange we compare our optimal policy with other submission strategies that use a subset of all available order types and show that ours significantly outperforms them.","PeriodicalId":8509,"journal":{"name":"arXiv: Trading and Market Microstructure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76213694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-02DOI: 10.1142/S2382626617500095
Charles-Albert Lehalle, Othmane Mounjid
This paper is split in three parts: first we use labelled trade data to exhibit how market participants accept or not transactions via limit orders as a function of liquidity imbalance; then we develop a theoretical stochastic control framework to provide details on how one can exploit his knowledge on liquidity imbalance to control a limit order. We emphasis the exposure to adverse selection, of paramount importance for limit orders. For a participant buying using a limit order: if the price has chances to go down the probability to be filled is high but it is better to wait a little more before the trade to obtain a better price. In a third part we show how the added value of exploiting a knowledge on liquidity imbalance is eroded by latency: being able to predict future liquidity consuming flows is of less use if you have not enough time to cancel and reinsert your limit orders. There is thus a rational for market makers to be as fast as possible as a protection to adverse selection. Thanks to our optimal framework we can measure the added value of latency to limit orders placement. To authors' knowledge this paper is the first to make the connection between empirical evidences, a stochastic framework for limit orders including adverse selection, and the cost of latency. Our work is a first stone to shed light on the roles of latency and adverse selection for limit order placement, within an accurate stochastic control framework.
{"title":"Limit Order Strategic Placement with Adverse Selection Risk and the Role of Latency","authors":"Charles-Albert Lehalle, Othmane Mounjid","doi":"10.1142/S2382626617500095","DOIUrl":"https://doi.org/10.1142/S2382626617500095","url":null,"abstract":"This paper is split in three parts: first we use labelled trade data to exhibit how market participants accept or not transactions via limit orders as a function of liquidity imbalance; then we develop a theoretical stochastic control framework to provide details on how one can exploit his knowledge on liquidity imbalance to control a limit order. We emphasis the exposure to adverse selection, of paramount importance for limit orders. For a participant buying using a limit order: if the price has chances to go down the probability to be filled is high but it is better to wait a little more before the trade to obtain a better price. In a third part we show how the added value of exploiting a knowledge on liquidity imbalance is eroded by latency: being able to predict future liquidity consuming flows is of less use if you have not enough time to cancel and reinsert your limit orders. There is thus a rational for market makers to be as fast as possible as a protection to adverse selection. Thanks to our optimal framework we can measure the added value of latency to limit orders placement. \u0000To authors' knowledge this paper is the first to make the connection between empirical evidences, a stochastic framework for limit orders including adverse selection, and the cost of latency. Our work is a first stone to shed light on the roles of latency and adverse selection for limit order placement, within an accurate stochastic control framework.","PeriodicalId":8509,"journal":{"name":"arXiv: Trading and Market Microstructure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73587078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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