Pattern graph tracking-based stock price prediction using big data

Jeon, Songhee; Hong, Bonghee; Chang, Victor

doi:10.1016/j.future.2017.02.010

Cited by 71 publications

(27 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jeon et al in [16] performed research on millisecond interval-based big dataset by using pattern graph tracking to complete stock price prediction tasks. The dataset they used is a millisecond interval-based big dataset of historical stock data from KOSCOM, from August 2014 to October 2014, 10G-15G capacity.…”

Section: Survey Of Related Workmentioning

confidence: 99%

Short-term stock market price trend prediction using a comprehensive deep learning system

2020

View full text Add to dashboard Cite

Stock market is one of the major fields that investors are dedicated to, thus stock market price trend prediction is always a hot topic for researchers from both financial and technical domains. In this research, our objective is to build a state-of-art prediction model for price trend prediction, which focuses on short-term price trend prediction. As concluded by Fama in [26], financial time series prediction is known to be a notoriously difficult task due to the generally accepted, semi-strong form of market efficiency and the high level of noise. Back in 2003, Wang et al. in [44] already applied artificial neural networks on stock market price prediction and focused on volume, as a specific feature of stock market. One of the key findings by them was that the volume was not found to be effective in improving the forecasting performance on the datasets they used, which was S&P 500 and DJI. Ince and Trafalis in [15] targeted short-term forecasting and applied support vector machine (SVM) model on the stock price prediction. Their main contribution is performing a comparison between multi-layer perceptron (MLP) and SVM then found that most of the scenarios SVM outperformed MLP, while

show abstract

Section: Survey Of Related Workmentioning

confidence: 99%

Short-term stock market price trend prediction using a comprehensive deep learning system

2020

View full text Add to dashboard Cite

show abstract

“…Jeon, Hong, and Chang () proposed a forecasting system based on big data processing and analysis tools for generating stock price predictions. In the study, ANN was used to create predicted data after feature selection.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market

Dosdoğru

2019

Manage Decis Econ

View full text Add to dashboard Cite

In this study, a new methodology is proposed to automatically determine six parameters of artificial neural network using population-based metaheuristics. We considered following three issues: What is the effect of used metaheuristic on performance? Which parameters are mostly selected? Is there a difference between the forecasting results when using stationary or nonstationary dataset that are selected according to the augmented Dickey-Fuller test statistics? Based upon results of performance measures, proposed method leads to significant opportunities to forecast stock market more effectively. We also expect proposed methodology can provide remarkable advantages for other complex, dynamic, and nonlinear forecasting problems. | INTRODUCTIONStock market is one of the most important investment ways due to the potential of earning higher returns. It is also considered as one of the key indicators of economic growth of countries. Over the years, researchers, practitioners, and investors analyzed the stock market, the stocks, and the relationships. However, stock market has high complexity and nonlinear constraints to grasp. Although anybody can own stocks to perform the aspired profit, many investors find stock market hard to detect which stock to buy, or when to buy and sell stocks. Furthermore, it is so difficult to exactly say when the stock market price will increase, how much it will increase or how long the increase of price will remain (Albeladi & Abdullah, 2018).Due to high volatilities and noises in stock market, unilateral traditional forecasting method and analysis cannot meet investors' need any more. Hence, new methods with recent advances in artificial intelligence have been proposed to extract useful information from stock market data. Depending on the nature of the stock market, a particular method can be used to gain profit. However, there is no single model exist in literature. Each method has its own strengths and limitations in terms of performance and reliability. In this case, artificial intelligence methods can be integrated to combine the strength of methods and overcome a single method's weakness. Especially, artificial neural network (ANN) method can be proposed for stock market forecasting in order to improve investors' decision support quality. In this paper, genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) are integrated with ANN to clarify the dynamic and nonstationary nature of the stock market. GA, DE, and PSO are population-based metaheuristics. The group of populationbased metaheuristics inherently has some significant advantages. First, they ensure information about the "surface" of an objective function.Second, population-based metaheuristics are less sensitive to "improper" pathways of certain individual solutions. Finally, they improve the probability of attaining the global optimum (Feoktistov, 2006). This study involves the following tasks: (a) determine the relevant technical indicators for the stocks, (b) determine the proper transfe...

show abstract

“…The authors in [17] utilized trading price, trading volume, high, low, open price and quantity of selling stocks as input to a neural network to predict stock exchange. They used matching patterns in past stock data to attain everyday stock values and solid rules to choose the major factors that considerably have an effect on the value.…”

Section: B Big Data Framework and Machine Learning Techniquesmentioning

confidence: 99%

Short Term Stock Movements with Big Data and Market Sentiments Analytics

Kalra¹,

Gupta²,

Prasad³

2019

IJITEE

View full text Add to dashboard Cite

Machine Learning Techniques and Big Data analytics are two central points of data science. Big Data is important for organizations to gain insights into it and machine learning techniques are one of the substantial assets for analyzing a massive amount of data. In this paper, a framework has been proposed to improve the short term stock trend prediction accuracy using Logistic Regression model by means of qualitative and quantitative data. This paper makes a comprehensive survey of stock market trend prediction with the accumulation of various data sources by applying machine learning techniques and by using big data analytics approach. The model has been implemented in Big data Framework with Hadoop and Apache Spark. For qualitative data Tweets sentiments and news sentiments has been taken in to account and for quantitative data Google trends and historical data are considered. The proposed system has enhanced the prediction accuracy about 3-4 % in comparison to existing models by supplying Google trend as input data in addition to market sentiments and historical data. The implemented model can help the investors to take short term decisions to make money in the security market and the survey would help in finding the most effective resources which overly influence the stock prices.

show abstract

Pattern graph tracking-based stock price prediction using big data

Cited by 71 publications

References 34 publications

Short-term stock market price trend prediction using a comprehensive deep learning system

Short-term stock market price trend prediction using a comprehensive deep learning system

Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market

Short Term Stock Movements with Big Data and Market Sentiments Analytics

Contact Info

Product

Resources

About