Prediction for Stock Marketing Using Machine Learning

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. This paper will showcase how to perform stock prediction using Machine Learning algorithms: Linear Regression, Random Forest and Multilayer Perceptron

Forecasting Stock Prices via Deep Learning During COVID-19: A Case Study from an Emerging Economy

In this study we apply a Deep Learning Technique to predict stock prices for the 30 stocks that compose the BIST30, Turkish Stock Market Index before and after the onset of Covid-19 crises. Specifically, we utilize the Bi-Directional Long-Short Term Memory (BiLSTM) model which is a variation of the Long-Short-Term Memory (LSTM) model to predict stock prices for the BIST30 stocks. We compare the performance of the model to other commonly used machine learning models like decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), artificial neural networks (ANN), and other deep Leaning models like recurrent neural network (RNN), and the Long-Short-Term Memory (LSTM) model. The BiLSTM model seems to have better performance compared to conventional models used for predicting stock prices and continues to have superior performance in the Covid19 period. The LSTM model seems to have a good overall performance and is the next best model.&nbsp

Comparing classification algorithms for prediction on CROBEX data

The main objective of this analysis is to evaluate and compare the various classification algorithms for the automatic identification of favourable days for intraday trading using the Croatian stock index CROBEX data. Intra-day trading refers to the acquisition and sale of financial instruments on the same trading day. If the increase between the opening price and the closing price of the same day is substantial enough to earn a profit by purchasing at the opening price and selling at the closing price, the day is considered to be favourable for intra-day trading. The goal is to discover relation between selected financial indicators on a given day and the market situation on the following day i.e. to determine whether a day is favourable for day trading or not. The problem is modelled as a binary classification problem. The idea is to test different algorithms and to give greater attention to those that are more rarely used than traditional statistical methods. Thus, the following algorithms are used: neural network, support vector machine, random forest, as well as k-nearest neighbours and naïve Bayes classifier as classifiers that are more common. The work is an extension of authors’ previous work in which the algorithms are compared on resamples resulting from tuning the algorithms, while here, each derived model is used to make predictions on new data. The results should add to the increasing corpus of stock market prediction research efforts and try to fill some gaps in this field of research for the Croatian market, in particular by using machine learning algorithms

A Systematic Study of Stock Markets Using Analytical and AI Techniques

Predicting stock market patterns is seen as a crucial and highly productive activity. Therefore, if investors make wise choices, stock prices will result in significant gains. Investors face a lot of difficulty making predictions about the stock market because of the noisy and stagnating data. As a result, making accurate stock market predictions is difficult for investors who want to put their money to work for them. Predictions of the stock market are made using mathematical techniques and study aids. Out of 30 research papers advocating approaches, this study offers a thorough analysis of each, including computational methodologies, AI algorithms( machine learning and deep learning), performance evaluation parameters, and chosen publications. Research questions are used to choose studies. As a result, these chosen studies contribute to the discovery of ML methods and their corresponding data set for predicting security markets. The majority of Artificial Neural Network and Neural Network techniques are employed for producing precise stock market forecasts. The most recent stock market-related prediction system has significant limitations despite the substantial amount of work that has gone into it. In this survey, one may infer that the stock price forecasting procedure is a comprehensive affair and it is very necessary to look more closely at the typical parameters for the stock market prediction

The political power of twitter

In June 2016, the British voted by 52 per cent to leave the EU, a club the UK joined in 1973. This paper examines Twitter public and political party discourse surrounding the BREXIT withdrawal agreement. In particular, we focus on tweets from four different BREXIT exit strategies known as “Norway”, “Article 50”, the “Backstop” and “No Deal” and their effect on the pound and FTSE 100 index from the period of December 10th 2018 to February 24th 2019. Our approach focuses on using a Naive Bayes classification algorithm to assess political party and public Twitter sentiment. A Granger causality analysis is then introduced to investigate the hypothesis that BREXIT public sentiment, as measured by the twitter sentiment time series, is indicative of changes in the GBP/EUR Fx and FTSE 100 Index. Our results from the Twitter public sentiment indicate that the accuracy of the “Article 50” scenario had the single biggest effect on short run dynamics on the FTSE 100 index, additionally the “Norway” BREXIT strategy has a marginal effect on the FTSE 100 index whilst there was no significant causation to the GBP/EUR Fx. The BREXIT Political party sentiment for the “No Deal” was indicative of short term dynamics on the GBP/EUR Fx at a marginal rate. Our test concluded that there was no causality on the FTSE 100

Data Mining for Detecting E-learning Courses Anomalies: An Application of Decision Tree Algorithm

E-learning adaptation has become the most important method that facilitates access to the appropriate content. Adaptive approaches consist of reducing the problems of incompatibilities between learner’s cognitive abilities and educational content’s difficulties. In some cases, the adapted curriculum cannot meet learner's skills completely seen its incoherent structure, its unsuitable methodologies and sometimes its complexity. Therefore, we need to measure the convenience of the content material to improve it and ensure learners’ satisfaction. In other words, it is necessary to estimate its appropriateness to each learner. That is why; we have proceeded by using decision tree (DT) algorithm which is a supervised data mining method. It helps to predict the convenience of the proposed content material for learners. Our system consists of classifying learning material into two classes: “good” if it is convenient, and “anomaly” if not. To achieve that, we have used an intelligent agent called Classifier Agent (CLA). It tracks learner’s behavior by collecting a set of attributes like score, learning time, and number of attempts, feedback and interactions with the tutor. Then, he calculates the predictive attribute by using the (DT) algorithm. The finding algorithm shows that the score is the most crucial indicator gives us more information about the conformity of curriculum to learners, followed by learning time, feedback and number of attempts

Feeling The Stock Market: A Study in the Prediction of Financial Markets Based on News Sentiment

Researchers are fascinated with predicting the stock market. Even though there is a large amount of supporting evidence that the dynamics of financial markets cannot be predicted, studies that employ creative prediction techniques continue to emerge. This study proposes a sentiment analysis model developed to infer the polarity of news articles related to a company. The process of collecting the dataset, as well as a diagram of the system architecture for the sentiment analysis engine used in this study is provided to readers. Insights from this research and experimental results are used to provide further proof that supports the Efficient Market Hypothesis