Pub Date : 2013-04-16DOI: 10.1109/CIFEr.2013.6611706
Andrius Paukste, A. Raudys
In the foreign exchange, market liquidity is represented by the best bid and the best ask price spread. We searched for liquidity patterns during 24h trading sessions After experimental comparison, we found that neural networks and regression trees are most suitable for liquidity forecasting and outperform simple averaging and regression. We also rated the factors that most influence forecasting accuracy. Time of day is the factor that influences liquidity the most, followed by day of the week. Month and day of the month have no effect on liquidity. As a final conclusion, we state that in most currency pairs the liquidity can be forecasted more accurately than the simple averaging which is often used in practice for planning large order execution.
{"title":"Intraday forex bid/ask spread patterns - Analysis and forecasting","authors":"Andrius Paukste, A. Raudys","doi":"10.1109/CIFEr.2013.6611706","DOIUrl":"https://doi.org/10.1109/CIFEr.2013.6611706","url":null,"abstract":"In the foreign exchange, market liquidity is represented by the best bid and the best ask price spread. We searched for liquidity patterns during 24h trading sessions After experimental comparison, we found that neural networks and regression trees are most suitable for liquidity forecasting and outperform simple averaging and regression. We also rated the factors that most influence forecasting accuracy. Time of day is the factor that influences liquidity the most, followed by day of the week. Month and day of the month have no effect on liquidity. As a final conclusion, we state that in most currency pairs the liquidity can be forecasted more accurately than the simple averaging which is often used in practice for planning large order execution.","PeriodicalId":226767,"journal":{"name":"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132149638","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 : 2013-04-16DOI: 10.1109/CIFEr.2013.6611707
V. Chellaboina, Anil Bhatia, S. Bhat
In this paper, we consider the problem of discrete-time optimal hedging for a portfolio of (illiquid) European contingent claims (ECCs) written on multiple underlying assets. First, we present a framework to find discrete-time hedging strategies that minimize the variance of terminal wealth using a hedging portfolio of liquid assets, also assumed to ECCs written on the same underlying assets. Next, we specialize the framework to the case of illiquid portfolio consisting of a simple ECC written on a single underlying asset and a hedging portfolio consisting of the underlying asset and another simple ECC written on the same underlying asset. For this special case, we provide a (computable) formula for the minimum variance hedging strategy. Finally, we show that the minimum variance hedging strategy converges to the Δ-Γ-neutral hedging strategy as the interspacing between the hedging times converge to zero.
{"title":"Explicit formulas for optimal hedging stratergies for European contingent claims","authors":"V. Chellaboina, Anil Bhatia, S. Bhat","doi":"10.1109/CIFEr.2013.6611707","DOIUrl":"https://doi.org/10.1109/CIFEr.2013.6611707","url":null,"abstract":"In this paper, we consider the problem of discrete-time optimal hedging for a portfolio of (illiquid) European contingent claims (ECCs) written on multiple underlying assets. First, we present a framework to find discrete-time hedging strategies that minimize the variance of terminal wealth using a hedging portfolio of liquid assets, also assumed to ECCs written on the same underlying assets. Next, we specialize the framework to the case of illiquid portfolio consisting of a simple ECC written on a single underlying asset and a hedging portfolio consisting of the underlying asset and another simple ECC written on the same underlying asset. For this special case, we provide a (computable) formula for the minimum variance hedging strategy. Finally, we show that the minimum variance hedging strategy converges to the Δ-Γ-neutral hedging strategy as the interspacing between the hedging times converge to zero.","PeriodicalId":226767,"journal":{"name":"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128378757","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 : 2013-04-16DOI: 10.1109/CIFEr.2013.6611696
Marco Signoretto, J. Suykens
Mean-variance (MV) portfolio theory leads to relatively simple and elegant numerical problems. Nonetheless, the approach has been criticized for treating the market parameters as if they were constant over time. We propose a novel convex optimization problem that extends an existing MV formulation with chance constraint(s) by accounting for the portfolio dynamics. The core idea is to consider a multiperiod scenario where portfolio weights are implicitly regarded as the output of a state-space dynamical system driven by external inputs. The approach leverages a result on realization theory and uses the nuclear norm to penalize complex dynamical behaviors. The proposed ideas are illustrated by two case studies.
{"title":"DynOpt: Incorporating dynamics into mean-variance portfolio optimization","authors":"Marco Signoretto, J. Suykens","doi":"10.1109/CIFEr.2013.6611696","DOIUrl":"https://doi.org/10.1109/CIFEr.2013.6611696","url":null,"abstract":"Mean-variance (MV) portfolio theory leads to relatively simple and elegant numerical problems. Nonetheless, the approach has been criticized for treating the market parameters as if they were constant over time. We propose a novel convex optimization problem that extends an existing MV formulation with chance constraint(s) by accounting for the portfolio dynamics. The core idea is to consider a multiperiod scenario where portfolio weights are implicitly regarded as the output of a state-space dynamical system driven by external inputs. The approach leverages a result on realization theory and uses the nuclear norm to penalize complex dynamical behaviors. The proposed ideas are illustrated by two case studies.","PeriodicalId":226767,"journal":{"name":"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123119885","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 : 2013-04-16DOI: 10.1109/CIFEr.2013.6611691
William M. Cheung
This paper studies how liquidity evolves in a limit order market. By considering the determinants and consequences of the limit and market orders submission, we find that the tradeoff between limit orders and market orders depends on liquidity supply, proxy by the limit order size and the bid-ask spread. We find that increase in limit orders attract market orders, which increase the liquidity demand. Spread only has a significant negative effect on the market orders. Order size only has significant negative effect on the limit orders.
{"title":"Empirical anaylsis of liquidity provision of an order driven market","authors":"William M. Cheung","doi":"10.1109/CIFEr.2013.6611691","DOIUrl":"https://doi.org/10.1109/CIFEr.2013.6611691","url":null,"abstract":"This paper studies how liquidity evolves in a limit order market. By considering the determinants and consequences of the limit and market orders submission, we find that the tradeoff between limit orders and market orders depends on liquidity supply, proxy by the limit order size and the bid-ask spread. We find that increase in limit orders attract market orders, which increase the liquidity demand. Spread only has a significant negative effect on the market orders. Order size only has significant negative effect on the limit orders.","PeriodicalId":226767,"journal":{"name":"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133958943","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 : 2013-04-16DOI: 10.1109/CIFER.2013.6611699
Jesus Soto, P. Melin, O. Castillo
This paper describes an architecture for Ensembles of ANFIS (adaptive network based fuzzy inference system), with integrators of type-1 FLS and interval type-2 FLS (Fuzzy Logic System), with emphasis on its application to the prediction of chaotic time series, where the goal is to minimize the prediction error. The time series that was considered is the Mackey-Glass. The methods used for the integration of the ensembles of ANFIS are: Integration by average, the integration by weighted average, integration by type-1 FLS and integration by interval type-2 FLS. The performance obtained with this architecture overcomes several standard statistical approaches and neural network models reported in the literature by various researchers. In the experiments we changed the type of membership functions and the desired goal error, thereby increasing the complexity of the training.
{"title":"A new approach for time series prediction using ensembles of ANFIS models with interval type-2 and type-1 fuzzy integrators","authors":"Jesus Soto, P. Melin, O. Castillo","doi":"10.1109/CIFER.2013.6611699","DOIUrl":"https://doi.org/10.1109/CIFER.2013.6611699","url":null,"abstract":"This paper describes an architecture for Ensembles of ANFIS (adaptive network based fuzzy inference system), with integrators of type-1 FLS and interval type-2 FLS (Fuzzy Logic System), with emphasis on its application to the prediction of chaotic time series, where the goal is to minimize the prediction error. The time series that was considered is the Mackey-Glass. The methods used for the integration of the ensembles of ANFIS are: Integration by average, the integration by weighted average, integration by type-1 FLS and integration by interval type-2 FLS. The performance obtained with this architecture overcomes several standard statistical approaches and neural network models reported in the literature by various researchers. In the experiments we changed the type of membership functions and the desired goal error, thereby increasing the complexity of the training.","PeriodicalId":226767,"journal":{"name":"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133105080","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 : 2013-04-16DOI: 10.1109/CIFEr.2013.6611698
A. Chen, Yun-Chia Liang, Chia-Chien Liu
Nature-inspired optimization methods have been known to have the capability of handling computationally complicated problems, especially when traditional methods have become insufficient to. In this work, we proposed an improved artificial bee colony (IABC) method as the solution approach to trace out an efficiency frontier of the general portfolio performance. Such portfolio optimization problem focuses on balancing the trade-off between risk and return and is also captured in multidimensional nature with cardinality and bounding constraints. The proposed IABC algorithm intends to balance the diversity and quality of solutions, and fulfill the characteristic of the portfolio optimization problem. To do so, we employ a hybrid encoding that mixes integer and real variables in the IABC algorithm, and test its performance on four global stock market indices from the OR-Library. In addition, computational results are compared among four other algorithms. Evidences indicate that IABC performs the best in terms of diversity, convergence, and effectiveness among all four test data sets. The effect of choosing different number of stocks to form a portfolio is also investigated. The results confirm that less number of stocks selected in a portfolio can help to build a better efficiency frontier with lower risk and higher return more quickly.
{"title":"Portfolio optimization using improved artificial bee colony approach","authors":"A. Chen, Yun-Chia Liang, Chia-Chien Liu","doi":"10.1109/CIFEr.2013.6611698","DOIUrl":"https://doi.org/10.1109/CIFEr.2013.6611698","url":null,"abstract":"Nature-inspired optimization methods have been known to have the capability of handling computationally complicated problems, especially when traditional methods have become insufficient to. In this work, we proposed an improved artificial bee colony (IABC) method as the solution approach to trace out an efficiency frontier of the general portfolio performance. Such portfolio optimization problem focuses on balancing the trade-off between risk and return and is also captured in multidimensional nature with cardinality and bounding constraints. The proposed IABC algorithm intends to balance the diversity and quality of solutions, and fulfill the characteristic of the portfolio optimization problem. To do so, we employ a hybrid encoding that mixes integer and real variables in the IABC algorithm, and test its performance on four global stock market indices from the OR-Library. In addition, computational results are compared among four other algorithms. Evidences indicate that IABC performs the best in terms of diversity, convergence, and effectiveness among all four test data sets. The effect of choosing different number of stocks to form a portfolio is also investigated. The results confirm that less number of stocks selected in a portfolio can help to build a better efficiency frontier with lower risk and higher return more quickly.","PeriodicalId":226767,"journal":{"name":"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116856726","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 : 2013-04-16DOI: 10.1109/CIFEr.2013.6611697
Yuan Zhou, Hai-Lin Liu, Wenqin Chen
This paper proposes a multi-project and multi-term portfolio model through considering the remaining funds in investment. The model is based on a new kind of Mean-Semi-covariance theory, which describes the uncertainty of return and risk in investment. Portfolio investment is a multi-objective optimization problem with constraints. Multi-objective evolutionary algorithm (MOEA) with greedy repair strategy is used to deal with the infeasible individuals and makes the investment reasonable. Finally, computer simulation shows that the proposed algorithm can be considered as a viable alternative.
{"title":"Multi-objective evolutionary algorithm for multi-project and multi-term portfolio problem","authors":"Yuan Zhou, Hai-Lin Liu, Wenqin Chen","doi":"10.1109/CIFEr.2013.6611697","DOIUrl":"https://doi.org/10.1109/CIFEr.2013.6611697","url":null,"abstract":"This paper proposes a multi-project and multi-term portfolio model through considering the remaining funds in investment. The model is based on a new kind of Mean-Semi-covariance theory, which describes the uncertainty of return and risk in investment. Portfolio investment is a multi-objective optimization problem with constraints. Multi-objective evolutionary algorithm (MOEA) with greedy repair strategy is used to deal with the infeasible individuals and makes the investment reasonable. Finally, computer simulation shows that the proposed algorithm can be considered as a viable alternative.","PeriodicalId":226767,"journal":{"name":"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132429718","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 : 2013-04-16DOI: 10.1109/CIFEr.2013.6611705
John Robert Yaros, T. Imielinski
Use of industry classifications in the finance community is pervasive. They are critical to deriving a balanced portfolio of stocks and, more broadly, to risk management. Businesses, academics and government agencies have all researched and developed various schemes with mixed success. Recognizing major brokerages and research firms tend to assign their analysts to cover highly similar companies, we propose a scheme that makes use of stock analyst coverage assignments. Although creating coverage groups of highly similar stocks is not the direct goal of research firms, it may be imperative to their success because increasing similarity in coverage helps maximize synergy and derive the most value per analyst. To create our industry scheme, we construct a hypergraph where vertices represent stocks and hyperedges represent analyst coverage, connecting his/her similar companies. Using no additional information, we perform hypergraph partitioning to form clusters of stocks. Our scalable scheme can produce any number of clusters and can automatically update as research firms change analyst coverage as opposed to today's leading industry schemes which have only fixed numbers of industries and require periodic expert review. Can our crowdsourced scheme match the quality of stock groups from the expert-driven schemes? We make head-to-head comparisons to a leading academic and a leading commercial scheme using a methodology from the finance community that measures the coincidence of stock price movements. We also compare our scheme against a clusterer that creates groups based on past return correlations. Our results rival and often exceed all three schemes.
{"title":"Crowdsourced stock clustering through equity analyst hypergraph partitioning","authors":"John Robert Yaros, T. Imielinski","doi":"10.1109/CIFEr.2013.6611705","DOIUrl":"https://doi.org/10.1109/CIFEr.2013.6611705","url":null,"abstract":"Use of industry classifications in the finance community is pervasive. They are critical to deriving a balanced portfolio of stocks and, more broadly, to risk management. Businesses, academics and government agencies have all researched and developed various schemes with mixed success. Recognizing major brokerages and research firms tend to assign their analysts to cover highly similar companies, we propose a scheme that makes use of stock analyst coverage assignments. Although creating coverage groups of highly similar stocks is not the direct goal of research firms, it may be imperative to their success because increasing similarity in coverage helps maximize synergy and derive the most value per analyst. To create our industry scheme, we construct a hypergraph where vertices represent stocks and hyperedges represent analyst coverage, connecting his/her similar companies. Using no additional information, we perform hypergraph partitioning to form clusters of stocks. Our scalable scheme can produce any number of clusters and can automatically update as research firms change analyst coverage as opposed to today's leading industry schemes which have only fixed numbers of industries and require periodic expert review. Can our crowdsourced scheme match the quality of stock groups from the expert-driven schemes? We make head-to-head comparisons to a leading academic and a leading commercial scheme using a methodology from the finance community that measures the coincidence of stock price movements. We also compare our scheme against a clusterer that creates groups based on past return correlations. Our results rival and often exceed all three schemes.","PeriodicalId":226767,"journal":{"name":"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124558487","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 : 2013-04-16DOI: 10.1109/CIFEr.2013.6611701
Leandro Maciel, F. Gomide, R. Ballini, R. Yager
Financial asset volatility modeling and forecasting play a central role in risk management, portfolio selection, and derivative pricing. The increasing availability of market data at intraday frequencies has led to the development of improved volatility measurements such as realized volatility. The literature has shown that simple realized volatility models outperform the popular GARCH and related stochastic volatility models in out-of-sample forecasting. Moreover, gains in performance are achieved by separately considering volatility jump components. This paper suggests a nonlinear approach for realized volatility forecasting with jumps using a simplified evolving fuzzy system based on the concept of data clouds. Such an approach offers an alternative nonparametric form of fuzzy rule antecedents that reflects the real data distribution without requiring any explicit aggregation operations or membership functions, thus providing a more autonomous and efficient algorithm. Empirical results based on the Brazilian stock market index Ibovespa reveal the high potential of the evolving cloud-based fuzzy approach in modeling time-varying realized volatility with jump components, outperforming a traditional benchmark based on a linear regression, as well as alternative evolving fuzzy systems.
{"title":"Simplified evolving rule-based fuzzy modeling of realized volatility forecasting with jumps","authors":"Leandro Maciel, F. Gomide, R. Ballini, R. Yager","doi":"10.1109/CIFEr.2013.6611701","DOIUrl":"https://doi.org/10.1109/CIFEr.2013.6611701","url":null,"abstract":"Financial asset volatility modeling and forecasting play a central role in risk management, portfolio selection, and derivative pricing. The increasing availability of market data at intraday frequencies has led to the development of improved volatility measurements such as realized volatility. The literature has shown that simple realized volatility models outperform the popular GARCH and related stochastic volatility models in out-of-sample forecasting. Moreover, gains in performance are achieved by separately considering volatility jump components. This paper suggests a nonlinear approach for realized volatility forecasting with jumps using a simplified evolving fuzzy system based on the concept of data clouds. Such an approach offers an alternative nonparametric form of fuzzy rule antecedents that reflects the real data distribution without requiring any explicit aggregation operations or membership functions, thus providing a more autonomous and efficient algorithm. Empirical results based on the Brazilian stock market index Ibovespa reveal the high potential of the evolving cloud-based fuzzy approach in modeling time-varying realized volatility with jump components, outperforming a traditional benchmark based on a linear regression, as well as alternative evolving fuzzy systems.","PeriodicalId":226767,"journal":{"name":"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132077108","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}
A dark pool is a securities trading venue with no published market depth feed. Such markets have traditionally been utilized by large institutions as an alternative to public exchanges to execute large block orders which might otherwise impact settlement price. It is estimated that the trading volume of dark pool markets was 9% to 12% of the total U.S. equity market share volume in 2010 [1]. This phenomenon raises questions regarding the fundamental value of securities traded through dark pool markets and their impact on the price discovery process in traditional “visible” markets. In this paper, we establish a modeling framework for dark pool markets through agent-based modeling. It presents and validates the costs and benefits of trading small orders in dark pool markets. Simulated trading of 78 selected stocks demonstrates that dark pool market traders can obtain better execution rate when the dark pool market has more uninformed traders relative to informed traders. In addition, trading stocks with larger market capitalization yields better price improvement in dark pool markets.
{"title":"A study of dark pool trading using an agent-based model","authors":"Sheung Yin Kevin Mo, M. Paddrik, Steve Y. Yang","doi":"10.2139/ssrn.2181209","DOIUrl":"https://doi.org/10.2139/ssrn.2181209","url":null,"abstract":"A dark pool is a securities trading venue with no published market depth feed. Such markets have traditionally been utilized by large institutions as an alternative to public exchanges to execute large block orders which might otherwise impact settlement price. It is estimated that the trading volume of dark pool markets was 9% to 12% of the total U.S. equity market share volume in 2010 [1]. This phenomenon raises questions regarding the fundamental value of securities traded through dark pool markets and their impact on the price discovery process in traditional “visible” markets. In this paper, we establish a modeling framework for dark pool markets through agent-based modeling. It presents and validates the costs and benefits of trading small orders in dark pool markets. Simulated trading of 78 selected stocks demonstrates that dark pool market traders can obtain better execution rate when the dark pool market has more uninformed traders relative to informed traders. In addition, trading stocks with larger market capitalization yields better price improvement in dark pool markets.","PeriodicalId":226767,"journal":{"name":"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116547405","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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