Stock Prediction

Stock price prediction is one of the crucial concepts in finance area. Machine learning can provide the opportunity for traders and investors to predict stock prices more accurately. In this paper, Closing Price is dependent variable and First Price, Last Price, Opening Price, Today’s High, Today’s Low, Volume, Total Index of Tehran Stock Exchange, Brent Index, WTI Index and Exchange Rate are independent variables. Seven different machine learning algorithms are implemented to predict stock prices. Those include Bayesian Linear, Boosted Tree, Decision Forest, Neural Network, Support Vector, and Ensemble Regression. The sample of the study is fifteen oil and gas companies active in the Tehran Stock Exchange. For each stock the data from the September 23, 2017 to September 23, 2019 gathered. Each algorithm provided two metrics for performance: Root Mean Square Error and Mean Absolute Error. By comparing the aforementioned metrics, the Bayesian Linear Regression had the best performance to predict stock price in the oil and gas industry in the Tehran Stock Exchange.

Today, social networks are fast and dynamic communication intermediaries that are a vital business tool. This study aims at examining the views of those involved with Facebook stocks so that we can summarize their views to predict the general behavior of this stock and collectively consider possible Facebook stock price movements, and create a more accurate pattern compared to previous patterns. In this study, we have analyzed two statistical samples, the first being a large dataset containing a variety of tweets with an emotional tag. That is, it needed a set that had already been extracted from each individual tweet by a trusted human or machine. Consequently, we have collected posts on Facebook in an eighty-day period. In this study, we used a tagged dataset using Python's programming language and vector-to-word algorithm. The research results show that, we need stock change information, machine learning and sentiment analysis, and on paper we conclude that positive news about a company excites people to have positive opinions about it which in turn results in people encouraging each other to buy and hold stocks. Meanwhile, the opposite trend is also true, but everything will not always be easy and clear, and it is in areas of high complexity and mental uncertainty that the art of using the three elements mentioned above is evident.