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Stock Price Prediction A Referential Approach On How To Predict The Stock Price Using Simple Time Series
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Book Synopsis Stock price Prediction a referential approach on how to predict the stock price using simple time series... by : Dr.N.Srinivasan
Download or read book Stock price Prediction a referential approach on how to predict the stock price using simple time series... written by Dr.N.Srinivasan and published by Clever Fox Publishing. This book was released on with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Stock price analysis through Statistical and Data Science tools: An Overview by : Vinaitheerthan Renganathan
Download or read book Stock price analysis through Statistical and Data Science tools: An Overview written by Vinaitheerthan Renganathan and published by Vinaitheerthan Renganathan. This book was released on 2021-04-30 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Precision in Predicting the Stock Prices - An Empirical Approach to Accuracy in Forecasting by : Dr. Suresh Kumar S
Download or read book Precision in Predicting the Stock Prices - An Empirical Approach to Accuracy in Forecasting written by Dr. Suresh Kumar S and published by . This book was released on 2017 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting the future prices of stock by analyzing the past and current price movements in determining the trend are always areas of interest of Chartists who believe in studying the action of the market itself rather than the past and current performances of the company. Stock price prediction has ignited the interest of researchers who strive to develop better predictive models with a fair degree of accuracy. The autoregressive integrated moving average (ARIMA)model introduced by Box and Jenkins in 1970has been in the limelight in econometrics literature for time series prediction, which has been at the core of explaining many economic and finance phenomena. ARIMA models in the research domain of finance and economics, especially stock markets, have shown an efficient capability to generate short-term forecasts and have hence beenable to outperform complex structural models in short-term prediction.This paper presents a stock price predictive model using the ARIMA model to analyze the sensitivity of such models to different time horizons used in the estimation of trends and verifies the validity of such forecasts in terms of their degree of precision. Published historical stock data, on an actively traded public sector bank's share and historical movements in the banking sector index in which the selected bank is a constituent, obtained from National Stock Exchange(NSE), India andwebsites of Yahoo finance are used to build and develop stock price forecasts and index movement predictive models. The experiments with dynamic as well as static forecasting methods used revealed that the ARIMA model has a strong potential for short-term prediction and can offer better precision than from long term trend estimates. As a stock price prediction or index movement forecast tool, it can be relied extensively in deciding entry and exit to and from the volatile markets,notwithstanding the fact the risk the investor faces on account of noise or shocks still can be erroneous making the entire prediction irrespective of its degree of precision irrelevant.
Book Synopsis Stock Price Predictions by : Azhar Ul Haque Sario
Download or read book Stock Price Predictions written by Azhar Ul Haque Sario and published by Independently Published. This book was released on 2023-07-13 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Stock Price Predictions: An Introduction to Probabilistic Models" is a comprehensive guide that delves into the intricate world of stock market prediction models. This book is a treasure trove of knowledge for both novice and seasoned investors, providing detailed explanations of traditional and modern approaches used to predict stock prices. In the first part of the book, "Traditional Approaches, " the author examines the most commonly used techniques for estimating share prices, such as Fundamental Analysis, Technical Analysis, and Quantitative Analysis. It also delves into more specific methods like Sentiment Analysis, Time Series Analysis, and Machine Learning Algorithms, among others. Each method is meticulously explained, providing readers with a sound understanding of the strengths and limitations of each approach. The second part, "Understanding the World of Probability-Based Models," introduces readers to the realm of probability models, explaining their role and different types. It covers a wide range of models like ARIMA, GARCH, VAR, MGARCH, Stochastic Volatility Models, and many more. Each model is discussed in depth, with explanations of how they can be used to estimate future share prices. This section serves as an excellent resource for those seeking to expand their knowledge and skills in using probability-based models for stock price prediction. The final section, "Instances of Successful Forecasts Using Probability-Based Models," provides real-world examples of successful forecasts using these models. It includes well-known models like the Black-Scholes Model, Monte Carlo Simulations, Brownian Motion Model, ARIMA, and GARCH Model. The book concludes with a discussion on the success of more contemporary models like LSTM and Facebook's Prophet.
Book Synopsis Stock Price Prediction Using Kernel Adaptive Filtering Within a Stock Market Interdependence Approach by : Sergio Garcia-Vega
Download or read book Stock Price Prediction Using Kernel Adaptive Filtering Within a Stock Market Interdependence Approach written by Sergio Garcia-Vega and published by . This book was released on 2019 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock prices are continuously generated by different data sources and depend on various factors such as financial policies and national economic growths. These financial time series are complex interconnected systems in which the price of one stock may be influenced by the economic factors of other stock markets. The prediction of stock prices, unlike traditional classification and regression problems, requires considering the sequential and interdependence nature of financial time series. This work proposes to sequentially predict stock prices using kernel adaptive filtering (KAF) within a stock market interdependence approach. Thus, unlike traditional approaches, stock prices are predicted using not only their local models but also the individual local models learned from other stocks, enhancing prediction performance. The proposed framework has been tested on 24 different stocks from three major economies. Simulation results show relatively low values of mean-square-error and better accuracy when compared with KAF-based methods.
Book Synopsis TIME-SERIES ANALYSIS: FORECASTING STOCK PRICE USING MACHINE LEARNING WITH PYTHON GUI by : Vivian Siahaan
Download or read book TIME-SERIES ANALYSIS: FORECASTING STOCK PRICE USING MACHINE LEARNING WITH PYTHON GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-02 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Prediction of Stocks with Gao's Equation by : Johnson Gao
Download or read book Prediction of Stocks with Gao's Equation written by Johnson Gao and published by Lulu.com. This book was released on 2004-11-30 with total page 55 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prediction of stock with Gao's equation is a unique book that discuss how to apply a new method (dynamic balancing of moving average) to predict stock price. A specially desined stock ruler, a worksheet, and an instruction of how to use the stock ruler are included. The idea of Feng Shui and Ba Gua is used to evaluate 9 grades of stock strength that can simplify the method of prediction of stock price of tomorrow with the sliding stock ruler. Some arts, peoms, and abstract of a tale are inserted. This is an economic version of the book (printed in black and white) to reduce the cost. The original version is printed in full color. A full color copy with color stock ruler and worksheet may find at Lulu.com under the same author. Refer to the web site http: //www.lulu.com/content/73939 which is printed with better quality paper
Book Synopsis The Art and Science of Predicting Stock Prices by : Luna Tjung
Download or read book The Art and Science of Predicting Stock Prices written by Luna Tjung and published by Lulu.com. This book was released on 2010-08-12 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Time Series Analysis of Stock Prices Using the Box-Jenkins Approach by : Shakira Green
Download or read book Time Series Analysis of Stock Prices Using the Box-Jenkins Approach written by Shakira Green and published by . This book was released on 2011 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Author's abstract: A time series is a sequence of data points, typically measured at uniform time intervals. Examples occur in a variety of fields ranging from economics to engineering, and methods of analyzing time series constitute an important part of Statistics. Time series analysis comprises methods for analyzing time series data in order to extract meaningful characteristics of the data and forecast future values. The Autoregressive Integrated Moving Average (ARIMA) models, or Box-Jenkins methodology, are a class of linear models that are capable of representing stationary as well as nonstationary time series. ARIMA models rely heavily on autocorrelation patterns. This paper will explore the application of the Box-Jenkins approach to stock prices, in particular sampling at different time intervals in order to determine if there is some optimal frame and if there are similarities in autocorrelation patterns of stocks within the same industry.
Book Synopsis Prediction of International Stock Market Movements Using a Statistical Time Series Analysis Method by : Jehan Shareef
Download or read book Prediction of International Stock Market Movements Using a Statistical Time Series Analysis Method written by Jehan Shareef and published by . This book was released on 2015-07-24 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stock Market Prediction Using Machine Learning Methods by : Subhadra Kompella
Download or read book Stock Market Prediction Using Machine Learning Methods written by Subhadra Kompella and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock price forecasting is a popular and important topic in financial and academic studies. Share market is an volatile place for predicting since there are no significant rules to estimate or predict the price of a share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc. are used to predict the price in tie share market but none of these methods are proved as a consistently acceptable prediction tool. In this paper, we implemented a Random Forest approach to predict stock market prices. Random Forests are very effectively implemented in forecasting stock prices, returns, and stock modeling. We outline the design of the Random Forest with its salient features and customizable parameters. We focus on a certain group of parameters with a relatively significant impact on the share price of a company. With the help of sentiment analysis, we found the polarity score of the new article and that helped in forecasting accurate result. Although share market can never be predicted with hundred per-cent accuracy due to its vague domain, this paper aims at proving the efficiency of Random forest for forecasting the stock prices.
Book Synopsis Predict Market Swings With Technical Analysis by : Michael McDonald
Download or read book Predict Market Swings With Technical Analysis written by Michael McDonald and published by John Wiley & Sons. This book was released on 2002-10-02 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fresh perspective on predicting the market The experience of Wall Street investment manager and analyst Michael McDonald offers a new perspective on how to navigate the turbulent ups and downs of the markets. His innovative approach to the stock market teaches investors how to use new investment strategies intended to replace the "buy and hold forever" strategies of yesterday. McDonald discusses what a "trading range" market is-a roller-coaster ride in which the market will neither gain nor lose much ground-and guides readers through this market with his proven investment strategies. This book provides an understandable way to make sense of the unpredictable stock market, taking into account more complex theories, including chaos and contrarian approaches. Along with his expert advice, McDonald presents four investing paradoxes that will help investors make smarter decisions now and predict where the market is heading, using his proven theories.
Book Synopsis Stock Price Prediction Using Adaptive Time Series Forecasting and Machine Learning Algorithms by : Lumeng Chen
Download or read book Stock Price Prediction Using Adaptive Time Series Forecasting and Machine Learning Algorithms written by Lumeng Chen and published by . This book was released on 2020 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, ARIMA model, Long Short Term Memory (LSTM) model and Extreme Gradient Boosting (XGBoost) models were developed to predict daily adjusted close price of selected stocks from January 3, 2017 to April 24, 2020. Daily stock price data includes columns of open, close, adjusted close, high, low and volume. In ARIMA and LSTM models, the only features we used as model inputs were previous N days' stock prices. Prediction on day N+1 was calculated based on previous N values. RMSE and MAPE were calculated from this rolling forecast and the actual price in the test dataset. Optimal parameters were selected to be the setting that yielded the lowest RMSE score. Residuals diagnostic was performed to check model assumption for the final ARIMA model. In XGBoost model, feature engineering was used to create two additional features from open, close, high and low price. Same with LSTM model, previous N days features were used as features in day N+1 for prediction. In both LSTM and XGBoost models, training dataset was scaled for model fitting. Features and output from cross-validation and test dataset were scaled too based on previous N days' values. The prediction results were then reverted back to original scale before calculation of RMSE and MAPE scores. In conclusion, looking at the prediction versus actual stock price plot for each stock and their RMSE and MAPE scores, all three models produced good forecast of next day's stock price. However, during the time with great volatility, the lag between forecast value and actual value is more noticeable. In our models, historical N days stock price on its own could provide a relatively accurate prediction on N+1 day's stock price. In XGBoost model particularly, we found out that N=2 provided better RMSE and MAPE(%) results than other larger values of N (previous N days). As N gets larger, prediction accuracy got lower in XGBoost. In XGBoost feature importance analysis, the most important factor to today's stock price is its price yesterday. Although the final ARIMA model achieved the lowest RMSE score, grid search for one-step ARIMA forecast model parameters took the longest computing time, while XGBoost model with the second lowest RMSE score required the least time for parameter tuning and forecast calculation.
Book Synopsis Forecasting the Monthly Movements of Stock Prices by : William Dunnigan
Download or read book Forecasting the Monthly Movements of Stock Prices written by William Dunnigan and published by . This book was released on 1930 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Forecasting the Time Series of Apple Inc.'s Stock Price by : Jordan Berninger
Download or read book Forecasting the Time Series of Apple Inc.'s Stock Price written by Jordan Berninger and published by . This book was released on 2018 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 21st Century has been defined by the exponential growth of the information technology (IT) industry, the smart phone and the personal computer. Apple Inc. has played a major role in that growth, with Apple products widely considered emblematic of the IT revolution. The health of Apple Inc. is a predictor of the health of the industry and industries that depend on it. Apple's stock price is an indicator of Apple's health. Therefore, it is of particular interest to have good models to predict the stock price of such a hallmark company of this IT revolution. Statistical time series analysis is of paramount importance to do that. This thesis compares the forecasting performance of univariate and multivariate time series models of Apple stock's opening price for the first day of each month. Among well-known univariate models, we consider Autoregressive Integrated Moving Average (ARIMA) models, ARIMA with Generalized Autoregressive Conditional Heteroscedascity (GARCH) and Exponential Smoothing, all models where the predicted opening price depends on past values of itself and nothing else. Among multivariate methods known in time series analysis, we consider Vector Autoregression (VAR), which endogenizes all the variables considered, and classical linear regression with ARIMA residuals. We fit these models to an ``in-sample`` (training set) of historical opening price from January 1990 to September 2016, and use them to forecast 12 months ``out of sample`` (test set) October 2016 to September 2017. In the multivariate models, the predictors of Apple's opening price are: the stock price of the $S\&P 500$, Microsoft and Texas Instruments. We hypothesize a positive correlation of Apple's stock with the $S\&P 500$ stock, a negative correlation with its competitor Microsoft and a positive correlation with Apple's supplier, Texas Instruments. We compare the forecasts from each model with the actual values of the ``out of sample`` time series (the test set) to assess forecasting performance according to the Root Mean Square Error (RMSE). We find that an average forecast consisting of the average of all those models' forecasts, what is known in the forecasting industry as "the consensus forecast," has the lowest RMSE for the 12-month test set. This is an important result, not only because it explains the decades' old practice of considering averages of forecasts from many models as `` the forecast`` of any relevant economic variable, but also because it highlights the benefits of integrating multiple classical models to obtain a good predictive performance.
Book Synopsis Stock Market Prediction Using Time Series Analysis by : Kamalakannan J
Download or read book Stock Market Prediction Using Time Series Analysis written by Kamalakannan J and published by . This book was released on 2018 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock market is a market that enables seamless exchange of buying and selling of company stocks. Every Stock Exchange has their own Stock Index value. Index is the average value that is calculated by combining several stocks. This helps in representing the entire stock market and predicting the market's movement over time. The Equity market can have a profound impact on people and the country's economy as a whole. Therefore, predicting the stock trends in an effective manner can minimize the risk of investing and maximize profit. In our paper, we are using the Time Series Forecasting methodology for predicting and visualizing the predictions. Our focus for prediction will be based on the technical analysis using historic data and ARIMA Model. Autoregressive Integrated Moving Average (ARIMA) model has been used extensively in the field of finance and economics as it is known to be robust, efficient and has a strong potential for short-term share market prediction.
Book Synopsis Stock Market Trading by : Geary Hooper
Download or read book Stock Market Trading written by Geary Hooper and published by Trafford Publishing. This book was released on 2003-10-09 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Buy low, sell high" If you have ever traded stocks in the stock market, then I am sure that you have heard of this phrase. Have you ever purchased a stock that you thought was low in price, only to see it go lower after you have purchased the stock. Well if you have, you are not alone. Because this happens to just about everyone that has invested in the stock market. The problem is, knowing when a stock is at a low price and it is time to purchase it. This is really very simple. The only time a stock is at a low price is when it goes up after you purchase it. By now you are saying to your self, I know that. Well could you have known that the stock was going up without a tip from some one? Even with that tip, could you have analyzed the stock to see for your self? If not, then this book is for you. Its main purpose is to enable you to find, analyze, purchase, and sell stocks on your own while making a hansom profit along the way. With this book you will be able to find main events in a stock's history right up to the current time to let you know if this is a stock to purchase and when, or to leave it alone.
