Forecasting Hong Kong Hang Seng Index Stock Price Movement Using Social Media Data Analysis PDF Download

Author: Wai Tak Lau
Publisher:
ISBN:
Category : Data mining
Languages : en
Pages : 104

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Book Description

Author: Ton Viet Ta
Publisher:
ISBN:
Category :
Languages : en
Pages : 98

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Book Description
https: //www.dinhxa.com One-Week Free Trial (subject to change) Do you want to earn up to a 610% annual return on your money by two trades per day on HANG SENG INDEX ^HSI Stock? Reading this book is the only way to have a specific strategy. This book offers you a chance to trade ^HSI Stock at predicted prices. Eight methods for buying and selling ^HSI Stock at predicted low/high prices are introduced. These prices are very close to the lowest and highest prices of the stock in a day. All methods are explained in a very easy-to-understand way by using many examples, formulas, figures, and tables. The BIG DATA of the 7858 consecutive trading days (from October 12, 1987 to March 4, 2021) are utilized. The methods do not require any background on mathematics from readers. Furthermore, they are easy to use. Each takes you no more than 30 seconds for calculation to obtain a specific predicted price. The methods are not transient. They cannot be beaten by Mr. Market in several years, even until the stock doubles its current age. They are traits of Mr. Market. The reason is that the author uses the law of large numbers in the probability theory to construct them. In other words, you can use the methods in a long time without worrying about their change. The efficiency of the methods can be checked easily. Just compare the predicted prices with the actual price of the stock while referring to the probabilities of success which are shown clearly in the book (click the LOOK INSIDE button to read more information before buying this book). The book is very useful for Investors who have decided to buy the stock and keep it for a long time (as the strategy of Warren Buffett), or to sell the stock and pay attention to other stocks. The methods will help them to maximize profits for their decision. Day traders who buy and sell the stock many times in a day. Although each method is valid one time per day, the information from the methods will help the traders buy/sell the stock in the second time, third time or more in a day. Beginners to ^HSI Stock. The book gives an insight about the behavior of the stock. They will surely gain their knowledge of ^HSI Stock after reading the book. Everyone who wants to know about the U.S. stock market. https: //www.dinhxa.com includes a software (app) for stock price forecasting using the methods in this book. The software gives 114 predictions while this book gives 16. One-Week Free Trial (subject to change)

Author: Nazif AYYILDIZ
Publisher: Özgür Publications
ISBN: 975447821X
Category : Business & Economics
Languages : en
Pages : 121

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Book Description
The book titled "Prediction of Stock Market Index Movements with Machine Learning" focuses on the performance of machine learning methods in forecasting the future movements of stock market indexes and identifying the most advantageous methods that can be used across different stock exchanges. In this context, applications have been conducted on both developed and emerging market stock exchanges. The stock market indexes of developed countries such as NYSE 100, NIKKEI 225, FTSE 100, CAC 40, DAX 30, FTSE MIB, TSX; and the stock market indexes of emerging countries such as SSE, BOVESPA, RTS, NIFTY 50, IDX, IPC, and BIST 100 were selected. The movement directions of these stock market indexes were predicted using decision trees, random forests, k-nearest neighbors, naive Bayes, logistic regression, support vector machines, and artificial neural networks methods. Daily dataset from 01.01.2012 to 31.12.2021, along with technical indicators, were used as input data for analysis. According to the results obtained, it was determined that artificial neural networks were the most effective method during the examined period. Alongside artificial neural networks, logistic regression and support vector machines methods were found to predict the movement direction of all indexes with an accuracy of over 70%. Additionally, it was noted that while artificial neural networks were identified as the best method, they did not necessarily achieve the highest accuracy for all indexes. In this context, it was established that the performance of the examined methods varied among countries and indexes but did not differ based on the development levels of the countries. As a conclusion, artificial neural networks, logistic regression, and support vector machines methods are recommended as the most advantageous approaches for predicting stock market index movements.

Author: Thapanun Punyavachira
Publisher:
ISBN:
Category :
Languages : en
Pages : 94

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Author: Ping Raymond Ho
Publisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 414

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Book Description

Author: Rui Wu
Publisher:
ISBN:
Category :
Languages : en
Pages : 40

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Book Description
The stock markets in the recent years have become an integral part of the global economy, any fluctuation in this market influences our personal and corporate financial lives. A good prediction model for stock market forecasting is always highly desirable and would of wider interest. Recent research suggests that very early indicators can be extracted from online social media (blogs, Twitter feeds, etc.) to predict changes in various economic and commercial indicators. In this project, daily sentiment features are generated from a Twitter dataset to build up a high accuracy prediction model for stock price movement. Google Search Queries and Investor Intelligence provide additional features to improve performance on weekly based models. Five sentiment features (Mt-Positive, Mt-Negative, Bullishness, Message Volume, Agreement) are extracted from Twitter using sentiment analysis. Tweets that can express opinion upon stocks or indices are filtered out and classified from a Twitter dataset, which holds more than 400 million records from July 31 to December 31 2009. Four finance features (Return, Close, Trade Volume, Volatility) are generated for 2 Market Indices NASDAQ-100, Dow Jones Average Indices and 13 leading technological companies. Second step, correlations on each finance features with all other features are calculated to verify their statistically relationships. Results show high correlations (up to 0.93 for DJIA with Close) with stock prices and twitter sentiment. Twitter Sentiment may have time delay on stock prices movement, so time lag by weeks are also included in this experiments. Furthermore, with confidence from the correlations, several Machine Learning algorithms like Gaussian Process, Neural Network and Decision Stump are applied on the feature set. Results show reliable models are built with strong correlations and low Root Mean Square Error (R: 0.94, RMSE: 0.065). Finally, a real time prediction system is built with an additional component of Twitter Streaming API collecting real time Twitter data. Overall, the experimental results show that this prediction system is working with satisfiable efficiency and accuracy.

Author: Mohammad Ayoub Khan
Publisher: CRC Press
ISBN: 1000382567
Category : Computers
Languages : en
Pages : 926

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Book Description
The field of SMART technologies is an interdependent discipline. It involves the latest burning issues ranging from machine learning, cloud computing, optimisations, modelling techniques, Internet of Things, data analytics, and Smart Grids among others, that are all new fields. It is an applied and multi-disciplinary subject with a focus on Specific, Measurable, Achievable, Realistic & Timely system operations combined with Machine intelligence & Real-Time computing. It is not possible for any one person to comprehensively cover all aspects relevant to SMART Computing in a limited-extent work. Therefore, these conference proceedings address various issues through the deliberations by distinguished Professors and researchers. The SMARTCOM 2020 proceedings contain tracks dedicated to different areas of smart technologies such as Smart System and Future Internet, Machine Intelligence and Data Science, Real-Time and VLSI Systems, Communication and Automation Systems. The proceedings can be used as an advanced reference for research and for courses in smart technologies taught at graduate level.

Author: Robert M.X. Wu
Publisher: BoD – Books on Demand
ISBN: 1789846846
Category : Business & Economics
Languages : en
Pages : 172

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Book Description
This book provides the latest viewpoints of scientific research in the field of e-business. It is organized into three sections: “Higher Education and Digital Economy Development”, “Artificial Intelligence in E-Business”, and “Business Intelligence Applications”. Chapters focus on China’s higher education in e-commerce, digital economy development, natural language processing applications in business, Information Technology Governance, Risk and Compliance (IT GRC), business intelligence, and more.

Author: Lokesh Badolia
Publisher: Educreation Publishing
ISBN:
Category : Self-Help
Languages : en
Pages : 63

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Book Description
This book is well-researched by the author, in which he has shared the experience and knowledge of some very much experienced and renowned entities from stock market. We want that everybody should have the knowledge regarding the different aspects of stock market, which would encourage people to invest and earn without any fear. This book is just a step forward toward the knowledge of market.

Author: Greg N. Gregoriou
Publisher: CRC Press
ISBN: 1420099558
Category : Business & Economics
Languages : en
Pages : 654

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Book Description
Up-to-Date Research Sheds New Light on This Area Taking into account the ongoing worldwide financial crisis, Stock Market Volatility provides insight to better understand volatility in various stock markets. This timely volume is one of the first to draw on a range of international authorities who offer their expertise on market volatility in devel