Relevant bibliographies by topics / Forecasting stock price

Academic literature on the topic 'Forecasting stock price'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Forecasting stock price"

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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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Sadorsky, Perry. "A Random Forests Approach to Predicting Clean Energy Stock Prices." Journal of Risk and Financial Management 14, no. 2 (January 24, 2021): 48. http://dx.doi.org/10.3390/jrfm14020048.

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Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest towards investing in companies that specialize in clean energy. Well informed investors need reliable methods for predicting the stock prices of clean energy companies. While the existing literature on forecasting stock prices shows how difficult it is to predict stock prices, there is evidence that predicting stock price direction is more successful than predicting actual stock prices. This paper uses the machine learning method of random forests to predict the stock price direction of clean energy exchange traded funds. Some well-known technical indicators are used as features. Decision tree bagging and random forests predictions of stock price direction are more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging and random forests methods produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. Tree bagging and random forests are easy to understand and estimate and are useful methods for forecasting the stock price direction of clean energy stocks.
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Marjuni, Aris. "Peramalan Harga Saham Serentak Menggunakan Model Multivariate Singular Spectrum Analysis." JURNAL SISTEM INFORMASI BISNIS 12, no. 1 (August 24, 2022): 17–25. http://dx.doi.org/10.21456/vol12iss1pp17-25.

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Stock price fluctuations in the stock market are widely influenced by financial environment changes in both micro and macro that are usually unpredictable and can not be controlled by stock players. On the other side, stock price information is very essential and much needed for both buyers and traders. Stock price forecasting is one of the analytical techniques to obtain stock price prediction based on the previous historical stock prices. The open and close prices are important information in stock trading. The opening price can influence the movement towards the closing price, and the closing price becomes important for the next day's opening price. In technical analysis, the relationship between the two stock prices can be parametric or non-parametric. This study discusses the stock price prediction or forecasting through the non-parametric approach using a multivariate singular spectrum analysis method with the consideration that open and close prices are simultaneously working in the same system and time. Performance evaluation using Mean Absolute Percentage Error shows that the multivariate singular spectrum analysis method can produce predicted open and close prices with an error rate of 3.18% and 3.21%, respectively. Hence, this method can be used as an alternative for stock price forecasting simultaneously.
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Fathi, Asmaa Y., Ihab A. El-Khodary, and Muhammad Saafan. "Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (September 1, 2022): 851. http://dx.doi.org/10.11591/ijai.v11.i3.pp851-858.

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<span>The main objective of stock market investors is to maximize their gains. As a result, stock price forecasting has not lost interest in recent decades. Nevertheless, stock prices are influenced by news, rumor, and various economic factors. Moreover, the characteristics of specific stock markets can differ significantly between countries and regions, based on size, liquidity, and regulations. Accordingly, it is difficult to predict stock prices that are volatile and noisy. This paper presents a hybrid model combining singular spectrum analysis (SSA) and nonlinear autoregressive neural network (NARNN) to forecast close prices of stocks. The model starts by applying the SSA to decompose the price series into various components. Each component is then used to train a NARNN for future price forecasting. In comparison to the autoregressive integrated moving average (ARIMA) and NARNN models, the SSA-NARNN model performs better, demonstrating the effectiveness of SSA in extracting hidden information and reducing the noise of price series.</span>
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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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Lu, Wenjie, Jiazheng Li, Yifan Li, Aijun Sun, and Jingyang Wang. "A CNN-LSTM-Based Model to Forecast Stock Prices." Complexity 2020 (November 23, 2020): 1–10. http://dx.doi.org/10.1155/2020/6622927.

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Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data.
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Kwang En, Tan. "IS STOCK PRICES REFLECTED IN MARKET RATIOS?" International Journal of Contemporary Accounting 2, no. 2 (December 23, 2020): 123. http://dx.doi.org/10.25105/ijca.v2i2.8224.

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<p>The most fascinating thing in stock market world is forecasting stock prices. Almost all players in stock market race to find the best method for forecast stock prices. After years of researching and practicing, we can divide all methods into two main methods, fundamental and technical analysis. Fundamental analysis based its forecasting method on macroeconomic factor, industry analysis, and company internal factors, while technical analysis based on studying financial accounting numbers and stock price trends in the past and present. This study will be focusing in the uses of technical analysing in forecasting stock prices.</p><p>There are many ways in technical analysis to forecast stock prices. Investors and analysts usually use stock price trends or financial ratios to do that. The latest is the most simple and powerful tools that almost everyone can use it, regardless to its limitations. When it comes to use financial ratios, there are a lot of contradicting results that make its users need to make a comparation between ratios and make a decision. </p><p>This paper try to use another solution to overcome those problem with using a composite indicators. The composite indicator will be compared with another market ratio to find out which method is the best on forecasting stock prices.</p><p>The result is composite indicator is the best method on forecasting stock prices compared with price to sales ratio, price to book value ratio, price to earnings per share ratio, and price to operating cash flow ratio.</p>
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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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Lv, Jiehua, Chao Wang, Wei Gao, and Qiumin Zhao. "An Economic Forecasting Method Based on the LightGBM-Optimized LSTM and Time-Series Model." Computational Intelligence and Neuroscience 2021 (September 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/8128879.

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Stock price prediction is very important in financial decision-making, and it is also the most difficult part of economic forecasting. The factors affecting stock prices are complex and changeable, and stock price fluctuations have a certain degree of randomness. If we can accurately predict stock prices, regulatory authorities can conduct reasonable supervision of the stock market and provide investors with valuable investment decision-making information. As we know, the LSTM (Long Short-Term Memory) algorithm is mainly used in large-scale data mining competitions, but it has not yet been used to predict the stock market. Therefore, this article uses this algorithm to predict the closing price of stocks. As an emerging research field, LSTM is superior to traditional time-series models and machine learning models and is suitable for stock market analysis and forecasting. However, the general LSTM model has some shortcomings, so this paper designs a LightGBM-optimized LSTM to realize short-term stock price forecasting. In order to verify its effectiveness compared with other deep network models such as RNN (Recurrent Neural Network) and GRU (Gated Recurrent Unit), the LightGBM-LSTM, RNN, and GRU are respectively used to predict the Shanghai and Shenzhen 300 indexes. Experimental results show that the LightGBM-LSTM has the highest prediction accuracy and the best ability to track stock index price trends, and its effect is better than the GRU and RNN algorithms.
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He, Ling T. "Forecasting of housing stock returns and housing prices." Journal of Financial Economic Policy 7, no. 2 (May 5, 2015): 90–103. http://dx.doi.org/10.1108/jfep-01-2014-0004.

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Purpose – The purpose of this paper is to create an endurance index of housing investor sentiment and use it to forecast housing stock returns. This study performs not only in-sample and out-of-sample forecasting, like many previous studies did, but also a true forecasting by using all lag terms of independent variables. In addition, an evaluation procedure is applied to quantify the quality of forecasts. Design/methodology/approach – Using a binomial probability distribution model, this paper creates an endurance index of housing investor sentiment. The index reflects the probability of the high or low stock price being the close price for the Philadelphia Stock Exchange Housing Sector Index. This housing investor sentiment endurance index directly uses housing stock price differentials to measure housing investor reactions to all relevant news. Empirical results in this study suggest that the index can not only play a significant role in explaining variations in housing stock returns but also have decent forecasting ability. Findings – Results of this study reveal the considerable forecasting ability of the index. Monthly forecasts of housing stock returns have an overall accuracy of 51 per cent, while the overall accuracy of 8-quarter rolling forecasts even reaches 84 per cent. In addition, the index has decent forecasting ability on changes in housing prices as suggested by the strong evidence of one-direction causal relations running from the endurance index to housing prices. However, extreme volatility of housing stock returns may impair the forecasting quality. Practical implications – The endurance index of housing investor sentiment is easy to construct and use for forecasting housing stock returns. The demonstrated predictability of the index on housing stock returns in this study can have broad implications on housing-related business practices through providing an effective forecasting tool to investors and analysts of housing stocks, as well as housing policy-makers. Originality/value – Despite different investor sentiment proxies suggested in the previous studies, few of them can effectively predict stock returns, due to some embedded limitations. Many increases and decreases inn prices cancel out each other during the trading day, as many unreliable sentiments cancel out each other. This dynamic process reveals not only investor sentiment but also resilience or endurance of sentiment. It is only long-lasting resilient sentiment that can be built in the closing price. It means that the only feasible way to use investor sentiment contained in stock prices to forecast future stock prices is to detach resilient investor sentiment from stock prices and construct an index of endurance of investor sentiment.
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Dissertations / Theses on the topic "Forecasting stock price"

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Kwan, Wai-ching Josephine. "Trend models for price movements in financial markets /." [Hong Kong] : University of Hong Kong, 1994. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13841397.

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Yiu, Fu-keung. "Time series analysis of financial index /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18003047.

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Shan, Yaowen School of Banking &amp finance UNSW. "Analysts' forecasts and future stock return volatility: a firm-level analysis for NYSE Firms." Awarded by:University of New South Wales. School of Banking & finance, 2006. http://handle.unsw.edu.au/1959.4/26963.

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This study demonstrates that financial analysts significantly affect short-term stock prices, by examining how non-accounting information particularly contained in analysts' forecasts contributes to the fluctuation of future stock returns. If current non-accounting information of future earnings is more unfavourable or more volatile, we could observe a larger shift in the current stock return. The empirical evidence strongly supports these theoretical predictions that stem from the combination of the accounting version of Campbell-Shiller model (Campbell and Shiller (1988) and Vuolteenaho (2002)) and Ohlson????s information dynamics (1995). In addition, the results are also valid for measures of both systematic and idiosyncratic volatilities.
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Rank, Christian. "Forecasting stock price movements using neural networks." Master's thesis, University of Cape Town, 2006. http://hdl.handle.net/11427/4392.

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Includes bibliographical references (p. 99-101).
The prediction of security prices has shown to be one of the most important but most difficult tasks in financial operations. Linear approaches failed to model the non-linear behaviour of markets and non-linear approaches turned out to posses too many constraints. Neural networks seem to be a suitable method to overcome these problems since they provide algorithms which process large sets of data from a non-linear context and yield thorough results. The first problem addressed by this research paper is the applicability of neural networks with respect to markets as a tool for pattern recognition. It will be shown that markets posses the necessary requirements for the use of neural networks, i.e. markets show patterns which are exploitable.
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Zhang, Yuzhao. "Essays on return predictability and volatility estimation." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1666139151&sid=3&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Zhang, Shaorong. "Essays on security issuance /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144472.

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Rangel, Jose Gonzalo. "Stock market volatility and price discovery three essays on the effect of macroeconomic information /." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3220417.

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Thesis (Ph. D.)--University of California, San Diego, 2006.
Title from first page of PDF file (viewed September 7, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 125-130).
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Chen, Gary. "Behavioural heterogeneity in ASX 200 a dissertation submitted to Auckland University of Technology in fulfilment of the requirements for the degree of Master of Business (MBus), 2009 /." Click here to access this resource online, 2009. http://hdl.handle.net/10292/758.

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Fodor, Bryan D. "The effect of macroeconomic variables on the pricing of common stock under trending market conditions." Thesis, Department of Business Administration, University of New Brunswick, 2003. http://hdl.handle.net/1882/49.

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Thesis (MBA) -- University of New Brunswick, Faculty of Administration, 2003.
Typescript. Bibliography: leaves 83-84. Also available online through University of New Brunswick, UNB Electronic Theses & Dissertations.
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Woodgate, Artemiza. "The impact of earnings management on price momentum /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/8755.

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Books on the topic "Forecasting stock price"

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Hess, Martin. The Determinants and the forecastability of Swiss stock prices. Bern: Studienzentrum Gerzensee, 2001.

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How the major stock indexes work: From the Dow to the S&P 500. New York: Rosen Pub., 2013.

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Rischbieth, Nick. Zur Eignung von Finanz-Kennzahlen für die Prognose von wesentlichen Ausschüttungsänderungen: Eine empirische Untersuchung anhand der Jahresabschlüsse börsennotierter Aktiengesellschaften in der Bundesrepublik Deutschland. Frankfurt am Main: P. Lang, 1987.

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O'Brien, Thomas J. A simple binomial no-arbitrage model of the term structure with applications to the valuation of interest-sensitive options and interest-rate swaps. New York, N.Y: Salomon Brothers Center for the Study of Financial Institutions, 1991.

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O'Brien, Thomas J. A simple binomial no-arbitrage model of the term structure with applications to the valuation of interest-sensitive options and interest-rate swaps. New York, N.Y: Salomon Brothers Center for the Study of Financial Institutions, 1991.

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Haskamp, Clemens Heinrich. Aktienkursprognose auf Grundlage der Identifikation von Trend- und Saisonkomponente: Eine empirische Untersuchung. Krefeld: Marchal und Matzenbacher, 1985.

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Phillips, Scott. Buying at the point of maximum pessimism: Six value investing trends from China to oil to agriculture. Upper Saddle River, N.J: FT Press, 2010.

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Buying at the point of maximum pessimism: Six value investing trends from China to oil to agriculture. Upper Saddle River, N.J: FT Press, 2010.

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S, Woodward Richard, and New York University, eds. Gains from stock market timing. New York, N.Y: Salomon Brothers Center for the Study of Financial Institutions, Graduate School of Business Administration, New York University, 1986.

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Chua, Jess H. Gains from stock-market timing. New York, N.Y. (90 Trinity Pl., New York 10006): Salomon Brothers Center for the Study of Financial Institutions, Graduate School of Business Administration, New York University, 1986.

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Book chapters on the topic "Forecasting stock price"

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Ravichandra, Thangjam, and Chintureena Thingom. "Stock Price Forecasting Using ANN Method." In Advances in Intelligent Systems and Computing, 599–605. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2757-1_59.

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Bao, Yukun, Yansheng Lu, and Jinlong Zhang. "Forecasting Stock Price by SVMs Regression." In Artificial Intelligence: Methodology, Systems, and Applications, 295–303. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30106-6_30.

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Roh, Tae Hyup. "Forecasting the Volatility of Stock Price Index." In Advanced Data Mining and Applications, 424–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_47.

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Touzani, Yassine, Khadija Douzi, and Fadoul Khoukhi. "Stock Price Forecasting: New Model for Stocks Selection and Price Forecasting Based on Convolutional Neural Network." In Advances in Intelligent Systems and Computing, 422–30. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36674-2_43.

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Do, Sang Thanh, Thi Thanh Nguyen, Dong-Min Woo, and Dong-Chul Park. "Standard Additive Fuzzy System for Stock Price Forecasting." In Intelligent Information and Database Systems, 279–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12101-2_29.

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Vantuch, Tomas, and Ivan Zelinka. "Evolutionary Based ARIMA Models for Stock Price Forecasting." In ISCS 2014: Interdisciplinary Symposium on Complex Systems, 239–47. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10759-2_25.

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Gupta, Bhupendra Kumar, Manas Kumar Mallick, and Sarbeswara Hota. "Survey on Stock Price Forecasting Using Regression Analysis." In Smart Innovation, Systems and Technologies, 147–56. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6202-0_16.

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Crato, Nuno, and Álvaro Assis Lopes. "Forecasting Price Trends at the Lisbon Stock Exchange." In A Reappraisal of the Efficiency of Financial Markets, 305–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74741-0_18.

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Ashik, A. Mohamed, and K. Senthamarai Kannan. "Time Series Model for Stock Price Forecasting in India." In Asset Analytics, 221–31. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0872-7_17.

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Kumar, Neeraj, Ritu Chauhan, and Gaurav Dubey. "Forecasting of Stock Price Using LSTM and Prophet Algorithm." In Lecture Notes in Electrical Engineering, 141–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3067-5_12.

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Conference papers on the topic "Forecasting stock price"

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Touzani, Yassine, Khadija Douzi, and Fadoul Khoukhi. "Stock Price Forecasting." In the 2018 2nd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3264560.3264566.

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Toliyat Abolhassani, AmirMohsen, and Mahdi Yaghoobi. "Stock price forecasting using PSOSVM." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579738.

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Ravichandra, Thangjam, and Chintureena Thingom. "Cumulative istributionfunction: Stock price forecasting." In 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE, 2017. http://dx.doi.org/10.1109/icimia.2017.7975643.

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Beyaz, Erhan, Firat Tekiner, Xiao-jun Zeng, and John Keane. "Stock Price Forecasting Incorporating Market State." In 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2018. http://dx.doi.org/10.1109/hpcc/smartcity/dss.2018.00263.

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Wu, Jui-Yu, and Chi-Jie Lu. "Computational Intelligence Approaches for Stock Price Forecasting." In 2012 International Symposium on Computer, Consumer and Control (IS3C). IEEE, 2012. http://dx.doi.org/10.1109/is3c.2012.23.

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Wang, Yifeng, Yuying Liu, Meiqing Wang, and Rong Liu. "LSTM Model Optimization on Stock Price Forecasting." In 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, 2018. http://dx.doi.org/10.1109/dcabes.2018.00052.

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Chen, Chia-Chi, Chun Kuo, Shu-Yu Kuo, and Yao-Hsin Chou. "Dynamic Normalization BPN for Stock Price Forecasting." In 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2015. http://dx.doi.org/10.1109/smc.2015.497.

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Thakur, Shivam, Shekar singh, and Seema Sharma. "Forecasting Stock Price Using Conditional Inference Tree." In 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). IEEE, 2018. http://dx.doi.org/10.1109/icacccn.2018.8748383.

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Coelho, Joseph, Dawson D'almeida, Scott Coyne, Nathan Gilkerson, Katelyn Mills, and Praveen Madiraju. "Social Media and Forecasting Stock Price Change." In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2019. http://dx.doi.org/10.1109/compsac.2019.10206.

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Aaryan and B. Kanisha. "Forecasting stock market price using LSTM-RNN." In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2022. http://dx.doi.org/10.1109/icacite53722.2022.9823818.

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Reports on the topic "Forecasting stock price"

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Chen, Joseph, Harrison Hong, and Jeremy Stein. Forecasting Crashes: Trading Volume, Past Returns and Conditional Skewness in Stock Prices. Cambridge, MA: National Bureau of Economic Research, May 2000. http://dx.doi.org/10.3386/w7687.

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