Journal articles on the topic 'Stock price prediction'
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1
BIKSHAM, V., B. VISHAL KUMAR, C. RAHUL, G. VENU, and M. BHARGAV SAI. "STOCK PRICE PREDICTION." YMER Digital 21, no. 05 (May 2, 2022): 1–6. http://dx.doi.org/10.37896/ymer21.05/01.
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Machine learning has many important applications in the stock price prediction. Here, we will discuss about predicting the returns on stocks. This has uncertainties and it is a very complex task. This project will be developed into two parts: First, we will learn how to predict stock price using the Long Short-Term Memory neural networks. Predicting stock market prices involves human-computer interaction. For stock market analysis, conventional batch processing methods cannot be utilized efficiently due to the correlated nature of stock prices. We suggest an algorithm that utilizes a kind of recurrent neural network (RNN) called Long Short-Term Memory (LSTM), where using stochastic gradient descent the weights are adjusted for individual data points
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Cheruvu, Sai Manoj. "Stock Price Prediction Using Time Series." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 375–81. http://dx.doi.org/10.22214/ijraset.2021.39296.
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Abstract: Predicting Stock price of a company has been a challenge for analysts due to the fluctuations and its changing nature with respect to time. This paper attempts to predict the stock prices using Time series technique that proposes to observe various changes in a given variable with respect to time and is appropriate for making predictions in financial sector [1] as the stock prices are time variant. Keywords: Stock prices, Analysis, Fluctuations, Prediction, Time series, Time variant
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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 (October 30, 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 improve model training and minimize the prediction error, as models were evaluated using MSE, RMSE, and R2. The models performed with high accuracy in predicting closing price movement, despite the Intense volatility of time series. The empirical study concluded the possibility of relying on the RNN-LSTM model in predicting close prices at the ISX as well as decisions making upon.
Keywords: Stock, LSTM, Prediction, ANN, RNN, ISX
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Peng, Luna. "Stock Price Prediction of “Google” based on Machine Learning." BCP Business & Management 34 (December 14, 2022): 912–18. http://dx.doi.org/10.54691/bcpbm.v34i.3111.
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By 2022, many countries have declared the epidemic's end, both an opportunity and a challenge for many investors. More and more investors are manipulating prices to influence the stock market. So investors want to predict the price of stocks to make suitable investments. The author wants to start with the platform YouTube to study the price trend of this stock and make predictions to analyze whether there are traces of the factors affecting the stock price based on linear regression and random forest regression models. The author first backtested the price of this stock and analyzed the data according to the highest and lowest day. Then, the author used the method of Linear Regression and Random Forest Regression to predict the price. The error of the Linear Regression prediction results was within 5%, within the normal range, but the Random Forest Regression 5 days prediction's accuracy is much lower (65%). It shows that the stock price prediction model--Linear Regression is more credible and is worthy of reference for investors.
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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 (October 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 study focuses on major IT firms considering the company’s low and high prices. But, mid-price, which is a mean of the low and close price, is considered for the prediction. LSTM based methodology employing mid-price is effective in predicting values compared to other attributes and accuracy of prediction using the LSTM model. We conclude with the present model is more efficient in stock price prediction with a decrease in mean square error.
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Bhavanagarwala, Mustafa Shabbir, Nagarjun K N, Tanzim Abbas Charolia, Vishal M, and Ashwini M. "STOCK AND CRYPTOCURRENCY PREDICTION." International Journal of Innovative Research in Advanced Engineering 9, no. 8 (August 12, 2022): 182–86. http://dx.doi.org/10.26562/ijirae.2022.v0908.06.
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In our project, the point is to anticipate long term esteem of the money related stocks of a company and crypto coins individually with fine precision. The future prices of stock and cryptocurrency are predicted by using the past available values. “Buy low, sell high" is a good saying but it is not a good choice for making speculations. Investment is best stock or crypto currency in awful time can have bad results, while investment in best stock or cryptocurrency at right time can have best benefits. Prediction for long term values is easy as compared to day-to-day basis as prices fluctuate a lot. So, our model predicts the price of stocks and cryptocurrencies, which helps the investors to invest in appropriate stocks and cryptocoins. The dataset used is taken from yahoo finance and twelve data using web scraping. The dataset retrieved is in raw format. It consists of collection of values of stock market data of various companies, and also data of various cryptocurrencies. First, raw data is converted into processed data, which is done using feature extraction. Then the dataset is splitted into training and test sets. We use the training dataset to train the model, and use test dataset to predict the future prices of stocks and cryptocurrencies. Now user can gain best knowledge about stock price trends of various companies and also cryptocurrency price trends, and can decide on for best investments in respective fields and gain best benefits.
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TOKMAK, Mahmut. "Stock Price Prediction Using Long-Short-Term Memory Network." Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi 6, no. 2 (September 29, 2022): 309–22. http://dx.doi.org/10.31200/makuubd.1164099.
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One of the most important transactions of the financial system is stock trading. Stock price data is handle as a financial time series. Stock price predictions using time series analysis are the activity of determining the future value of stocks listed on the stock market. Predicting the price of the stock correctly reduces the risk factor in the decisions to be taken by the investors. Therefore, it is an important issue for the investor. However, because there are many variables that affect the stock price, it is a very complex process to predict. Machine learning methods, especially deep learning algorithms, are frequently used in prediction in the field of finance, as in many other fields. In this study, stock price prediction was made using Long-Short-Term Memory networks, which is one of the deep learning methods. Four stocks within the scope of Borsa İstanbul Technology Index were determined and a 2578-day data set was created between 2012 and 2022, and training and testing was carried out with the established model. As a result of the test process, consistent and realistic predictions were obtained.
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Zhong, Shan, and David Hitchcock. "S&P 500 Stock Price Prediction Using Technical, Fundamental and Text Data." Statistics, Optimization & Information Computing 9, no. 4 (November 18, 2021): 769–88. http://dx.doi.org/10.19139/soic-2310-5070-1362.
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We summarized both common and novel predictive models used for stock price prediction and combined them with technical indices, fundamental characteristics and text-based sentiment data to predict S&P stock prices. A 66.18% accuracy in S&P 500 index directional prediction and 62.09% accuracy in individual stock directional prediction was achieved by combining different machine learning models such as Random Forest and LSTM together into state-of-the-art ensemble models. The data we use contains weekly historical prices, finance reports, and text information from news items associated with 518 different common stocks issued by current and former S&P 500 large-cap companies, from January 1, 2000 to December 31, 2019. Our study's innovation includes utilizing deep language models to categorize and infer financial news item sentiment; fusing different models containing different combinations of variables and stocks to jointly make predictions; and overcoming the insufficient data problem for machine learning models in time series by using data across different stocks.
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Zhao, Cheng, Xiaohui Liu, Jie Zhou, Yuefeng Cen, and Xiaomin Yao. "GCN-based stock relations analysis for stock market prediction." PeerJ Computer Science 8 (August 11, 2022): e1057. http://dx.doi.org/10.7717/peerj-cs.1057.
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Most stock price predictive models merely rely on the target stock’s historical information to forecast future prices, where the linkage effects between stocks are neglected. However, a group of prior studies has shown that the leverage of correlations between stocks could significantly improve the predictions. This article proposes a unified time-series relational multi-factor model (TRMF), which composes a self-generating relations (SGR) algorithm that can extract relational features automatically. In addition, the TRMF model integrates stock relations with other multiple dimensional features for the price prediction compared to extant works. Experimental validations are performed on the NYSE and NASDAQ data, where the model is compared with the popular methods such as attention Long Short-Term Memory network (Attn-LSTM), Support Vector Regression (SVR), and multi-factor framework (MF). Results show that compared with these extant methods, our model has a higher expected cumulative return rate and a lower risk of return volatility.
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L, Dushyanth. "A SURVEY ON STOCK PRICE PREDICTION USING DEEP LEARNING." International Research Journal of Computer Science 9, no. 2 (February 28, 2022): 5–8. http://dx.doi.org/10.26562/irjcs.2022.v0902.002.
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Stock is a curve with a lot of unknowns. Stock market forecasting is fraught with complications and unpredictability. One of the most challenging and sophisticated methods of doing business is investing in the stock market. Stock forecasting is a difficult and time-consuming activity since the stock market is extremely volatile with stock prices fluctuating due to a variety of variables. Investors nowadays want quick and precise information to make informed decisions, thanks to the rapid growth of technology in stock price prediction. Understanding a company's stock price pattern and estimating its future development and financial growth will be quite advantageous. As the stock is made up of dynamic data, data is the critical source of efficiency. In the current trend of predicting stocks, deep learning is the most popular among the prediction of datasets. To forecast and automate operations, deep learning employs several prediction models and algorithms. The paper briefs about different algorithms and methods used for stock market prediction.
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Jing, Nan, Qi Liu, and Hefei Wang. "Stock price prediction based on stock price synchronicity and deep learning." International Journal of Financial Engineering 08, no. 02 (June 2021): 2141010. http://dx.doi.org/10.1142/s2424786321410103.
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Deep learning technology has been widely used in the financial industry, primarily for improving financial time series prediction based on stock prices. To solve the problem of low fitting and poor accuracy in traditional stock price prediction models, this paper proposes a stock price prediction model based on stock price synchronicity and deep learning methods, which applied the stock price synchronicity theory in stock price trend analysis. This paper first uses the affinity propagation algorithm to build stock clusters, and then, based on convolution neural network (CNN), and feature weight to construct the stock price synchronicity factor. At last, the long short-term memory (LSTM) network with multifactor is built for stock price trend analysis. According to the theory of stock price synchronicity, the affinity propagation algorithm can find the potential related stocks of the target stock. The spatial data analysis ability of the CNN model provides a guarantee for the application in stock price synchronicity factor analysis. The LSTM model can better analyze the information contained in the stock price time series and predict the future price. The experimental results show that, compared with the traditional multilayer neural network model, the LSTM model has better accuracy in the trend prediction of the stock price. Simultaneously, the application of stock price synchronicity effectively improves the performance of the multifactor LSTM network.
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Yu, Menghan, Panji Wang, and Tong Wang. "Application of Hidden Markov Models in Stock Forecasting." Proceedings of Business and Economic Studies 5, no. 6 (December 7, 2022): 14–21. http://dx.doi.org/10.26689/pbes.v5i6.4453.
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In this paper, we tested our methodology on the stocks of four representative companies: Apple, Comcast Corporation (CMCST), Google, and Qualcomm. We compared their performance to several stocks using the hidden Markov model (HMM) and forecasts using mean absolute percentage error (MAPE). For simplicity, we considered four main features in these stocks: open, close, high, and low prices. When using the HMM for forecasting, the HMM has the best prediction for the daily low stock price and daily high stock price of Apple and CMCST, respectively. By calculating the MAPE for the four data sets of Google, the close price has the largest prediction error, while the open price has the smallest prediction error. The HMM has the largest prediction error and the smallest prediction error for Qualcomm’s daily low stock price and daily high stock price, respectively.
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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 (October 1, 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 financial probabilistic lexicon (ThaiFinLex) derived from Thai financial news and stock market historical prices. The PLSP development consists of three steps. Firstly, we constructed ThaiFinLex by extracting event terms from news articles and calculating their associated probability of increasing/decreasing values of stock prices. Then, event terms with bad prediction performance were filtered out. Finally, the stock price directions were predicted using the PLSP and the remaining effective event terms. Our results indicated that the proposed model can be used for predicting stock price movement. The performance is as high as 83.33% when PLSP is used to predict stocks from the financial sector.
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Serbin, V., and U. Zhenisserov. "ANALYSIS OF MACHINE LEARNING METHODS FOR PREDICTIONS OF STOCK EXCHANGE SHARE PRICES." Scientific Journal of Astana IT University, no. 5 (July 27, 2021): 94–100. http://dx.doi.org/10.37943/aitu.2021.47.22.009.
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Since the stock market is one of the most important areas for investors, stock market price trend prediction is still a hot subject for researchers in both financial and technical fields. Lately, a lot of work has been analyzed and done in the field of machine learning algorithms for analyzing price patterns and predicting stock prices and index changes. Currently, machine-learning methods are receiving a lot of attention for predicting prices in financial markets. The main goal of current research is to improve and develop a system for predicting future prices in financial markets with higher accuracy using machine-learning methods. Precise predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and machine learning, forecasting methods have become more effective at predicting stock prices. In this article, we looked at the machine learning techniques that have been used to trade stocks to predict price changes before an actual rise or fall in the stock price occurs. In particular, the article discusses in detail the use of support vector machines, linear regression, and prediction using decision stumps, classification using the nearest neighbor algorithm, and the advantages and disadvantages of each method. The paper introduces parameters and variables that can be used to recognize stock price patterns that might be useful in future stock forecasting, and how the boost can be combined with other learning algorithms to improve the accuracy of such forecasting systems.
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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 (January 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 prediction. Aside from predictions, the limitations, and future works are discussed in the papers reviewed. The commonly used technique for achieving effective stock price prediction is the RF, LSTM, and SVM techniques. Despite the research efforts, the current stock price prediction technique has many limits. From this survey, it is observed that the stock market prediction is a complicated task, and other factors should be considered to accurately and efficiently predict the future.
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Talati, Drashti, Dr Miral Patel, and Prof Bhargesh Patel. "Stock Market Prediction Using LSTM Technique." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1820–28. http://dx.doi.org/10.22214/ijraset.2022.43976.
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Abstract: One of the most intricate machine learning problems is the share value prediction. Stock market prediction is an activity in which investors need fast and accurate information to make effective decisions. Moreover, the behavior of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. Prices of stocks are depicted by time series data and neural networks are trained to learn the patterns from trends in the existing data. This system employed algorithm using LSTM to improve the accuracy of stock price prediction.
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Shrikhande, Paresh, Raghu Ramani, and Rushi Bhalerao. "Stock Market Analysis and Prediction." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 1254–63. http://dx.doi.org/10.22214/ijraset.2022.42239.
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Abstract: Stock price Analysis & Prediction is a one of the sought after and popular topic throughout the last decade. By using machine learning and deep learning (RNN & LSTM) methods to make stock price prediction using real time data. While using Deep learning functions to predict and analyze stock prices are becoming more prevalent in these recent days. Its observed and assumed that machine learning as well as deep learning methods with RNN and LSTM could produce accurate results in stock price prediction. That is why we would like to try our own methods for this project. We have used a number of stocks from the S&P 500 namely as inputs and target. The data will contain open, high, and low, close, adjacent close and volume as its 6 variables. The adjacent close of each stock will become the target, and rest of the variable for a particular stock as well as all the other variables of other respective stocks will become as inputs. Index Terms: RNN: Recurrent Neural Network, S&P 500: Standard and Poor’s 500, LSTM Long short-term memory, DSP: Digital Signal Processing.
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Hersugondo, Hersugondo, Imam Ghozali, Eka Handriani, Trimono Trimono, and Imang Dapit Pamungkas. "Price Index Modeling and Risk Prediction of Sharia Stocks in Indonesia." Economies 10, no. 1 (January 6, 2022): 17. http://dx.doi.org/10.3390/economies10010017.
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This study aimed to predict the JKII (Jakarta Islamic Index) price as a price index of sharia stocks and predict the loss risk. This study uses geometric Brownian motion (GBM) and Value at Risk (VaR; with the Monte Carlo Simulation approach) on the daily closing price of JKII from 1 August 2020–13 August 2021 to predict the price and loss risk of JKII at 16 August 2021–23 August 2021. The findings of this study were very accurate for predicting the JKII price with a MAPE value of 2.03%. Then, using VaR with a Monte Carlo Simulation approach, the loss risk prediction for 16 August 2021 (one-day trading period after 13 August 2021) at the 90%, 95%, and 99% confidence levels was 2.40%, 3.07%, and 4.27%, respectively. Most Indonesian Muslims have financial assets in the form of Islamic investments as they offer higher returns within a relatively short time. The movement of all Islamic stock prices traded on the Indonesian stock market can be seen through the Islamic stock price index, namely the JKII (Jakarta Islamic Index). Therefore, the focus of this study was predicting the price and loss risk of JKII as an index of Islamic stock prices in Indonesia. This study extends the previous literature to determine the prediction of JKII price and the loss risk through GBM and VaR using a Monte Carlo simulation approach.
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Peñalvo, Francisco José García, Tamanna Maan, Sunil K. Singh, Sudhakar Kumar, Varsha Arya, Kwok Tai Chui, and Gaurav Pratap Singh. "Sustainable Stock Market Prediction Framework Using Machine Learning Models." International Journal of Software Science and Computational Intelligence 14, no. 1 (January 1, 2022): 1–15. http://dx.doi.org/10.4018/ijssci.313593.
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Prediction of stock prices is a challenging task owing to its volatile and constantly fluctuating nature. Stock price prediction has sparked the interest of various investors, data analysists, and researchers because of high returns on their investments. A sustainable framework for stock price prediction is proposed to quantify the factors affecting the stock price and impact of technology on the ever-changing business world. The proposed framework also helps to understand how technology can be used to predict the future price of stocks by using some historical dataset to produce desirable results using machine learning algorithms. The aim of this research paper is to learn about stock price prediction by using different machine learning algorithms and comparing their performance. The results reveal that Fb-prophet should be preferred for more precise prediction among different ML algorithms.
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Li, Jianyao. "A Comparative Study of LSTM Variants in Prediction for Tesla’s Stock Price." BCP Business & Management 34 (December 14, 2022): 30–38. http://dx.doi.org/10.54691/bcpbm.v34i.2861.
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Long short-term memory (LSTM) is widely used in the stock market to train the prediction model and forecast future stock prices. Applying the LSTM method to research may incur some problems and facilitate the improvement of the method. Therefore, many LSTM variants are put forward under different circumstances. This paper surveys four LSTM variants, including Vanilla, Stacked, Bi-directional, and CNN LSTM on two different data sets regarding Tesla's stock price. Two data sets mentioned in this paper represent different stock types. To be more specific, data set 1 refers to stocks with a single long-term trend, while data set 2 can be seen as an example of stocks with more complexity. The result shows that the Vanilla LSTM reaches the highest prediction accuracy on the data set without any irregular shift in the long-term trend. CNN LSTM also provides decent predictions for the stock price. Otherwise, the Stacked LSTM performs the best for stock prediction. Bi-LSTM and CNN LSTM are also suitable for stock forecasting in more complicated situations. The change in preference for model selection proves that a company's operation situation and market circumstances also influence the prediction performance of LSTM variants.
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Kadam, Mr Yash, Mr Sujay Kulkarni, Mr Suyog Lonsane, and Prof Anjali S. Khandagale. "A Survey on Stock Market Price Prediction System using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 322–30. http://dx.doi.org/10.22214/ijraset.2022.40635.
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Abstract: Prediction of stock prices is one of the most researched topics and gathers interest from academia and the industry alike. In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market using machine learning is Python. In this paper we propose a Machine Learning (ML) approach that will be trained from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. The paper focuses on the use of Linear Regression, Moving Average, K-Nearest Neighbours, Auto ARIMA, Prophet, and LSTM based Machine learning techniques to predict stock values. Factors considered are open, close, low, high and volume. The models are evaluated using standard strategic indicators: RMSE and MAPE. The low values of these two indicators show that the models are efficient in predicting stock closing price. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for stock market price prediction. This work contributes to the stock analysis research community both in the financial and technical domains. Keywords: Stock Market, Machine Learning, Prediction, LSTM, Python, Analysis, Linear Regression, Feature Engineering.
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Jaiswal, Rupashi, Kunal Mahato, Pankaj Kapoor, and Sudipta Basu Pal. "A Comparative Analysis on Stock Price Prediction Model using DEEP LEARNING Technology." American Journal of Electronics & Communication 2, no. 3 (January 3, 2022): 12–19. http://dx.doi.org/10.15864/ajec.2303.
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In today's world, Artificial Intelligence and Deep Learning are getting popular regularly. The various applications areas of artificial intelligence are related to human activity. One of the general application areas of neural networks and artificial intelligence is prediction analysis. In this paper, the authors also have performed one comparative study based on artificial intelligence. Authors have performed stock market predictions using different models. In reality, stock markets are entirely volatile, so there is very much a requirement of good prediction analysis for judging the stocks prices and their ups and downs with time. The stock prices can easily be predicted using machine learning algorithms on data available in financial news, as this data can also change investors' interests. However, traditional prediction methods have become obsolete and do not provide accurate predictions over non-stationary time series data. This paper proposes a stock price prediction method that gives accurate results with the advancements in deep learning technologies.
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Prasetyo, Dian Angga, and Rofikoh Rokhim. "Indonesian Stock Price Prediction using Deep Learning during COVID-19 Financial Crisis." International Journal of Business, Economics, and Social Development 3, no. 2 (May 6, 2022): 64–70. http://dx.doi.org/10.46336/ijbesd.v3i2.273.
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This research paper aims to use the deep learning model Long Short-Term Memory (LSTM) for the stock prediction model under the financial crisis of COVID-19. The financial impact of the COVID-19 has brought many of the world's indexes down. The impact of the financial crisis is even riskier for an emerging country such as Indonesia where foreign investors tend to take out their investments in emerging countries in financial crisis events. The application of deep learning in financial time series applications such as stock price prediction has been researched extensively. This study used the (Bidirectional LSTM) BiLSTM model which is a variation of the LSTM model to predict stock closing price. The stock prediction is applied to a selected company from the Indonesian stock market using historical prices. The model is then evaluated using metrics Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE). A graphical comparison between the actual price and predicted price of the stock is charted to study the stock price movement. To study the impact during COVID-19 on the stock prices, an intervention analysis is conducted along with the Wilcoxon model. The stock price prediction model can forecast the price of stocks before and during the financial crisis with minimal error. The intervention analysis result showed that health sectors have a positive effect while other sectors such as transportation, finance, information technology, and entertainment have a negative effect during the financial crisis of COVID-19. Being able to analyze and study the stock price movement of stocks is beneficial to investors in understanding the impact of the financial crisis on some industries and the behavior of certain stocks or industries under the circumstances which can lead to alternate investment strategies and decision making.
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Zheng, Hongying, Hongyu Wang, and Jianyong Chen. "Evolutionary Framework with Bidirectional Long Short-Term Memory Network for Stock Price Prediction." Mathematical Problems in Engineering 2021 (October 5, 2021): 1–8. http://dx.doi.org/10.1155/2021/8850600.
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As an important part of the social economy, stock market plays an important role in economic development, and accurate prediction of stock price is important as it can lower the risk of investment decision-making. However, the task of predicting future stock price is very difficult. This difficulty arises from stocks with nonstationary behavior and without any explicit form. In this paper, we propose a novel bidirectional Long Short-Term Memory Network (BiLSTM) framework called evolutionary BiLSTM (EBiLSTM) for the prediction of stock price. In the framework, three independent BiLSTMs correspond to different objective functions and act as mutation individuals, then their respective losses for evolution are calculated, and finally, the optimal objective function is identified by the minimum of loss. Since BiLSTM is effective in the prediction of time series and the evolutionary framework can get an optimal solution for multiple objectives, their combination well adapts to the nonstationary behavior of stock prices. Experiments on several stock market indexes demonstrate that EBiLSTM can achieve better prediction performance than others without the evolutionary operator.
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Horák, Jakub, and Tomáš Krulický. "Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company." SHS Web of Conferences 61 (2019): 01006. http://dx.doi.org/10.1051/shsconf/20196101006.
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Accurate stock price prediction is very difficult in today's economy. Accurate prediction plays an important role in helping investors improve return on equity. As a result, a number of new approaches and technologies have logically evolved in recent years to predict stock prices. One is also the method of artificial neural networks, which have many advantages over conventional methods. The aim of this paper is to compare a method of exponential time series alignment and time series alignment using artificial neural networks as tools for predicting future stock price developments on the example of the company Unipetrol. Time series alignment is performed using artificial neural networks, exponential alignment of time series, and then a comparison of time series of predictions of future stock price trends predicted using the most successful neural network and price prediction calculated by exponential time series alignment is performed. Predictions for 62 business days were obtained. The realistic picture of further possible development is surprisingly given based on the exponential alignment of time series.
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Cheng, Li-Chen, Yu-Hsiang Huang, Ming-Hua Hsieh, and Mu-En Wu. "A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions." Mathematics 9, no. 23 (November 30, 2021): 3094. http://dx.doi.org/10.3390/math9233094.
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The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. Most studies propose machine learning models to predict stock prices. However, constructing trading strategies is helpful for traders to avoid making mistakes and losing money. We propose an automatic trading framework using LSTM combined with deep Q-learning to determine the trading signal and the size of the trading position. This is more sophisticated than traditional price prediction models. This study used price data from the Taiwan stock market, including daily opening price, closing price, highest price, lowest price, and trading volume. The profitability of the system was evaluated using a combination of different states of different stocks. The profitability of the proposed system was positive after a long period of testing, which means that the system performed well in predicting the rise and fall of stocks.
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Islam, Noman, Misbah Afzal, Muhammad Arsal Wali, and Hamza Shakeel. "Data Analysis, Visualization and Prediction of Stock Market Prices of K-Electric." Pakistan Journal of Engineering and Technology 5, no. 2 (September 28, 2022): 226–33. http://dx.doi.org/10.51846/vol5iss2pp226-233.
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Predicting stock price is a trend yet very challenging task. It is because the stock prices depend upon several internal and external factors. Stock price prediction can be very useful for financial sectors and the government and help in informed decision-making. This paper analyzes the stock market prices of K-Electric Karachi. It is found that the stock prices of K-electric depend on the stock prices of the refinery sector. The paper analyzes the stock price data of the two sectors. Also, the paper compares the stock price prediction based on moving average, auto-regressive integrated moving average (ARIMA), convolutional neural network and long short-term memory (LSTM) model. It is found that ARIMA outperforms the other algorithms. A set of experiments were conducted to test the performance of algorithms. The algorithms were analyzed based on different metrics such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).
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Mishra, Shambhavi, Tanveer Ahmed, Vipul Mishra, Sami Bourouis, and Mohammad Aman Ullah. "An Online Kernel Adaptive Filtering-Based Approach for Mid-Price Prediction." Scientific Programming 2022 (February 16, 2022): 1–13. http://dx.doi.org/10.1155/2022/3798734.
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The idea of multivariate and online stock price prediction via the kernel adaptive filtering (KAF) paradigm is proposed in this article. The prediction of stock prices is traditionally done with regression and classification, thereby requiring a large set of batch-oriented and independent training samples. This is problematic considering the nonstationary nature of a financial time series. In this research, we propose an online kernel adaptive filtering-based approach for stock price prediction to overcome this challenge. To examine a stock's performance and demonstrate the work's superiority, we use ten different KAF family of algorithms. In this paper, we take on this challenge and propose an approach for predicting stock prices. To analyze a stock's performance and demonstrate the work's superiority, we use ten distinct KAF algorithms. Besides, the results are analyzed on nine-time windows such as one day, sixty minutes, thirty minutes, twenty five minutes, twenty minutes, fifteen minutes, ten minutes, five minutes, and one minute. We are the first to experiment with several time windows for all fifty stocks on the Indian National Stock Exchange, to the best of our knowledge. It should be noted here that the experiments are performed on stocks making up the main index: Nifty-50. In terms of performance and compared to existing methods, we have a 66% probability of correctly predicting a stock's next upward or downward movement. This number clearly shows the edge that the proposed method has in actual deployment. Furthermore, the experimental findings show that KAF is not only a better option for predicting stock prices but that it may also be used as an alternative in high-frequency trading due to its low latency.
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Chang, Zhichao, and Zuping Zhang. "Judging Stock Trends According to the Sentiments of Stock Comments in Expert Forums." Electronics 12, no. 3 (February 1, 2023): 722. http://dx.doi.org/10.3390/electronics12030722.
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Machine learning has been proven to be very effective and it can help to boost the performance of stock price predictions. However, most researchers mainly focus on the historical data of stocks and predict the future trends of stock prices by designing prediction models. They believe that past data must hide useful information in the future. Due to the lack of human participation, the result of this practice must be accidental. To solve this problem, we propose a novel model called Convolutional Neural Network with Sentiment Check (CNN-SC) in this paper. The model recommended by the authors refers to and expands upon the ideas of experts, and then takes the sentiment value in expert comments as the basis for stock price prediction. This model reflects the humanization of stock price prediction and eliminates the problem of a lack of supervision in machine learning. To demonstrate the effectiveness of our novel method, we compare it with five other popular and excellent methods. Although the C-E-SVR&RF and GC-CNN models are also quite effective, our results indicate the superiority of CNN-SC and it is accurately used to calculate the short-term (seven days later) stock price fluctuation of a single stock.
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Saud, Arjun Singh, and Subarna Shakya. "Analysis of Gradient Descent Optimization Techniques with Gated Recurrent Unit for Stock Price Prediction: A Case Study on Banking Sector of Nepal Stock Exchange." Journal of Institute of Science and Technology 24, no. 2 (December 31, 2019): 17–21. http://dx.doi.org/10.3126/jist.v24i2.27247.
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The stock price is the cost of purchasing a security or stock in a stock exchange. The stock price prediction has been the aim of investors since the beginning of the stock market. It is the act of forecasting the future price of a company's stock. Nowadays, deep learning techniques are widely used for identifying the stock trends from large amounts of past data. This research has experimented two big and robust commercial banks listed in the Nepal Stock Exchange (NEPSE) and compared stock price prediction performance of GRU with three widely used gradient descent optimization techniques: Momentum, RMSProp, and Adam. GRU with Adam is more accurate and consistent approach for predicting stock prices from the present study.
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KITA, Eisuke, Masaaki Harada, and Takao Mizuno. "Application of Bayesian Network to stock price prediction." Artificial Intelligence Research 1, no. 2 (September 26, 2012): 171. http://dx.doi.org/10.5430/air.v1n2p171.
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Authors present the stock price prediction algorithm by using Bayesian network. The present algorithm uses the networktwice. First, the network is determined from the daily stock price and then, it is applied for predicting the daily stock pricewhich was already observed. The prediction error is evaluated from the daily stock price and its prediction. Second, thenetwork is determined again from both the daily stock price and the daily prediction error and then, it is applied for thefuture stock price prediction. The present algorithm is applied for predicting NIKKEI stock average and Toyota motorcorporation stock price. Numerical results show that the maximum prediction error of the present algorithm is 30% inNIKKEI stock average and 20% in Toyota Motor Corporation below that of the time-series prediction algorithms such asAR, MA, ARMA and ARCH models.
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Kaninde, Sumedh, Manish Mahajan, Aditya Janghale, and Bharti Joshi. "Stock Price Prediction using Facebook Prophet." ITM Web of Conferences 44 (2022): 03060. http://dx.doi.org/10.1051/itmconf/20224403060.
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Estimating stock prices has always been a challenging task for researchers in the financial sector. Although the Efficient Market Hypothesis states that it is impossible to accurately predict stock prices, there is work in the literature that has shown that stock price movements can be predicted with the right level of accuracy, if the right variables are selected and appropriate predictor models are developed. those that are flexible. The Stock Market is volatile in nature and the prediction of the same is a cumbersome task. Stock prices depend upon not only economic factors, but they relate to various physical, psychological, rational and other important parameters. In this research work, the stock prices are predicted using Facebook Prophet. Stock price predictive models have been developed and run-on published stock data acquired from Yahoo Finance. Prophet is capable of generating daily, weekly and yearly seasonality along with holiday effects, by implementing regression models. The experimental results lead to the conclusion that Facebook Prophet can be used to predict stock prices for a long period of time with reasonable accuracy.
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Cao, Mengya. "Predicting the Link between Stock Prices and Indices with Machine Learning in R Programming Language." Journal of Mathematics 2021 (December 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/1275637.
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This paper provides an in-depth analysis machine study of the relationship between stock prices and indices through machine learning algorithms. Stock prices are difficult to predict by a single financial formula because there are too many factors that can affect stock prices. With the development of computer science, the author now uses many computer science techniques to make more accurate predictions of stock prices. In this project, the author uses machine learning in R Studio to predict the prices of 35 stocks traded on the New York Stock Exchange and to study the interaction between the prices of four indices in different countries. Further, it is proposed to find the link between stocks and indices in different countries and then use the predictions to optimize the portfolio of these stocks. To complete this project, the author used Linear Regression, LASSO, Regression Trees, Bagging, Random Forest, and Boosted Trees to perform the analysis. The experimental results show that the MRDL deep multiple regression model proposed in this paper predicts the closing price trend of stocks with a mean square error interval [0.0043, 0.0821]. Additionally, 80% of the proposed DMISV, KDJSV, MACDV, and DKB stock buying and selling strategies have a return greater than 10%. The experimental results validate the effectiveness of the proposed buying and selling strategies and stock price trend prediction methods in this paper. Compared with other algorithms, the accuracy of the algorithm in this study is increased by 15%, and the efficiency of prediction is increased by 25%.
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Jin, Beijia, Shuning Gao, and Zheng Tao. "ARIMA and Facebook Prophet Model in Google Stock Price Prediction." Proceedings of Business and Economic Studies 5, no. 5 (October 21, 2022): 60–66. http://dx.doi.org/10.26689/pbes.v5i5.4386.
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We use the Autoregressive Integrated Moving Average (ARIMA) model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’ predictions. We first examine the stationary of the dataset and use ARIMA(0,1,1) to make predictions about the stock price during the pandemic, then we train the Prophet model using the stock price before January 1, 2021, and predict the stock price after January 1, 2021, to present. We also make a comparison of the prediction graphs of the two models. The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.
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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 “AAPL” can achieve a good expected return within a low risk. In addition, the Artificial Neural Network method has the highest accuracy in predicting the multiclass stock price movement. Our research has a reference significance for the investors in the application of risk quantification and stock price prediction.
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Zhang, Junhao, and Yifei Lei. "Deep Reinforcement Learning for Stock Prediction." Scientific Programming 2022 (April 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/5812546.
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Investors are frequently concerned with the potential return from changes in a company’s stock price. However, stock price fluctuations are frequently highly nonlinear and nonstationary, rendering them to be uncontrollable and the primary reason why the majority of investors earn low long-term returns. Historically, people have always simulated and predicted using classic econometric models and simple machine learning models. In recent years, an increasing amount of research has been conducted using more complex machine learning and deep learning methods to forecast stock prices, and their research reports also indicate that their prediction accuracy is gradually improving. While the prediction results and accuracy of these models improve over time, their adaptability in a volatile market environment is questioned. Highly optimized machine learning algorithms include the following: FNN and the RNN are incapable of predicting the stock price of random walks and their results are frequently not consistent with stock price movements. The purpose of this article is to increase the accuracy and speed of stock price volatility prediction by incorporating the PG method’s deep reinforcement learning model. Finally, our tests demonstrate that the new algorithm’s prediction accuracy and reward convergence speed are significantly higher than those of the traditional DRL algorithm. As a result, the new algorithm is more adaptable to fluctuating market conditions.
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Zhu, Tianlei, Yuexin Liao, and Zheng Tao. "Predicting Google’s Stock Price with LSTM Model." Proceedings of Business and Economic Studies 5, no. 5 (October 21, 2022): 82–87. http://dx.doi.org/10.26689/pbes.v5i5.4361.
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Stock market has a profound impact on the market economy, Hence, the prediction of future movement of stocks is of great significance to investors. Therefore, an efficient prediction system can solve this problem to a great extent. In this paper, we used the stock price of Google Inc. as a prediction object, selected 3810 adjusted closing prices, and used long short-term memory (LSTM) method to predict the future price trend of the stock. We built a three-layer LSTM model and divided the entire data into a test set and a training set according to the ratio of 8 to 2. The final results show that while the LSTM model can predict the stock trend of Google Inc. very well, it cannot predict the specific price accurately.
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Vochozka, Marek, Jakub Horak, and Tomas Krulicky. "Innovations in Management Forecast: Time Development of Stock Prices with Neural Networks." Marketing and Management of Innovations, no. 2 (2020): 324–39. http://dx.doi.org/10.21272/mmi.2020.2-24.
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Accurate prediction of stock market values is a challenging task for over decades. Prediction of stock prices is associated with numerous benefits including but not limited to helping investors make wise decisions to accumulate profits. The development of the share price is a dynamic and nonlinear process affected by several factors. What is interesting is the unpredictability of share prices due to the global financial crisis. However, classical methods are no longer sufficient for the application of share price development prediction.However, over-relying on prediction data can lead to losses in the case of software malfunction. This paper aims to innovate the prediction management when predicting the share price development over time by the use of neural networks. For the contribution, the data on the prices of CEZ, a.s. shares obtained from the Prague Stock Exchange database. The stock price data are available for the period 2012-2017. In the case of Statistica software, the multilayer perceptron networks (MLP) and the radial basis function networks (RBF) are generated. In the case of Matlab software, the Support Vector Regression (SVR) and the Back-Propagation Neural Network (BPNN) are generated. The networks with the best characteristics are retained and based on the statistical interpretation of the results, and all are applicable in practice. In all data sets, MLP networks show stable performance better than in the case of SVR and BPNN networks. As for the final assessment, the deviation of 2.26% occurs in the most significant differential of the maximal and the minimal prediction. It is not necessarily significant regarding the price of one stock. However, in the case of purchasing or selling a large number of stocks, the difference may seem significant. Therefore, in practice, the application of two networks is recommended: MLP 1-2-1 and MLP 1-5-1. The first network always represents a pessimistic, minimal prediction. The second one of the recommended networks is an optimistic, maximal prediction. The actual situation should correspond to the interval of the difference between the optimistic and pessimistic prediction. Keywords: Statistica software, Matlab software, stock price development, neural networks, prediction.
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Yan, Qiuyu. "The Stock Price Analysis of Netflix Prediction." BCP Business & Management 34 (December 14, 2022): 964–68. http://dx.doi.org/10.54691/bcpbm.v34i.3117.
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Since the inception of the stock market, people have been using various data models, machine learning, and data mining to predict the future movement of stock prices to make huge profits. The rise and fall of stock prices are influenced by many factors, such as political, economic, social, and market factors. For stock investors, the prediction of stock market trends is directly related to profit capture. In this paper, we use Netflix's stock price for the past ten years as the dataset for this paper. An LSTM model will be built to predict the stock price trend of NETFLIX in the next 30 days. The dataset will be divided into a training set and a test set to test the degree of fit of the data. The results show that the LSTM model is a good fit for the predicted data and the real data. Finally, Netflix's stock price for the next 30 days will be predicted using Netflix's stock price for the past 10 years, and the results show that Netflix's stock price is on an upward trend for the next 30 days.
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Teng, Xiao, Tuo Wang, Xiang Zhang, Long Lan, and Zhigang Luo. "Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method." Complexity 2020 (February 17, 2020): 1–8. http://dx.doi.org/10.1155/2020/6737951.
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Stock trend prediction refers to predicting future price trend of stocks for seeking profit maximum of stock investment. Although it has aroused broad attention in stock markets, it is still a tough task not only because the stock markets are complex and easily volatile but also because real short-term stock data is so limited that existing stock prediction models could be far from perfect, especially for deep neural networks. As a kind of time-series data, the underlying patterns of stock data are easily influenced by any tiny noises. Thus, how to augment limited stock price data is an open problem in stock trend prediction, since most data augmentation schemes adopted in image processing cannot be brutally used here. To this end, we devise a simple yet effective time-sensitive data augmentation method for stock trend prediction. To be specific, we augment data by corrupting high-frequency patterns of original stock price data as well as preserving low-frequency ones in the frame of wavelet transformation. The proposed method is motivated by the fact that low-frequency patterns without noisy corruptions do not hurt the true patterns of stock price data. Besides, a transformation technique is proposed to recognize the importance of the patterns at varied time points, that is, the information is time-sensitive. A series of experiments carried out on a real stock price dataset including 50 corporation stocks verify the efficacy of our data augmentation method.
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Nagaya, Shigeki, Zhang Chenli, and Osamu Hasegawa. "A Proposal of Stock Price Predictor Using Associated Memory." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 2 (March 20, 2011): 145–55. http://dx.doi.org/10.20965/jaciii.2011.p0145.
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The novel method [1] we propose for predicting stock prices is a case-based reasoning predictor based on associative stock price data memory using Self-Organizing and IncrementalNeural Networks (SOINN) [2]. When a user inputs stock price data, the predictor outputs the most likely prediction based on statistically summarizing similar stock price pattern. It also outputs all cases included in the prediction. Our method has following advantages: (a) our predictor gives users grounds by giving all cases consisting of the prediction using associative memory. Users thereby recognize and are ready for prediction risk. (b) Our predictor avoids large prediction failures because it modifies itself through online learning and continues to learn without its learning parameters being reassigned. This makes it much safer where investment loss may be large. (c) Our predictor is as profitable as previous work while realizing unique, useful functions, as shown by experimental results using actual stock price data from the US and Japan markets between 1998 and 2005.
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Bhatia, Sahil. "Stock Price Trend Prediction." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (June 30, 2020): 1787–92. http://dx.doi.org/10.22214/ijraset.2020.6293.
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Liu, Hui, Liangchen Qi, and Mingsong Sun. "Short-Term Stock Price Prediction Based on CAE-LSTM Method." Wireless Communications and Mobile Computing 2022 (June 22, 2022): 1–7. http://dx.doi.org/10.1155/2022/4809632.
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Artificial intelligence methods are important tools for mining information for forecasting in the stock market. Most of the literature related to short-term stock price prediction focuses on the technical data, but in the real market, many individual investors make investment decisions more from stock price shape characteristics rather than specific stock price values. Compared with traditional measurement methods, deep neural networks perform better in processing high-dimensional complex data such as images. This paper proposes a model that combines CAE (convolutional autoencoder) and LSTM (long short-term memory) neural network, uses CAE to extract stock price image feature data, and combines technical data to predict short-term stock prices. The results show that the CAE-LSTM model, based on stock price image morphological feature data and technical data, performs well in short-term stock price prediction and has good generalization ability. The root mean square error of the CAE-LSTM model decreased by about 4% from that of LSTM. CAE-LSTM models have better predictive power than LSTM models that only use technical indicator data as valid inputs.
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Mahtab, Md Tanvir, A. G. M. Zaman, Montasir Rahman Mahin, Mohammad Nazim Mia, and Md. Tanjirul Islam. "Stock Price Prediction: An Incremental Learning Approach Model of Multiple Regression." AIUB Journal of Science and Engineering (AJSE) 21, no. 3 (December 31, 2022): 159–66. http://dx.doi.org/10.53799/ajse.v21i3.490.
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The endeavour of predicting stock prices using different mathematical and technological methods and tools is not new. But the recent advancements and curiosity regarding big data and machine learning have added a new dimension to it. In this research study, we investigated the feasibility and performance of the multiple regression method in the prediction of stock prices. Here, multiple regression was used on the basis of the incremental machine learning setting. The study conducted an experiment to predict the closing price of stocks of six different organizations enlisted in the Dhaka Stock Exchange (DSE). Three years of historical stock market data (2017-2019) of these organizations have been used. Here, the Multiple Regression, Squared Loss Function, and Stochastic Gradient Descent (SGD) algorithms are used as a predictor, loss function, and optimizer respectively. The model incrementally learned from the data of several stock-related attributes and predicted the closing price of the next day. The performance of prediction was then analysed and assessed on the basis of the rolling Mean Absolute Error (MAE) metric. The rolling MAE scores found in the experiment are quite promising.
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Chen, Ruoyang, and Bin Pan. "Chinese Stock Index Futures Price Fluctuation Analysis and Prediction Based on Complementary Ensemble Empirical Mode Decomposition." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/3791504.
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Since the CSI 300 index futures officially began trading on April 15, 2010, analysis and predictions of the price fluctuations of Chinese stock index futures prices have become a popular area of active research. In this paper, the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method is used to decompose the sequences of Chinese stock index futures prices into residue terms, low-frequency terms, and high-frequency terms to reveal the fluctuation characteristics over different time scales of the sequences. Then, the CEEMD method is combined with the Particle Swarm Optimization (PSO) algorithm-based Support Vector Machine (SVM) model to forecast Chinese stock index futures prices. The empirical results show that the residue term determines the long-term trend of stock index futures prices. The low-frequency term, which represents medium-term price fluctuations, is mainly affected by policy regulations under the analysis of the Iterated Cumulative Sums of Squares (ICSS) algorithm, whereas short-term market disequilibrium, which is represented by the high-frequency term, plays an important local role in stock index futures price fluctuations. In addition, in forecasting the daily or even intraday price data of Chinese stock index futures, the combination prediction model is superior to the single SVM model, which implies that the accuracy of predicting Chinese stock index futures prices will be improved by considering fluctuation characteristics in different time scales.
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Dai, Shu Yan, and Ning Li. "Using SVM to Predict Stock Price Changes from Online Financial News." Applied Mechanics and Materials 157-158 (February 2012): 1586–90. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1586.
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Many technical analysis use financial indices to predict stock price changes. In this paper, we present a different approach for prediction stock price fluctuations using financial news. Our method approaches the stock price prediction problem from an information retrieval perspective. We apply both text analysis and pattern classification techniques to search for important online news that are relevant for stock price changes. First, the online financial news and the corresponding stocks are extracted. Then we apply Support Vector Machine (SVM) to construct a model that predicts the price changes for the stocks. Finally, the stock changes prediction model is used to classify and extract upcoming important financial news. The experimental results demonstrate our method is effective for seeking the important financial news for stock price changes.
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Ji, Xuan, Jiachen Wang, and Zhijun Yan. "A stock price prediction method based on deep learning technology." International Journal of Crowd Science 5, no. 1 (March 5, 2021): 55–72. http://dx.doi.org/10.1108/ijcs-05-2020-0012.
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Purpose Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data. Design/methodology/approach This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price. Findings The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price. Originality/value In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.
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Kolte, Geeta, Varadraj Kini, Harikrishnan Nair, and Prof Suresh Babu K. S. "Stock Market Prediction using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 26–32. http://dx.doi.org/10.22214/ijraset.2022.41159.
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Abstract: Stock market is very uncertain and highly volatile as the prices of stocks keep fluctuating due to several factors that make prediction of stocks a very difficult and complicated task. In the finance and trading world stock analysis and trading is a method for investors and traders to make buying and selling decisions. Investors and traders try to gain an edge in the markets by taking informed decisions by studying and evaluating past and current data. Stock market prediction has always been an important research topic in the financial and trading field [2]. Prediction of stock market is the act of trying to determine the future value of a company stock (nifty & sensex) or other financial instrument traded on an exchange. Our project explains the prediction of a stock using Machine Learning, which itself employs different models to make prediction easier and authentic. The paper focuses on the use of Recurrent Neural Networks (RNN) called Long Short Term Memory (LSTM) to predict stock values. This will help us provide more accurate results when compared to existing stock price prediction algorithms. The eminent analysis of the stock will be an asset for the stock market investors and will provide real-life solutions to the problems and also yield significant profit. Keywords: Stock Price Prediction, Machine Learning, Long Short-Term Memory, Recurrent Neural Networks
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Ilyas, Qazi Mudassar, Khalid Iqbal, Sidra Ijaz, Abid Mehmood, and Surbhi Bhatia. "A Hybrid Model to Predict Stock Closing Price Using Novel Features and a Fully Modified Hodrick–Prescott Filter." Electronics 11, no. 21 (November 3, 2022): 3588. http://dx.doi.org/10.3390/electronics11213588.
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Forecasting stock market prices is an exciting knowledge area for investors and traders. Successful predictions lead to high financial revenues and prevent investors from market risks. This paper proposes a novel hybrid stock prediction model that improves prediction accuracy. The proposed method consists of three main components, a noise-filtering technique, novel features, and machine learning-based prediction. We used a fully modified Hodrick–Prescott filter to smooth the historical stock price data by removing the cyclic component from the time series. We propose several new features for stock price prediction, including the return of firm, return open price, return close price, change in return open price, change in return close price, and volume per total. We investigate traditional and deep machine learning approaches for prediction. Support vector regression, auto-regressive integrated moving averages, and random forests are used for conventional machine learning. Deep learning techniques comprise long short-term memory and gated recurrent units. We performed several experiments with these machine learning algorithms. Our best model achieved a prediction accuracy of 70.88%, a root-mean-square error of 0.04, and an error rate of 0.1.
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Pawaskar, Shreya. "Stock Price Prediction using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 667–73. http://dx.doi.org/10.22214/ijraset.2022.39891.
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Abstract: Machine learning has broad applications in the finance industry. Risk Analytics, Consumer Analytics, Fraud Detection, and Stock Market Predictions are some of the domains where machine learning methods can be implemented. Accurate prediction of stock market returns is extremely difficult due to volatility in the market. The main factor in predicting a stock market is a high level of accuracy and precision. With the introduction of artificial intelligence and high computational capacity, efficiency has increased. In the past few decades, the highly theoretical and speculative nature of the stock market has been examined by capturing and using repetitive patterns. Various machine learning algorithms like Multiple Linear Regression, Polynomial Regression, etc. are used here. The financial data contains factors like Date, Volume, Open, High, Low Close, and Adj Close prices. The models are evaluated using standard strategic indicators RMSE and R2 score. Lower values of these two indicators mean higher efficiency of the trained models. Various companies employ different types of analysis tools for forecasting and the primary aim is the accuracy to obtain the maximum profit. The successful prediction of the stock will be an invaluable asset for the stock market institutions and will provide real-life solutions to the problems of the investors. Keywords: Stock prices, Analysis, Accuracy, Prediction, Machine Learning, Regression, Finance