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Design and implementation of NN5 for Hong Kong stock price forecasting

Philip M. Tsang, Paul Kwok, S.O. Choy, Reggie Kwan, S.C. Ng, Jacky Mak, Jonathan Tsang, Kai Koong, Tak-Lam Wong
2007 Engineering applications of artificial intelligence  
The system is tested with data from one Hong Kong stock, The Hong Kong and Shanghai Banking Corporation (HSBC) Holdings. The system is shown to achieve an overall hit rate of over 70%.  ...  A number of published techniques have emerged in the trading community for stock prediction tasks. Among them is neural network (NN).  ...  Acknowledgments The authors wish to thank all the 2005-2006 tutors and students in the OUHK class of T396 AI for Technology for inspiring the write-up of this paper. Gratitude also extends to Dr.  ... 
doi:10.1016/j.engappai.2006.10.002 fatcat:acojhh535nccbh7ntzvb5e2hku

Regional Tourism Demand Forecasting with Machine Learning Models: Gaussian Process Regression vs. Neural Network Models in a Multiple-Input Multiple-Output Setting

Oscar Claveria, Enric Monte, Salvador Torra
2017 Social Science Research Network  
The authors use a sparse GPR model to predict tourism demand to Hong Kong and find that its forecasting capability outperforms those of the ARMA and SVR models.  ...  When comparing the forecasting accuracy of a sparse GPR model than with ARMA and SVR models for tourist arrivals to Hong Kong, Wu et al. (2012) obtained better forecasting results with the GPR model.  ... 
doi:10.2139/ssrn.2945556 fatcat:focps6ion5ahnkvxaoxqq7qacm

Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Bhukya Ramadevi, Kishore Bingi
2022 Symmetry  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY  ...  Simulation Modeling ANN Chaotic dynamical Embedding - RMSE - Practice and Theory system dimension [102], 2014 Hong Kong Building and ANN-chaotic PSO Air quality Particulate matter Mulleven R, MSE - Environment  ...  Therefore, this paper presents a comprehensive review of the performance of traditional and machine learningbased methods for chaotic time series forecasting and their implementation on nonlinear dynamical  ... 
doi:10.3390/sym14050955 dblp:journals/symmetry/RamadeviB22 fatcat:3oa3go7rdzdurjl4yxcivjsbf4


Mr Yuvraj, M Wadghule, V Sonawane
International Journal of Engineering Sciences & Research Technology   unpublished
NN and Markov Model can be used exclusively in the finance markets and forecasting of stock price.  ...  The Traditional techniques are not cover all the possible relation of the stock price fluctuations. There are new approaches to known in-depth of an analysis of stock price variations.  ...  I am also immensely grateful to Prof.Shaikh I.R for their comments on an earlier version of the manuscript, although any errors are my own and should not tarnish the reputations of these esteemed persons  ... 

The Town and County Planning Fees [chapter]

2018 Spon's Architect's and Builders' Price Book 2016  
AECOM's median forecasts for tender price inflation are 3.5- 5.8% in 2016 Q1, and 3.5-7.5% in 2017 Q1.  ...  This fifth edition provides overarching construction cost data for 16 countries: Brunei, Cambodia, China, Hong Kong, India, Indonesia, Japan, Malaysia, Myanmar, Philippines, Singapore, South Korea, Sri  ...  and design information for architects and architecture students.  ... 
doi:10.1201/9781315270623-30 fatcat:bjazgvcufjdwtdbcep6oarlhvi