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Muhammad Umer
Muhammad Awais
Muhammad Muzammul
Vol. 8 No. 4 (2019), Articles, pages 97-116
Accepted: Apr 24, 2020
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Stocks are possibly the most popular financial instrument invented for building wealth and are the centerpiece of any investment portfolio. The advances in trading technology has opened up the markets so that nowadays nearly anybody can own stocks. From last few decades, there seen explosive increase in the average person’s interest for stock market. In a financially explosive market, as the stock market, it is important to have a very accurate prediction of a future trend. Because of the financial crisis and recording profits, it is compulsory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires progressive algorithms of machine learning with help of Artificial Intelligence (AI).In our research, we are going to use Machine Learning Algorithm specially focus on Linear Regression (LR), Three month Moving Average(3MMA), Exponential Smoothing (ES) and Time Series Forecasting using MS Excel as best statistical tool for graph and tabular representation of prediction results. We obtained data from Yahoo Finance for Amazon (AMZN) stock, AAPL stock and GOOGLE stock after implementation LR we successfully predicted stock market trend for next month and also measured accuracy according to measurements.


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Tae Kyun Lee et al. "Global stock market investment strategies based on financial network indicators using machine learning tech niques", Expert Systems with Applications, 117(2019):228-242.

Bruno et al. "Literature review: Machine learning techniques applied to financial market prediction", Expert Systems with Applications, 124(2019): 226-251.

Thomas Fischer et al. "Deep learning with long short-term memory networks for financial market predictions", European Journal of Operational Research, 270.2(2018): 654-669.

Kang Zhang et al. "Stock Market Prediction Based on Generative Adversarial Network", Procardia Computer Science, 147(2019):400-406

Ben Moews et at. "Lagged correlation-based deep learning for directional trend change prediction in financial time series". Expert Systems with Applications, 120(2019):197-206.

Fische Mospel et al. "Deep learning with machine learning algorithms for financial market predictions", European Journal of scientific Research, 220.2(2019):654-669.

Markku Karhunen et al. "Algorithmic sign prediction and covariate selection across eleven international stock markets", Expert Systems with Applications, 115(2019):256-263.

Eunsuk Chong et at. "Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies", Expert Systems with Applications,8 (2017): 187-205.

WangQili et al. "Combining the wisdom of crowds and technical analysis for financial market prediction using deep random subspace ensembles", Neurocomputing, 299(2018):51-61.

ChangjuLee et al. "Explaining future market return and evaluating market condition with common preferred spread index", Physica A: Statistical Mechanics and its Applications, 220(2019):220-229.

Mu-YenChen et al. "Modeling public mood and emotion: Stock market trend prediction with anticipatory computing approach", Computers in Human Behavior (2018).

Hiransha et al. "NSE Stock Market Prediction Using Deep-Learning Models", Procedia Computer Science, 132(2018): 1351-1362.

FengmeiYang et al. "A novel hybrid stock selection method with stock prediction", Applied Soft Computing (2019).

Yue-gang Song et al.Corrigendum to "Towards a new approach to predict business performance using machine learning" [Cogn. Syst. Res. 52 (2018): 1004-1012].