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| Author | : Georgios Sermpinis |
| Publisher | : |
| Total Pages | : |
| Release | : 2009 |
| Genre | : |
| ISBN | : |
| Author | : Georgios Sermpinis |
| Publisher | : |
| Total Pages | : |
| Release | : 2009 |
| Genre | : |
| ISBN | : |
| Author | : Fahed Mostafa |
| Publisher | : Springer |
| Total Pages | : 177 |
| Release | : 2017-02-28 |
| Genre | : Technology & Engineering |
| ISBN | : 331951668X |
This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing, and value-at-risk modeling. These features mean that they can be applied to market-risk problems to overcome classic problems associated with statistical models.
| Author | : Wayne Ferson |
| Publisher | : MIT Press |
| Total Pages | : 497 |
| Release | : 2019-03-12 |
| Genre | : Business & Economics |
| ISBN | : 0262039370 |
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 | : Alan White |
| Publisher | : Routledge |
| Total Pages | : 224 |
| Release | : 2014-02-04 |
| Genre | : Business & Economics |
| ISBN | : 1135687897 |
This book examines the applicability of a relatively new and powerful tool, genetic adaptive neural networks, to the field of option valuation. A genetic adaptive neural network model is developed to price option contracts with futures-style margining. This model is capable of estimating complex, non-linear relationships without having prior knowledge of the specific nature of the relationships. Traditional option pricing models require that the researcher or practitioner specify the distribution of the underlying asset. In addition, the methodology is able to easily accommodate additional inputs(something that cannot be preformed with existing models. Since 1973, options on stock have been traded on organized exchanges in the United States. An option on a stock gives the option owner the right to buy or sell the stock for a pre-set price.. Since the introduction of stock options, the options market has experienced tremendous growth and has spawned even more exotic types of derivative securities. Obviously, valuing these securities is an issue of great importance to investors and hedgers in the financial marketplace. Existing pricing models produce systematic pricing errors and new models have to be developed for options with differing characteristics. The genetic adaptive neural network is found to provide more accurate valuation than a traditional option pricing model when applied to the 3-month Eurodollar futures-option contract traded on the London International Financial Futures and Options Exchange.
| Author | : Paul D. McNelis |
| Publisher | : Elsevier |
| Total Pages | : 261 |
| Release | : 2005-01-20 |
| Genre | : Computers |
| ISBN | : 0080479650 |
This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website
| Author | : Ali Roghani |
| Publisher | : Createspace Independent Publishing Platform |
| Total Pages | : 108 |
| Release | : 2016-08-09 |
| Genre | : |
| ISBN | : 9781536976830 |
Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal. Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style. Neural networks have been touted as all-powerful tools in stock-market prediction. Companies such as MJ Futures claim amazing 199.2% returns over a 2-year period using their neural network prediction methods. They also claim great ease of use; as technical editor John Sweeney said in a 1995 issue of "Technical Analysis of Stocks and Commodities," "you can skip developing complex rules (and redeveloping them as their effectiveness fades) . . . just define the price series and indicators you want to use, and the neural network does the rest."
| Author | : Antonios K. Alexandridis |
| Publisher | : John Wiley & Sons |
| Total Pages | : 262 |
| Release | : 2014-04-24 |
| Genre | : Mathematics |
| ISBN | : 1118596293 |
A step-by-step introduction to modeling, training, and forecasting using wavelet networks Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification presents the statistical model identification framework that is needed to successfully apply wavelet networks as well as extensive comparisons of alternate methods. Providing a concise and rigorous treatment for constructing optimal wavelet networks, the book links mathematical aspects of wavelet network construction to statistical modeling and forecasting applications in areas such as finance, chaos, and classification. The authors ensure that readers obtain a complete understanding of model identification by providing in-depth coverage of both model selection and variable significance testing. Featuring an accessible approach with introductory coverage of the basic principles of wavelet analysis, Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification also includes: • Methods that can be easily implemented or adapted by researchers, academics, and professionals in identification and modeling for complex nonlinear systems and artificial intelligence • Multiple examples and thoroughly explained procedures with numerous applications ranging from financial modeling and financial engineering, time series prediction and construction of confidence and prediction intervals, and classification and chaotic time series prediction • An extensive introduction to neural networks that begins with regression models and builds to more complex frameworks • Coverage of both the variable selection algorithm and the model selection algorithm for wavelet networks in addition to methods for constructing confidence and prediction intervals Ideal as a textbook for MBA and graduate-level courses in applied neural network modeling, artificial intelligence, advanced data analysis, time series, and forecasting in financial engineering, the book is also useful as a supplement for courses in informatics, identification and modeling for complex nonlinear systems, and computational finance. In addition, the book serves as a valuable reference for researchers and practitioners in the fields of mathematical modeling, engineering, artificial intelligence, decision science, neural networks, and finance and economics.
| Author | : Ing-Chyuan Wu |
| Publisher | : |
| Total Pages | : 346 |
| Release | : 2002 |
| Genre | : Over-the-counter markets |
| ISBN | : |
We propose a neural network option pricing model that can fit listed option prices accurately, and be used to recover the implied asset price distribution and asset price dynamics. The observable market option prices are noisy and insufficient. To overcome the problem, two option pricing models constructed using multilayer feedforward neural networks are investigated. The first one uses a neural network to learn the implied volatility function of Black-Scholes-Merton model. To price an option, this neural network must work together with Black-Scholes-Merton formulas. The other one uses a neural network to learn the function mapping between the option price and observable affecting factors. This neural network is a complete option pricing model and can function independently of any option pricing formula. Based on a theory derived by Breeden and Litzenberger, the implied risk-neutral probability density surface can be extracted from the second partial derivative of the option price function with respect to the strike price. While both neural network option pricing models fit observed option prices well, only the first model is suitable for extracting a risk-neutral probability density surface. Risk-neutral valuation method is used to perform in-sample and out-of-sample tests. Based on the Fokker-Plank equation, an implied Ito process can be derived from the first and second partial derivatives of the option price function with respect to the strike price and the maturity. Similarly, only the first neural network option pricing model is suitable for deriving an Ito process. Monte Carlo simulation is used to perform in-sample and out-of-sample tests. The pricing errors from the extracted risk-neutral probability density surface and Ito process are only slightly larger than that directly from the neural network option pricing model. The small difference indicates that little information has been lost in the extracted risk-neutral probability density surface and Ito process. As a result, exotic options can be priced with the extracted information.
| Author | : German Creamer |
| Publisher | : Routledge |
| Total Pages | : 131 |
| Release | : 2021-04-05 |
| Genre | : Business & Economics |
| ISBN | : 1000372006 |
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.
