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Relevant bibliographies by topics / Stock Movement Prediction

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Academic literature on the topic 'Stock Movement Prediction'

Author: Grafiati

Published: 20 February 2023

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Journal articles on the topic "Stock Movement Prediction"

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Al-Hasnawi, Salim Sallal, and Laith Haleem Al-Hchemi*. "CLOSING PRICE PREDICTION OF STOCK LISTED ON THE IRAQ STOCK EXCHANGE USING ANN-LSTM." JURISMA : Jurnal Riset Bisnis & Manajemen 12, no. 2 (2022): 173–85. http://dx.doi.org/10.34010/jurisma.v12i2.8103.

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Financial markets are highly reactive to events and situations, as seen by the very volatile movement of stock values. As a result, investors are having difficulties guessing prices and making investment decisions, especially when statistical techniques have failed to model historical prices. This paper aims to propose an RNNs-based predictive model using the LSTM model for predicting the closing price of four stocks listed on the Iraq Stock Exchange (ISX). The data used are historical closing prices provided by ISX for the period from 2/1/2019 to 24/12/2020. Several attempts were conducted to

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Chen, Fang, Jinglun Gao, and Zhiwen Zhang. "US Stocks Market Movements Prediction: Classification of SP-500 Using Machine Learning Technology." BCP Business & Management 26 (September 19, 2022): 1043–50. http://dx.doi.org/10.54691/bcpbm.v26i.2068.

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In the field of quantified investment, risk quantification and maximum expected return are the problems focused on by the investors. Besides, a powerful toolkit for predicting the stock price movement is also very important for investors. In this paper, five stocks that are components of the SP-500 Index are selected, and the Mean-Variance method is used to optimize the portfolio of the above stocks. Moreover, five machine learning methods are compared to evaluate the performance in the application of stock price movement prediction. The results show that the combination of “AMZN”, “MSFT” and

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Yuvaraj, K., Dr J. Sreerambabu, and S. Kalidasan. "Trading View API and Prediction Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 978–81. http://dx.doi.org/10.22214/ijraset.2022.46313.

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Abstract: Stock market may be a market that permits seamless exchange of shopping for and commercialism of company stocks. each stock market has their own index price. Index is that the average price that's calculated by combining many stocks. Everyday billions of bucks ar listed on the exchange, ANd behind every greenback is an capitalist hoping to profit in a method or another. Entire corporations rise and fall daily supported the behaviour of the market. ought to AN capitalist be able to accurately predict market movements, it offers a tantalizing guarantees of wealth and influence.This hel

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Rammurthy, Shruthi Komarla, and Sagar B. Patil. "An LSTM-Based Approach to Predict Stock Price Movement for IT Sector Companies." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–12. http://dx.doi.org/10.4018/ijcini.20211001.oa3.

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A stock market is an aggregation of buyers and sellers where issuance, buying, and selling of stocks happen. Predicting stock price is a significant concern due to volatility. Historical stock price and historical price data reveal the effect of such factors. Since stock data is time series and prediction can be made accurately with time series forecasting model. LSTM (Long Short Term Memory) model, a particular kind of RNN (Recurrent Neural Network), based on time series forecasting used to predict stock price. LSTM doesn’t have long term dependencies because of its distinctive structure. The

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Nguyen, Thi-Thu, and Seokhoon Yoon. "A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning." Applied Sciences 9, no. 22 (2019): 4745. http://dx.doi.org/10.3390/app9224745.

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Stock price prediction has always been an important application in time series predictions. Recently, deep neural networks have been employed extensively for financial time series tasks. The network typically requires a large amount of training samples to achieve high accuracy. However, in the stock market, the number of data points collected on a daily basis is limited in one year, which leads to insufficient training samples and accordingly results in an overfitting problem. Moreover, predicting stock price movement is affected by various factors in the stock market. Therefore, choosing appr

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Sakphoowadon, Surinthip, Nawaporn Wisitpongphan, and Choochart Haruechaiyasak. "Predicting stock price movement using effective Thai financial probabilistic lexicon." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 4313. http://dx.doi.org/10.11591/ijece.v11i5.pp4313-4324.

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Predicting stock price fluctuation during critical events remains a big challenge for many researchers because the stock market is extremely vulnerable and sensitive during such time. Most existing works rely on various numerical data of related factors which can impact the stock price direction. However, very few research papers analyzed the effect of information appearing in financial news articles. In this paper, a novel probabilistic lexicon based stock market prediction (PLSP) algorithm is proposed to predict the direction of stock price movement. Our approach used the proposed thai finan

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Khanna, Munish, Mohak Kulshrestha, Law K. Singh, Shankar Thawkar, and Kapil Shrivastava. "Performance Evaluation of Machine Learning Algorithms for Stock Price and Stock Index Movement Prediction Using Trend Deterministic Data Prediction." International Journal of Applied Metaheuristic Computing 13, no. 1 (2022): 1–30. http://dx.doi.org/10.4018/ijamc.292511.

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This experimental study addresses the problem of predicting the direction of stocks and the movement of stock price indices for three major stocks and stock indices. The proposed approach for processing input data involves the computation of ten technical indicators using stock trading data. The dataset used for the evaluation of all the prediction models consists of 11 years of historical data from January 2007 to December 2017. The study comprises four prediction models which are Long Short-Term Memory, XGBoost, Support Vector Machine ( and Random forests. Accuracy scores and F1 scores for e

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Ansah, Kwabena, Ismail Wafaa Denwar, and Justice Kwame Appati. "Intelligent Models for Stock Price Prediction." Journal of Information Technology Research 15, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/jitr.298616.

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Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price

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Yang, Can, Junjie Zhai, and Guihua Tao. "Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory." Mathematical Problems in Engineering 2020 (July 16, 2020): 1–13. http://dx.doi.org/10.1155/2020/2746845.

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The prediction of stock price movement direction is significant in financial studies. In recent years, a number of deep learning models have gradually been applied for stock predictions. This paper presents a deep learning framework to predict price movement direction based on historical information in financial time series. The framework combines a convolutional neural network (CNN) for feature extraction and a long short-term memory (LSTM) network for prediction. We specifically use a three-dimensional CNN for data input in the framework, including the information on time series, technical i

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Ho, Trang-Thi, and Yennun Huang. "Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation." Sensors 21, no. 23 (2021): 7957. http://dx.doi.org/10.3390/s21237957.

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Determining the price movement of stocks is a challenging problem to solve because of factors such as industry performance, economic variables, investor sentiment, company news, company performance, and social media sentiment. People can predict the price movement of stocks by applying machine learning algorithms on information contained in historical data, stock candlestick-chart data, and social-media data. However, it is hard to predict stock movement based on a single classifier. In this study, we proposed a multichannel collaborative network by incorporating candlestick-chart and social-m

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Dissertations / Theses on the topic "Stock Movement Prediction"

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Saha, Suman. "Stock market movement prediction using machine learning techniques and graph-based approaches." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/30018.

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Machine learning techniques are preferred now than the statistical methods for stock movement prediction due to their efficiency and effectiveness. Stock market movement prediction is impacted significantly by choice of input features and prediction algorithms. We focus on a specific event of ex-dividend day and use event-specific input features of cum-dividend period for predicting price movement on the ex-dividend day. Performance improves significantly when these event-specific optimum input features are used along with machine learning models. The relative order or ranking of stocks is mo

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Li, Edwin. "LSTM Neural Network Models for Market Movement Prediction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231627.

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Interpreting time varying phenomena is a key challenge in the capital markets. Time series analysis using autoregressive methods has been carried out over the last couple of decades, often with reassuring results. However, such methods sometimes fail to explain trends and cyclical fluctuations, which may be characterized by long-range dependencies or even dependencies between the input features. The purpose of this thesis is to investigate whether recurrent neural networks with LSTM-cells can be used to capture these dependencies, and ultimately be used as a complement for index trading decisi

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Elena, Podasca. "Predicting the Movement Direction of OMXS30 Stock Index Using XGBoost and Sentiment Analysis." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21119.

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Background. Stock market prediction is an active yet challenging research area. A lot of effort has been put in by both academia and practitioners to produce accurate stock market predictions models, in the attempt to maximize investment objectives. Tree-based ensemble machine learning methods such as XGBoost have proven successful in practice. At the same time, there is a growing trend to incorporate multiple data sources in prediction models, such as historical prices and text, in order to achieve superior forecasting performance. However, most applications and research have so far focused o

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Caley, Jeffrey Allan. "A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/2001.

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In this work, we propose and investigate a series of methods to predict stock market movements. These methods use stock market technical and macroeconomic indicators as inputs into different machine learning classifiers. The objective is to survey existing domain knowledge, and combine multiple techniques into one method to predict daily market movements for stocks. Approaches using nearest neighbor classification, support vector machine classification, K-means classification, principal component analysis and genetic algorithms for feature reduction and redefining the classification rule were

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Tang, Tsun-Hsien, and 湯忠憲. "News-Oriented Stock Movement Prediction on Dense Temporal Sequence Using Implicit News." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5t23bt.

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碩士<br>國立臺灣大學<br>資料科學學位學程<br>107<br>Analyzing online news content benefits stock price trend prediction. Previous studies on news-oriented stock market prediction focus mainly on news with explicit stock mentions for a specific prediction target, and may suffer from data sparsity. As taking into consideration other related news - e.g., sector-related news - is a crucial part of real-world decision-making, we explore the use of news without explicit target mentions to enrich the information for the prediction model. We first conduct an empirical analysis on real-world news collected from a well-

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"An application of two forecasting models for predicting price movements of a number of selected stocks in Hong Kong." Chinese University of Hong Kong, 1986. http://library.cuhk.edu.hk/record=b5885605.

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CHEN, YU-WEN, and 陳煜文. "Predicting Daily Direction of Stock Price Movement by Using Limit Book Information with LSTM Artificial Neural Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/e4fk26.

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碩士<br>輔仁大學<br>金融與國際企業學系金融碩士班<br>106<br>This study uses the intraday data information to forecasts the trend of stock prices within one day by using Long Short-Term Memory(LSTM) neural network model, and refers to the previous literature in Parlour(1998), Bacidore et al(2003) Cao and Wang(2003) ,the data of limit order book are adopted to as input parameters. The data period is from December 1, 2017 to December 31, 2017. Every data in limit order book will be collected in each transaction. In this study we will discussed the degree of influence of the limit order book information and the predic

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Andrews, Pete. "Trading on mood? Analysing the efficacy of sentiment and behavioural biases in predicting stock market movements and investor behaviour." Master's thesis, 2021. http://hdl.handle.net/10362/133423.

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Traditional finance theory rests on the assumption that investors are rational in aggregation. However, a wealth of behavioural finance research has shown this not to be the case. This paper examines whether biases that have been evidenced to impact individual’s behaviour can be witnessed in the stock market as a whole, and whether these can be utilised as a bellwether to future price changes. The results mirror the findings of the behaviourists, evidencing a susceptibility to biases among investors, and a promising forecasting ability when incorporated into a systematic volatility t

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Books on the topic "Stock Movement Prediction"

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Daneth, Roger K. Stock Investing for Income and Capital Growth Using Economic Indicators for Predicting Market Movements. Independently Published, 2017.

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Book chapters on the topic "Stock Movement Prediction"

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Hapsari, V. A. W., and R. Rokhim. "Stock movement prediction." In Contemporary Research on Business and Management. CRC Press, 2021. http://dx.doi.org/10.1201/9781003196013-12.

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Chen, Peibin, and Ying Tan. "Stock Market Movement Prediction by Gated Hierarchical Encoder." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78811-7_48.

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Upadhyay, Anand, Santosh Singh, Ranjit Patra, and Shreyas Patwardhan. "Prediction of Stock Movement Using Learning Vector Quantization." In Second International Conference on Sustainable Technologies for Computational Intelligence. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4641-6_22.

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Zheng, Yangjia, Xia Li, Junteng Ma, and Yuan Chen. "Fundamental Analysis Based Neural Network for Stock Movement Prediction." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18315-7_22.

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Samal, Sidharth, and Rajashree Dash. "Stock Index Movement Prediction: A Crow Search-ELM Approach." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7076-3_30.

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Chen, Po-Wei, Hao Yuan, Jiaying Huang, and Po-Ju Chen. "Multi-model based Attention Mechanism for Stock Movement Prediction." In Proceedings of the 2022 International Conference on Mathematical Statistics and Economic Analysis (MSEA 2022). Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-042-8_183.

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Liu, Jian, Yubo Chen, Kang Liu, and Jun Zhao. "Attention-Based Event Relevance Model for Stock Price Movement Prediction." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7359-5_5.

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Das, Nabanita, and Satyajit Chakrabati. "A Comparative Study for Analysis and Prediction of Stock Market Movement." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9927-9_77.

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Gong, Jiaying, and Hoda Eldardiry. "Multi-stage Hybrid Attentive Networks for Knowledge-Driven Stock Movement Prediction." In Neural Information Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92273-3_41.

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Dash, Rajashree, Sidharth Samal, Rasmita Rautray, and Rasmita Dash. "A TOPSIS Approach of Ranking Classifiers for Stock Index Price Movement Prediction." In Soft Computing in Data Analytics. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0514-6_63.

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Conference papers on the topic "Stock Movement Prediction"

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Li, Wei, Ruihan Bao, Keiko Harimoto, Deli Chen, Jingjing Xu, and Qi Su. "Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/626.

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Stock movement prediction is a hot topic in the Fintech area. Previous works usually predict the price movement in a daily basis, although the market impact of news can be absorbed much shorter, and the exact time is hard to estimate. In this work, we propose a more practical objective to predict the overnight stock movement between the previous close price and the open price. As no trading operation occurs after market close, the market impact of overnight news will be reflected by the overnight movement. One big obstacle for such task is the lacking of data, in this work we collect and publi

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Ding, Qianggang, Sifan Wu, Hao Sun, Jiadong Guo, and Jian Guo. "Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/640.

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Predicting the price movement of finance securities like stocks is an important but challenging task, due to the uncertainty of financial markets. In this paper, we propose a novel approach based on the Transformer to tackle the stock movement prediction task. Furthermore, we present several enhancements for the proposed basic Transformer. Firstly, we propose a Multi-Scale Gaussian Prior to enhance the locality of Transformer. Secondly, we develop an Orthogonal Regularization to avoid learning redundant heads in the multi-head self-attention mechanism. Thirdly, we design a Trading Gap Splitter

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Feng, Fuli, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, and Tat-Seng Chua. "Enhancing Stock Movement Prediction with Adversarial Training." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/810.

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This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model. The rationality of adversarial training here is that the input features to stock prediction are typically based on stock price, which is essentially a stochastic variable and continuously changed with time by nature. As such, normal training with static price-based features (e.g. the close

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Tang, Tsun-Hsien, Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. "Retrieving Implicit Information for Stock Movement Prediction." In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2021. http://dx.doi.org/10.1145/3404835.3462999.

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Xu, Junjie H., Shiwen An, Liangcheng Lyu, and Yiming Bai. "Overnight Stock Movement Prediction with Contextualized Embedding Incorporating News and Stock." In 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE). IEEE, 2022. http://dx.doi.org/10.1109/gcce56475.2022.10014070.

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Xu, Yumo, and Shay B. Cohen. "Stock Movement Prediction from Tweets and Historical Prices." In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/p18-1183.

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Zhao, Shunan. "Nepal Stock Market Movement Prediction with Machine Learning." In ICISDM 2021: 2021 the 5th International Conference on Information System and Data Mining. ACM, 2021. http://dx.doi.org/10.1145/3471287.3471289.

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Chen, Deli, Yanyan Zou, Keiko Harimoto, Ruihan Bao, Xuancheng Ren, and Xu Sun. "Incorporating Fine-grained Events in Stock Movement Prediction." In Proceedings of the Second Workshop on Economics and Natural Language Processing. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-5105.

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Latha, R. S., G. R. Sreekanth, R. C. Suganthe, et al. "Stock Movement Prediction using KNN Machine Learning Algorithm." In 2022 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2022. http://dx.doi.org/10.1109/iccci54379.2022.9740781.

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Nelson, David M. Q., Adriano C. M. Pereira, and Renato A. de Oliveira. "Stock market's price movement prediction with LSTM neural networks." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966019.

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Reports on the topic "Stock Movement Prediction"

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Dassanayake, Wajira, Chandimal Jayawardena, Iman Ardekani, and Hamid Sharifzadeh. Models Applied in Stock Market Prediction: A Literature Survey. Unitec ePress, 2019. http://dx.doi.org/10.34074/ocds.12019.

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Stock market prices are intrinsically dynamic, volatile, highly sensitive, nonparametric, nonlinear, and chaotic in nature, as they are influenced by a myriad of interrelated factors. As such, stock market time series prediction is complex and challenging. Many researchers have been attempting to predict stock market price movements using various techniques and different methodological approaches. Recent literature confirms that hybrid models, integrating linear and non-linear functions or statistical and learning models, are better suited for training, prediction, and generalisation performan

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Caley, Jeffrey. A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.2000.

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