scholarly journals Stock Price Forecast Based on CNN-BiLSTM-ECA Model

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yu Chen ◽  
Ruixin Fang ◽  
Ting Liang ◽  
Zongyu Sha ◽  
Shicheng Li ◽  
...  
Keyword(s):  
Network Model ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Short Term Memory ◽  
Composite Index ◽  
Multimedia Data ◽  
Series Data ◽  
High Noise ◽  
Stock Price Prediction

Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.

scholarly journals Sentiment-Guided Adversarial Learning for Stock Price Prediction

2021 ◽  
Vol 7 ◽  
Author(s):  
Yiwei Zhang ◽  
Jinyang Li ◽  
Haoran Wang ◽  
Sou-Cheng T. Choi
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Short Term Memory ◽  
Simulated Data ◽  
Series Data ◽  
Generative Adversarial Networks ◽  
Related Factors ◽  
Adversarial Learning ◽  
Stock Price Prediction

Prediction of stock prices or trends have attracted financial researchers’ attention for many years. Recently, machine learning models such as neural networks have significantly contributed to this research problem. These methods often enable researchers to take stock-related factors such as sentiment information into consideration, improving prediction accuracies. At present, Long Short-Term Memory (LSTM) networks is one of the best techniques known to learn knowledge from time-series data and to predict future tendencies. The inception of generative adversarial networks (GANs) also provides researchers with diversified and powerful methods to explore the stock prediction problem. A GAN network consists of two sub-networks known as generator and discriminator, which work together to minimize maximum loss on both actual and simulated data. In this paper, we developed a sentiment-guided adversarial learning and predictive models of stock prices, adopting a popular variation of GAN called conditional GAN (CGAN). We adopted an LSTM network in the generator and a multilayer perceptron (MLP) network in the discriminator. After extensively pre-processing historical stock price datasets, we analyzed the sentiment information from daily tweets and computed sentiment scores as an additional model feature. Our experiments demonstrated that the average forecast accuracies of the CGAN models were improved with sentiment data. Moreover, our GAN and CGAN models outperformed LSTM and other traditional methods on 11 out of 36 processed stock price datasets, potentially playing a part in ensemble methods.

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 A CNN-LSTM-Based Model to Forecast Stock Prices

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wenjie Lu ◽  
Jiazheng Li ◽  
Yifan Li ◽  
Aijun Sun ◽  
Jingyang Wang
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Short Term Memory ◽  
Memory Function ◽  
Series Data ◽  
Price Forecasting ◽  
Stock Price Forecasting ◽  
Forecasting Method

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.

scholarly journals LSTM-based sentiment analysis for stock price forecast

2021 ◽  
Vol 7 ◽  
pp. e408 ◽  
Author(s):  
Ching-Ru Ko ◽  
Hsien-Tsung Chang
Keyword(s):  
Language Processing ◽  
Stock Prices ◽  
Financial Management ◽  
Stock Price ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Price Forecast ◽  
Natural Language Processing Tool ◽  
Long Short Term Memory

Investing in stocks is an important tool for modern people’s financial management, and how to forecast stock prices has become an important issue. In recent years, deep learning methods have successfully solved many forecast problems. In this paper, we utilized multiple factors for the stock price forecast. The news articles and PTT forum discussions are taken as the fundamental analysis, and the stock historical transaction information is treated as technical analysis. The state-of-the-art natural language processing tool BERT are used to recognize the sentiments of text, and the long short term memory neural network (LSTM), which is good at analyzing time series data, is applied to forecast the stock price with stock historical transaction information and text sentiments. According to experimental results using our proposed models, the average root mean square error (RMSE ) has 12.05 accuracy improvement.

scholarly journals Comparison of ARIMA, ANN and LSTM for Stock Price Prediction

2020 ◽  
Vol 218 ◽  
pp. 01026
Author(s):  
Qihang Ma
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Moving Average ◽  
Arima Model ◽  
Predictive Ability ◽  
Research Direction ◽  
Ann Model ◽  
Advantages And Disadvantages ◽  
Stock Price Prediction

The prediction of stock prices has always been a hot topic of research. However, the autoregressive integrated moving average (ARIMA) model commonly used and artificial neural networks (ANN) still have their own advantages and disadvantages. The use of long short-term memory (LSTM) networks model for prediction also shows interesting possibilities. This article compares three models specifically through the analysis of the principles of the three models and the prediction results. In the end, it is believed that the LSTM model may have the best predictive ability, but it is greatly affected by the data processing. The ANN model performs better than that of the ARIMA model. The combination of time series and external factors may be a worthy research direction.

A Sectoral Analysis of the Role of Stock Market Development on Economic Growth: Empirical Evidence from Indian Economy

2017 ◽  
Vol 18 (4) ◽  
pp. 911-923 ◽  
Author(s):  
Madhu Sehrawat ◽  
A.K. Giri
Keyword(s):  
Economic Growth ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Trade Openness ◽  
Series Data ◽  
Causality Test ◽  
Long Run ◽  
Short Run

The present study examines the relationship between Indian stock market and economic growth from a sectoral perspective using quarterly time-series data from 2003:Q4 to 2014:Q4. The results of the autoregressive distributed lag (ARDL) approach bounds test confirm the existence of a cointegrating relationship between sector-specific gross domestic product (GDP) and sector-specific stock indices. The empirical results reveal that sector-specific economic growth are significantly influenced by changes in the respective sector-specific stock price indices in the long run as well as in the short run. Apart from that, the control variables, such as trade openness and inflation, act as the instrument variables in explaining the variations in the sector-specific GDP of the economy. The results of Granger causality test demonstrate unidirectional long-run as well as short-run causality running from sector specific stock prices to respective sector GDP. The findings suggest that economic growth of the country is sensitive to respective sub-sector stock market investments. The findings highlight the reasons for cyclical and counter-cyclical business phase for the overall economy.

Terrorism and Stock Market Linkages: An Empirical Study from a Front-line State

2017 ◽  
Vol 18 (2) ◽  
pp. 365-378 ◽  
Author(s):  
Imtiaz Arif ◽  
Tahir Suleman
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Negative Impact ◽  
Stock Exchange ◽  
Series Data ◽  
Front Line ◽  
Long Run ◽  
Factor Index ◽  
The Impact

This article investigates the impact of prolonged terrorist activities on stock prices of different sectors listed in the Karachi Stock Exchange (KSE) by using the newly developed terrorism impact factor index with lingering effect (TIFL) and monthly time series data from 2002 (January) to 2011 (December). Johansen and Juselius (JJ) cointegration revealed a long-run relationship between terrorism and stock price. Normalized cointegration vectors are used to test the effect of terrorism on stock price. Results demonstrate a significantly mixed positive and negative impact of prolonged terrorism on stock prices of different sectors and show that the market has not become insensitive to the prolonged terrorist attacks.

scholarly journals Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Sidra Mehtab ◽  
Abhishek Dutta
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Short Term Memory ◽  
Efficient Market Hypothesis ◽  
Training Data ◽  
Stock Price Prediction ◽  
Price Prediction

Prediction of stock prices has been an important area of research for a long time. While supporters of the <i>efficient market hypothesis</i> believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. Researchers have also worked on technical analysis of stocks with a goal of identifying patterns in the stock price movements using advanced data mining techniques. In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models. For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records from December 29, 2014 till December 28, 2018. Using these regression models, we predicted the <i>open</i> values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forecasting framework by building four deep learning-based regression models using long-and short-term memory (LSTM) networks with a novel approach of walk-forward validation. Using the grid-searching technique, the hyperparameters of the LSTM models are optimized so that it is ensured that validation losses stabilize with the increasing number of epochs, and the convergence of the validation accuracy is achieved. We exploit the power of LSTM regression models in forecasting the future NIFTY 50 <i>open</i> values using four different models that differ in their architecture and in the structure of their input data. Extensive results are presented on various metrics for all the regression models. The results clearly indicate that the LSTM-based univariate model that uses one-week prior data as input for predicting the next week's <i>open</i> value of the NIFTY 50 time series is the most accurate model.

scholarly journals Predicting Stock Market Prices using Fine-Tuned IndRNN

2020 ◽  
Vol 9 (7) ◽  
pp. 309-315
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Price ◽  
Linear Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Activation Function ◽  
Series Data ◽  
Current Time

Prediction and analysis of stock market data have a vital role in current time’s economy. The various methods used for the prediction can be classified into 1) Linear Algorithms like Moving Average (MA) and Auto-Regressive Integrated Moving Average (ARIMA). 2) Non-Linear Models like Artificial Neural Networks and Deep Learning. In this work, we are using the results of previous research papers to demonstrate the potential of some models like ARIMA, Multi-Layer Perception (MLP) ), Convolutional Neural Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long-Short Term Memory (LSTM) for forecasting the stock price of an organization based on its available historical data. Then, implementing some of these methods to check and compare their efficiency within the same issue. We used Independently RNN (IndRNN) to explore a better efficiency for stock prediction and we found that it gives better accuracy prevailing methods in the current time. We also proposed an enhancement to IndRNN by replacing its default activation function with a more effective function called Parametric Rectified Linear Unit (PreLU). Our proposed approach can be used as an alternative method for predicting time series data efficiently other than the typical approaches today

scholarly journals PENGARUH RETURN ON EQUITY, DEBT TO EQUITY RATIO DAN SIZE TERHADAP HARGA SAHAM PADA PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BURSA EFEK INDONESIA (BEI)

BISMA ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 27
Author(s):  
Marzuki Marzuki
Keyword(s):  
Firm Size ◽  
Significant Influence ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Manufacturing Companies ◽  
Return On Equity ◽  
Section Data

The objective of this study is to examine the effect of ROE, DER, and firm size on stock prices of the manufacturing companies listed on the Indonesia Stock Exchange (IDX). The data used in this study were panel data sourced from the combination of cross section data and time series data. This research used purposive sampling method with the sample consisted of 86 manufacturing companies listed on IDX in 2017. Data were analyzed using multiple linear regression. The results showed that ROE and firm size had a positive and significant influence on stock price. However, DER did not have a significant influence on stock price. Keywords : ROE, DER, company size, stock price

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