Utilizing Neural Networks for Stocks Prices Prediction in Stocks Markets

Abstract

The neural networks, AI applications, are effective prediction methods. Therefore, in the current research a prediction system was proposed using these neural networks. It studied the technical share indices, viewing price not only as a function of time, but also as a function depending on several indices among which were the opening and closing, top and bottom trading session prices or trading volume. The above technical indices of a number of Egyptian stock market shares during the period from 2007 to 2017, which can be used for training the proposed system, were collected and used as follows: The data were divided into two sets. The first one contained 67% of the total data and was used for training neural networks and the second contained 33% and was used for testing the proposed system. The training set was segmented into subsets used for training a number of neural networks. The output of such networks was used for training another network hierarchically. The system was, then, tested using the rest of the data.

Data Driven Through Machine Learning on Electricity Production by Anode Respiring Bacteria Using the Microbial Fuel Cells

“…This article utilizes the effectiveness of AI to evaluate the role of Anode Respiring Bacteria in current generation via MFCs. Data was collected by using artificial intelligence techniques and ANN modeling was performed because of its capability to process huge volumes of data [53][54]. It gave a detailed description of best possible methods for the generation of electricity through microbial fuel cells.…”

Management and Policy Modeling of the Market Using Artificial Intelligence

“…In the above, P L,l (t) is the load demand at time t and N L is the number of load levels in the system. (2) each dispatchable generator or unit is allowed to produce in its limited range, as below: P Gi,min (t) ≤ P Gi (t) ≤ P Gi,max (t) P grid,min (t) ≤ P Grid (t) ≤ P grid,max (t) P sj,min (t) ≤ P sj (t) ≤ P sj,max (t) (12) In (12), the min/max indices technically show the least and highest possible values.…”

“…In [12], the market price is forecasted using a hybrid generative adversarial network model and the results are compared with the some of the most well-known algorithms in the field. It is shown in [13] that the market price and market demand both have high nonlinearity and non-stationary characteristics which, if combined with seasonality, would create a big problem for accurate prediction.…”