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1999 International Journal of Theoretical and Applied Finance  
Based on the rescaled range analysis, a backpropagation neural network is used to capture the relationship between the technical indicators and the levels of the index in the market under study over time  ...  The paper, however, also discusses the problems associated with technical forecasting using neural networks, such as the choice of "time frames" and the "recency" problems.  ...  Acknowledgments Parts of this article have been presented at a seminar at National University of Singapore, International Conference On Neural Networks in the Capital Markets and IEEE International Conference  ... 
doi:10.1142/s0219024999000145 fatcat:5yzv4wojnnc45kvya4esltpdju

A Survey on Impact of Bio-inspired Computation on Stock Market Prediction

Smruti Rekha Das, Debahuti Mishra, Minakhi Rout
2017 Journal of Engineering Science and Technology Review  
To predict the stock price most Artificial Neural Network (ANN) based model are used in the historical data along with statistical measures, technical indicators etc.  ...  ANNs particularly Back Propagation (BP) has overlooked the non-stationary and noise characteristics of stock market data, as the training of BP is intricate due to the noise data and the network fall into  ...  The proposed Multi Objective Optimization trained intraday trading agents for an artificial market which encourages an application to end-of-day market data.  ... 
doi:10.25103/jestr.103.15 fatcat:hash5nemh5fntcgwscnifoavay

A Channeled Multilayer Perceptron as Multi-Modal Approach for Two Time-Frames Algo-Trading Strategy

Noussair Fikri, Khalid Moussaid, Mohamed Rida, Amina El Omri, Noureddine Abghour
2022 International Journal of Advanced Computer Science and Applications  
to prevent false indications in a short time frame.  ...  FOREX (Foreign Exchanges) is a 24H open market with an enormous daily volume. Most of the used Trading strategies, used individually, are not providing accurate signals.  ...  Most of the methods cited on the state of the art are methods used for algo-trading based on a single indicator and are not based on neural networks, while we target multiple and different type indicators  ... 
doi:10.14569/ijacsa.2022.0130259 fatcat:s5zisuzvlrabfapzilikfca5oq

Improving Trading Systems Using the RSI Financial Indicator and Neural Networks [chapter]

Alejandro Rodríguez-González, Fernando Guldrís-Iglesias, Ricardo Colomo-Palacios, Juan Miguel Gomez-Berbis, Enrique Jimenez-Domingo, Giner Alor-Hernandez, Rubén Posada-Gomez, Guillermo Cortes-Robles
2010 Lecture Notes in Computer Science  
Particularly, Trading-oriented Decision Support Systems based on the Chartist or Technical Analysis Relative Strength Indicator (RSI) have been published and used worldwide.  ...  and Feed-Forward Neural Networks (FFNN).  ...  White [13] was the first to use neural networks for market forecasting in the late 80s.  ... 
doi:10.1007/978-3-642-15037-1_3 fatcat:rqwvnkja55bcnkcxivicvzrske

Stock Market Forecasting Model From Multi News Data Source Using a Two-Level Learning Algorithm

El Bousty Hicham, Et. al.
2021 Turkish Journal of Computer and Mathematics Education  
Data are collected from four financial news websites and proceeded individually by Support Vector Machine (SVM) algorithm then we aggregate outputs using an Artificial Neural Network (ANN) algorithm.  ...  The emergence of new indicators mostly extracted from the web makes this domain of research challenging and in a continuous evolution.  ...  Results show that random forest outperforms Artificial Neural Network (ANN), Support Vector Machine (SVM) and naive-Bayes algorithms. David M.  ... 
doi:10.17762/turcomat.v12i5.1746 fatcat:tepc63mmurfrfilmkg7pkw4ggq

Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Alvin Ho, CHRIST (Deemed to be University), Bengaluru, India *Corresponding Author, Ramesh Vatambeti, Sathish Kumar Ravichandran
2021 Indian Journal of Science and Technology  
This paper compares the prediction outcomes of a machine learning model and an artificial neural network model.  ...  This makes it very difficult to predict its value and hence with the help of Machine Learning Algorithm and Artificial Neural Network Model this predictor is tested.  ...  These platforms are very easy to use and it does not take much time to create an account and start trading.  ... 
doi:10.17485/ijst/v14i27.878 fatcat:hwreht3uwnhdjjhb7ciqx37qzm

A Survey on Stock Market Price Prediction System using Machine Learning Techniques

Mr. Yash Kadam, Mr. Sujay Kulkarni, Mr. Suyog Lonsane, Prof. Anjali S. Khandagale
2022 International Journal for Research in Applied Science and Engineering Technology  
The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions.  ...  Factors considered are open, close, low, high and volume. The models are evaluated using standard strategic indicators: RMSE and MAPE.  ...  Previous methods of stock predictions involve the use of Artificial Neural Networks and Convolution Neural Networks which has an error loss at an average of 20%.  ... 
doi:10.22214/ijraset.2022.40635 fatcat:ba57sntpc5hftd7x6ixtltukve

Short term wind power forecasting using hybrid intelligent systems

M. Negnevitsky, P. Johnson, S. Santoso
2007 IEEE Power Engineering Society General Meeting  
This panel paper summarizes the current trends in wind power development and describes a proposed approach for short term wind power forecasting using a hybrid intelligent system.  ...  of Artificial Neural Networks (ANNs).  ...  This innovative approach applies the combination of two Artificial Intelligence techniques, Fuzzy Logic and Artificial Neural Networks in the form of a hybrid model called an Adaptive Neural Fuzzy Inference  ... 
doi:10.1109/pes.2007.385453 fatcat:olqnsrxzxjcs7msykvggpkymrm

YOLO Object Recognition Algorithm and "Buy-Sell Decision" Model over 2D Candlestick Charts

Serdar Birogul, Gunay Temur, Utku Kose
2020 IEEE Access  
Indicators and formations within the scope of technical analysis constitute the most significant basis of this perspective.  ...  Besides, the model can be used for all the time series for which candlestick charts can be created.  ...  APPENDIX The authors would like to thank BIST for providing past stock price data used in the study free of charge.  ... 
doi:10.1109/access.2020.2994282 fatcat:2jcqapiry5bgrhwfg3m7ikcpyi

The Co-Movement between International and Emerging Stock Markets Using ANN and Stepwise Models: Evidence from Selected Indices

Dania Al-Najjar, Faheem Aslam
2022 Complexity  
To validate the availability of the linkage between the indices, the author includes various tests of a correlation coefficient, stepwise regression analysis, and artificial neural network (ANN).  ...  neural network support this relationship.  ...  Acknowledgments is work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (Project no. GRANT320).  ... 
doi:10.1155/2022/7103553 fatcat:yieuwvtihrbs5jtps7jd4hykxi

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

Nusrat Rouf, Majid Bashir Malik, Tasleem Arif, Sparsh Sharma, Saurabh Singh, Satyabrata Aich, Hee-Cheol Kim
2021 Electronics  
Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms.  ...  The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.  ...  Daily weekly [51] Apple, yahoo Market data, technical indicators 60 day and 90-day prediction [63] Microsoft company Twitter Daily [42] NASDAQ, DJIA, Apple Stock (AAPL) Market data, technical indicators  ... 
doi:10.3390/electronics10212717 fatcat:cfvbgrcnn5hpfo276fquqwsfxa

Univariate and Multivariate LSTM Model for Short-Term Stock Market Prediction [article]

Vishal Kuber, Divakar Yadav, Arun Kr Yadav
2022 arXiv   pre-print
Experimental outcomes revel that approach one is useful to determine the future trend but multivariate LSTM model with technical indicators found to be useful in accurately predicting the future price  ...  For the approach second, technical indicators values are calculated from the closing prices and then collectively applied on Multivariate LSTM model.  ...  Technical indicators based on short-term duration, such as SMA or EMA 10 days or 50 days, are available in this paper, but can also be researched and validated for longer timeframes if long-term predictions  ... 
arXiv:2205.06673v1 fatcat:bb3jbzi7qbhr7lg7gwo3efcsj4

A Neural Networks Adoption Framework for Predicting Stock Market Trends: Case of the Zimbabwe Stock Exchange

Ezekiel Tinashe Mukanga
2017 Business & Social Sciences Journal (BSSJ)  
This dissertation seeks to rationalise and advocate for the use of Artificial Neural Networks (ANN) by this key department in predicting, yearly turnovers, as well as daily stock market, counters price's  ...  each type of neural network model are assessed.  ...  Also widely used in market predictions is the delay neural network (TDNN) as well as the time recurrent neural network (RNN).  ... 
doi:10.26831/bssj.2016.2.2.27-60 fatcat:h3s7y3ixxncsvknbmqlrs5wxtq

Predicting Stock Exchange using Supervised Learning Algorithms

More and more researchers invest their time every day in coming up with ways to arrive at techniques that can further improve the accuracy of the stock prediction model.  ...  More and more researchers invest their time every day in coming up with ways to arrive at techniques that can further improve the accuracy of the stock prediction model.  ...  Several researchers had already examined the artificial neural networks as models for predicting exchange rates and shown that neural networks could be one of the quite useful tools in foreign currency  ... 
doi:10.35940/ijitee.a4144.119119 fatcat:drvor6prtbe3rhebwxosp4qnw4

Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning [article]

Ben Moews, Gbenga Ibikunle
2020 arXiv   pre-print
In order to measure the effect of time horizons, we predict intraday and daily stock price movements in varying interval lengths and gauge the complexity of the problem at hand with a modification of our  ...  Our findings show that accuracies, while remaining significant and demonstrating the exploitability of lagged correlations in stock markets, decrease with shorter prediction horizons.  ...  Acknowledgements We wish to express our gratitude to Peter Zimmerman and other discussants of this paper at the Third Workshop of the European Capital Markets Cooperative Research Centre (ECMCRC) on July  ... 
arXiv:2002.10385v1 fatcat:czgyqfjhf5hqtne5wve2u2ljye
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