Stock Market Modeling and Forecasting

Stock Market Modeling and Forecasting PDF

Author: Xiaolian Zheng

Publisher: Springer

Published: 2013-04-05

Total Pages: 166

ISBN-13: 1447151550

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Stock Market Modeling and Forecasting translates experience in system adaptation gained in an engineering context to the modeling of financial markets with a view to improving the capture and understanding of market dynamics. The modeling process is considered as identifying a dynamic system in which a real stock market is treated as an unknown plant and the identification model proposed is tuned by feedback of the matching error. Like a physical system, a financial market exhibits fast and slow dynamics corresponding to external (such as company value and profitability) and internal forces (such as investor sentiment and commodity prices) respectively. The framework presented here, consisting of an internal model and an adaptive filter, is successful at considering both fast and slow market dynamics. A double selection method is efficacious in identifying input factors influential in market movements, revealing them to be both frequency- and market-dependent. The authors present work on both developed and developing markets in the shape of the US, Hong Kong, Chinese and Singaporean stock markets. Results from all these sources demonstrate the efficiency of the model framework in identifying significant influences and the quality of its predictive ability; promising results are also obtained by applying the model framework to the forecasting of major market-turning periods. Having shown that system-theoretic ideas can form the core of a novel and effective basis for stock market analysis, the book is completed by an indication of possible and likely future expansions of the research in this area.

Introduction to Financial Forecasting in Investment Analysis

Introduction to Financial Forecasting in Investment Analysis PDF

Author: John B. Guerard, Jr.

Publisher: Springer Science & Business Media

Published: 2013-01-04

Total Pages: 245

ISBN-13: 1461452392

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Forecasting—the art and science of predicting future outcomes—has become a crucial skill in business and economic analysis. This volume introduces the reader to the tools, methods, and techniques of forecasting, specifically as they apply to financial and investing decisions. With an emphasis on "earnings per share" (eps), the author presents a data-oriented text on financial forecasting, understanding financial data, assessing firm financial strategies (such as share buybacks and R&D spending), creating efficient portfolios, and hedging stock portfolios with financial futures. The opening chapters explain how to understand economic fluctuations and how the stock market leads the general economic trend; introduce the concept of portfolio construction and how movements in the economy influence stock price movements; and introduce the reader to the forecasting process, including exponential smoothing and time series model estimations. Subsequent chapters examine the composite index of leading economic indicators (LEI); review financial statement analysis and mean-variance efficient portfolios; and assess the effectiveness of analysts’ earnings forecasts. Using data from such firms as Intel, General Electric, and Hitachi, Guerard demonstrates how forecasting tools can be applied to understand the business cycle, evaluate market risk, and demonstrate the impact of global stock selection modeling and portfolio construction.

Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network

Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network PDF

Author: Joish Bosco

Publisher: GRIN Verlag

Published: 2018-09-18

Total Pages: 76

ISBN-13: 3668800456

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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.

Modeling and Forecasting Stock Market Prices with Sigmoidal Curves

Modeling and Forecasting Stock Market Prices with Sigmoidal Curves PDF

Author: Daniel Tran

Publisher:

Published: 2017

Total Pages: 150

ISBN-13: 9781369846188

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Pricing stock market data is difficult because it is inherently noisy and prone to unexpected events. However, stock market data generally exhibits trends in the medium and long term. A typical successful stock index exhibits an initiation phase, rapid growth, and then saturation whereby the price plateaus. Sigmoidal curves can effectively model and forecast stock market data because it can represent nonlinear stock behavior within confidence interval bounds. This thesis surveys various members of the sigmoidal family of curves and determines which curves best fit stock market data. We explore several techniques to filter our data, such as the moving average, single exponential smoothing, and the Hodrick-Prescott filter. We fit the sigmoidal curves to raw data using the Levenberg-Marquardt algorithm. This thesis aggregates these analysis techniques and apply them towards gauging the opportune time point to sell stocks.

11th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions and Artificial Intelligence - ICSCCW-2021

11th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions and Artificial Intelligence - ICSCCW-2021 PDF

Author: Rafik A. Aliev

Publisher: Springer Nature

Published: 2022-01-04

Total Pages: 803

ISBN-13: 3030921271

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This book presents the proceedings of the 11th Conference on Theory and Applications of Soft Computing, Computing with Words and Perceptions and Artificial Intelligence, ICSCCW-2021, held in Antalya, Turkey, on August 23–24, 2021. The general scope of the book covers uncertain computation, decision making under imperfect information, neuro-fuzzy approaches, natural language processing, and other areas. The topics of the papers include theory and application of soft computing, computing with words, image processing with soft computing, intelligent control, machine learning, fuzzy logic in data mining, soft computing in business, economics, engineering, material sciences, biomedical engineering, and health care. This book is a useful guide for academics, practitioners, and graduates in fields of soft computing and computing with words. It allows for increasing of interest in development and applying of these paradigms in various real-life fields.

Forecasting Volatility in the Financial Markets

Forecasting Volatility in the Financial Markets PDF

Author: John L. Knight

Publisher: Butterworth-Heinemann

Published: 2002

Total Pages: 428

ISBN-13: 9780750655156

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This text assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting edge modeling and forecasting techniques. It then uses a technical survey to explain the different ways to measure risk and define the different models of volatility and return.

A Practical Guide to Forecasting Financial Market Volatility

A Practical Guide to Forecasting Financial Market Volatility PDF

Author: Ser-Huang Poon

Publisher: John Wiley & Sons

Published: 2005-08-19

Total Pages: 236

ISBN-13: 0470856157

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Financial market volatility forecasting is one of today's most important areas of expertise for professionals and academics in investment, option pricing, and financial market regulation. While many books address financial market modelling, no single book is devoted primarily to the exploration of volatility forecasting and the practical use of forecasting models. A Practical Guide to Forecasting Financial Market Volatility provides practical guidance on this vital topic through an in-depth examination of a range of popular forecasting models. Details are provided on proven techniques for building volatility models, with guide-lines for actually using them in forecasting applications.

Modeling and Forecasting Primary Commodity Prices

Modeling and Forecasting Primary Commodity Prices PDF

Author: Walter C. Labys

Publisher: Routledge

Published: 2017-03-02

Total Pages: 247

ISBN-13: 1351917080

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Recent economic growth in China and other Asian countries has led to increased commodity demand which has caused price rises and accompanying price fluctuations not only for crude oil but also for the many other raw materials. Such trends mean that world commodity markets are once again under intense scrutiny. This book provides new insights into the modeling and forecasting of primary commodity prices by featuring comprehensive applications of the most recent methods of statistical time series analysis. The latter utilize econometric methods concerned with structural breaks, unobserved components, chaotic discovery, long memory, heteroskedasticity, wavelet estimation and fractional integration. Relevant tests employed include neural networks, correlation dimensions, Lyapunov exponents, fractional integration and rescaled range. The price forecasting involves structural time series trend plus cycle and cyclical trend models. Practical applications focus on the price behaviour of more than twenty international commodity markets.

Computational Methods in Decision-Making, Economics and Finance

Computational Methods in Decision-Making, Economics and Finance PDF

Author: Erricos John Kontoghiorghes

Publisher: Springer Science & Business Media

Published: 2013-11-11

Total Pages: 626

ISBN-13: 1475736134

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Computing has become essential for the modeling, analysis, and optimization of systems. This book is devoted to algorithms, computational analysis, and decision models. The chapters are organized in two parts: optimization models of decisions and models of pricing and equilibria.