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