Stock Market Prediction And Efficiency Analysis Using Recurrent Neural Network

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Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network

Joish Bosco,Fateh Khan
Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network Book PDF

Author : Joish Bosco,Fateh Khan
Publisher : GRIN Verlag
Page : 76 pages
File Size : 40,7 Mb
Release : 2018-09-18
Category : Computers
ISBN : 9783668800458

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Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network by Joish Bosco,Fateh Khan Pdf

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.

Conference Proceedings of ICDLAIR2019

Meenakshi Tripathi,Sushant Upadhyaya
Conference Proceedings of ICDLAIR2019 Book PDF

Author : Meenakshi Tripathi,Sushant Upadhyaya
Publisher : Springer Nature
Page : 376 pages
File Size : 52,8 Mb
Release : 2021-02-08
Category : Computers
ISBN : 9783030671877

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Conference Proceedings of ICDLAIR2019 by Meenakshi Tripathi,Sushant Upadhyaya Pdf

This proceedings book includes the results from the International Conference on Deep Learning, Artificial Intelligence and Robotics, held in Malaviya National Institute of Technology, Jawahar Lal Nehru Marg, Malaviya Nagar, Jaipur, Rajasthan, 302017. The scope of this conference includes all subareas of AI, with broad coverage of traditional topics like robotics, statistical learning and deep learning techniques. However, the organizing committee expressly encouraged work on the applications of DL and AI in the important fields of computer/electronics/electrical/mechanical/chemical/textile engineering, health care and agriculture, business and social media and other relevant domains. The conference welcomed papers on the following (but not limited to) research topics: · Deep Learning: Applications of deep learning in various engineering streams, neural information processing systems, training schemes, GPU computation and paradigms, human–computer interaction, genetic algorithm, reinforcement learning, natural language processing, social computing, user customization, embedded computation, automotive design and bioinformatics · Artificial Intelligence: Automatic control, natural language processing, data mining and machine learning tools, fuzzy logic, heuristic optimization techniques (membrane-based separation, wastewater treatment, process control, etc.) and soft computing · Robotics: Automation and advanced control-based applications in engineering, neural networks on low powered devices, human–robot interaction and communication, cognitive, developmental and evolutionary robotics, fault diagnosis, virtual reality, space and underwater robotics, simulation and modelling, bio-inspired robotics, cable robots, cognitive robotics, collaborative robotics, collective and social robots and humanoid robots It was a collaborative platform for academic experts, researchers and corporate professionals for interacting their research in various domain of engineering like robotics, data acquisition, human–computer interaction, genetic algorithm, sentiment analysis as well as usage of AI and advanced computation in various industrial challenges based applications such as user customization, augmented reality, voice assistants, reactor design, product formulation/synthesis, embedded system design, membrane-based separation for protecting environment along with wastewater treatment, rheological properties estimation for Newtonian and non-Newtonian fluids used in micro-processing industries and fault detection.

2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)

IEEE Staff
2018 First International Conference on Secure Cyber Computing and Communication ICSCCC Book PDF

Author : IEEE Staff
Publisher : Unknown
Page : 128 pages
File Size : 44,6 Mb
Release : 2018-12-15
Category : Electronic
ISBN : 1538663740

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2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) by IEEE Staff Pdf

It includes Object oriented analysis, continuous process modelling and improvement, cost estimation and project planning and effectiveness of agile lean practices, Web Engineering, Quality Management, Managing Software Projects, Multimedia and Visual Software Engineering, Software Maintenance and Testing, Languages and Formal Methods, Web based Education Systems and Learning Applications, Software Engineering Decision Making This track focuses on Natural language processing, artificial intelligence, and computational linguistics with respect to the interactions between computers and human languages It includes Genetic Algorithms, Grammatical Evolution, Differential Evolution, Probabilistic Meta heuristic, Swarm Intelligence, Ant Colony Algorithms, Artificial Immune Systems, High Performance Computing and Computational Intelligence, Fuzzy Logic, Bayesian statistical methods, Neural Networks, Multi Agent Systems, Stochastic Optimization It also focuses on focuses on Cloud and its servi

Stock Market Price Prediction using Machine Learning Techniques

Mahfuz Islam Khan Jabed
Stock Market Price Prediction using Machine Learning Techniques Book PDF

Author : Mahfuz Islam Khan Jabed
Publisher : Ocleno
Page : 172 pages
File Size : 48,7 Mb
Release : 2024-02-16
Category : Business & Economics
ISBN : 8210379456XXX

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Stock Market Price Prediction using Machine Learning Techniques by Mahfuz Islam Khan Jabed Pdf

Predicting stock market prices is a challenging task in the financial sector, where the Efficient Market Hypothesis (EMH) posits the impossibility of accurate prediction due to the inherent uncertainty and complexity of stock price behaviour. However, introducing Machine Learning algorithms has shown the feasibility of stock market price forecasting. This study employs advanced Machine Learning models that can predict stock price movements with the right level of accuracy if the correct parameter tuning and appropriate predictor models are developed. In this research work, the LSTM model, which is a type of Recurrent Neural Network (RNN), time series forecasting Facebook Prophet algorithm and Random Forest Regressor model have been implemented on 10 Dhaka Stock Market (DSEbd) listed companies and six international giants for predicting the stock and forecasting the future price. The dataset of domestic companies is extracted from the graphical representation of the DSEbd website, and the international companies' dataset is imported from Yahoo Finance. In this experiment, Facebook Prophet demonstrates a long period of forecasting with reasonable accuracy, capturing daily, weekly, and yearly seasonality, including holiday effects for market trend analysis. Remarkably, the LSTM model exhibits significant accuracy, yielding the best results with evaluation metrics, including RMSE (0.35), MAPE (0.50%), and MAE (0.30). The experimental results underscore the efficiency of LSTM for future stock forecasting, observed over 15 days of upcoming market prices. A comparison of the results shows that the LSTM model efficiently forecasts the next day's closing price.

Recurrent Neural Networks

Amit Kumar Tyagi,Ajith Abraham
Recurrent Neural Networks Book PDF

Author : Amit Kumar Tyagi,Ajith Abraham
Publisher : CRC Press
Page : 426 pages
File Size : 51,9 Mb
Release : 2022-08-08
Category : Computers
ISBN : 9781000626179

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Recurrent Neural Networks by Amit Kumar Tyagi,Ajith Abraham Pdf

The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, learning algorithm, neural turning machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding. FEATURES Covers computational analysis and understanding of natural languages Discusses applications of recurrent neural network in e-Healthcare Provides case studies in every chapter with respect to real-world scenarios Examines open issues with natural language, health care, multimedia (Audio/Video), transportation, stock market, and logistics The text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology.

Hands-On Machine Learning for Algorithmic Trading

Stefan Jansen
Hands On Machine Learning for Algorithmic Trading Book PDF

Author : Stefan Jansen
Publisher : Packt Publishing Ltd
Page : 668 pages
File Size : 46,7 Mb
Release : 2018-12-31
Category : Computers
ISBN : 9781789342710

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Hands-On Machine Learning for Algorithmic Trading by Stefan Jansen Pdf

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key FeaturesImplement machine learning algorithms to build, train, and validate algorithmic modelsCreate your own algorithmic design process to apply probabilistic machine learning approaches to trading decisionsDevelop neural networks for algorithmic trading to perform time series forecasting and smart analyticsBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML workflow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learnImplement machine learning techniques to solve investment and trading problemsLeverage market, fundamental, and alternative data to research alpha factorsDesign and fine-tune supervised, unsupervised, and reinforcement learning modelsOptimize portfolio risk and performance using pandas, NumPy, and scikit-learnIntegrate machine learning models into a live trading strategy on QuantopianEvaluate strategies using reliable backtesting methodologies for time seriesDesign and evaluate deep neural networks using Keras, PyTorch, and TensorFlowWork with reinforcement learning for trading strategies in the OpenAI GymWho this book is for Hands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.

Stock price Prediction a referential approach on how to predict the stock price using simple time series...

Dr.N.Srinivasan
Stock price Prediction a referential approach on how to predict the stock price using simple time series Book PDF

Author : Dr.N.Srinivasan
Publisher : Clever Fox Publishing
Page : 56 pages
File Size : 46,9 Mb
Release : 2024-06-16
Category : Business & Economics
ISBN : 8210379456XXX

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Stock price Prediction a referential approach on how to predict the stock price using simple time series... by Dr.N.Srinivasan Pdf

This book is about the various techniques involved in the stock price prediction. Even the people who are new to this book, after completion they can do stock trading individually with more profit.

A Performance Analysis of Machine Learning Techniques in Stock Price Prediction

Hasan Al-Quaid
A Performance Analysis of Machine Learning Techniques in Stock Price Prediction Book PDF

Author : Hasan Al-Quaid
Publisher : Unknown
Page : 0 pages
File Size : 40,6 Mb
Release : 2023
Category : Electronic
ISBN : OCLC:1424641557

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A Performance Analysis of Machine Learning Techniques in Stock Price Prediction by Hasan Al-Quaid Pdf

Stock market trends are of great interest to investors and corporations worldwide. The global financial system is intricately interconnected with the stock market, playing a central role in driving economic activity. In today's interconnected world, trading stocks has become a popular and accessible means for individuals and entities to generate income. Numerous academic researchers have explored the use of Artificial Intelligence (AI) for stock prediction and have claimed that their models can accurately forecast stock performance. The issue is that many of these studies rely on a single data source, namely, daily stock data and cannot predict future stock prices, more than 1 or 2 days, with a large degree of success. Additionally, the single data source may be influenced by a multitude of economic factors as well as public sentiment, which is the most significant. In this research paper, several of these AI models are tested to evaluate their claims regarding stock prediction capabilities. Based on our experiments utilizing AI models and the results gathered, it was concluded that it was not possible to predict future stock prices using one method alone. Therefore, in order to provide a greater accuracy in predicting future stocks, the use of an ensemble approach was proposed. While many researchers build their ensemble models by combining various Artificial Neural Network models with sentiment analysis. We have suggested a different approach using other kinds of AI models, along with enhancements to traditional sentiment analysis techniques.

Advances in Artificial Intelligence: From Theory to Practice

Salem Benferhat,Karim Tabia,Moonis Ali
Advances in Artificial Intelligence From Theory to Practice Book PDF

Author : Salem Benferhat,Karim Tabia,Moonis Ali
Publisher : Springer
Page : 642 pages
File Size : 43,8 Mb
Release : 2017-06-10
Category : Computers
ISBN : 9783319600420

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Advances in Artificial Intelligence: From Theory to Practice by Salem Benferhat,Karim Tabia,Moonis Ali Pdf

The two-volume set LNCS 10350 and 10351 constitutes the thoroughly refereed proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, held in Arras, France, in June 2017. The 70 revised full papers presented together with 45 short papers and 3 invited talks were carefully reviewed and selected from 180 submissions. They are organized in topical sections: constraints, planning, and optimization; data mining and machine learning; sensors, signal processing, and data fusion; recommender systems; decision support systems; knowledge representation and reasoning; navigation, control, and autonome agents; sentiment analysis and social media; games, computer vision; and animation; uncertainty management; graphical models: from theory to applications; anomaly detection; agronomy and artificial intelligence; applications of argumentation; intelligent systems in healthcare and mhealth for health outcomes; and innovative applications of textual analysis based on AI.

Stock price analysis through Statistical and Data Science tools: An Overview

Vinaitheerthan Renganathan
Stock price analysis through Statistical and Data Science tools An Overview Book PDF

Author : Vinaitheerthan Renganathan
Publisher : Vinaitheerthan Renganathan
Page : 107 pages
File Size : 52,7 Mb
Release : 2021-04-30
Category : Business & Economics
ISBN : 9789354579738

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Stock price analysis through Statistical and Data Science tools: An Overview by Vinaitheerthan Renganathan Pdf

Stock price analysis involves different methods such as fundamental analysis and technical analysis which is based on data related to price movement of the stock in the past. Price of the stock is affected by various factors such as company’s performance, current status of economy and political factor. These factors play an important role in supply and demand of the stock which makes the price to be volatile in the short term. Investors and stock traders aim to book profit through buying and selling the stocks. There are different statistical and data science tools are being used to predict the stock price. Data Science and Statistical tools assume only the stock price’s historical data in predicting the future stock price. Statistical tools include measures such as Graph and Charts which depicts the general trend and time series tools such as Auto Regressive Integrated Moving Averages (ARIMA) and regression analysis. Data Science tools include models like Decision Tree, Support Vector Machine (SVM), Artificial Neural Network (ANN) and Long Term and Short Term Memory (LSTM) Models. Current methods include carrying out sentiment analysis of tweets, comments and other social media discussion to extract the hidden sentiment expressed by the users which indicate the positive or negative sentiment towards the stock price and the company. The book provides an overview of the analyzing and predicting stock price movements using statistical and data science tools using R open source software with hypothetical stock data sets. It provides a short introduction to R software to enable the user to understand analysis part in the later part. The book will not go into details of suggesting when to purchase a stock or what at price. The tools presented in the book can be used as a guiding tool in decision making while buying or selling the stock. Vinaitheerthan Renganathan www.vinaitheerthan.com/book.php

Neural Networks in Business Forecasting

G. Peter Zhang
Neural Networks in Business Forecasting Book PDF

Author : G. Peter Zhang
Publisher : IGI Global
Page : 314 pages
File Size : 52,7 Mb
Release : 2004-01-01
Category : Computers
ISBN : 1591402158

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Neural Networks in Business Forecasting by G. Peter Zhang Pdf

Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. There are considerable interests and applications in forecasting using neural networks. This book provides for researchers and practitioners some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.

Deep Learning

Josh Patterson,Adam Gibson
Deep Learning Book PDF

Author : Josh Patterson,Adam Gibson
Publisher : "O'Reilly Media, Inc."
Page : 532 pages
File Size : 46,7 Mb
Release : 2017-07-28
Category : Computers
ISBN : 9781491914212

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Deep Learning by Josh Patterson,Adam Gibson Pdf

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool Learn how to use DL4J natively on Spark and Hadoop

Engineering Applications of Neural Networks

John Macintyre,Lazaros Iliadis,Ilias Maglogiannis,Chrisina Jayne
Engineering Applications of Neural Networks Book PDF

Author : John Macintyre,Lazaros Iliadis,Ilias Maglogiannis,Chrisina Jayne
Publisher : Springer
Page : 546 pages
File Size : 55,6 Mb
Release : 2019-05-14
Category : Computers
ISBN : 9783030202576

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Engineering Applications of Neural Networks by John Macintyre,Lazaros Iliadis,Ilias Maglogiannis,Chrisina Jayne Pdf

This book constitutes the refereed proceedings of the 19th International Conference on Engineering Applications of Neural Networks, EANN 2019, held in Xersonisos, Crete, Greece, in May 2019. The 35 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on AI in energy management - industrial applications; biomedical - bioinformatics modeling; classification - learning; deep learning; deep learning - convolutional ANN; fuzzy - vulnerability - navigation modeling; machine learning modeling - optimization; ML - DL financial modeling; security - anomaly detection; 1st PEINT workshop.

Prediction of Stock Market Index Movements with Machine Learning

Nazif AYYILDIZ
Prediction of Stock Market Index Movements with Machine Learning Book PDF

Author : Nazif AYYILDIZ
Publisher : Özgür Publications
Page : 121 pages
File Size : 49,7 Mb
Release : 2023-12-16
Category : Business & Economics
ISBN : 9789754478211

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Prediction of Stock Market Index Movements with Machine Learning by Nazif AYYILDIZ Pdf

The book titled "Prediction of Stock Market Index Movements with Machine Learning" focuses on the performance of machine learning methods in forecasting the future movements of stock market indexes and identifying the most advantageous methods that can be used across different stock exchanges. In this context, applications have been conducted on both developed and emerging market stock exchanges. The stock market indexes of developed countries such as NYSE 100, NIKKEI 225, FTSE 100, CAC 40, DAX 30, FTSE MIB, TSX; and the stock market indexes of emerging countries such as SSE, BOVESPA, RTS, NIFTY 50, IDX, IPC, and BIST 100 were selected. The movement directions of these stock market indexes were predicted using decision trees, random forests, k-nearest neighbors, naive Bayes, logistic regression, support vector machines, and artificial neural networks methods. Daily dataset from 01.01.2012 to 31.12.2021, along with technical indicators, were used as input data for analysis. According to the results obtained, it was determined that artificial neural networks were the most effective method during the examined period. Alongside artificial neural networks, logistic regression and support vector machines methods were found to predict the movement direction of all indexes with an accuracy of over 70%. Additionally, it was noted that while artificial neural networks were identified as the best method, they did not necessarily achieve the highest accuracy for all indexes. In this context, it was established that the performance of the examined methods varied among countries and indexes but did not differ based on the development levels of the countries. As a conclusion, artificial neural networks, logistic regression, and support vector machines methods are recommended as the most advantageous approaches for predicting stock market index movements.

Advances in Machine Learning and Computational Intelligence

Srikanta Patnaik,Xin-She Yang,Ishwar K. Sethi
Advances in Machine Learning and Computational Intelligence Book PDF

Author : Srikanta Patnaik,Xin-She Yang,Ishwar K. Sethi
Publisher : Springer Nature
Page : 853 pages
File Size : 47,5 Mb
Release : 2020-07-25
Category : Technology & Engineering
ISBN : 9789811552434

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Advances in Machine Learning and Computational Intelligence by Srikanta Patnaik,Xin-She Yang,Ishwar K. Sethi Pdf

This book gathers selected high-quality papers presented at the International Conference on Machine Learning and Computational Intelligence (ICMLCI-2019), jointly organized by Kunming University of Science and Technology and the Interscience Research Network, Bhubaneswar, India, from April 6 to 7, 2019. Addressing virtually all aspects of intelligent systems, soft computing and machine learning, the topics covered include: prediction; data mining; information retrieval; game playing; robotics; learning methods; pattern visualization; automated knowledge acquisition; fuzzy, stochastic and probabilistic computing; neural computing; big data; social networks and applications of soft computing in various areas.