scholarly journals Data Mining on Romanian Stock Market Using Neural Networks for Price Prediction

2013 ◽  
Vol 17 (3/2013) ◽  
pp. 125-136 ◽  
Author(s):  
Magdalena Daniela NEMES ◽  
Alexandru BUTOI
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Stock Market ◽  
Price Prediction

scholarly journals Stock Price Prediction Based on Procedural Neural Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Jiuzhen Liang ◽  
Wei Song ◽  
Mei Wang
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Stock Price ◽  
Time Window ◽  
Support Vector ◽  
Sustained Improvement ◽  
Spatiotemporal Model ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Multidimensional Time Series

We present a spatiotemporal model, namely, procedural neural networks for stock price prediction. Compared with some successful traditional models on simulating stock market, such as BNN (backpropagation neural networks, HMM (hidden Markov model) and SVM (support vector machine)), the procedural neural network model processes both spacial and temporal information synchronously without slide time window, which is typically used in the well-known recurrent neural networks. Two different structures of procedural neural networks are constructed for modeling multidimensional time series problems. Learning algorithms for training the models and sustained improvement of learning are presented and discussed. Experiments on Yahoo stock market of the past decade years are implemented, and simulation results are compared by PNN, BNN, HMM, and SVM.

scholarly journals BITCOIN PRICE PREDICTION USING MACHINE LEARNING

2021 ◽  
Vol 8 (5) ◽  
pp. 20-28
Author(s):  
Vaidehi M ◽  
Alivia Pandit ◽  
Bhaskar Jindal ◽  
Minu Kumari ◽  
Rupali Singh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Stock Market ◽  
Recurrent Neural Networks ◽  
Common View ◽  
Price Prediction

In this paper, we use the LSTM version of Recurrent Neural Networks, pricing for Bitcoin. To develop a better understanding of its price influence and a common view of this good invention, we first give a brief overview of Bitcoin again economics. After that, we define the database, including data from stock market indices, sentiment, and . in this investigation, we demonstrate the use of LSTM structures with the series of time mentioned above. In conclusion, we draw the Bitcoin pricing forecast results 30 and 60 days in advance.

TLBO-FLN: Teaching-Learning Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 522-532 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Subhranginee Das ◽  
Mohammad Dilsad Ansari
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Fine Tuning ◽  
Functional Link ◽  
Price Prediction ◽  
Closing Price ◽  
Teaching Learning Based Optimization ◽  
Artificial Neural ◽  
Teaching Learning

Background and Objective: Stock closing price prediction is enormously complicated. Artificial Neural Networks (ANN) are excellent approximation algorithms applied to this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters either leads toward additional computational cost or local optima. Methods: Teaching Learning Based Optimization (TLBO) is a newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of Functional Link Artificial Neural Network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data made it popular and got wide applications in the stock market prediction. This article presents a hybrid model termed as Teaching Learning Based Optimization of Functional Neural Networks (TLBO-FLN) by combining the advantages of both TLBO and FLANN. Results and Conclusion: The model is evaluated by predicting the short, medium, and long-term closing prices of four emerging stock markets. The performance of the TLBO-FLN model is measured through Mean Absolute Percentage of Error (MAPE), Average Relative Variance (ARV), and coefficient of determination (R2); compared with that of few other state-of-the-art models similarly trained and found superior.

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