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.

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.

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.

A Historical Data based Ensemble System for Efficient Stock Price Prediction

2019 ◽  
Vol 12 ◽  
Author(s):  
Vijay Kumar Dwivedi ◽  
Manoj Madhava Gore
Keyword(s):  
Stock Price ◽  
Prediction Models ◽  
Historical Data ◽  
Stock Exchange ◽  
Primary Objective ◽  
Lasso Regression ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Consistent Manner ◽  
Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

scholarly journals A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 731
Author(s):  
Mengxia Liang ◽  
Xiaolong Wang ◽  
Shaocong Wu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Stock Price ◽  
Multivariate Time Series ◽  
Temporal Features ◽  
Proposed Model ◽  
Time Series Segmentation ◽  
Similarity Model ◽  
Dynamic Time ◽  
Investment Portfolios

Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model’s effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising.

Stock Market Reaction on Green-Bond Issue: Evidence from Indian Green-Bond Issuers

2021 ◽  
pp. 097226292110225
Author(s):  
Rakesh Kumar Verma ◽  
Rohit Bansal
Keyword(s):  
Stock Returns ◽  
Financial Institutions ◽  
Stock Price ◽  
Stock Exchange ◽  
Abnormal Returns ◽  
Developed Countries ◽  
Low Carbon ◽  
Bond Issue ◽  
The Government ◽  
The Impact

Purpose: A green bond is a financial instrument issued by governments, financial institutions and corporations to fund green projects, such as those involving renewable energy, green buildings, low carbon transport, etc. This study analyses the effect of green-bond issue announcement on the issuer’s stock price movement. It shows the reaction of the stock price after the issue of green bonds. Methodology: This study is based on secondary data. Green-bond issue dates have been collected from newspaper articles from different online sources, such as Business Standard, The Economic Times, Moneycontrol, etc. The closing prices of stocks have been taken from the NSE (National Stock Exchange of India Limited) website. An event window of 21 days has been fixed for the study, including the 10 days before and after the issue date. Data analysis is carried out through the event study method using the R software. Calculation of abnormal returns is done using three models: mean-adjusted returns model, market-adjusted returns model and risk-adjusted returns model. Findings: The results show that the issue of green bonds has a significant positive effect on the stock price. Returns increase after the green-bond issue announcement. Although the announcement day shows a negative return for all the samples taken for the study, the 10-day cumulative abnormal return (CAR) is positive. Thus, green-bond issues lead to positive sentiments among investors. Research implications: This research article will help the government issue more green bonds so that the proceeds can be utilized for green projects. The government should motivate corporations and financial institutions to issue more green bonds to help the economy grow. In India, very few organizations have issued a green bond. It will be beneficial if these players issue green bonds, as it will increase the firms’ value and boost returns to the investors. Originality/value: The effect of green-bond issue on stock returns has been analysed in some studies in developed countries. This is the first study to examine the impact of green-bond issue on stock returns in the Indian context, to the best of our knowledge.

Do Financially Constrained Firms Earn Higher Returns than the Unconstrained Firms? A Comprehensive Evidence from Pakistan Stock Exchange

2020 ◽  
Keyword(s):  
Time Series ◽  
Asset Pricing ◽  
Capital Structure ◽  
Stock Returns ◽  
Financial Constraints ◽  
Stock Exchange ◽  
Cash Flows ◽  
Total Asset ◽  
Cash Holding ◽  
The Relationship

This paper examines the relationship between financial constraints and the stock returns explaining the pricing of stock through financially constrained and unconstrained firms in Pakistan. Three proxies; total assets, tangible to total assets and cash holding to total assets ratios) have been used for financial constraints and the study tried to investigate that either the investors are compensated for taking the extra risk or not in Pakistan Stock Exchange (PSX). We find that the financially constrained firms don’t earn higher returns when their capital structure is heavy with liquid assets and their cash flows are more than the unconstrained firms in PSX. Moreover, the time series results showed that the risk-adjusted returns of the most constrained firms give the mix and somewhat negative and significant and insignificant results for the Pakistani firms listed in PSX sorted based on tangible to total assets and Cash holding to total asset ratios. Keywords: Asset Pricing, Financial constraints, risk-adjusted performance of portfolios

scholarly journals Margin requirements and Stock Market Volatility in Bangladesh

2012 ◽  
Vol 3 (2) ◽  
pp. 29
Author(s):  
A. F. M. Mainul Ahsan ◽  
Mohammad Osman Gani ◽  
Md. Bokhtiar Hasan
Keyword(s):  
Stock Returns ◽  
Stock Price ◽  
Stock Exchange ◽  
Price Volatility ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Margin Requirements ◽  
Margin Requirement ◽  
Daily Stock Returns ◽  
The Impact

Officially margin requirements in bourses in Bangladesh were initiated on April 28, 1999, to limit the amount of credit available for the purpose of buying stocks. The goal of this paper is to measure the impact of changing margin requirement on stock returns' volatility in Dhaka Stock Exchange (DSE). The impact of margin requirement on stock price volatility has been extensively studied with mixed and ambiguous results. Using daily stock returns, we found mixed evidence that SEC's margin requirements have significant impact on market volatility in DSE.

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