Stock Closing Price Forecasting Using Ensembles of Constructive Neural Networks

2014 ◽  
Author(s):  
Rafael Stoffalette Joao ◽  
Tarcisio Fonseca Guidoni ◽  
Joao Roberto Bertini ◽  
Maria do Carmo Nicoletti ◽  
Almir Olivette Artero
Keyword(s):  
Neural Networks ◽  
Price Forecasting ◽  
Constructive Neural Networks ◽  
Closing Price

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.

Online filtering of CO2 signals from a bioreactor gas outflow using a committee of constructive neural networks

2007 ◽  
Vol 31 (2) ◽  
pp. 101-109 ◽  
Author(s):  
Roberto C. Giordano ◽  
João R. Bertini ◽  
Maria C. Nicoletti ◽  
Raquel L. C. Giordano
Keyword(s):  
Neural Networks ◽  
Constructive Neural Networks

scholarly journals Electricity Market Price Forecasting: Neural Networks versus Weighted-Distance k Nearest Neighbours

2002 ◽  
pp. 321-330 ◽  
Author(s):  
Alicia Troncoso Lora ◽  
Jose Riquelme Santos ◽  
Jesus Riquelme Santos ◽  
Jose Luis Martinez Ramos ◽  
Antonio Gomez Exposito
Keyword(s):  
Neural Networks ◽  
Electricity Market ◽  
Market Price ◽  
Price Forecasting ◽  
Weighted Distance ◽  
Nearest Neighbours

Simulation of Stock Prediction System using Artificial Neural Networks

2020 ◽  
pp. 511-530
Author(s):  
Omisore Olatunji Mumini ◽  
Fayemiwo Michael Adebisi ◽  
Ofoegbu Osita Edward ◽  
Adeniyi Shukurat Abidemi
Keyword(s):  
Neural Networks ◽  
Test Data ◽  
Stock Prices ◽  
Prediction Accuracy ◽  
Stock Exchange ◽  
Training Data ◽  
Prediction System ◽  
Closing Price ◽  
Non Linear ◽  
Predicted Values

Stock trading, used to predict the direction of future stock prices, is a dynamic business primarily based on human intuition. This involves analyzing some non-linear fundamental and technical stock variables which are recorded periodically. This study presents the development of an ANN-based prediction model for forecasting closing price in the stock markets. The major steps taken are identification of technical variables used for prediction of stock prices, collection and pre-processing of stock data, and formulation of the ANN-based predictive model. Stock data of periods between 2010 and 2014 were collected from the Nigerian Stock Exchange (NSE) and stored in a database. The data collected were classified into training and test data, where the training data was used to learn non-linear patterns that exist in the dataset; and test data was used to validate the prediction accuracy of the model. Evaluation results obtained from WEKA shows that discrepancies between actual and predicted values are insignificant.

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