Innovative Derivative Pricing and Time Series Simulation Techniques Via Machine and Deep Learning PDF Download

Author: Weilong Fu
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
It is challenging because of the complex statistical properties of the real financial data. In Chapter 5, we introduce two generative adversarial networks, which utilize the convolutional networks with attention and the transformers, for financial time series simulation. The networks learn the statistical properties in a data-driven manner and the attention mechanism helps to replicate the long-range dependencies. The proposed models are tested on the S&P 500 index and its option data, examined by scores based on the stylized facts and are compared with the pure convolutional network, i.e. QuantGAN. The attention-based networks not only reproduce the stylized facts, including heavy tails, autocorrelation and cross-correlation, but also smooth the autocorrelation of returns.

Author: German Creamer
Publisher: Routledge
ISBN: 1000372006
Category : Business & Economics
Languages : en
Pages : 131

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Book Description
The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm. Machine Learning and AI in Finance explores the most recent advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options. The chapters in this book were originally published as a special issue of the Quantitative Finance journal.

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: Jaromír Vrbka
Publisher: Springer Nature
ISBN: 3030756491
Category : Technology & Engineering
Languages : en
Pages : 197

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Book Description
The aim of this publication is to identify and apply suitable methods for analysing and predicting the time series of gold prices, together with acquainting the reader with the history and characteristics of the methods and with the time series issues in general. Both statistical and econometric methods, and especially artificial intelligence methods, are used in the case studies. The publication presents both traditional and innovative methods on the theoretical level, always accompanied by a case study, i.e. their specific use in practice. Furthermore, a comprehensive comparative analysis of the individual methods is provided. The book is intended for readers from the ranks of academic staff, students of universities of economics, but also the scientists and practitioners dealing with the time series prediction. From the point of view of practical application, it could provide useful information for speculators and traders on financial markets, especially the commodity markets.

Author: Tingting Ye
Publisher:
ISBN:
Category :
Languages : en
Pages : 40

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Book Description
In this paper, we combine the theory of stochastic process and techniques of machine learning with the regression analysis, first proposed by Longstaff and Schwartz 2001 and apply the new methodologies on financial derivatives pricing. Rigorous convergence proofs are provided for some of the methods we propose. Numerical examples show good applicability of the algorithms.

Author: Stefan Nagel
Publisher: Princeton University Press
ISBN: 0691218714
Category : Business & Economics
Languages : en
Pages : 168

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Book Description
A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

Author: Dmytro Zherlitsyn
Publisher: BPB Publications
ISBN: 9355516894
Category : Computers
Languages : en
Pages : 480

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Book Description
DESCRIPTION Python's intuitive syntax and beginner-friendly nature makes it an ideal programming language for financial professionals. It acts as a bridge between the world of finance and data analysis. This book will introduce essential concepts in financial analysis methods and models, covering time-series analysis, graphical analysis, technical and fundamental analysis, asset pricing and portfolio theory, investment and trade strategies, risk assessment and prediction, and financial ML practices. The Python programming language and its ecosystem libraries, such as Pandas, NumPy, SciPy, Statsmodels, Matplotlib, Seaborn, Scikit-learn, Prophet, and other data science tools will demonstrate these rooted financial concepts in practice examples. This book will help you understand the concepts of financial market dynamics, estimate the metrics of financial asset profitability, predict trends, evaluate strategies, optimize portfolios, and manage financial risks. You will also learn data analysis techniques using Python programming language to understand the basics of data preparation, visualization, and manipulation in the world of financial data. KEY FEATURES ● Comprehensive guide to Python for financial data analysis and modeling. ● Practical examples and real-world applications for immediate implementation. ● Covers advanced topics like regression, Machine Learning and time series forecasting. WHAT YOU WILL LEARN ● Learn financial data analysis using Python data science libraries and techniques. ● Learn Python visualization tools to justify investment and trading strategies. ● Learn asset pricing and portfolio management methods with Python. ● Learn advanced regression and time series models for financial forecasting. ● Learn risk assessment and volatility modeling methods with Python. WHO THIS BOOK IS FOR This book is designed for financial analysts and other professionals interested in the financial industry with a basic understanding of Python programming and statistical analysis. It is also suitable for students in finance and data science who wish to apply Python tools to financial data analysis and decision-making. TABLE OF CONTENTS 1. Getting Started with Python for Finance 2. Python Tools for Data Analysis: Primer to Pandas and NumPy 3. Financial Data Manipulation with Python 4. Exploratory Data Analysis for Finance 5. Investment and Trading Strategies 6. Asset Pricing and Portfolio Management 7. Time Series Analysis and Financial Data Forecasting 8. Risk Assessment and Volatility Modelling 9. Machine Learning and Deep Learning in Finance 10. Time Series Analysis and Forecasting with FB Prophet Library Appendix A: Python Code Examples for Finance Appendix B: Glossary Appendix C: Valuable Resources

Author: Oktay Akpolat
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 91

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Book Description
The forecasting of financial time series has been very important in both finance industry and academia for several years due to the volatile and unstable nature of financial systems. However, researchers believe that financial time series data can be modeled accurately, despite its noisy and unstable nature. Therefore, they have created several applications of time series modeling, which have been very successful when applied to financial markets, the prediction of stock prices being one of the main examples. In order to build better models for predicting stock prices, advanced data analysis techniques such as machine learning, deep learning, time series analysis, and probabilistic pattern recognition have been developed by researchers among from various areas in the past decades. Lately Recently, Deep Learning and Probabilistic Models are have become popular regarding due to developing their success in financial time series forecasting. This study focuses on a Deep Learning Model called Long-Short Term Memory (LSTM) and a Probabilistic Model called Hidden Markov Model (HMM) implementation for financial time series forecasting and on building a hybrid model by combining these two models to enhance prediction accuracy. More Specifically, the main objective is to build a novel approach of by combining LSTM with HMM, train the resulting model by using historical data, and predict future behavior of financial time series indicators. The results of our study indicate that the HMM is better than LSTM in predicting the stock closing price. The experimental result of our hybrid model process showed shows that the prediction accuracy of LSTM model increased when model combined with the HMM.

Author: Hariom Tatsat
Publisher: "O'Reilly Media, Inc."
ISBN: 1492073008
Category : Computers
Languages : en
Pages : 432

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Book Description
Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Author: Sofien Kaabar
Publisher: "O'Reilly Media, Inc."
ISBN: 1098148355
Category : Computers
Languages : en
Pages : 369

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Book Description
Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar—financial author, trading consultant, and institutional market strategist—introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Understand and create machine learning and deep learning models Explore the details behind reinforcement learning and see how it's used in time series Understand how to interpret performance evaluation metrics Examine technical analysis and learn how it works in financial markets Create technical indicators in Python and combine them with ML models for optimization Evaluate the models' profitability and predictability to understand their limitations and potential