Journal articles on the topic 'Forecasting stock price'
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1
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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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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Atmaja, Dinul Darma, Widowati Widowati, and Budi Warsito. "FORECASTING STOCK PRICES ON THE LQ45 INDEX USING THE VARIMAX METHOD." MEDIA STATISTIKA 14, no. 1 (March 8, 2021): 98–107. http://dx.doi.org/10.14710/medstat.14.1.98-107.
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Forecasting using the Autoregressive Integrated Moving Average (ARIMA) method is not appropriate to predict more than one stock price because this method is only able to model one dependent variable. Therefore, to expect more than one stock prices, the ARIMA method expansion can be used, namely the Vector Autoregressive Integrated Moving Average (VARIMA) method. Furthermore, this research will discuss forecasting stock prices on the LQ45 index using the Vector Autoregressive Integrated Moving Average with Exogenous Variable (VARIMAX) method. Then, after the initial model formation process, the best model is the VARIMAX (0,1,2) model. Finally, the results of this study using the VARIMAX (0,1,2) model obtained the predictive value of the prices and the error values of stocks on the LQ45 index.
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Zhang, Fengyi, Zhigao Liao, and Hongping Hu. "Application of Multi-Input Hamacher-ANFIS Ensemble Model on Stock Price Forecast." Advances in Data Science and Adaptive Analysis 11, no. 01n02 (April 2019): 1950004. http://dx.doi.org/10.1142/s2424922x19500049.
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The stock market is a complex, evolving, and nonlinear dynamic system. Forecasting stock prices has been regarded as one of the most challenging applications of modern time series forecasting. This paper proposes a novel multi-input Hamacher-ANFIS (adaptive network-based fuzzy inference system based on Hamacher operator) ensemble model to forecast stock prices in China’s stock market and achieve good prediction performance. We selected five stocks with the largest total market capitalization from the Shanghai and Shenzhen Stock Exchanges, measured their historical volatility over the same time period, and weighed the performance of each stock forecasting model based on the above volatility. Then, the experiment was repeated 100 times for each data set, and we calculated the comprehensive [Formula: see text] of the testing set according to the weight that we obtained earlier. The statistical test of the experimental results shows that: (1) In terms of comprehensive [Formula: see text] of the stock price, the multi-input Hamacher-ANFIS model is superior to other conventional models; (2) when compared with the nonensemble forecasting strategy, the ensemble strategy of the Hamacher-ANFIS model has significant advantages.
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He, Wu, Lin Guo, Jiancheng Shen, and Vasudeva Akula. "Social Media-Based Forecasting." Journal of Organizational and End User Computing 28, no. 2 (April 2016): 74–91. http://dx.doi.org/10.4018/joeuc.2016040105.
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Social media-based forecasting has received significant attention from academia and industries in recent years. With a focus on Twitter, this paper investigates whether sentiments of the tweets regarding the 7 largest US financial service companies (in U.S. dollars) are related to the stock price changes of these companies. The authors' findings indicate, in the financial services context, negative sentiments predict firms' future stock prices. However, the number of and the positive sentiment of tweets are not correlated with stock prices. The findings of this paper suggest the possible predictive value of social media data on stock prices at the company level.
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Rahmawati, Nila, and Trianingsih Eni Lestari. "Implementasi Model Fungsi Transfer dan Neural Network untuk Meramalkan Harga Penutupan Saham (Close Price)." Jurnal Matematika 9, no. 1 (June 30, 2019): 11. http://dx.doi.org/10.24843/jmat.2019.v09.i01.p107.
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The multivariate forecasting model is a model of forecasting that takes into the causal relationship between a prediction factor with one or more independent variables. This study uses multivariate forecasting model that are transfer function and neural network model. The transfer function and neural network model are used for forecasting of closing stock price data by considering the opening stock price data as the independent variable in the forecasting model. The data used in this study is the monthly closing stock price and opening stock price data of PT. Bank Central Asia, Tbk. The best model for forecasting of closing stock price is a transfer function model that has MSE, MAPE, and MAE values ??smaller than the neural network model.
Keywords: transfer function, neural network, opening stock price, closing stock price
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Zili, Arman Haqqi Anna, Derick Hendri, and Selly Anastassia Amellia Kharis. "Peramalan Harga Saham Dengan Model Hybrid Arima-Garch dan Metode Walk Forward." Jurnal Statistika dan Aplikasinya 6, no. 2 (December 31, 2022): 341–54. http://dx.doi.org/10.21009/jsa.06218.
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For an Investor, modelling and forecasting the stock prices are very important. Stock price fluctuate as time goes and these changes vary from one point of time to another. These changes can be really dangerous if ignored because the risk of loss it might create. Many models have been created with the purpose of minimizing the risk of loss. In this study, the ARIMA-GARCH model will be used to predict closing price in the stock prices which contain volatility. The reason for using the combination of the two models is due to ARIMA model unable to handle large volatility along with non-linear data. Thus, it is hoped the use of this combined model can solve this problem. The data that is used on this study is the closing price of 2 stocks that is part of the LQ45 index. In this research, the data will be used on the combined model to get the forecast price of the next day. Then, the rest of the forecast price will be found using a process called Walk Forward. After acquiring all the forecasted price, it is found that the combination of ARIMA (1,1,1)-GARCH (1,1) yield the best result in forecasting the stock prices. Then, by using MAE and RMSE to check the error of the results, it can be concluded that the ARIMA-GARCH model is a model that is able to predict stock prices well.
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Gurav, U. P., and S. Kotrappa. "Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model." Engineering, Technology & Applied Science Research 10, no. 5 (October 26, 2020): 6356–61. http://dx.doi.org/10.48084/etasr.3805.
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Stock market historical information is often utilized in technical analyses for identifying and evaluating patterns that could be utilized to achieve profits in trading. Although technical analysis utilizing various measures has been proven to be helpful for forecasting and predicting price trends, its utilization in formulating trading orders and rules in an automated system is complex due to the indeterminate nature of the rules. Moreover, it is hard to define a specific combination of technical measures that identify better trading rules and points, since stocks might be affected by different external factors. Thus, it is important to incorporate investors’ sentiments in forecasting operations, considering dynamically the varying stock behavior. This paper presents a sentiment aware stock forecasting model using a Log BiLinear (LBL) model for learning short term stock market sentiment patterns, and a Recurrent Neural Network (RNN) for learning long-term stock market sentiment patterns. The Sentiment Aware Stock Price Forecasting (SASPF) model achieves a much superior performance compared to standard deep learning based stock price forecasting models.
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Eka, Gatri, Vebriani Safitry, and Yesika Kristin. "Penentuan Model Terbaik untuk Peramalan Data Saham Closing PT. CIMB Niaga Indonesia Menggunakan Metode Arch-Garch." Jurnal Statistika dan Aplikasinya 1, no. 1 (September 8, 2017): 1–12. http://dx.doi.org/10.21009/jsa.01101.
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This study explains about forecasting model stock closing price data PT. CIMB Niaga Indonesia on June 2017. The goal of this research is to formulated forecasting model stock data of PT. CIMB Niaga Indonesia specially is that stock closing price data by using ARCH/GARCH model. The result that got is that ARCH(1) model is the suitable model to forecasting stock closing price data. Result of forecasting for daily later so that stock data specially stock closing price is being increase.
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Srinivasulu, Akasam. "Validating The Assumptions of Residuals in ARIMA Model for Daily Stock Price Data By using R." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 971–78. http://dx.doi.org/10.22214/ijraset.2021.38942.
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Abstract: Identifying the past data and plannig for future is very important for every organization . Now a days Stock market playes a major role for the development of economy. For the countries economic development, stock market plays a vital role. For this modelling, forecasting is the best way to know the future stock prices based on the past stock prices data. In stock price data, forecasting of closed price plays a major role in financing economic decisions. The Arima model has developed and implemented in many applications .So the researchers utilize arima model in forecasting the closed prices of AMAZON stock price data for future which have been collected from AMAZON 2007-01-03, to 2020-10-12.In this paper the researcher aim is to forecast by using the ARIMA time series model with particular reference to Box and Jenkins approach on daily stock prices of AMAZON With open statistical software R. The validity of ARIMA model is tested by using the standard statistical tests. Keywords: Auto Regressive Integrated Moving Average, Auto Correlation Function, Partial Auto Correlation Function, Akaikae Information Criterion, Auto Regressive Conditional Heteroscedasticity
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Fajar Dwi Wibowo, Thanh-Tuan Dang, and Chia-Nan Wang. "FORECASTING INDONESIA STOCK PRICE USING TIME SERIES ANALYSIS AND MACHINE LEARNING IN R." Indonesian Scholars Scientific Summit Taiwan Proceeding 4 (August 17, 2022): 103–8. http://dx.doi.org/10.52162/4.2022166.
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This study investigated the appropriate model to predict 30 days ahead of Unilever Indonesia stock price and Telekomunikasi Indonesia stock price using time series analysis and machine learning in R, time series forecasting is a fun and interesting way to learn data science. The data is format Close Price. The goal of this project is to predict the future stock price of unilever indonesia and telekomunikasi indonesia using various predictive forecasting models and then analyze the various models. The dataset for unilever stocks is obtained from yahoo finance using Quantmod package in R. The final results that have been compared show that using the arima and neural network methods produces good accuracy values. Research and analysis of stock prices will help investors carry out investment is more accurate, investors can determine what steps will be taken, either buying a share or selling acquired shares the right step in taking an action. The data model used to predict close stock prices in this study unilever Indonesia using arima has an accuracy of 98.87%. and using neural network model has an of 98.92%. Telekomunikasi Indonesia using arima has an accuracy of 98.74%. and using neural network Model has an accuracy of 98.77% there are suggestions that can be given for further research and development. Trying to add to the existing historical data to be more complete so as to improve the accuracy of forecasting.
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Weng, Qiangwei, Ruohan Liu, and Zheng Tao. "Forecasting Tesla’s Stock Price Using the ARIMA Model." Proceedings of Business and Economic Studies 5, no. 5 (October 21, 2022): 38–45. http://dx.doi.org/10.26689/pbes.v5i5.4331.
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The stock market is an important economic information center. The economic benefits generated by stock price prediction have attracted much attention. Although the stock market cannot be predicted accurately, the stock market’s prediction of the trend of stock prices helps in grasping the operation law of the stock market and the influence mechanism on the economy. The autoregressive integrated moving average (ARIMA) model is one of the most widely accepted and used time series forecasting models. Therefore, this paper first compares the return on investment (ROI) of Apple and Tesla, revealing that the ROI of Tesla is much greater than that of Apple, and subsequently focuses on ARIMA model’s prediction on the available time series data, thus concluding that the ARIMA model is better than the Naïve method in predicting the change in Tesla’s stock price trend.
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Katterbauer, Klemens, and Philippe Moschetta. "An Innovative Artificial Intelligence and Natural Language Processing Framework for Asset Price Forecasting Based on Islamic Finance: A Case Study of the Saudi Stock Market." Econometric Research in Finance 6, no. 2 (December 1, 2021): 183–96. http://dx.doi.org/10.2478/erfin-2021-0009.
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Abstract Artificial intelligence has transformed the forecasting of stock prices and the evaluation of companies. Novel techniques, allowing the real-time processing of large amounts of data, have enabled the use of data on various external factors to improve the forecasting of the company’s value and stock price. Although conventional approaches solely focus on the use of quantitative data, history has shown that news announcements and statements may significantly affect the performance of the stock value of companies. We present an innovative framework for integrating a nonlinear autoregressive network with a natural language processing approach to analyze stock price movements and forecast stock prices. The framework analyzes and processes the company’s financial statements, determining indicative factors and transforming them into categorical parameters which are then integrated into a nonlinear autoregressive network to estimate and forecast the company’s stock price. The analysis of several Saudi companies listed in the Tadawul index affirms the improved estimation of the stock price and the possibility of a more precise prediction of long-term stock price evolution.
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Katterbauer, Klemens, and Philippe Moschetta. "An Innovative Artificial Intelligence and Natural Language Processing Framework for Asset Price Forecasting Based on Islamic Finance: A Case Study of the Saudi Stock Market." Econometric Research in Finance 6, no. 2 (December 1, 2021): 183–96. http://dx.doi.org/10.2478/erfin-2021-0009.
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Abstract Artificial intelligence has transformed the forecasting of stock prices and the evaluation of companies. Novel techniques, allowing the real-time processing of large amounts of data, have enabled the use of data on various external factors to improve the forecasting of the company’s value and stock price. Although conventional approaches solely focus on the use of quantitative data, history has shown that news announcements and statements may significantly affect the performance of the stock value of companies. We present an innovative framework for integrating a nonlinear autoregressive network with a natural language processing approach to analyze stock price movements and forecast stock prices. The framework analyzes and processes the company’s financial statements, determining indicative factors and transforming them into categorical parameters which are then integrated into a nonlinear autoregressive network to estimate and forecast the company’s stock price. The analysis of several Saudi companies listed in the Tadawul index affirms the improved estimation of the stock price and the possibility of a more precise prediction of long-term stock price evolution.
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Olena Nikolaieva, Anzhela Petrova, and Rostyslav Lutsenko. "FORECASTING OF THE STOCK RATE OF LEADING WORLD COMPANIES USING ECONOMETRIC METHODS AND DCF ANALYSIS." International Journal of Innovative Technologies in Economy, no. 2(29) (May 31, 2020): 33–41. http://dx.doi.org/10.31435/rsglobal_ijite/31052020/7067.
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In this article, we will cover various models for forecasting the stock price of global companies, namely the DCF model, with well-reasoned financial analysis and the ARIMA model, an integrated model of autoregression − moving average, as an econometric mechanism for point and interval forecasting. The main goal is to compare the obtained forecasting results and evaluate their real accuracy. The article is based on forecasting stock prices of two companies: Coca-Cola HBC AG (CCHGY) and Nestle S.A. (NSRGF). At the moment, it is not determined which approach is better for predicting the stock price − the analysis of financial indicators or the use of econometric data analysis methods.
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Mucci, Paul, Eun-Joo Lee, and Seung-Hwan Lee. "Stock Price Forecasting Using A Dependence Structure." European Journal of Mathematics and Statistics 3, no. 3 (May 20, 2022): 21–29. http://dx.doi.org/10.24018/ejmath.2022.3.3.114.
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It is important to incorporate diverse dependence structures between stocks when managing a stock portfolio. Copulas are a useful statistical tool to capture dependence structure, dealing with both the linear and non-linear association that may occur in the tails of data. Financial time series datasets often exhibit volatility clustering that affects price forecasting accuracy. This work proposes the initial use of the principal component analysis followed by a copula and GARCH model that filters the effect of the volatility clustering in the series. For illustration, we consider ten banks from which Bank of America and PNC Financial Services Group are chosen, and then we project their future price movements through simulations. Since they are selected in terms of the principal component analysis, the procedures could help the proposed model to become a more widely used tool in forecasting financial stock performance.
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Jagtap, Ajitkumar, Yash Patil, and Darshan Oswal. "Visualizing and Forecasting Stocks Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2562–66. http://dx.doi.org/10.22214/ijraset.2022.41846.
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Abstract: India's stock market is exceedingly changing and reductionism, which has a countless number of features that control the directions and trends of the stock price; therefore, prediction of uptrend and downtrend is a complex process. This paper point of view to demonstrate the use of recurrent neural network in finance to prediction of the closing price of a selected stock and analyse opinions around it in real-time. By combining both techniques, the submitted model can give buy or sell recommendation. In Stock Market Prediction, the aim is to predict the upcoming future value of the financial stocks of the company. The latest trend in stock market prediction technologies is the use of machine learning which makes predictions depending on the values of current stock market indices by training on their previous stock values. Machine learning itself use different models to make prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to prediction of stock values. Factors for stocks considered are open, close, low, high and volume. Keywords: Machine learning, Stock Market, Long Short-Term Memory, Recurrent Neural Network.
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Alwi, Wahidah, Aprilia Pratiwi S, and Ilham Syata. "Forcasting Stock Price PT. Indonesian Telecomunication with ARCH-GARCH Model." Jurnal Varian 5, no. 2 (April 26, 2022): 125–36. http://dx.doi.org/10.30812/varian.v5i2.1543.
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This research discusses the modeling of time series using R software, focusing on forecasting the stock price of PT. Indonesian telecommunications with ARCH-GARCH model. The data used daily closing data on stock prices from January 6, 2020, to January 6, 2021 was obtained from the website www.finance.yahoo.com. The goal is to find out the best model arch-garch on PT. Indonesian telecommunications to find out the results of stock price forecasting the next day using the ARCH-GARCH model. The best model was ARIMA (2,1,3). The results of the ARCH-LM test showed the data contained heteroskedasticity effects or ARCH elements. The research models proposed in this study are ARCH (1) and ARCH-GARCH (1,1). The smallest AIC and BIC values of these two models are ARCH-GARCH (1,1) which is the best model for forecasting the stock price of PT. Indonesian telecommunications for the next 10 days. The study attempts to conduct stock price forecasting with the ARCH-GARCH model. The result of the forecasting of the share price of PT. Indonesian telecommunications from January 07, 2021 to January 20, 2021 respectively except for holidays is IDR 3374.884, IDR 3379.617,IDR 3378.305, IDR 3376.610, IDR 3380.050, IDR 3376.372, IDR 3379.071, IDR 3377.964, IDR 3377.515, IDR 3379.002. Forecasting results are close to factual data for forecasting the next 10 days so that they can be taken into consideration in investing by investors.
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Dong, Can. "Stock Trend Forecasting Using the ARIMA Model." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 56–62. http://dx.doi.org/10.54097/hset.v16i.2239.
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Stocks have always been a very important tool in the investment market. Nowadays, the stock market attracts a large number of investors as more and more people are exposed to investing their money. One of the most attractive features of equities for investors is the high returns, however, the high risks are also affecting investors’ confidence. Therefore, predicting the long-term performance of the stock market can lower the risk and give investors more ideas on how to invest and help them understand the future trend of their preferred stocks, thus reducing their risk rate. In this paper, the author proposed a stock predicting method based on the closing prices of Ford Motor Company over the past 50 years. In the selection of the model, the ADF test was utilized and by analyzing the dataset, the author demonstrated that the stock closing price is a non-stationary series and therefore the ARIMA model is selected. After differencing the series, a stationary series was obtained, and the best parameter was chosen based on Auto-ARIMA analyses. Furthermore, the author obtained the long-term trend graph based on the model output and analyzed the accuracy of the prediction results by RMSE and MAPE tests. In addition, by bringing data from other stock markets into the experiment, it is evident that the ARIMA model can be effective when predicting long-term trend of stocks and is able to be used as a method to forecast stock prices.
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Maya Citra. "Comparative Study of Stock Price Forecasting Models PT. Unilever Indonesia Tbk Using Arima and Garch." International Journal of Community Service (IJCS) 2, no. 1 (June 30, 2021): 1–22. http://dx.doi.org/10.55299/ijcs.v2i1.220.
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The purpose of this study is to know the comparison of forecasting models in predicting the stock price of PT. Unilever Indonesia Tbk. In this study, there are 2 forecasting models, namely ARIMA and GARCH forecasting. The population in this study is data on the daily closing price of PT. Unilever Indonesia Tbk for the period January 2018 to June 2021, so the sample in this study is 1090 time series data. The results showed that the best forecasting model to predict the stock price of PT. Unilever Indonesia Tbk, namely ARIMA (1,1,1) and GARCH (1,1). In the ARIMA model (1,1,1) there are assumptions that are not met, namely the assumption of homoscedasticity or in the model there is an element of heteroscedasticity so that the GARCH (1,1) model with MAPE 1.91% is selected as the best forecasting model to predict stock prices of PT. Unilever Indonesia Tbk.
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Endress, Tobias. "“Deliberated Intuition” in Stock Price Forecasting." Economics & Sociology 11, no. 3 (September 2018): 11–27. http://dx.doi.org/10.14254/2071-789x.2018/11-3/1.
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Singh Saud, Arjun, and Subarna Shakya. "Evaluation of Weight Decay Regularization Techniques for Stock Price Prediction using Gated Recurrent Unit Network." Nepal Journal of Science and Technology 19, no. 2 (October 10, 2021): 9–15. http://dx.doi.org/10.3126/njst.v20i1.39379.
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Stock price forecasting in the field of interest for many stock investors to earn more profit from stock trading. Nowadays, machine learning researchers are also involved in this research field so that fast, accurate and automatic stock price forecasting can be achieved. This research paper evaluated GRU network’s performance with weight decay reg-ularization techniques for predicting price of stocks listed NEPSE. Three weight decay regularization technique analyzed in this research work were (1) L1 regularization (2) L2 regularization and (3) L1_L2 regularization. In this research work, six randomly selected stocks from NEPSE were experimented. From the experimental results, we observed that L2 regularization could outperform L1 and L1_L2 reg-ularization techniques for all six stocks. The average MSE obtained with L2 regularization was 4.12% to 33.52% lower than the average MSE obtained with L1 regularization, and it was 10.92% to 37.1% lower than the average MSE obtained with L1_L2 regularization. Thus, we concluded that the L2 regularization is best choice among weight regularization for stock price prediction.
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Ma, Guifen, Ping Chen, Zhaoshan Liu, and Jia Liu. "The Prediction of Enterprise Stock Change Trend by Deep Neural Network Model." Computational Intelligence and Neuroscience 2022 (August 2, 2022): 1–9. http://dx.doi.org/10.1155/2022/9193055.
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This study aims to accurately predict the changing trend of stocks in stock trading so that company investors can obtain higher returns. In building a financial forecasting model, historical data and learned parameters are used to predict future stock prices. Firstly, the relevant theories of stock forecasting are discussed, and problems in stock forecasting are raised. Secondly, the inadequacies of deep neural network (DNN) models are discussed. A prediction trend model of enterprise stock is established based on long short-term memory (LSTM). The uniqueness and innovation lie in using the stock returns of Bank of China securities in 2022 as the training data set. LSTM prediction models are used to perform error analysis on company data training. The 20-day change trend of the company’s stock returns under different models is predicted and analyzed. The results show that as the number of iterations increases, the loss rate of the LSTM training curve keeps decreasing until 0. The average return price of the LSTM prediction model is 14.01. This figure is closest to the average real return price of 13.89. Through the forecast trend analysis under different models, LSTM predicts that the stock change trend of the enterprise model is closest to the changing trend of the actual earnings price. The prediction accuracy is better than other prediction models. In addition, this study explores the characteristics of high noise and complexity of corporate stock time series, designs a DNN prediction model, and verifies the feasibility of the LSTM model to predict corporate stock changes with high accuracy.
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Shahi, Tej Bahadur, Ashish Shrestha, Arjun Neupane, and William Guo. "Stock Price Forecasting with Deep Learning: A Comparative Study." Mathematics 8, no. 9 (August 27, 2020): 1441. http://dx.doi.org/10.3390/math8091441.
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The long short-term memory (LSTM) and gated recurrent unit (GRU) models are popular deep-learning architectures for stock market forecasting. Various studies have speculated that incorporating financial news sentiment in forecasting could produce a better performance than using stock features alone. This study carried a normalized comparison on the performances of LSTM and GRU for stock market forecasting under the same conditions and objectively assessed the significance of incorporating the financial news sentiments in stock market forecasting. This comparative study is conducted on the cooperative deep-learning architecture proposed by us. Our experiments show that: (1) both LSTM and GRU are circumstantial in stock forecasting if only the stock market features are used; (2) the performance of LSTM and GRU for stock price forecasting can be significantly improved by incorporating the financial news sentiments with the stock features as the input; (3) both the LSTM-News and GRU-News models are able to produce better forecasting in stock price equally; (4) the cooperative deep-learning architecture proposed in this study could be modified as an expert system incorporating both the LSTM-News and GRU-News models to recommend the best possible forecasting whichever model can produce dynamically.
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Sedighi, Mojtaba, Hossein Jahangirnia, Mohsen Gharakhani, and Saeed Farahani Fard. "A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine." Data 4, no. 2 (May 22, 2019): 75. http://dx.doi.org/10.3390/data4020075.
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This paper intends to present a new model for the accurate forecast of the stock’s future price. Stock price forecasting is one of the most complicated issues in view of the high fluctuation of the stock exchange and also it is a key issue for traders and investors. Many predicting models were upgraded by academy investigators to predict stock price. Despite this, after reviewing the past research, there are several negative aspects in the previous approaches, namely: (1) stringent statistical hypotheses are essential; (2) human interventions take part in predicting process; and (3) an appropriate range is complex to be discovered. Due to the problems mentioned, we plan to provide a new integrated approach based on Artificial Bee Colony (ABC), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM). ABC is employed to optimize the technical indicators for forecasting instruments. To achieve a more precise approach, ANFIS has been applied to predict long-run price fluctuations of the stocks. SVM was applied to create the nexus between the stock price and technical indicator and to further decrease the forecasting errors of the presented model, whose performance is examined by five criteria. The comparative outcomes, obtained by running on datasets taken from 50 largest companies of the U.S. Stock Exchange from 2008 to 2018, have clearly demonstrated that the suggested approach outperforms the other methods in accuracy and quality. The findings proved that our model is a successful instrument in stock price forecasting and will assist traders and investors to identify stock price trends, as well as it is an innovation in algorithmic trading.
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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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You, Zixuan. "Evaluation of two models for predicting Amazon stock based on machine learning." BCP Business & Management 34 (December 14, 2022): 39–47. http://dx.doi.org/10.54691/bcpbm.v34i.2862.
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With the fast growth of artificial intelligence and technology, the use of machine learning techniques in financial markets is gaining popularity. As a result, many opportunities arise, such as predicting future stock movements. Financial markets are complex and constantly evolving environments, so analyzing them can be challenging and interesting. There are no specific rules to predict or estimate the value of a stock in the stock market, so one can do stock price prediction by various methods. In this project, the stock price data of Amazon for the past five years, ‘Date’, ‘Starting price’, ‘Closing price’, ‘Highest price’, ‘Lowest price’, ‘Adjusted close price’, and 'Volume’ are all included. The data were obtained from Yahoo Finance to predict the future stock price. Two forecasting models, the Linear Regression model, and the Long short-term memory model were analyzed, and based on the comparison of Mean Absolute Error, it was concluded that the LSTM forecasting model was shown to be more effective for forecasting the time-series data category.
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Agung, Ignatius Wiseto Prasetyo. "Input Parameters Comparison on NARX Neural Network to Increase the Accuracy of Stock Prediction." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 6, no. 1 (July 21, 2022): 82–90. http://dx.doi.org/10.31289/jite.v6i1.7158.
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The trading of stocks is one of the activities carried out all over the world. To make the most profit, analysis is required, so the trader could determine whether to buy or sell stocks at the right moment and at the right price. Traditionally, technical analysis which is mathematically processed based on historical price data can be used. Parallel to technological development, the analysis of stock price and its forecasting can also be accomplished by using computer algorithms e.g. machine learning. In this study, Nonlinear Auto Regressive network with eXogenous inputs (NARX) neural network simulations were performed to predict the stock index prices. Experiments were implemented using various configurations of input parameters consisting of Open, High, Low, Closed prices in conjunction with several technical indicators for maximum accuracy. The simulations were carried out by using stock index data sets namely JKSE (Indonesia Jakarta index) and N225 (Japan Nikkei index). This work showed that the best input configurations can predict the future 13 days Close prices with 0.016 and 0.064 mean absolute error (MAE) for JKSE and N225 respectively.
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Chang, To-Han, Nientsu Wang, and Wen-Bin Chuang. "Stock Price Prediction Based on Data Mining Combination Model." Journal of Global Information Management 30, no. 7 (September 2022): 1–19. http://dx.doi.org/10.4018/jgim.296707.
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Predicting stock indexes is a common concern in the financial world. This work uses neural network, support vector machine (SVM), mixed data sampling (MIDAS), and other methods in data mining technology to predict the daily closing price of the next 20 days and the monthly average closing price of the future expected daily closing price on the basis of the market performance of stock prices. Additionally, by the mutual ratio of weighted mean square error the study achieves the best prediction result. Combining value investment effectively with nonlinear models, a complete stock forecasting model is established, and empirical research is conducted on it. Results indicate that SVM and MIDAS have good results for stock price forecasting. Among them, MIDAS has a better mid-term forecast, which is approximately 10% higher than the forecast accuracy of the SVM model; Meanwhile, SVM is more accurate in the short-term forecast.
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Alrub, Ahmad Abu, Tahir Abu Awwad, and Emad Al-Saadi. "Autoregressive Neural Network EURO STOXX 50 Forecasting Model Based on Principal Component Stock Selection." International Journal of Finance 6, no. 2 (September 1, 2021): 71–81. http://dx.doi.org/10.47941/ijf.667.
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Purpose: The given study looks into forecast accuracy of a traditional ARIMA model while comparing it to Autoregressive Neural Network (AR-NN) model for 984 trading days on EURO STOXX 50 Index.
Methodology: A hybrid model is constructed by combining ARIMA model and feed-forward neural network model aiming to attain linear and non-linear price fluctuations. The study also incorporates the investigation of component stock prices of the index, that can be selected to improve the predictability of the hybrid model.
Findings:The reached ARIMA (1,1,3) model showed higher scores than AR-NN model however integrating selected exogenous stock prices from the index components gave much notable accuracy results. The selected exogenous stocks were extracted after conducting PCA and model scores were compared via MAPE and RMSE.
Unique contribution to theory, practice and policy: The major contribution of this work is to provide the researcher and fnancial analyst a systematic approach for development of intelligent methodology to forecast stock market. This paper also presents the outlines of proposed work with the aim to enhance the performance of existing techniques. Therefore, Empirical analysis is employed along with a hybrid model based on a feed-forward Neural Network. Lesser error is attained on the test set of Index stock price by comparing the performance of ARIMA and AR-NN while forecasting. Hence, The components of extracted Index stock price like exogenous features are added to make an influence from the AR-NN model.
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Wankhade, Sunil B., Divyesh Surana, Neel J. Mansatta, and Karan Shah. "Hybrid Model based on unification of Technical Analysis and Sentiment Analysis for Stock Price Prediction." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 9 (December 5, 2013): 3025–33. http://dx.doi.org/10.24297/ijct.v11i9.3415.
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Stock price forecasting phenomenon has been majorly made on the basis of quantitative information. Over the time, with the advent of technology, stock forecasting used technical analysis to get more accurate predictions. Until recently, studies have demonstrated that sentiment information hidden in corporate reports can be effectively incorporated to predict short-run stock price returns. Soft computing methods, like neural networks, fuzzy models and support vector regression, have shown great results in the forecasting of stock price due to their ability to model complex non-linear systems.In this paper we propose a hybrid method for stock price predication, which is combinational feature from technical analysis and sentiment analysis (SA). The features of sentiment analysis are based on a Point wise Mutual Information (PMI) and we apply neural network and ε-support vector regression models to predict the yearly change in the stock price.
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C, Liyanagamage, and Madusanka P.H.A.C. "Modeling for Stock Price Forecasting in Colombo Stock Exchange: An Historical Analysis of Stock Prices." International Journal of Applied Economics, Finance and Accounting 9, no. 1 (2021): 1–7. http://dx.doi.org/10.33094/8.2017.2021.91.1.7.
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Sadik, Zryan A., Paresh M. Date, and Gautam Mitra. "Forecasting crude oil futures prices using global macroeconomic news sentiment." IMA Journal of Management Mathematics 31, no. 2 (July 22, 2019): 191–215. http://dx.doi.org/10.1093/imaman/dpz011.
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Abstract We propose a method of incorporating macroeconomic news into a predictive model for forecasting prices of crude oil futures contracts. Since these futures contracts are more liquid than the underlying commodity itself, accurate forecasting of their prices is of great value to multiple categories of market participants. We utilize the Kalman filtering framework for forecasting arbitrage-free (futures) prices and assume that the volatility of oil (futures) price is influenced by macroeconomic news. The impact of quantified news sentiment on the price volatility is modelled through a parametrized, non-linear functional map. This approach is motivated by the successful use of a similar model structure in our earlier work, for predicting individual stock volatility using stock-specific news. We claim the proposed model structure for incorporating macroeconomic news together with historical (market) data is novel and improves the accuracy of price prediction quite significantly. We report results of extensive numerical experiments which justify our claim.
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Roy Choudhury, Ahana, Soheila Abrishami, Michael Turek, and Piyush Kumar. "Enhancing profit from stock transactions using neural networks." AI Communications 33, no. 2 (September 22, 2020): 75–92. http://dx.doi.org/10.3233/aic-200629.
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Financial time-series forecasting, and profit maximization is a challenging task, which has attracted the interest of several researchers and is immensely important for investors. In this paper, we present a deep learning system, which uses a variety of data for a subset of the stocks on the NASDAQ exchange to forecast the stock price. Our framework allows the use of a variational autoencoder (VAE) to remove noise and time-series data engineering to extract higher-level features. A Stacked LSTM Autoencoder is used to perform multi-step-ahead prediction of the stock closing price. This prediction is used by two profit-maximization strategies that include greedy approach and short selling. Besides, we use reinforcement learning as a third profit-enhancement strategy and compare these three strategies to offline strategies that use the actual future prices. Results show that the proposed methods outperform the state-of-the-art time-series forecasting approaches in terms of predictive accuracy and profitability.
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Reddy, Dinesh, and Abhinav Karthik. "Forecasting Stock Price using LSTM-CNN Method." International Journal of Engineering and Advanced Technology 11, no. 1 (October 30, 2021): 1–8. http://dx.doi.org/10.35940/ijeat.a3117.1011121.
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Foreseeing assumes an indispensable part in setting an exchanging methodology or deciding the ideal opportunity to purchase or sell stock. We propose an element combination long transient memory-convolutional neural organization (LSTM-CNN) model, which joins highlights gained from various presentations of similar information, i.e., stock timetable and stock outline pictures, to anticipate stock costs. The proposed model is created by LSTM and CNN, which extricate impermanent and picture components. We assessed the proposed single model (CNN and LSTM) utilizing SPDR S&P 500 ETF information. Our LSTM-CNN combination highlight model surpasses single models in foreseeing evaluating. Also, we track down that the candle graph is the most precise image of a stock diagram that you can use to anticipate costs. Subsequently, this examination shows that prescient mistake can be viably decreased by utilizing a blend of transitory and picture components from similar information as opposed to utilizing these provisions independently.
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Reddy, Madhusudan, Arun Gade, Sreekarreddy ., and P. Prabhu. "Stock Market Price Forecasting by Using Deep Learning." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 627. http://dx.doi.org/10.14419/ijet.v7i3.12.16442.
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Stock market forecasts are an attempt to determine the future value of corporate capital or other financial products consumed in the stock market. If the future stock price forecast succeeds, you can gain great profit. The efficient market presents all the current stock price information, which shows that price fluctuations are not the basis for unnecessary new information. Others disagree that people who have these ideas have many methods and techniques to help them get future information. [1]
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Zaheer, Shahzad, Nadeem Anjum, Saddam Hussain, Abeer D. Algarni, Jawaid Iqbal, Sami Bourouis, and Syed Sajid Ullah. "A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model." Mathematics 11, no. 3 (January 22, 2023): 590. http://dx.doi.org/10.3390/math11030590.
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Financial data are a type of historical time series data that provide a large amount of information that is frequently employed in data analysis tasks. The question of how to forecast stock prices continues to be a topic of interest for both investors and financial professionals. Stock price forecasting is quite challenging because of the significant noise, non-linearity, and volatility of time series data on stock prices. The previous studies focus on a single stock parameter such as close price. A hybrid deep-learning, forecasting model is proposed. The model takes the input stock data and forecasts two stock parameters close price and high price for the next day. The experiments are conducted on the Shanghai Composite Index (000001), and the comparisons have been performed by existing methods. These existing methods are CNN, RNN, LSTM, CNN-RNN, and CNN-LSTM. The generated result shows that CNN performs worst, LSTM outperforms CNN-LSTM, CNN-RNN outperforms CNN-LSTM, CNN-RNN outperforms LSTM, and the suggested single Layer RNN model beats all other models. The proposed single Layer RNN model improves by 2.2%, 0.4%, 0.3%, 0.2%, and 0.1%. The experimental results validate the effectiveness of the proposed model, which will assist investors in increasing their profits by making good decisions.
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Aishwarya, Nadar, Nair Divya, and Karkera Poornima. "STOCK PRICE FORECASTING: A MACHINE LEARNING MODEL." International Journal of Engineering Applied Sciences and Technology 5, no. 4 (August 1, 2020): 245–50. http://dx.doi.org/10.33564/ijeast.2020.v05i04.036.
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Amiens, Ernest Oseghale, and Ifuero Osad Osamwonyi. "Stock price forecasting using hidden Markov model." International Journal of Information and Decision Sciences 14, no. 1 (2022): 39. http://dx.doi.org/10.1504/ijids.2022.122721.
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Amiens, Ernest Oseghale, and Ifuero Osad Osamwonyi. "Stock price forecasting using hidden Markov model." International Journal of Information and Decision Sciences 14, no. 1 (2022): 39. http://dx.doi.org/10.1504/ijids.2022.10047265.
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Rajan, G. B. Sabari. "Forecasting of stock price volatility: An evaluation." Asian Journal of Multidimensional Research (AJMR) 8, no. 9 (2019): 23. http://dx.doi.org/10.5958/2278-4853.2019.00272.6.
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