The role of investor attention in predicting stock prices: The long short-term memory networks perspective

2020 ◽  
pp. 101484 ◽  
Author(s):  
Yongjie Zhang ◽  
Gang Chu ◽  
Dehua Shen
Keyword(s):  
Stock Prices ◽  
Short Term Memory ◽  
Short Term ◽  
Investor Attention ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Daniel Štifanić ◽  
Jelena Musulin ◽  
Adrijana Miočević ◽  
Sandi Baressi Šegota ◽  
Roman Šubić ◽  
...  
Keyword(s):  
Crude Oil ◽  
Stock Prices ◽  
Short Term Memory ◽  
Oil Price ◽  
Stationary Wavelet Transform ◽  
Crude Oil Price ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
The Impact

COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&P 500, and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM + WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.

scholarly journals Stock Prediction Using Sentiment Analysis and LSTM

2021 ◽  
Vol 9 (9) ◽  
pp. 1752-1756
Author(s):  
Ms. Anjima K. S
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Programming Language ◽  
Stock Prices ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Python Programming Language ◽  
Long Short Term Memory ◽  
Python Programming

Abstract: The stock market is a difficult area to anticipate since it is influenced by a variety of variables at the same time. The stock exchange is where equities are exchanged, transferred, and circulated. This research proposes a hybrid algorithm that predicts a stock's next day closing prices using sentiment analysis and Long Short Term Memory. The LSTM model seems to be quite popular in time-series forecasting, which is why it was selected for this project. Our proposed methodology makes use of the temporal association between public opinion and stock prices. Part-of-speech tagging is used to do sentiment analysis, and Long Short Term Memory is utilized to predict the stock's next day closing price. When these two factors are combined, we get a good picture of the stock's future. In this project, two main datasets have been used: HCLTECH company stock data and the news related to each stock of the HCL company for each day. The project is implemented by using the python programming language. The python programming language has been used to execute the project. This also incorporates machine learning along with public feedback. Sentiment analysis enables us to evaluate a diversity of political and economic factors, which have a significant impact on the stock market. Keywords: LSTM, sentiment analysis, RNN, Back propagation neural network.

scholarly journals Implementasi Long Short-Term Memory Pada Harga Saham Perusahaan Perkebunan Di Indonesia

2020 ◽  
Vol 6 (01) ◽  
pp. 9-18
Author(s):  
Rahmadi Yotenka ◽  
Fazano Fikri El Huda
Keyword(s):  
Stock Prices ◽  
Short Term Memory ◽  
Commodity Price ◽  
Share Price ◽  
Short Term ◽  
Term Memory ◽  
Price Fluctuations ◽  
Climate Forecasting ◽  
Long Short Term Memory ◽  
Hidden Neurons

  The decline and increase in the price of shares of plantation companies is a problem for investors in making decisions to buy or sell shares. Factors influencing the movement of plantation stock prices include CPO commodity price fluctuations, world oil price fluctuations, Rupiah exchange rate fluctuations, government regulations and policies, demands from importing countries, and climate. Forecasting stock prices is expected to help investors to deal with uncertainty in the movement of plantation stock prices. This study applies the Long Short-Term Memory (LSTM) to predict the stock prices of plantation companies using SSMS, LSIP, and SIMP share price data from the period 1 July 2014 - 22 July 2019. Based on the results of the study it was found that the best LSTM model on SSMS shares by using the RMSProp optimizer and 70 hidden neurons produced an RMSE value of 21,328. Then the best LSTM model on LSIP stock by using Adam optimizer and 80 hidden neurons produces an RMSE value of 33,097. Whereas the best LSTM model on SIMP shares using Adamax optimizer and 100 hidden neurons produced an RMSE value of 8,3337.    

scholarly journals Implementation of Long Short-Term Memory and Gated Recurrent Units on Grouped Time-Series Data to Predict Stock Prices Accurately

2021 ◽  
Author(s):  
Armin Lawi ◽  
Hendra Mesra ◽  
Supri Amir
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Percentage Error ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract Stocks are an attractive investment option since they can generate large profits compared to other businesses. The movement of stock price patterns on the stock market is very dynamic; thus it requires accurate data modeling to forecast stock prices with a low error rate. Forecasting models using Deep Learning are believed to be able to accurately predict stock price movements using time-series data, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. However, several previous implementation studies have not been able to obtain convincing accuracy results. This paper proposes the implementation of the forecasting method by classifying the movement of time-series data on company stock prices into three groups using LSTM and GRU. The accuracy of the built model is evaluated using loss functions of Rooted Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results showed that the performance evaluation of both architectures is accurate in which GRU is always superior to LSTM. The highest validation for GRU was 98.73% (RMSE) and 98.54% (MAPE), while the LSTM validation was 98.26% (RMSE) and 97.71% (MAPE).

scholarly journals Systematic optimization of Long Short-Term Memory model for predicting NYSE Arca Airline Index (XAL) during COVID-19

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Sarah Dong ◽  
Amber Wang
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Airline Industry ◽  
Stock Price ◽  
Short Term Memory ◽  
The United States ◽  
Short Term ◽  
Term Memory ◽  
Stock Price Prediction ◽  
Long Short Term Memory

Predicting stock prices has been both challenging and controversial. Since it first spread through the United States, the COVID-19 pandemic has impacted the stock market in a multitude of ways. Thus, stock price prediction has become even more challenging. Recurrent neural networks (RNN) have been widely used in many fields to predict financial time series. In this study, Long Short-Term Memory (LSTM), a special form of RNN, is used to predict the stock market direction for the US airline industry by using NYSE Arca Airline Index (XAL). The LSTM model was optimized through changing different hyperparameters of the model architecture to find the best combination for increased accuracy and performance evaluated by several metrics, including raw RMSE (3.51) and MAPA (4.6%), and very high MAPA (95.4%) and R^2 (0.978).

scholarly journals Analysis of Lumber Prices Time Series Using Long Short-Term Memory Artificial Neural Networks

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 428
Author(s):  
Dercilio Junior Verly Lopes ◽  
Gabrielly dos Santos Bobadilha ◽  
Amanda Peres Vieira Bedette
Keyword(s):  
Stock Prices ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Short Term ◽  
Term Memory ◽  
Squared Error ◽  
Dense Output ◽  
The Usa ◽  
Long Short Term Memory

This manuscript confirms the feasibility of using a long short-term memory (LSTM) recurrent neural network (RNN) to forecast lumber stock prices during the great and Coronavirus disease 2019 (COVID-19) pandemic recessions in the USA. The database was composed of 5012 data entries divided into recession periods. We applied a timeseries cross-validation that divided the dataset into an 80:20 training/validation ratio. The network contained five LSTM layers with 50 units each followed by a dense output layer. We evaluated the performance of the network via mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) for 30, 60, and 120 timesteps and the recession periods. The metrics results indicated that the network was able to capture the trend for both recession periods with a remarkably low degree of error. Timeseries forecasting may help the forest and forest product industries to manage their inventory, transportation costs, and response readiness to critical economic events.

scholarly journals A Novel Synergetic LSTM-GA Stock Trading Suggestion System in Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jimmy Ming-Tai Wu ◽  
Lingyun Sun ◽  
Gautam Srivastava ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Genetic Algorithms ◽  
Stock Prices ◽  
Short Term Memory ◽  
Leading Indicators ◽  
Short Term ◽  
Trading System ◽  
Stock Trading ◽  
Data Set ◽  
Term Memory ◽  
Long Short Term Memory

The Internet of Things (IoT) play an important role in the financial sector in recent decades since several stock prediction models can be performed accurately according to IoT-based services. In real-time applications, the accuracy of the stock price fluctuation forecast is very important to investors, and it helps investors better manage their funds when formulating trading strategies. It has always been a goal and difficult problem for financial researchers to use predictive tools to obtain predicted values closer to actual values from a given financial data set. Leading indicators such as futures and options can reflect changes in many markets, such as the industry’s prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. In this research, a trading strategy for finding stock trading signals is proposed that combines long short-term memory neural networks with genetic algorithms. This new framework is called long short-term memory neural network with leading index, or LSTMLI for short. We thus take the stock markets of the United States and Taiwan as the research objects and use historical data, futures, and options as data sets to predict the stock prices of these two markets. After that, we use genetic algorithms to find trading signals for the designed stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.

scholarly journals Evolutionary Framework with Bidirectional Long Short-Term Memory Network for Stock Price Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongying Zheng ◽  
Hongyu Wang ◽  
Jianyong Chen
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Optimal Solution ◽  
Short Term ◽  
Term Memory ◽  
Stock Price Prediction ◽  
Memory Network ◽  
Long Short Term Memory

As an important part of the social economy, stock market plays an important role in economic development, and accurate prediction of stock price is important as it can lower the risk of investment decision-making. However, the task of predicting future stock price is very difficult. This difficulty arises from stocks with nonstationary behavior and without any explicit form. In this paper, we propose a novel bidirectional Long Short-Term Memory Network (BiLSTM) framework called evolutionary BiLSTM (EBiLSTM) for the prediction of stock price. In the framework, three independent BiLSTMs correspond to different objective functions and act as mutation individuals, then their respective losses for evolution are calculated, and finally, the optimal objective function is identified by the minimum of loss. Since BiLSTM is effective in the prediction of time series and the evolutionary framework can get an optimal solution for multiple objectives, their combination well adapts to the nonstationary behavior of stock prices. Experiments on several stock market indexes demonstrate that EBiLSTM can achieve better prediction performance than others without the evolutionary operator.

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