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Author: Azhar ul Haque Sario
Publisher: Azhar Sario Authorship and Publishing
ISBN: 3759250637
Category : Business & Economics
Languages : en
Pages : 178
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Book Description
This third edition in the "Stock Predictions" series builds upon its predecessors, offering a deep dive into the quantitative methods used in stock price prediction. It presents a comprehensive guide to advanced financial models, ranging from the foundational Brownian Motion to cutting-edge machine learning techniques. The book explores key concepts like Geometric Brownian Motion for modeling exponential growth, Mean Reversion Models for capturing price reversion tendencies, and GARCH models for understanding volatility. It also delves into the world of machine learning, showcasing how Support Vector Machines, Neural Networks, and LSTMs can enhance prediction accuracy. Monte Carlo simulations and Copula Models are further discussed for their roles in risk assessment and portfolio management. Throughout the book, mathematical formulations, parameter estimation techniques, and practical applications are presented with clarity. The strengths and limitations of each model are highlighted, enabling readers to make informed choices. This edition is an invaluable resource for anyone in finance and investments seeking to master the quantitative tools used in stock price prediction. Whether a student, researcher, or practitioner, this book empowers you to leverage advanced models and navigate the complexities of today's markets.
Author: Azhar ul Haque Sario
Publisher: Azhar Sario Authorship and Publishing
ISBN: 3759250637
Category : Business & Economics
Languages : en
Pages : 178
Get Book Here
Book Description
This third edition in the "Stock Predictions" series builds upon its predecessors, offering a deep dive into the quantitative methods used in stock price prediction. It presents a comprehensive guide to advanced financial models, ranging from the foundational Brownian Motion to cutting-edge machine learning techniques. The book explores key concepts like Geometric Brownian Motion for modeling exponential growth, Mean Reversion Models for capturing price reversion tendencies, and GARCH models for understanding volatility. It also delves into the world of machine learning, showcasing how Support Vector Machines, Neural Networks, and LSTMs can enhance prediction accuracy. Monte Carlo simulations and Copula Models are further discussed for their roles in risk assessment and portfolio management. Throughout the book, mathematical formulations, parameter estimation techniques, and practical applications are presented with clarity. The strengths and limitations of each model are highlighted, enabling readers to make informed choices. This edition is an invaluable resource for anyone in finance and investments seeking to master the quantitative tools used in stock price prediction. Whether a student, researcher, or practitioner, this book empowers you to leverage advanced models and navigate the complexities of today's markets.
Author: Azhar ul Haque Sario
Publisher: tredition
ISBN: 3384403339
Category : Business & Economics
Languages : en
Pages : 200
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Book Description
This third volume in the "Stock Predictions" series builds on the success of the first edition, "Stock Price Predictions: An Introduction to Probabilistic Models" (ISBN 979-8223912712), and the second edition, "Forecasting Stock Prices: Mathematics of Probabilistic Models" (ISBN 979-8223038993). This new edition delves deeper into the complex world of quantitative finance, providing readers with a comprehensive guide to advanced financial models used in stock price prediction. The book covers a wide array of models, beginning with the foundational concept of Brownian Motion, which represents the random movement of stock prices and underpins many financial models. It then progresses to Geometric Brownian Motion, a model that accounts for the exponential growth often observed in stock prices. Mean Reversion Models are introduced to capture the tendency of stock prices to revert to their long-term average, offering a counterpoint to trend-following strategies. The book explores the world of volatility modeling with GARCH models, which capture the clustering and persistence of volatility in financial markets, crucial for risk management and option pricing. Extensions of GARCH, such as EGARCH and TGARCH, are examined to address the asymmetric impact of positive and negative news on volatility. In the latter part of the book, the focus shifts to Machine Learning, demonstrating how techniques like Support Vector Machines and Neural Networks can uncover complex patterns in financial data and enhance prediction accuracy. Recurrent Neural Networks, particularly LSTMs, are highlighted for their ability to model sequential data, making them ideal for capturing the temporal dynamics of stock prices. Monte Carlo simulations are discussed as a powerful tool for generating a range of possible future outcomes, enabling investors to assess risk and make informed decisions. Finally, Copula Models are introduced to model the dependence structure between multiple assets, critical for portfolio management and risk assessment. Throughout the book, each model is presented with a clear explanation of its mathematical formulation, parameter estimation techniques, and practical applications in stock price prediction. The book emphasizes the strengths and limitations of each model, equipping readers with the knowledge to select the most appropriate model for their specific needs. This book is an invaluable resource for students, researchers, and practitioners in finance and investments seeking to master the quantitative tools used in stock price prediction. With its rigorous yet accessible approach, this book empowers readers to leverage advanced financial models and make informed investment decisions in today's dynamic markets. The book is based on 95 research studies, which are listed on the references page and uploaded on Harvard University's Dataverse for transparency. As a published book, it has undergone review for originality.
Author: Renuka Sharma
Publisher: John Wiley & Sons
ISBN: 1394214316
Category : Computers
Languages : en
Pages : 358
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Book Description
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.
Author: Dr. Hemalatha N.
Publisher: REDSHINE Publication
ISBN: 9358096519
Category : Education
Languages : en
Pages : 340
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Book Description
Whiteflies pose a significant threat to agricultural productivity worldwide, causing damage to crops and economic losses. In this study, we present an Android-based whiteflies detection system utilizing deep learning techniques, specifically leveraging the YOLOv5 algorithm. The objective is to create a robust and efficient solution capable of real-time whitefly detection in agricultural fields. The proposed system capitalizes on the powerful capabilities of YOLOv5, a state-of-the-art object detection algorithm, known for its accuracy and speed.
Author: Rim El Khoury
Publisher: Springer Nature
ISBN: 3031634020
Category :
Languages : en
Pages : 581
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Book Description
Author: Vladimir Vapnik
Publisher: Springer Science & Business Media
ISBN: 1475732643
Category : Mathematics
Languages : en
Pages : 324
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Book Description
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
Author: Shuigeng Zhou
Publisher: Springer Science & Business Media
ISBN: 3642355277
Category : Computers
Languages : en
Pages : 812
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Book Description
This book constitutes the refereed proceedings of the 8th International Conference on Advanced Data Mining and Applications, ADMA 2012, held in Nanjing, China, in December 2012. The 32 regular papers and 32 short papers presented in this volume were carefully reviewed and selected from 168 submissions. They are organized in topical sections named: social media mining; clustering; machine learning: algorithms and applications; classification; prediction, regression and recognition; optimization and approximation; mining time series and streaming data; Web mining and semantic analysis; data mining applications; search and retrieval; information recommendation and hiding; outlier detection; topic modeling; and data cube computing.
Author: Wayne Ferson
Publisher: MIT Press
ISBN: 0262039370
Category : Business & Economics
Languages : en
Pages : 497
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Book Description
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Author: Wing-Keung Wong
Publisher:
ISBN: 9783039435746
Category :
Languages : en
Pages : 232
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Book Description
Mathematical finance plays a vital role in many fields within finance and provides the theories and tools that have been widely used in all areas of finance. Knowledge of mathematics, probability, and statistics is essential to develop finance theories and test their validity through the analysis of empirical, real-world data. For example, mathematics, probability, and statistics could help to develop pricing models for financial assets such as equities, bonds, currencies, and derivative securities.
Author: Rabinarayan Satpathy
Publisher: John Wiley & Sons
ISBN: 111978560X
Category : Computers
Languages : en
Pages : 433
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Book Description
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
