Author: Milla Mäkinen
Publisher:
ISBN:
Category :
Languages : en
Pages : 27
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Book Description
This paper proposes a new method for predicting jump arrivals in stock markets with high-frequency limit order book data. We introduce a new model architecture, based on Convolutional Long Short-Term Memory with attention, to apply time series representation learning with memory and to focus the prediction attention on the most important features to improve performance. Using order book data on five liquid U.S. stocks, we provide empirical evidence on the efficacy of the proposed approach. We find that the proposed approach with an attention mechanism outperforms the multi-layer perceptron network as well as the convolutional neural network and Long Short-Term memory model. The use of limit order book data was found to improve the performance of the proposed model in jump prediction, either clearly or marginally, depending on the underlying stock.
Author: Joish Bosco
Publisher: GRIN Verlag
ISBN: 3668800456
Category : Computers
Languages : en
Pages : 76
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Book Description
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.
Author: Mukesh Saraswat
Publisher: Springer Nature
ISBN: 9811966346
Category : Technology & Engineering
Languages : en
Pages : 908
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Book Description
This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2022), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from 26 to 27 March 2022. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.
Author: Yuxiao Dong
Publisher: Springer Nature
ISBN: 3030865142
Category : Computers
Languages : en
Pages : 579
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Book Description
The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.
Author: Zhuowen Wang
Publisher:
ISBN:
Category : University of Ottawa theses
Languages : en
Pages : 232
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Book Description
Author: Frédéric Abergel
Publisher: Cambridge University Press
ISBN: 1316870480
Category : Mathematics
Languages : en
Pages : 242
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Book Description
A limit order book is essentially a file on a computer that contains all orders sent to the market, along with their characteristics such as the sign of the order, price, quantity and a timestamp. The majority of organized electronic markets rely on limit order books to store the list of interests of market participants on their central computer. A limit order book contains all the information available on a specific market and it reflects the way the market moves under the influence of its participants. This book discusses several models of limit order books. It begins by discussing the data to assess their empirical properties, and then moves on to mathematical models in order to reproduce the observed properties. Finally, the book presents a framework for numerical simulations. It also covers important modelling techniques including agent-based modelling, and advanced modelling of limit order books based on Hawkes processes. The book also provides in-depth coverage of simulation techniques and introduces general, flexible, open source library concepts useful to readers studying trading strategies in order-driven markets.
Author: Glenn Fung
Publisher: Frontiers Media SA
ISBN: 2889718115
Category : Science
Languages : en
Pages : 135
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Book Description
Luisa Fernanda Polania Cabrera is an Experienced Professional at Target Corporation (United States). Victor Wu is a Product Manager at GitLab Inc, San Francisco, United States. Sou-Cheng Choi is a Consulting Principle Data Scientist at Allstate Corporation. Lawrence Kwan Ho Ma is the Founder, Director and Chief Scientist of Valigo Limited and Founder, CEO and Chief Scientist of EMALI.IO Limited. Glenn M. Fung is the Chief Research Scientist at American Family Insurance.
Author: Dr.N.Srinivasan
Publisher: Clever Fox Publishing
ISBN:
Category : Business & Economics
Languages : en
Pages : 56
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Book Description
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.
Author: Frédéric Abergel
Publisher: Springer Science & Business Media
ISBN: 8847017661
Category : Business & Economics
Languages : en
Pages : 316
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Book Description
The primary goal of the book is to present the ideas and research findings of active researchers from various communities (physicists, economists, mathematicians, financial engineers) working in the field of "Econophysics", who have undertaken the task of modelling and analyzing order-driven markets. Of primary interest in these studies are the mechanisms leading to the statistical regularities ("stylized facts") of price statistics. Results pertaining to other important issues such as market impact, the profitability of trading strategies, or mathematical models for microstructure effects, are also presented. Several leading researchers in these fields report on their recent work and also review the contemporary literature. Some historical perspectives, comments and debates on recent issues in Econophysics research are also included.
Author: Jacob Brewer
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
This research explores how to leverage system identification and machine learning to predict stock prices using NASDAQ order book data. It begins by providing essential background information of stock market trading mechanics and then gives a brief explanation of how machine learning is used for feedback system identification. The project then applies these principles to create a price impact model of NASDAQ stock prices. After describing detailed results, we show that prediction margins appear to increase for our testing set when we incorporate order book data. This project is a core element of a greater project, which explores the possibility of stock price manipulation and control--currently a great concern to organizations such as the Department of Homeland Security (DHS). Since our findings suggest that stock prices on our sampled data set are at least slightly more predictable than a baseline algorithm when incorporating order data, it is likely that the growing number of similar high frequency trading algorithms would be affected by a significant change in the order distribution. This would mean that stock prices could be influenced without cost by strategically placing orders.
