Neuro Fuzzy Based Stock Market Prediction System

Neuro Fuzzy Based Stock Market Prediction System
Author: M. Gunasekaran
Publisher:
Total Pages: 6
Release: 2013
Genre:
ISBN:

Neural networks have been used for forecasting purposes for some years now. Often arises the problem of a black-box approach, i.e. after having trained neural networks to a particular problem, it is almost impossible to analyze them for how they work. Fuzzy Neuronal Networks allow adding rules to neural networks. This avoids the black-box-problem. Additionally they are supposed to have a higher prediction precision in unlike situations. Applying artificial neural network, genetic algorithm and fuzzy logic for the stock market prediction has attracted much attention recently, which has better correlated the non-quantitative factors with the stock market performance. However these approaches perform less satisfactorily due to the memoryless nature of the stock market performance. In this paper, we propose a data compression-based portfolio prediction model hybridized with the fuzzy logic and genetic algorithm. In the model, the quantifiable microeconomic stock data are first optimized through the genetic algorithms to generate the most effective microeconomic data in relation to the stock market performance.

Computational Science - ICCS 2001

Computational Science - ICCS 2001
Author: Vassil Alexandrov
Publisher: Springer Science & Business Media
Total Pages: 1068
Release: 2001-05-24
Genre: Computers
ISBN: 3540422331

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.

Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance

Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance
Author: Tom Rutkowski
Publisher: Springer Nature
Total Pages: 167
Release: 2021-06-07
Genre: Technology & Engineering
ISBN: 3030755215

The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.

A Neuro-fuzzy Logic Forecasting System in Stock Investment Decision Making Processes

A Neuro-fuzzy Logic Forecasting System in Stock Investment Decision Making Processes
Author: Xu Wang
Publisher:
Total Pages:
Release: 2007
Genre: Artificial intelligence
ISBN:

"The sophisticated financial investment world is characterized by highly random variations in stock prices, financial indexes and trading volumes so that it is quite difficult to get fundamental understanding of the financial investment process and to predict the stock market. This research attempts to develop a new and innovative approach to predict the stock time series with artificial intelligence techniques. Specifically, a fuzzy logic analysis has been made to predict the stock time series with different characteristic variables and different investments horizons, respectively. A neural network is designed to fine-tune the parameters involved and thus a neuron-fuzzy logic time series forecasting model has been developed" - abstract.

Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network

Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network
Author: Joish Bosco
Publisher: GRIN Verlag
Total Pages: 82
Release: 2018-09-18
Genre: Computers
ISBN: 3668800456

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.

Artificial Intelligence in Financial Markets

Artificial Intelligence in Financial Markets
Author: Christian L. Dunis
Publisher: Springer
Total Pages: 349
Release: 2016-11-21
Genre: Business & Economics
ISBN: 1137488808

As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.

Forecast of Financial Markets Stock Prices Using Neural Networks and ANFIS

Forecast of Financial Markets Stock Prices Using Neural Networks and ANFIS
Author: Luis Alberto Valencia Vega
Publisher:
Total Pages:
Release: 2011
Genre: Finance
ISBN:

The financial market is a very complex nonlinear series of time. There have been a lot of opinions in the topic of the predictability of it. The need to predict a next day, week, or month has always existed for the final purpose of making money. The most common way of forecasting this time series is with statistic methods and linear regression models. However, the use of artificial intelligence algorithms may have a better outcome, due to the capability of them to handle nonlinear data. The present thesis will be focused on evaluating the use of artificial intelligence algorithms as forecasters for financial markets stock prices. Two algorithms will be used, Feed-Forward Neural networks and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). All forecasts are made with the purpose of a short term trading strategy. Three stocks will be used as an example of the consistency of the method; Google, Apple and the Mexican stock ALFA. These three stocks have different distributed data and different behavior from the neural networks and ANFIS ¡s expected.

Machine Intelligence

Machine Intelligence
Author: Peter Sincak
Publisher: World Scientific
Total Pages: 480
Release: 2004
Genre: Computers
ISBN: 9789812562531

This book brings together the contributions of leading researchers inthe field of machine intelligence, covering areas such as fuzzy logic, neural networks, evolutionary computation and hybrid systems.There is wide coverage of the subject from simple tools, throughindustrial applications, to applications in high-level intelligentsystems which are biologically motivated, such as humanoid robots (andselected parts of these systems, like the visual cortex). Readers willgain a comprehensive overview of the issues in machine intelligence, afield which promises to play a very important role in the informationsociety of the future

ELLIOTT WAVE PRINCIPLE - KEY T

ELLIOTT WAVE PRINCIPLE - KEY T
Author: Robert R. Prechter Jr
Publisher: New Classics Library
Total Pages: 258
Release: 2005-02
Genre: Business & Economics
ISBN: 9781616040499

A Great Classic for Three Decades: Now In Its 10th Edition, Consider What This "Definitive Text" Offers You Take a moment to look over your books about investing. Have any of them given you a successful method for making profits and reducing risks? Is there even one such book that has proven reliable over the years? Alas, most investors would say "no." That's because so few investment books are "classic" in the true sense: For years investors keep buying the book, and they keep using the method to make the most of their opportunities. Three decades years ago -- 1978 -- is one of the last times an investment book was written that is worthy of being called "classic." One of the two men who authored that book was a 26 year-old market analyst working at Merrill Lynch's headquarters on Wall Street. The young man had earned a lot of attention in a short time by using a forecasting tool that almost no one had heard of. Yet his market forecasts were startlingly accurate: Robert Prechter was the young man's name, and he used a method called the "Elliott Wave Principle." A. J. Frost was one of the few other financial professionals who used the Wave Principle. In a distinguished 20-year career, Frost had likewise made many astonishingly accurate forecasts. His colleagues regarded him as the consummate technical analyst. Frost and Prechter met in May of 1977 and became fast friends. Eighteen months later, they published Elliott Wave Principle - Key to Market Behavior. The Dow Industrials stood at 790. But the brash forecast in this new book called for a Great Bull Market. It became a runaway best seller. Three decades is enough time for investors to deem a book about an investment method as "classic," and surely the jury is in on this one: Elliott Wave Principle is now published in seven languages, and continues to sell thousands of copies every year. In Europe, Asia and the Americas, literally millions of investors worldwide use or recognize the Elliott Wave method for profitable investing. Elliott Wave International is proud to present the 10th edition of this investment classic. It's designed to help the Elliott Wave novice and the veteran practitioner. It's time to consider what this definitive text offers you. Here's a sample of what you'll learn: The basic tenets of Wave Theory: You'll read simple explanations of the terms, and how to identify all 13 waves that can occur in the movement of stock market averages. The rules and guidelines of Wave analysis: You'll learn the basics of counting waves, how to recognize the "right look" of a wave, plus lots of simple steps for applying the rules. The scientific background of the Wave Principle: How you can see it in nature and the universe, in art and mathematics, even in the shape of the human body. Long-term waves: You'll see how the Wave Principle gives history greater meaning, from the fall of the Roman Empire through the Middle Ages into the financial upheavals of the 20th Century. Understanding these monumental trends will help you position yourself for long-term profit and protection. Stocks, commodities and gold: The Wave Principle is your guide to the movements of any financial market. Few pleasures can match the exhilaration you'll feel when a Wave Principle forecast has you in the market when it moves up, or takes you out just before it moves down. Obviously, Elliott Wave Principle - Key to Market Behavior is the perfect companion to Bob Prechter's Elliott Wave Theorist publication. The book is essential reading for you to receive the most from what the Theorist says every month -- in fact, all of EWI's publications continually reference this book.

Intelligent and Fuzzy Techniques: Smart and Innovative Solutions

Intelligent and Fuzzy Techniques: Smart and Innovative Solutions
Author: Cengiz Kahraman
Publisher: Springer Nature
Total Pages: 1701
Release: 2020-07-10
Genre: Technology & Engineering
ISBN: 3030511561

This book gathers the most recent developments in fuzzy & intelligence systems and real complex systems presented at INFUS 2020, held in Istanbul on July 21–23, 2020. The INFUS conferences are a well-established international research forum to advance the foundations and applications of intelligent and fuzzy systems, computational intelligence, and soft computing, highlighting studies on fuzzy & intelligence systems and real complex systems at universities and international research institutions. Covering a range of topics, including the theory and applications of fuzzy set extensions such as intuitionistic fuzzy sets, hesitant fuzzy sets, spherical fuzzy sets, and fuzzy decision-making; machine learning; risk assessment; heuristics; and clustering, the book is a valuable resource for academics, M.Sc. and Ph.D. students, as well as managers and engineers in industry and the service sectors.