Download or read book Computational Science ICCS 2001 written by Vassil N. Alexandrov and published by Springer. This book was released on 2003-05-15 with total page 1068 pages. Available in PDF, EPUB and Kindle. Book excerpt: LNCS volumes 2073 and 2074 contain the proceedings of the International Conference on Computational Science, ICCS 2001, held in San Francisco, California, May 27-31, 2001. The two volumes consist of more than 230 contributed and invited papers that reflect the aims of the conference to bring together researchers and scientists from mathematics and computer science as basic computing disciplines, researchers from various application areas who are pioneering advanced application of computational methods to sciences such as physics, chemistry, life sciences, and engineering, arts and humanitarian fields, along with software developers and vendors, to discuss problems and solutions in the area, to identify new issues, and to shape future directions for research, as well as to help industrial users apply various advanced computational techniques.

Download or read book Stock Market Intelligence Analysis written by Cameron Sae and published by . This book was released on 2020-09-15 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Why do others always make money in the stock market, but they fail repeatedly? Why others, can be fried from silk into successful people, but they are still silk? The road of stock speculation is always full of doubts, the stock god taught you how to speculate so that your path to stock speculation more successful. The book content from shallow deep, easy to understand, detailed explanation, easy to learn, is the best choice for stock investors. The book combines the author's 20 years of fund management experience to help you analyze the stock market, master the laws of the stock market so that your investment risk is smaller, the investment success rate is higher. This book will protect your stock investments.

Download or read book Market Intelligence written by Per V. Jenster and published by Copenhagen Business School Press DK. This book was released on 2009 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Market Intelligence provides an overview of the most important tools and concepts relevant to intelligence analysis for strategic decision making. The book's focus is not only on competitors, but also on customers, suppliers, and a range of other stakeholders. It gives the reader tools used to analyze both micro and macro factors in the organization's environment to predict future outcomes better and to improve decision making. The field of competitive intelligence is studied by a diverse research community. Contributions to this field are made to aid States - on a national, regional, and local level - as well as to aid the military, non-profit organizations, and private companies. These contributions are mostly done in isolation, even though all these fields of study have much in common. The authors draw from these various fields and provide the essential insights to aid management thinking.

Download or read book The Research Driven Investor written by Timothy Hayes and published by McGraw-Hill Companies. This book was released on 2001 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: The editor of "Investment Strategy" shows how individual investors can access institutional-quality tools, data, and indicators and consistently beat the market. Hayes presents walk-through examples of a wide variety of investment models based on more than 100 years of stock market data and research from Ned Davis Research to achieve top results. 120 illustrations. 60 tables.

Download or read book Technical Analysis of the Financial Markets written by John J. Murphy and published by Penguin. This book was released on 1999-01-01 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: John J. Murphy has now updated his landmark bestseller Technical Analysis of the Futures Markets, to include all of the financial markets. This outstanding reference has already taught thousands of traders the concepts of technical analysis and their application in the futures and stock markets. Covering the latest developments in computer technology, technical tools, and indicators, the second edition features new material on candlestick charting, intermarket relationships, stocks and stock rotation, plus state-of-the-art examples and figures. From how to read charts to understanding indicators and the crucial role technical analysis plays in investing, readers gain a thorough and accessible overview of the field of technical analysis, with a special emphasis on futures markets. Revised and expanded for the demands of today's financial world, this book is essential reading for anyone interested in tracking and analyzing market behavior.

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

Download or read book Stock Analysis in the Twenty First Century and Beyond written by Thomas E. Berghage and published by Xlibris Corporation. This book was released on 2014-07-30 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock Analysis in the Twenty-First Century and Beyond For years, financial analysts have struggled with the fact that practically all the financial measures used to analyze corporate performance lack predictive power when it comes to forecasting the market performance of the company’s stock. Numerous academic studies have documented and reported this lack of predictability. Correlation coefficients close to zero have been reported for the relationship between stock market performance and such critical financial measures as earnings growth, sales growth, price/earnings ratio, return on equity, intrinsic value (models based on discounted cash flow or dividends), and many more. It is this disconnect between traditional financial measures and the performance of stocks in the marketplace that has led to the now-famous efficient market hypothesis, the cornerstone of modern portfolio theory. To accept the idea that the future performance of stocks is unpredictable is to say that nothing a company does will affect the future performance of its stock in the market, and that is absurd. It would be more accurate to say that everything a company does will affect the future performance of its stock in the market. The problem with this statement is that it makes the forecasting of future stock performance so complex that it removes it from the realm of human solution. Confident in the belief that something other than chance and irrational investors determine future stock prices, several research groups around the world have started exploring the use of intelligent computer programs (programs that self-organize based on environmental feedback). Early results are very promising and have provided a glimpse of the economic forces described by Adam Smith as the invisible hand that guides economic activity. Stock Analysis in the Twenty-First Century and Beyond describes the stock analysis problem and explores one of the more successful efforts to harness the new intelligent computer technology. Many people mistakenly classify Artificially Intelligent (AI) computer systems as a form of quantitative analysis. There are two distinct differences between advanced AI systems and traditional quantitative analysis. They are (1) who makes up the selection rules and weighting and (2) what information is used to discriminate between good- and poor-performing securities. In most quantitative systems, even in an advanced expert system form, humans make up the investment rules and mathematically derive the weightings associated with the rules. Computer systems that depend on outside human intelligence to program their actions are not inherently intelligent. In advanced AI systems, the computer makes up its own rules and weightings. The computer learns from examples of good- and poor-performing stocks and determines its own ways for discriminating between them. The procedures that are derived by the computer are often so complex that they defy human understanding. In addition to making up its own rules, advanced AI systems look at corporate financial data differently. Just like in the human brain, where information is not stored in the brain cells but rather in the connections and relationships between cells, so too is corporate performance information stored in the relationships between financial numbers. Assessing the performance of companies is not so much in the numbers as it is in the connections between the numbers. Financial analysts recognized this early on and have used first-order relational information in the form of financial ratios for many years (price/book, debt/equity, current assets / current liabilities, price/earnings, etc.). Now with advanced AI systems, we are finally able to look at and evaluate high-order interrelationships in financial data that have been far too complex to analyze with less sophisticated systems. These then are the fundamental differences between what has been used in the past and what will be used in the future. Cdr. Thomas E. Berghage

Download or read book Mapping the Markets written by Deborah Owen and published by John Wiley & Sons. This book was released on 2006-10-01 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: The global financial markets turn over billions of dollars daily. An array of different instruments is available to trade in these markets, ranging from simple stocks and shares to exotic creatures such as butterfly spreads. Participation at any level involves taking a view as to which way the market in question will move. There are essentially only two methods for analysing the future direction of the markets in equities, currencies, interest rates or commodities: one involves fundamental analysis, the other technical analysis. The two camps of investment analysts are separated by a wide gulf of distrust and suspicion. This book seeks to bridge the gap between the two disciplines and show how you can benefit from both, highlighting: • The tools you can use for mapping the markets—to understand what causes shifts in the trend and underlying forces that affect the economy and therefore the financial markets • The long-term cyclical drivers—how economic change is triggered by technological change, and the technological changes that will drive the markets in the future • Downward phases of the cycle—and the factors that cause them • The markets and sectors that will prosper in the future. As the world of investment gets ever more complicated and faster, Mapping the Markets will provide an invaluable route to improving your chances of investment success and avoiding investment distress, whether you are a long-term investor or a short-term trader.

Download or read book Stock Market Analysis Using the SAS System written by and published by . This book was released on 1995 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improve your market timing and investment strategies by using SAS for technical analysis of stock market data. Numerous step-by-step examples show you how to generate practical results easily and quickly. Topics include forecasting with time-series models, using crossover models to generate trading signals, calculating and using of price and volume rates of change, momentum and relative strength indicators, and a variety of other indicators. This book is designed for users with little previous experience with SAS who want to perform technical analysis of stock market data.

Download or read book High frequency data analysis written by Nadine Hirte and published by GRIN Verlag. This book was released on 2004-06-23 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seminar paper from the year 2003 in the subject Mathematics - Statistics, grade: 2.0 (B), European University Viadrina Frankfurt (Oder), language: English, abstract: Today the financial market becomes more complex and includes more competition. Reasons are trends like globalization, liberalization and lower-cost trading mechanism. The market microstructure research has the aim of an efficient market. It is focused on the structure of the financial market. The investigation becomes possible through the availability of high- frequency data. Those data exist especially in the United States and like that most of the research focuses this market. To explain the phenomena, which have been found adequate, models that fit the characteristics of high- frequency data have to be developed. The research is important to understand actions on the market as well as develop new efficient mechanism. One part of the market microstructure field is the bid-ask spread. It will be focus of this paper. In the first two parts it will be discussed theoretically. In the last part one model will be empirically analyzed and tested on its usefulness and validity. The second part of this paper explains the basic elements surrounding the research of bid-ask spread. Those are the financial market, market microstructure as well as high-frequency data. In the following part the bid-ask spread itself, approaches, researches and models focussing the spread will be discussed. The model of Roll (1984) will be explained in detail. The last part will be the empirical analysis of the model of Roll. It is analyzed with data from the NASDAQ.

Download or read book Option Theory with Stochastic Analysis written by Fred Espen Benth and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a very basic and accessible introduction to option pricing, invoking a minimum of stochastic analysis and requiring only basic mathematical skills. It covers the theory essential to the statistical modeling of stocks, pricing of derivatives with martingale theory, and computational finance including both finite-difference and Monte Carlo methods.

Download or read book Technical Analysis Modern Perspectives written by Gordon Scott and published by CFA Institute Research Foundation. This book was released on 2016-11-14 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Analysis Machine Learning and Applications written by Christine Preisach and published by Springer Science & Business Media. This book was released on 2008-04-13 with total page 714 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007.

Download or read book Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network written by Joish Bosco and published by GRIN Verlag. This book was released on 2018-09-18 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Deep Learning Tools for Predicting Stock Market Movements written by Renuka Sharma and published by John Wiley & Sons. This book was released on 2024-05-14 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis. The book: details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average; explains the rapid expansion of quantum computing technologies in financial systems; provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions; explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers. Audience The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.

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:

Download or read book Graphing Stock Market Data in R written by Hanna Kattilakoski and published by GRIN Verlag. This book was released on 2020-06-22 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seminar paper from the year 2018 in the subject Computer Science - Commercial Information Technology, grade: 90.00, Cologne Business School Köln, language: English, abstract: R is a programming language similar to S, designed for statistical computing and graphics. R is a GNU project developed at Bell Laboratories, with the first version launched in 2000. This paper is a demonstration of different graphing applications that can be accomplished through the R programming language. The majority of the focus will be on the analysis of stock market information in R. The starting point for this paper is with the first project that was conducted: a scatterplot combining aesthetic elements. With a basic code, the project added a creative twist to graphing in R. The outcome of this project was a scatterplot graphing heartweight and bodyweight of male and female cats. This project was found on R-Bloggers, and changes were made accordingly to the code. Instead of using normal points on the graph, the dots were replaced with .png images of cats. This provided a fun, visual example that made differentiating between male and female cats easier, therefore allowing for easier analysis of trends based on the sex of the cat. A linear regression trend line is also implemented, with paw prints, to further illustrate the correlation between the data.