scholarly journals A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yang Yujun ◽  
Yang Yimei ◽  
Xiao Jianhua
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Method ◽  
Reference Value ◽  
Prediction Methods ◽  
Hybrid Prediction ◽  
Original Stock ◽  
Stock Price Prediction ◽  
Comparison Results

The stock market is a chaotic, complex, and dynamic financial market. The prediction of future stock prices is a concern and controversial research issue for researchers. More and more analysis and prediction methods are proposed by researchers. We proposed a hybrid method for the prediction of future stock prices using LSTM and ensemble EMD in this paper. We use comprehensive EMD to decompose the complex original stock price time series into several subsequences which are smoother, more regular and stable than the original time series. Then, we use the LSTM method to train and predict each subsequence. Finally, we obtained the prediction values of the original stock price time series by fused the prediction values of several subsequences. In the experiment, we selected five data to fully test the performance of the method. The comparison results with the other four prediction methods show that the predicted values show higher accuracy. The hybrid prediction method we proposed is effective and accurate in future stock price prediction. Hence, the hybrid prediction method has practical application and reference value.

scholarly journals Investor Sentiment Combined with Multisource Information to Predict Stock Prices: An Analysis of China’s A-Share Market

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin Huang ◽  
Huilin Song
Keyword(s):  
Social Media ◽  
Time Series ◽  
News Media ◽  
Multilayer Perceptron ◽  
Stock Prices ◽  
Stock Price ◽  
Investor Sentiment ◽  
Prediction Method ◽  
Stock Price Prediction ◽  
Price Prediction

Investor sentiment has been widely used in the research of the stock market, and how to accurately measure investor sentiment is still being explored. With the rise of social media, investor sentiment is no longer only influenced by macroeconomic data and news media, but also guided by We-Media and fragmented information. We take the data of China A-shares from January 2020 to December 2020 as the research object and propose a stock price prediction method that combines investor sentiment with multisource information. Firstly, the sentiment of macroeconomic data, brokerage research reports, news, and We-Media is calculated, respectively, and then the investor sentiment vector combining multisource information is obtained by the multilayer perceptron. Finally, the LSTM model is used to represent the stock time series characteristics. The results show that (1) the proposed algorithm is superior to the benchmark algorithm in terms of accuracy and F1-score, (2) investor sentiment vector can effectively measure the investment sentiment of stocks, and (3) compared with vector concatenation, multilayer perceptron can better represent investor sentiment.

scholarly journals Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company

2019 ◽  
Vol 61 ◽  
pp. 01006 ◽  
Author(s):  
Jakub Horák ◽  
Tomáš Krulický
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Stock Prices ◽  
Stock Price ◽  
Exponential Time ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Artificial Neural ◽  
Time Series Alignment

Accurate stock price prediction is very difficult in today's economy. Accurate prediction plays an important role in helping investors improve return on equity. As a result, a number of new approaches and technologies have logically evolved in recent years to predict stock prices. One is also the method of artificial neural networks, which have many advantages over conventional methods. The aim of this paper is to compare a method of exponential time series alignment and time series alignment using artificial neural networks as tools for predicting future stock price developments on the example of the company Unipetrol. Time series alignment is performed using artificial neural networks, exponential alignment of time series, and then a comparison of time series of predictions of future stock price trends predicted using the most successful neural network and price prediction calculated by exponential time series alignment is performed. Predictions for 62 business days were obtained. The realistic picture of further possible development is surprisingly given based on the exponential alignment of time series.

scholarly journals Stock Price Prediction Using Time Series

2021 ◽  
Vol 9 (12) ◽  
pp. 375-381
Author(s):  
Sai Manoj Cheruvu
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Price ◽  
Financial Sector ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Prediction Time ◽  
A Company ◽  
Series Technique

Abstract: Predicting Stock price of a company has been a challenge for analysts due to the fluctuations and its changing nature with respect to time. This paper attempts to predict the stock prices using Time series technique that proposes to observe various changes in a given variable with respect to time and is appropriate for making predictions in financial sector [1] as the stock prices are time variant. Keywords: Stock prices, Analysis, Fluctuations, Prediction, Time series, Time variant

scholarly journals An Economic Forecasting Method Based on the LightGBM-Optimized LSTM and Time-Series Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiehua Lv ◽  
Chao Wang ◽  
Wei Gao ◽  
Qiumin Zhao
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Large Scale ◽  
Stock Index ◽  
Short Term ◽  
Economic Forecasting ◽  
Stock Price Prediction

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.

scholarly journals Climate Finance: Mapping Air Pollution and Finance Market in Time Series

Econometrics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 43
Author(s):  
Zheng Fang ◽  
Jianying Xie ◽  
Ruiming Peng ◽  
Sheng Wang
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Financial Market ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Local Environment ◽  
Climate Finance ◽  
Time Series Regression ◽  
Stock Price Prediction

Climate finance is growing popular in addressing challenges of climate change because it controls the funding and resources to emission entities and promotes green manufacturing. In this study, we determined that PM2.5, PM10, SO2, NO2, CO, and O3 are the target pollutant in the atmosphere and we use a deep neural network to enhance the regression analysis in order to investigate the relationship between air pollution and stock prices of the targeted manufacturer. We also conduct time series analysis based on air pollution and heavy industry manufacturing in China, as the country is facing serious air pollution problems. Our study uses Convolutional-Long Short Term Memory in 2 Dimension (ConvLSTM2D) to extract the features from air pollution and enhance the time series regression in the financial market. The main contribution in our paper is discovering a feature term that impacts the stock price in the financial market, particularly for the companies that are highly impacted by the local environment. We offer a higher accurate model than the traditional time series in the stock price prediction by considering the environmental factor. The experimental results suggest that there is a negative linear relationship between air pollution and the stock market, which demonstrates that air pollution has a negative effect on the financial market. It promotes the manufacturer’s improving their emission recycling and encourages them to invest in green manufacture—otherwise, the drop in stock price will impact the company funding process.

scholarly journals Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market

2008 ◽  
Vol 2008 ◽  
pp. 1-8 ◽  
Author(s):  
Junhai Ma ◽  
Lixia Liu
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Stock Returns ◽  
Stock Price ◽  
Linear Prediction ◽  
Stock Exchange ◽  
Multivariate Time Series ◽  
Prediction Method ◽  
Nonlinear Prediction ◽  
Stock Price Prediction

This study attempts to characterize and predict stock returns series in Shanghai stock exchange using the concepts of nonlinear dynamical theory. Surrogate data method of multivariate time series shows that all the stock returns time series exhibit nonlinearity. Multivariate nonlinear prediction methods and univariate nonlinear prediction method, all of which use the concept of phase space reconstruction, are considered. The results indicate that multivariate nonlinear prediction model outperforms univariate nonlinear prediction model, local linear prediction method of multivariate time series outperforms local polynomial prediction method, and BP neural network method. Multivariate nonlinear prediction model is a useful tool for stock price prediction in emerging markets.

Forecasting based on spectral time series analysis: prediction of the Aurubis stock price

2020 ◽  
Vol 17 (4) ◽  
pp. 215-227
Author(s):  
Julia Babirath ◽  
Karel Malec ◽  
Rainer Schmitl ◽  
Kamil Maitah ◽  
Mansoor Maitah
Keyword(s):  
Time Series ◽  
Risk Management ◽  
Time Series Analysis ◽  
Wiener Process ◽  
Stock Price ◽  
Percentage Error ◽  
Portfolio Performance ◽  
Stock Price Prediction ◽  
Series Analysis ◽  
Augmented Dickey Fuller

The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.

scholarly journals Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series

2021 ◽  
Author(s):  
Sidra Mehtab ◽  
Jaydip Sen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Research Work ◽  
Hybrid Approach ◽  
Forecast Horizon ◽  
Stock Price Prediction ◽  
Price Prediction

Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.

scholarly journals A stock price prediction method based on deep learning technology

2021 ◽  
Vol 5 (1) ◽  
pp. 55-72
Author(s):  
Xuan Ji ◽  
Jiachen Wang ◽  
Zhijun Yan
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Stock Price ◽  
Prediction Method ◽  
Series Data ◽  
Learning Technology ◽  
Content Type ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Text Feature

Purpose Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data. Design/methodology/approach This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price. Findings The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price. Originality/value In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

scholarly journals Predictability of Stock Price Fluctuations Based on Business Relationships: A Comparison of Normal and the COVID-19 Pandemic Periods in Japan

2021 ◽  
Vol 13 (18) ◽  
pp. 10146
Author(s):  
Shoma Sakamoto ◽  
Shintaro Sengoku
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Factor Model ◽  
Customer Relationships ◽  
Business Relationships ◽  
Monthly Basis ◽  
Limited Region ◽  
Stock Price Prediction ◽  
Available Information ◽  
The Impact

The stock prices of a company are significantly influenced by changes of its business relationships. However, the effectiveness of stock price prediction based on such inter-firm business relationships has been partially confirmed in limited region and/or timeframe cases. In particular, it has not been verified under highly volatile market conditions such as those caused by the COVID-19 pandemic. To address these issues, we analyzed the impact of supplier–customer relationships on stock prices in the case of the Japanese stock market using The Fama-French three-factor model and publicly available information of business relationships. The subjects were classified into two conditions—normal and COVID-19—and the stock price predictability associated with changes of stock prices of related companies for both short and long holding periods. As a result, the significance of stock price predictability was confirmed on a daily and monthly basis in the given region. In addition, specific factors including a volatile event caused by a customer company, a stock price downturn, and the company size of a customer particularly improved stock price predictability in the pandemic.

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