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

Cluster analysis of high-dimensional high-frequency financial time series 高维高频金融时间序列的聚类分析

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611700

S. A. Pasha, P. Leong

引用次数: 7

Forecasting foreign exchange rates using Support Vector Regression 利用支持向量回归预测外汇汇率

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611694

F. Bahramy, S. Crone

{"title":"Forecasting foreign exchange rates using Support Vector Regression","authors":"F. Bahramy, S. Crone","doi":"10.1109/CIFEr.2013.6611694","DOIUrl":"https://doi.org/10.1109/CIFEr.2013.6611694","url":null,"abstract":"Support Vector Regression (SVR) algorithms have received increasing interest in forecasting, promising nonlinear, non-parametric and data driven regression capabilities for time series prediction. But despite evidence on the nonlinear properties of foreign exchange markets, applications of SVR in price or return forecasting have demonstrated only mixed results. However, prior studies were limited to using only autoregressive time series inputs to SVR. This paper evaluates the efficacy of SVR to predict the Euro-US Dollar exchange rate using input vectors enhanced with explanatory variables on mean-reversion movements derived from Bollinger Bands technical indicators. Using a rigorous empirical out-of-sample evaluation of multiple rolling forecast origins, we assess the accuracy of different SVR input vectors, including upper and lower BB, binary trading signals of BB, and combinations of the above. As a result, a local SVR model using autoregressive lags in conjunction with BB bands and BB indicators, and recalibrated yearly, outperforms the random walk on directional and all other error metrics, showing some promise for an SVR application.","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":"131602460","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

How (in)efficient is after-hours trading? 盘后交易的效率如何?

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611693

A. Raudys, Esther Mohr, G. Schmidt

引用次数: 1

Price variation limits and financial market bubbles: Artificial market simulations with agents' learning process 价格变动限制与金融市场泡沫:具有智能体学习过程的人工市场模拟

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611689

T. Mizuta, K. Izumi, S. Yoshimura

引用次数: 14

A probabilistic risk-to-reward measure for evaluating the performance of financial securities 一种评估金融证券表现的概率风险回报度量

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611704

P. Maguire, Philippe Moser, J. McDonnell, R. Kelly, Simon Fuller, R. Maguire

引用次数: 3

Optimum quantizing of monotonic nondecreasing arrays 单调非递减阵列的最佳量化

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611703

William W. Y. Hsu, Cheng-Yu Lu, M. Kao, Jan-Ming Ho

引用次数: 0

Monte Carlo methods in spatio-temporal regression modeling of migration in the EU 蒙特卡罗方法在欧盟移民时空回归建模中的应用

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611708

M. Manuguerra, G. Sofronov, M. Tani, G. Heller

{"title":"Monte Carlo methods in spatio-temporal regression modeling of migration in the EU","authors":"M. Manuguerra, G. Sofronov, M. Tani, G. Heller","doi":"10.1109/CIFEr.2013.6611708","DOIUrl":"https://doi.org/10.1109/CIFEr.2013.6611708","url":null,"abstract":"Spatio-temporal regression models are well developed in disciplines such as, for example, climate and geostatistics, but have had little application in the modelling of economic phenomena. In this study we have modelled migrations of skilled workers and firms across the European Union during the period 1998-2010. The data set has been extracted from Eurostats Labour Force Survey (LFS) and contains information stratified by European region. We investigate whether the spatial component in the migration patterns is based either on neighbourhood or on some other metric (such as the existence of a flight connection). The complete spatio-temporal model has been implemented using conditional autoregressive (CAR) random effects in the Bayesian framework. In recent years, Bayesian methods have been widely applied to spatio-temporal modelling since they enable the use of Markov chain Monte Carlo (MCMC) samplers to estimate model parameters. In this paper, we consider the Bayesian Adaptive Independence Sampler (BAIS) for estimation, and compare different computing schemes. The results suggest that the regions with a stronger increase of skilled workers are more likely to have similarities with other advanced regions which they are connected to by flight connections, than with the regions at their border. The conclusion of this study is that graphical proximity is not a sufficient condition to reduce differences in skill endowments between regions.","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":"130687476","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: 2

Balance sheet outlier detection using a graph similarity algorithm 资产负债表异常检测使用图相似算法

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611709

Steve Y. Yang, R. Cogill

{"title":"Balance sheet outlier detection using a graph similarity algorithm","authors":"Steve Y. Yang, R. Cogill","doi":"10.1109/CIFEr.2013.6611709","DOIUrl":"https://doi.org/10.1109/CIFEr.2013.6611709","url":null,"abstract":"Graph similarity measurement has been used in many applications, such as computational biology, text mining, pattern recognition, and computer vision. In this paper, we apply similarity measurement on graphs to measure structural differences in financial statements. Unconventional financial statement structures may potentially reveal deceptive intention of hiding certain information while making technically “correct” financial statements. Furthermore, unconventional financial statements may also lead to investment opportunities if legitimacy is not questioned. We construct an algorithm based on the metric of string edit distance as an approximation of graph similarity, and apply the Levenshtein algorithm with modified string edit costs to measure string edit distance. We demonstrate the effectiveness of this algorithm in capturing the sensitive changes of balance sheet structures by applying the algorithm in two experiments. The first experiment shows the algorithm is sensitive to all three basic edits (namely deletion, insertion and substitution) on a particular balance sheet, and the second experiment shows more than 90% clustering accuracy on real balance sheets.","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":"115514266","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: 5

Analysis on the number of XCS agents in agent-based computational finance 基于agent的计算金融中XCS代理数量的分析

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611690

Tomohiro Nakada, K. Takadama

引用次数: 8

Empirical analysis of model selection criteria for genetic programming in modeling of time series system 时间序列系统建模中遗传规划模型选择准则的实证分析

2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) Pub Date : 2013-04-16 DOI: 10.1109/CIFEr.2013.6611702

A. Garg, S. Sriram, K. Tai

{"title":"Empirical analysis of model selection criteria for genetic programming in modeling of time series system","authors":"A. Garg, S. Sriram, K. Tai","doi":"10.1109/CIFEr.2013.6611702","DOIUrl":"https://doi.org/10.1109/CIFEr.2013.6611702","url":null,"abstract":"Genetic programming (GP) and its variants have been extensively applied for modeling of the stock markets. To improve the generalization ability of the model, GP have been hybridized with its own variants (gene expression programming (GEP), multi expression programming (MEP)) or with the other methods such as neural networks and boosting. The generalization ability of the GP model can also be improved by an appropriate choice of model selection criterion. In the past, several model selection criteria have been applied. In addition, data transformations have significant impact on the performance of the GP models. The literature reveals that few researchers have paid attention to model selection criterion and data transformation while modeling stock markets using GP. The objective of this paper is to identify the most appropriate model selection criterion and transformation that gives better generalized GP models. Therefore, the present work will conduct an empirical analysis to study the effect of three model selection criteria across two data transformations on the performance of GP while modeling the stock indexed in the New York Stock Exchange (NYSE). It was found that FPE criteria have shown a better fit for the GP model on both data transformations as compared to other model selection criteria.","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":"117212971","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: 45