최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Expert systems with applications, v.73, 2017년, pp.187 - 200
Nakano, M. , Takahashi, A. , Takahashi, S.
This paper proposes a generalized exponential moving average (EMA) model, a new stochastic volatility model with time-varying expected return in financial markets. In particular, we effectively apply a particle filter (PF) to sequential estimation of states and parameters in a state space framework....
Bayesian Statistics Aguilar 6 1 1 1998 Bayesian inference on latent structure in time series
Journal of Business and Economic Statistics Aguilar 18 338 2000 10.1080/07350015.2000.10524875 Bayesian dynamic factor models and variance matrix discounting for portfolio allocation
Expert Systems with Applications Araújo 42 8 4081 2015 10.1016/j.eswa.2015.01.004 A hybrid model for high-frequency stock market forecasting
Expert Systems with Applications Ballings 42 20 7046 2015 10.1016/j.eswa.2015.05.013 Evaluating multiple classifiers for stock price direction prediction
Box 1970 Time Series Analysis: Forecasting and Control
Braga 2016 Risk-Based Approaches to Asset Allocation
Journal of Computational Information Systems Cai 18 7273 2013 Particle filtering with observation anomaly detection in wireless sensor networks
Review of Financial Studies Campbell 21 4 1509 2008 10.1093/rfs/hhm055 Predicting excess stock returns out of sample: Can anything beat the historical average?
Journal of Geodesy Chang 88 4 391 2014 10.1007/s00190-013-0690-8 Robust kalman filtering based on mahalanobis distance as outlier judging criterion
Journal of Portfolio Management, Winter 1993 Chopra 19 2 1993 The effect of errors in means, variances, and covariances on optimal portfolio choice
The Journal of Business Fama 38 1 34 1965 10.1086/294743 The behavior of stock-market prices
Journal of Financial Economics Fama 33 1 3 1993 10.1016/0304-405X(93)90023-5 Common risk factors in the returns on stocks and bonds
Journal of Financial Economics Fama 105 3 457 2012 10.1016/j.jfineco.2012.05.011 Size, value, and momentum in international stock returns
Fukui 2016 Preprint, CARF-f-383, university of tokyo Estimating style weights of mutual funds by monte carlo filter with generalized simulated annealing
Radar and Signal Processing, IEE Proceedings F Gordon 140 2 107 1993 10.1049/ip-f-2.1993.0015 Novel approach to nonlinear/non-gaussian bayesian state estimation
Expert Systems with Applications Hsu 61 215 2016 10.1016/j.eswa.2016.05.033 Bridging the divide in financial market forecasting: machine learners vs. financial economists
Expert Systems with Applications Huang 34 4 2870 2008 10.1016/j.eswa.2007.05.035 Application of wrapper approach and composite classifier to the stock trend prediction
Computers & Operations Research Huang 32 10 2513 2005 10.1016/j.cor.2004.03.016 Forecasting stock market movement direction with support vector machine
European Journal of Operational Research Huck 207 3 1702 2010 10.1016/j.ejor.2010.06.043 Pairs trading and outranking: The multi-step-ahead forecasting case
Journal of Business & Economic Statistics Jacquier 12 69 1994 Bayesian analysis of stochastic volatility models
The Journal of Finance Johannes 69 2 611 2014 10.1111/jofi.12121 Sequential learning, predictability, and optimal portfolio returns
Expert Systems with applications Khashei 37 1 479 2010 10.1016/j.eswa.2009.05.044 An artificial neural network (p, d, q) model for timeseries forecasting
Expert Systems with Applications Khashei 39 4 4344 2012 10.1016/j.eswa.2011.09.157 A new class of hybrid models for time series forecasting
Applied Soft Computing Kim 7 2 569 2007 10.1016/j.asoc.2006.03.004 A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets
Journal of Computational and Graphical statistics Kitagawa 5 1 1 1996 10.1080/10618600.1996.10474692 Monte carlo filter and smoother for non-gaussian nonlinear state space models
Journal of the American Statistical Association Kitagawa 1203 1998 A self-organizing state-space model
Knorn 1 2008 INFOCOM workshops 2008, IEEE Adaptive kalman filtering for anomaly detection in software appliances
IEEE Transactions on Neural Networks Kwon 18 3 851 2007 10.1109/TNN.2007.891629 A hybrid neurogenetic approach for stock forecasting
Expert Systems with Applications Lee 36 8 10896 2009 10.1016/j.eswa.2009.02.038 Using support vector machine with a hybrid feature selection method to the stock trend prediction
The Journal of Finance Lettau 56 3 815 2001 10.1111/0022-1082.00347 Consumption, aggregate wealth, and expected stock returns
Liu 197 2001 Sequential monte carlo methods in practice Combined parameter and state estimation in simulation-based filtering
Lopes 515 2010 Rethinking risk measurement and reporting: Uncertainty, bayesian analysis and expert judgement’, riskbooks Bayesian inference for stochastic volatility modeling
Expert Systems with Applications Mundnich 54 228 2016 10.1016/j.eswa.2016.01.052 Early online detection of high volatility clusters using particle filters
Forthcoming in The Journal of Economics Nakano 2016 Optimal portfolio with particle filtering (in japanese)
Expert Systems with Applications de Oliveira 40 18 7596 2013 10.1016/j.eswa.2013.06.071 Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index-case study of PETR4, petrobras, brazil
Omega Pai 33 6 497 2005 10.1016/j.omega.2004.07.024 A hybrid ARIMA and support vector machines model in stock price forecasting
Expert Systems with Applications Patel 42 1 259 2015 10.1016/j.eswa.2014.07.040 Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques
Expert Systems with Applications Patel 42 4 2162 2015 10.1016/j.eswa.2014.10.031 Predicting stock market index using fusion of machine learning techniques
Microelectronics Reliability Patil 52 3 482 2012 10.1016/j.microrel.2011.10.017 A prognostic approach for non-punch through and field stop IGBTs
Journal of the American Statistical Association Pitt 94 446 590 1999 10.1080/01621459.1999.10474153 Filtering via simulation: Auxiliary particle filters
Expert Systems with Applications Rather 42 6 3234 2015 10.1016/j.eswa.2014.12.003 Recurrent neural network and a hybrid model for prediction of stock returns
Rios 23 2013 State-space models The extended liu and west filter: Parameter learning in markov switching stochastic volatility models
Omega Tay 29 4 309 2001 10.1016/S0305-0483(01)00026-3 Application of support vector machines in financial time series forecasting
Taylor 1986 Modelling Financial Time Series
IEEE Transactions on Neural Networks Van Gestel 12 4 809 2001 10.1109/72.935093 Financial time series prediction using least squares support vector machines within the evidence framework
Omega Wang 40 6 758 2012 10.1016/j.omega.2011.07.008 Stock index forecasting based on a hybrid model
Review of Financial Studies Welch 21 4 1455 2008 10.1093/rfs/hhm014 A comprehensive look at the empirical performance of equity premium prediction
West 409 1993 Journal of the royal statistical society. series b (methodological) Approximating posterior distributions by mixture
West 1993 Computing science and statistics Mixture models, monte carlo, bayesian updating, and dynamic models
International Journal of Forecasting Zhou 30.4 963 2014 10.1016/j.ijforecast.2014.03.017 Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.