doaa450 / Hybrid-Model-for-stock-Price-Predictin

Master Project

Hybrid-Model

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The objective of this study is to reach an appropriate model to predict the stock close price of One-Step for Telecom Egypt (ETEL).

First Model:

Dependent Variable :

Stock Close Price

Independent Variables from the previous day :

( open , Close , High , Low Prices, Volume,and Change)

Statistical Model :

Long Short Term Memory (LSTM) network.

Second Model:

Dependent Variable :

Stock Close Price

Independent Variables ( Technical Indicators) from the previous day :

(MACD Indicator, Momentum, Rate of change (ROC), Relative strength Indicator (RSI), STOCHASTIC Indicator, Bollinger Bands Indicator, Simple Moving Average for 30 days, Simple Moving Average for 10 days, Exponential Moving Average for 30 days, Exponential Moving Average for 10 days, Average Price, Median Price, and Typical Price)

Statistical Model :

Applying Lasso Regression to select the significant Indicators. Long Short Term Memory (LSTM) network for making prediction.

Third Model (Hybrid Model using stacking ensemble):

Dependent Variable :

Stock Close Price

Independent Variables from the previous day :

Predicated value from the first model, Predicated value from the Second model,and Actual value for stock close Price

Statistical Model :

Ridge Regression