Richard Cruz¹, Johnson Kinyua^1,*, Charles Mutigwe²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3405-3423, 2023, DOI:10.32604/iasc.2023.035906

Abstract The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from new perspectives. The meme stock mania of 2021 brought together stock traders and investors that were also active on social media. This mania was in good part driven by retail investors’ discussions on investment strategies that occurred on social media platforms such as Reddit during the COVID-19 lockdowns. The stock trades by these retail investors were then executed using services like Robinhood. In this paper, machine learning models… More >

J. Faritha Banu^1,*, S. B. Rajeshwari², Jagadish S. Kallimani², S. Vasanthi³, Ahmed Mateen Buttar⁴, M. Sangeetha⁵, Sanjay Bhargava⁶

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1393-1405, 2022, DOI:10.32604/iasc.2022.024176

Abstract The stock market is an important domain in which the investors are focused to, therefore accurate prediction of stock market trends remains a hot research area among business-people and researchers. Because of the non-stationary features of the stock market, the stock price prediction is considered a challenging task and is affected by several factors. Anticipating stock market trends is a difficult endeavor that requires a lot of attention, because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, noisy, and chaotic data, stock market prediction is a huge difficulty, and as… More >

Singamaneni Kranthi Kumar^1,*, Alhassan Alolo Abdul-Rasheed Akeji², Tiruvedula Mithun³, M. Ambika⁴, L. Jabasheela⁵, Ranjan Walia⁶, U. Sakthi⁷

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1217-1227, 2022, DOI:10.32604/iasc.2022.024311

Abstract In recent times, stock price prediction helps to determine the future stock prices of any financial exchange. Accurate forecasting of stock prices can result in huge profits to the investors. The prediction of stock market is a tedious process which involves different factors such as politics, economic growth, interest rate, etc. The recent development of social networking sites enables the investors to discuss the stock market details such as profit, future stock prices, etc. The proper identification of sentiments posted by the investors in social media can be utilized for predicting the upcoming stock prices. With this motivation, this paper… More >

Qunhui Zhang¹, Mengzhe Lu^3,4, Liang Dai^2,*

Computer Systems Science and Engineering, Vol.41, No.2, pp. 795-809, 2022, DOI:10.32604/csse.2022.019987

Abstract Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. But the fact is that forecasting stock prices by using various models has been suffering from low accuracy, slow convergence, and complex parameters. This study aims to employ a mixed model to improve the accuracy of stock price prediction. We present how to use a random walk based on jump-diffusion, to obtain stock predictions with a good-fitting degree by adjusting different parameters. Aimed at getting better parameters and then using the time series model to predict the data, we employed the time… More >

Ansari Saleh Ahmar¹, Pawan Kumar Singh², Nguyen Van Thanh^3,*, Nguyen Viet Tinh³, Vo Minh Hieu³

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 523-534, 2022, DOI:10.32604/cmc.2022.017068

Abstract The novel coronavirus has played a disastrous role in many countries worldwide. The outbreak became a major epidemic, engulfing the entire world in lockdown and it is now speculated that its economic impact might be worse than economic deceleration and decline. This paper identifies two different models to capture the trend of closing stock prices in Brazil (BVSP), Russia (IMOEX.ME), India (BSESN), and China (SSE), i.e., (BRIC) countries. We predict the stock prices for three daily time periods, so appropriate preparations can be undertaken to solve these issues. First, we compared the ARIMA, SutteARIMA and Holt-Winters (H-W) methods to determine… More >

Daxing Zhang^1,*, Erguan Cai²

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3931-3943, 2021, DOI:10.32604/cmc.2021.012302

Abstract Previous research in the area of using deep learning algorithms to forecast stock prices was focused on news headlines, company reports, and a mix of daily stock fundamentals, but few studies achieved excellent results. This study uses a convolutional neural network (CNN) to predict stock prices by considering a great amount of data, consisting of financial news headlines. We call our model N-CNN to distinguish it from a CNN. The main concept is to narrow the diversity of specific stock prices as they are impacted by news headlines, then horizontally expand the news headline data to a higher level for… More >

Tian Liwei^1,2,*, Feng Li¹, Sun Yu³, Guo Yuankai⁴

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 855-868, 2021, DOI:10.32604/iasc.2021.016805

Abstract The prediction of the “ups and downs” of stock market prices is one of the important undertakings of the financial market. Since accurate prediction helps foster considerable economic benefits, stock market prediction has attracted significant interest by both investors and researchers. Efforts into building an accurate, stable and effective model to predict stock prices’ movements have been proliferating at a fast pace, to meet such a challenge. Firstly, this paper uses a correlation analysis to analyze the attributes of a stock dataset, processing missing values, determining the data attributes to be retained data, then divide it in a training set… More >

Ajla Kulaglic^1,*, Burak Berk Ustundag²

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3577-3593, 2021, DOI:10.32604/cmc.2021.014768

Abstract Machine Learning (ML) algorithms have been widely used for financial time series prediction and trading through bots. In this work, we propose a Predictive Error Compensated Wavelet Neural Network (PEC-WNN) ML model that improves the prediction of next day closing prices. In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs. An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence. The performance of the proposed model is evaluated using six different stock data samples… More >

Mei Sun¹, Qingtao Li², Peiguang Lin^2,*

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 369-378, 2021, DOI:10.32604/iasc.2021.014962

Abstract Stocks are the key components of most investment portfolios. The accurate forecasting of stock prices can help investors and investment brokerage firms make profits or reduce losses. However, stock forecasting is complex because of the intrinsic features of stock data, such as nonlinearity, long-term dependency, and volatility. Moreover, stock prices are affected by multiple factors. Various studies in this field have proposed ways to improve prediction accuracy. However, not all of the proposed features are valid, and there is often noise in the features—such as political, economic, and legal factors—which can lead to poor prediction results. To overcome such limitations,… More >

Guang Sun¹, Jingjing Lin^1,*, Chen Yang¹, Xiangyang Yin¹, Ziyu Li¹, Peng Guo^1,2, Junqi Sun³, Xiaoping Fan¹, Bin Pan¹

Computer Systems Science and Engineering, Vol.36, No.3, pp. 509-520, 2021, DOI:10.32604/csse.2021.014189

Abstract Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean… More >