Stock price Prediction a referential approach on how to predict the stock price using simple time series... PDF Download
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Author: Dr.N.Srinivasan
Publisher: Clever Fox Publishing
ISBN:
Category : Business & Economics
Languages : en
Pages : 56
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Book Description
This book is about the various techniques involved in the stock price prediction. Even the people who are new to this book, after completion they can do stock trading individually with more profit.
Author: Dr.N.Srinivasan
Publisher: Clever Fox Publishing
ISBN:
Category : Business & Economics
Languages : en
Pages : 56
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Book Description
This book is about the various techniques involved in the stock price prediction. Even the people who are new to this book, after completion they can do stock trading individually with more profit.
Author: Vinaitheerthan Renganathan
Publisher: Vinaitheerthan Renganathan
ISBN: 9354579736
Category : Business & Economics
Languages : en
Pages : 107
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Book Description
Stock price analysis involves different methods such as fundamental analysis and technical analysis which is based on data related to price movement of the stock in the past. Price of the stock is affected by various factors such as company’s performance, current status of economy and political factor. These factors play an important role in supply and demand of the stock which makes the price to be volatile in the short term. Investors and stock traders aim to book profit through buying and selling the stocks. There are different statistical and data science tools are being used to predict the stock price. Data Science and Statistical tools assume only the stock price’s historical data in predicting the future stock price. Statistical tools include measures such as Graph and Charts which depicts the general trend and time series tools such as Auto Regressive Integrated Moving Averages (ARIMA) and regression analysis. Data Science tools include models like Decision Tree, Support Vector Machine (SVM), Artificial Neural Network (ANN) and Long Term and Short Term Memory (LSTM) Models. Current methods include carrying out sentiment analysis of tweets, comments and other social media discussion to extract the hidden sentiment expressed by the users which indicate the positive or negative sentiment towards the stock price and the company. The book provides an overview of the analyzing and predicting stock price movements using statistical and data science tools using R open source software with hypothetical stock data sets. It provides a short introduction to R software to enable the user to understand analysis part in the later part. The book will not go into details of suggesting when to purchase a stock or what at price. The tools presented in the book can be used as a guiding tool in decision making while buying or selling the stock. Vinaitheerthan Renganathan www.vinaitheerthan.com/book.php
Author: Hasan Al-Quaid
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Stock market trends are of great interest to investors and corporations worldwide. The global financial system is intricately interconnected with the stock market, playing a central role in driving economic activity. In today's interconnected world, trading stocks has become a popular and accessible means for individuals and entities to generate income. Numerous academic researchers have explored the use of Artificial Intelligence (AI) for stock prediction and have claimed that their models can accurately forecast stock performance. The issue is that many of these studies rely on a single data source, namely, daily stock data and cannot predict future stock prices, more than 1 or 2 days, with a large degree of success. Additionally, the single data source may be influenced by a multitude of economic factors as well as public sentiment, which is the most significant. In this research paper, several of these AI models are tested to evaluate their claims regarding stock prediction capabilities. Based on our experiments utilizing AI models and the results gathered, it was concluded that it was not possible to predict future stock prices using one method alone. Therefore, in order to provide a greater accuracy in predicting future stocks, the use of an ensemble approach was proposed. While many researchers build their ensemble models by combining various Artificial Neural Network models with sentiment analysis. We have suggested a different approach using other kinds of AI models, along with enhancements to traditional sentiment analysis techniques.
Author: Joish Bosco
Publisher: GRIN Verlag
ISBN: 3668800456
Category : Computers
Languages : en
Pages : 76
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Book Description
Project Report from the year 2018 in the subject Computer Science - Technical Computer Science, , course: Computer Science, language: English, abstract: Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environment during the 2 sample period. In this project, we use stock data instead of daily data in order to reduce the probability of uncertain noise, and relatively increase the sample size within a certain period of time. By non-stationarity, one means that the distribution of stock data is various during time changing. Non-linearity implies that feature correlation of different individual stocks is various. Efficient Market Hypothesis was developed by Burton G. Malkiel in 1991.
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 463
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Book Description
Stock trading and financial instrument markets offer significant opportunities for wealth creation. The ability to predict stock price movements has long intrigued researchers and investors alike. While some theories, like the Efficient Market Hypothesis, suggest that consistently beating the market is nearly impossible, others contest this viewpoint. Stock price prediction involves forecasting the future value of a given stock. In this project, we focus on the S&P 500 Index, which consists of 500 stocks from various sectors of the US economy and serves as a key indicator of US equities. To tackle this task, we utilize the Yahoo stock price history dataset, which contains 1825 rows and 7 columns including Date, High, Low, Open, Close, Volume, and Adj Close. To enhance our predictions, we incorporate technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation. In this book, for the forecasting task, we employ various regression algorithms including Linear Regression, Random Forest Regression, Decision Tree Regression, Support Vector Regression, Naïve Bayes Regression, K-Nearest Neighbor Regression, Adaboost Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, Catboost Regression, MLP Regression, Lasso Regression, and Ridge Regression. These models aim to predict the future Adj Close price of the stock based on historical data. In addition to stock price prediction, we also delve into predicting stock daily returns using machine learning models. We utilize K-Nearest Neighbor Classifier, Random Forest Classifier, Naive Bayes Classifier, Logistic Regression Classifier, Decision Tree Classifier, Support Vector Machine Classifier, LGBM Classifier, Gradient Boosting Classifier, XGB Classifier, MLP Classifier, and Extra Trees Classifier. These models are trained to predict the direction of daily stock returns (positive or negative) based on various features and technical indicators. To assess the performance of these machine learning models, we evaluate several important metrics. Accuracy measures the overall correctness of the predictions, while recall quantifies the ability to correctly identify positive cases (upward daily returns). Precision evaluates the precision of positive predictions, and the F1 score provides a balanced measure of precision and recall. Additionally, we consider macro average, which calculates the average metric value across all classes, and weighted average, which provides a balanced representation considering class imbalances. To enhance the user experience and facilitate data exploration, we develop a graphical user interface (GUI). The GUI is built using PyQt and offers an interactive platform for users to visualize and interact with the data. It provides features such as plotting boundary decisions, visualizing feature distributions and importance, comparing predicted values with true values, displaying confusion matrices, learning curves, model performance, and scalability analysis. The GUI allows users to customize the analysis by selecting different models, time periods, or variables of interest, making it accessible and user-friendly for individuals without extensive programming knowledge. The combination of exploring the dataset, forecasting stock prices, predicting daily returns, and developing a GUI creates a comprehensive framework for analyzing and understanding stock market trends. By leveraging machine learning algorithms and evaluating performance metrics, we gain valuable insights into the accuracy and effectiveness of our predictions. The GUI further enhances the accessibility and usability of the analysis, enabling users to make data-driven decisions and explore the stock market with ease.
Author: Luna Tjung
Publisher: Lulu.com
ISBN: 0557602483
Category :
Languages : en
Pages : 135
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Book Description
This study presents a Business Intelligence (BI) approach to forecast daily changes in 27 stocks’ prices from 8 industries. The BI approach uses a financial data mining technique specifically Neural Network to assess the feasibility of financial forecasting compared to regression model using ordinary least squares estimation method. We used eight indicators such as macroeconomic indicators, microeconomic indicators, political indicators, market indicators, market sentiment indicators, institutional investor, business cycles, and calendar anomaly to predict changes in stocks’ prices. The results shows NN model better predicts stock prices with up to 92% of forecasting accuracy.
Author: Ton Viet Ta
Publisher:
ISBN:
Category :
Languages : en
Pages : 86
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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 13908% annual return on your money by two trades per day on Dropbox Inc DBX Stock? Reading this book is the only way to have a specific strategy. This book offers you a chance to trade DBX Stock at predicted prices. Eight methods for buying and selling DBX 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 765 consecutive trading days (from March 23, 2018 to April 7, 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 DBX Stock. The book gives an insight about the behavior of the stock. They will surely gain their knowledge of DBX 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: Francis Eng-Hock Tay
Publisher: World Scientific
ISBN: 9812380752
Category : Business & Economics
Languages : en
Pages : 198
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Book Description
Exotic methods refer to a particular function within a general soft computing method such as genetic algorithms, neural networks and rough sets theory. They are applied to ordinary shares for a variety of financial purposes, such as portfolio selection and optimization, classification of market states, forecasting of market states and data mining. This is in contrast to the wide spectrum of work done on exotic financial instruments, wherein advanced mathematics is used to construct financial instruments for hedging risks and for investment. In this book, particular aspects of the general method are used to create interesting applications. For instance, genetic niching produces a family of portfolios for the trader to choose from. Support vector machines, a special form of neural networks, forecast the financial markets; such a forecast is on market states, of which there are three -- uptrending, mean reverting and downtrending. A self-organizing map displays in a vivid manner the states of the market. Rough sets with a new discretization method extract information from stock prices.
Author: Ethan Shaotran
Publisher:
ISBN: 9781092671101
Category :
Languages : en
Pages : 111
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Book Description
For centuries, human beings have tried to predict the future, whether it be NBA playoffs, weather, or elections. In this book, we tackle the common misconception that the stock market cannot be predicted, and build a stock prediction algorithm to beat the stock market, using Deep Learning, Data Analysis, and Natural Language Processing techniques.If you're new to Artificial Intelligence and Python, and are curious to learn more, this is a great book for you! Industry experts also have plenty to learn from the variety of methods and techniques used in data collection and manipulation.ABOUT THE AUTHOREthan Shaotran is an AI developer, researcher, and author of "Stock Prediction with Deep Learning". He is the founder of Energize.AI, where he built a financial stock prediction algorithm that outperformed the stock market in 2017. He is currently working on a thought experiment series to raise awareness on AI-related societal challenges within the AI community, regarding regulation and potential moral hazards, as well as autonomous vehicle driving software. Ethan has studied Economics and AI courses from Harvard, Stanford, and USF, is an affiliate with the Harvard Kennedy School's AI Initiative and is a member of the Association for Computing Machinery.
Author: Raymond Hon Fu Chan
Publisher: World Scientific Publishing Company
ISBN: 9814436267
Category : Business & Economics
Languages : en
Pages : 204
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Book Description
Technical analysis is defined as the tracking and prediction of asset price movements using charts and graphs in combination with various mathematical and statistical methods. More precisely, it is the quantitative criteria used in predicting the relative strength of buying and selling forces within a market to determine what to buy, what to sell, and when to execute trades. This book introduces simple technical analysis tools like moving averages and Bollinger bands, and also advanced techniques such as wavelets and empirical mode decomposition. It first discusses some traditional tools in technical analysis, such as trend, trend Line, trend channel, Gann's Theory, moving averages, and Bollinger bands. It then introduces a recent indicator developed for stock market and two recent techniques used in the technical analysis field: wavelets and the empirical mode decomposition in financial time series. The book also discusses the theory to test the performance of the indicators and introduces the MATLAB Financial Toolbox, some of the functions/codes of which are used in our numerical experiments.
