发布求助
文献互助 智能选刊 最新文献

ERN: Neural Networks & Related Topics (Topic)最新文献

筛选
英文 中文
The Economic Value of Nonlinear Predictions in Asset Allocation 非线性预测在资产配置中的经济价值
ERN: Neural Networks & Related Topics (Topic) Pub Date : 2012-01-01 DOI: 10.2139/ssrn.1600716
F. Kruse, M. Rudolf
{"title":"The Economic Value of Nonlinear Predictions in Asset Allocation","authors":"F. Kruse, M. Rudolf","doi":"10.2139/ssrn.1600716","DOIUrl":"https://doi.org/10.2139/ssrn.1600716","url":null,"abstract":"Predictions of asset returns and volatilities are heavily discussed and analyzed in the finance research literature. In this paper, we compare linear and nonlinear predictions for stock- and bond index returns and their covariance matrix. We show in-sample and out-of-sample prediction accuracy as well as their impact on asset allocation results for short-horizon investors. Our data comprises returns from the German DAX stock market index and the REXP bond market index as well as their joint covariance matrix over the period 01/1988 - 12/2007. The comparison of a linear and nonlinear prediction approach is the focus of this study. The results show that while out-of-sample prediction accuracies are weak in terms of statistical significance, asset allocation performances based on linear predictions result in significant Jensen's alpha measures and Sharpe-ratio and are further improved by nonlinear predictions.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117055372","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: 0
Self Organizing Maps, Pattern Recognition and Financial Crises 自组织地图，模式识别和金融危机
ERN: Neural Networks & Related Topics (Topic) Pub Date : 2011-05-25 DOI: 10.2139/ssrn.2168795
S. Barthélémy, Pascal Devaux, F. Faure, Matthieu Pautonnier
{"title":"Self Organizing Maps, Pattern Recognition and Financial Crises","authors":"S. Barthélémy, Pascal Devaux, F. Faure, Matthieu Pautonnier","doi":"10.2139/ssrn.2168795","DOIUrl":"https://doi.org/10.2139/ssrn.2168795","url":null,"abstract":"The aim of this paper is to present preliminary results of an ongoing research on vulnerable pattern recognition for predicting economic and financial crises in developing countries. This research has been conducted in close cooperation between TAC & BNP Paribas. But the views expressed are those of the authors and do not necessarily reflect the position of TAC or BNP Paribas.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122177611","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: 1
Effectiveness of Artificial Neural Networks in Forecasting BSE Sensex Index Values 人工神经网络预测BSE Sensex指数的有效性
ERN: Neural Networks & Related Topics (Topic) Pub Date : 2010-06-12 DOI: 10.2139/ssrn.1863187
T. Soni
{"title":"Effectiveness of Artificial Neural Networks in Forecasting BSE Sensex Index Values","authors":"T. Soni","doi":"10.2139/ssrn.1863187","DOIUrl":"https://doi.org/10.2139/ssrn.1863187","url":null,"abstract":"Tarun Soni* Abstract Since stock markets are volatile, dynamic and complicated, forecasting stock market return is considered as a challenging task. Nevertheless, researchers have developed various linear and non linear methods for effective forecasting. Among these neural networks are most suitable for forecasting non linear and chaotic relationships among variables. The current study attempts to forecast the future returns of B.S.E, highly volatile index, with the help of conventional method i.e. ARIMA (Auto Regression Integrated Moving Average) and Artificial Neural Network M.L.P (Multilayer Perceptron). To examine the efficiency of the models, MAD (Mean Absolute Deviation) and MSE (Mean Square Error) of the two models are compared. The study results revealed that neural network is better for forecasting in comparison to ARIMA.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134021594","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: 0
Neuro Fuzzy Based Stock Market Prediction System 基于神经模糊的股票市场预测系统
ERN: Neural Networks & Related Topics (Topic) Pub Date : 2010-05-07 DOI: 10.2139/ssrn.2335293
M. Gunasekaran, S. Anitha, S. KaviPriya
{"title":"Neuro Fuzzy Based Stock Market Prediction System","authors":"M. Gunasekaran, S. Anitha, S. KaviPriya","doi":"10.2139/ssrn.2335293","DOIUrl":"https://doi.org/10.2139/ssrn.2335293","url":null,"abstract":"Neural networks have been used for forecasting purposes for some years now. Often arises the problem of a black-box approach, i.e. after having trained neural networks to a particular problem, it is almost impossible to analyze them for how they work. Fuzzy Neuronal Networks allow adding rules to neural networks. This avoids the black-box-problem. Additionally they are supposed to have a higher prediction precision in unlike situations. Applying artificial neural network, genetic algorithm and fuzzy logic for the stock market prediction has attracted much attention recently, which has better correlated the non-quantitative factors with the stock market performance. However these approaches perform less satisfactorily due to the memoryless nature of the stock market performance. In this paper, we propose a data compression-based portfolio prediction model hybridized with the fuzzy logic and genetic algorithm. In the model, the quantifiable microeconomic stock data are first optimized through the genetic algorithms to generate the most effective microeconomic data in relation to the stock market performance.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123992332","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
ARIMA and Neural Networks: An Application to the Real GNP Growth Rate and the Unemployment Rate of U.S.A. ARIMA和神经网络:在美国实际国民生产总值增长率和失业率中的应用
ERN: Neural Networks & Related Topics (Topic) Pub Date : 2009-03-26 DOI: 10.2139/ssrn.1368675
Eleftherios Giovanis
{"title":"ARIMA and Neural Networks: An Application to the Real GNP Growth Rate and the Unemployment Rate of U.S.A.","authors":"Eleftherios Giovanis","doi":"10.2139/ssrn.1368675","DOIUrl":"https://doi.org/10.2139/ssrn.1368675","url":null,"abstract":"This paper examines the estimation and forecasting performance of ARIMA models in comparison with some of the most popular and common models of neural networks. Specifically we provide the estimation results of AR-GRNN (Generalized regression neural networks) and the AR-RBF (Radial basis function). We show that neural networks models outperform the ARIMA forecasting. We found that the best model in the case of real US GNP is the AR-GRNN and for US unemployment rate is the AR-MLP.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114259194","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
The Arbitrage Pricing Theory and the Capital Asset Pricing Models and Artificial Neural Networks Modeling with Particle Swarm Optimization (PSO) 套利定价理论与资本资产定价模型及粒子群优化人工神经网络建模
ERN: Neural Networks & Related Topics (Topic) Pub Date : 2009-03-01 DOI: 10.2139/ssrn.1351249
Eleftherios Giovanis
{"title":"The Arbitrage Pricing Theory and the Capital Asset Pricing Models and Artificial Neural Networks Modeling with Particle Swarm Optimization (PSO)","authors":"Eleftherios Giovanis","doi":"10.2139/ssrn.1351249","DOIUrl":"https://doi.org/10.2139/ssrn.1351249","url":null,"abstract":"We examine two stocks of Athens Exchange Stock Market, that of 'Coca-Cola' and 'Compucon'. We analyze the arbitrage pricing theory (APT) model and the Capital Asset Pricing Model (CAPM) and we compare the performance between them. Then we develop a neural network model in Synapse Software with the particle swarm optimization algorithm and show the flexibility of hybrid models and the Synapse software, as the superiority in forecasting performance, in relation to the traditional econometric methodology , like Ordinary least square and ARCH-GARCH estimations.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130719112","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: 3
Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns 基于支持向量回归的GARCH模型及其在财务收益波动性预测中的应用
ERN: Neural Networks & Related Topics (Topic) Pub Date : 2008-01-31 DOI: 10.2139/ssrn.2894286
Shiyi Chen, Kiho Jeong, W. Härdle
{"title":"Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns","authors":"Shiyi Chen, Kiho Jeong, W. Härdle","doi":"10.2139/ssrn.2894286","DOIUrl":"https://doi.org/10.2139/ssrn.2894286","url":null,"abstract":"In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a moving average (MA), a recurrent NN and a parametric GACH in terms of their ability to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange rates from July 2, 2003 to June 30, 2005 and New York Stock Exchange (NYSE) daily composite index from July 3, 2003 to June 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms the MA, the recurrent NN and the parametric GARCH based on the criteria of mean absolute error (MAE) and directional accuracy (DA). No structured way being available to choose the free parameters of SVR, the sensitivity of performance is also examined to the free parameters.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133874684","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: 25
Using Adaptation and Optimization Techniques in Neural Networks to Predict Stock Market Exchange 利用神经网络的自适应和优化技术预测股票市场行情
ERN: Neural Networks & Related Topics (Topic) Pub Date : 2007-03-20 DOI: 10.2139/ssrn.1947060
Nikitas Goumatianos
{"title":"Using Adaptation and Optimization Techniques in Neural Networks to Predict Stock Market Exchange","authors":"Nikitas Goumatianos","doi":"10.2139/ssrn.1947060","DOIUrl":"https://doi.org/10.2139/ssrn.1947060","url":null,"abstract":"This paper presents important concepts of using properly technical analysis as input system in neural networks to predict stock market.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128991088","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: 0
Using Self-Organizing Maps to Adjust Intra-Day Seasonality 使用自组织地图调整日内季节性
ERN: Neural Networks & Related Topics (Topic) Pub Date : 2005-05-06 DOI: 10.2139/ssrn.720441
Walid Ben Omrane, Eric de Bodt
{"title":"Using Self-Organizing Maps to Adjust Intra-Day Seasonality","authors":"Walid Ben Omrane, Eric de Bodt","doi":"10.2139/ssrn.720441","DOIUrl":"https://doi.org/10.2139/ssrn.720441","url":null,"abstract":"The existence of an intra-day seasonality component within financial market variables (volatility, volume, activity,. . .), has been highlighted in many previous works. To adjust raw data from their cyclical component, many studies start by implementing the intra-daily average observations model (IAOM) and/or some smoothing techniques (e.g. the kernel method) in order to remove the day of the week effect. When seasonality involves only a deterministic component, IAOM method succeed in estimating periodicity almost without estimation error. However, when seasonality contains both deterministic and stochastic components (e.g. closed days), we show that either the IAOM or the kernel method fail to capture it. We introduce the use of the self-organizing maps (SOM) as a solution. SOM are based on neural network learning and nonlinear projections. Their flexibility allows capturing seasonality even in the presence of stochastic cycles.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115566089","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: 17
Reliability Conditions in Quadrature Algorithms 正交算法中的可靠性条件
ERN: Neural Networks & Related Topics (Topic) Pub Date : 2003-03-06 DOI: 10.1016/S0010-4655(03)00282-0
G. Adam, S. Adam, N. Plakida
{"title":"Reliability Conditions in Quadrature Algorithms","authors":"G. Adam, S. Adam, N. Plakida","doi":"10.1016/S0010-4655(03)00282-0","DOIUrl":"https://doi.org/10.1016/S0010-4655(03)00282-0","url":null,"abstract":"","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124231856","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信