2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)最新文献_第2页

Intraday forex bid/ask spread patterns - Analysis and forecasting 日内外汇买卖价差模式-分析和预测

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611706

Andrius Paukste, A. Raudys

引用次数: 5

Explicit formulas for optimal hedging stratergies for European contingent claims 欧洲或有债权最优对冲策略的明确公式

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611707

V. Chellaboina, Anil Bhatia, S. Bhat

引用次数: 3

DynOpt: Incorporating dynamics into mean-variance portfolio optimization DynOpt:将动态整合到均值-方差组合优化中

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611696

Marco Signoretto, J. Suykens

引用次数: 1

Empirical anaylsis of liquidity provision of an order driven market 订单驱动市场流动性供给的实证分析

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611691

William M. Cheung

引用次数: 0

A new approach for time series prediction using ensembles of ANFIS models with interval type-2 and type-1 fuzzy integrators 基于区间2型和1型模糊积分器的ANFIS模型的时间序列预测新方法

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFER.2013.6611699

Jesus Soto, P. Melin, O. Castillo

引用次数: 22

Portfolio optimization using improved artificial bee colony approach 基于改进人工蜂群方法的投资组合优化

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611698

A. Chen, Yun-Chia Liang, Chia-Chien Liu

{"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}

引用次数: 12

Multi-objective evolutionary algorithm for multi-project and multi-term portfolio problem 多项目多期限投资组合问题的多目标进化算法

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611697

Yuan Zhou, Hai-Lin Liu, Wenqin Chen

引用次数: 0

Crowdsourced stock clustering through equity analyst hypergraph partitioning 基于股票分析师超图划分的众包股票聚类

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611705

John Robert Yaros, T. Imielinski

{"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}

引用次数: 4

Simplified evolving rule-based fuzzy modeling of realized volatility forecasting with jumps 基于演化规则的简化模糊建模实现了有跳变的波动率预测

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611701

Leandro Maciel, F. Gomide, R. Ballini, R. Yager

{"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}

引用次数: 10

A study of dark pool trading using an agent-based model 基于代理模型的暗池交易研究

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2012-11-27 DOI: 10.2139/ssrn.2181209

Sheung Yin Kevin Mo, M. Paddrik, Steve Y. Yang

引用次数: 15