243,826 research outputs found
Clustering Evolutionary Stock Market Model
As a typical representation of complex networks studied relatively
thoroughly, financial market presents some special details, such as its
nonconservation and opinions spreading. In this model, agents congregate to
form some clusters, which may grow or collapse with the evolution of the
system. To mimic an open market, we allow some ones participate in or exit the
market suggesting that the number of the agents would fluctuate. Simulation
results show that the large events are frequent in the fluctuations of the
stock price generated by the artificial stock market when compared with a
normal process and the price return distribution is a \emph{l\'{e}vy}
distribution in the central part followed by an approximately exponential
truncation.Comment: 9 pages, 8 figure
Highly Interconnected Subsystems of the Stock Market
The stock market is a complex system that affects economic and financial
activities around the world. Analysis of stock price data can improve
our understanding of the past price movements of stocks. In this work,
we develop a method to determine the highly interconnected subsystems of
the stock market. Our method relies on a k-core decomposition scheme to
analyze large networks. Our approach illustrates that the stock market
is a nearly decomposable system which comprises hierarchic subsystems.
This work also presents results from the analysis of a network derived
from a large data set of stock prices. This network analysis technique
is a new promising approach to analyze and classify stocks based on
price interactions and to decompose the complex system embodied in the
stock market
Stock Market Prediction via Deep Learning Techniques: A Survey
The stock market prediction has been a traditional yet complex problem
researched within diverse research areas and application domains due to its
non-linear, highly volatile and complex nature. Existing surveys on stock
market prediction often focus on traditional machine learning methods instead
of deep learning methods. Deep learning has dominated many domains, gained much
success and popularity in recent years in stock market prediction. This
motivates us to provide a structured and comprehensive overview of the research
on stock market prediction focusing on deep learning techniques. We present
four elaborated subtasks of stock market prediction and propose a novel
taxonomy to summarize the state-of-the-art models based on deep neural networks
from 2011 to 2022. In addition, we also provide detailed statistics on the
datasets and evaluation metrics commonly used in the stock market. Finally, we
highlight some open issues and point out several future directions by sharing
some new perspectives on stock market prediction
Volatility Modeling for Currency Pairs and Stock Indices by Means of Complex Networks
Financial markets are complex systems. Network analysis is an innovative method for improving data sharing and knowledge discovery in financial data. Oriented weighted networks were created for the Shanghai Composite, S&P500, DAX30, CAC40, Nikkei225, FTSE100, IBEX35 indexes, for CNY-JPY, EUR-USD, GBP-EUR, RUB-CNY and for cryptocurrency BTC-USD. We considered data since January 6, 2006 to September 6, 2019. The complex networks had a similar structure for both types of markets, which was divided into the central part (core) and the outer one (loops). The emergence of such a structure reflects the fact that, for the most part, the stock and currency markets develop around some significant state of volatility, but occasionally anomalies occur when the states of volatility deviate from the core. Comparing the topology of evolutionary networks and the differences found for the stock and currency markets networks, we can conclude that stock markets are characterized by a greater variety of volatility patterns than currency ones. At the same time, the cryptocurrency market network showed a special mechanism of volatility evolution compared to the currency and stock market networks
Predicting stock market movements using network science: An information theoretic approach
A stock market is considered as one of the highly complex systems, which
consists of many components whose prices move up and down without having a
clear pattern. The complex nature of a stock market challenges us on making a
reliable prediction of its future movements. In this paper, we aim at building
a new method to forecast the future movements of Standard & Poor's 500 Index
(S&P 500) by constructing time-series complex networks of S&P 500 underlying
companies by connecting them with links whose weights are given by the mutual
information of 60-minute price movements of the pairs of the companies with the
consecutive 5,340 minutes price records. We showed that the changes in the
strength distributions of the networks provide an important information on the
network's future movements. We built several metrics using the strength
distributions and network measurements such as centrality, and we combined the
best two predictors by performing a linear combination. We found that the
combined predictor and the changes in S&P 500 show a quadratic relationship,
and it allows us to predict the amplitude of the one step future change in S&P
500. The result showed significant fluctuations in S&P 500 Index when the
combined predictor was high. In terms of making the actual index predictions,
we built ARIMA models. We found that adding the network measurements into the
ARIMA models improves the model accuracy. These findings are useful for
financial market policy makers as an indicator based on which they can
interfere with the markets before the markets make a drastic change, and for
quantitative investors to improve their forecasting models.Comment: 13 pages, 7 figures, 3 table
Stock Value Prediction System
The use of artificial neural network is gaining popularity in the research field. Neural network consist of interconnected neurons which deciphers value by using input data by feeding network values. The main aim of our project is to use backpropagation process to predict the future value.Stock market prediction models are the most challenging fields in computer science. The aim of this project is implementation of neural networks with back propagation algorithm for stock value prediction .A neural network is a powerful data-modeling tool that is able to capture and represent complex input/output relationships. We apply Data mining technology to the stock in order to research the trend of the market. Our proposed system provides methods to develop machine learning stock market predictor based on Neural Networks using Back propagationalgorithm, with intent of improving the accuracy. In this paper we have used data mining process along with artificial neural network networking to predict the future value of the stock. This paper overcomes the all traditional statistical methods of the stock market value prediction.
DOI: 10.17762/ijritcc2321-8169.16049
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