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Granger causality is proposed to fuse stock prices and social media sentiment information for stock market prediction. Sentiment extraction is performed on the Twitter data from major stock companies. Analysis shows that authoritative user's sentiment affects the other users after an event with the lag of 3 days. The prediction is performed for Twitter and stock data from four companies. The sentiment...
The proposed model is intended to assessment of company's operation effectiveness, which is an important factor at investment decisions making. There are compared indicators of the growth rate, profitability and risk for shares placed on various stock exchanges with an assessment of the intrinsic value and management efficiency of company. The information received is useful for investors and company...
In present days, the social media and networking act as one of the key platforms for sharing information and opinions. Many people share ideas, express their view points and opinions on various topic of their interest. Social media text has rich information about the companies, their products and various services offered by them. In this research we focus exploring the association of sentiments of...
Stock price prediction is a challenging problem as the market is quite unpredictable. We propose a method for price prediction using Dynamic Mode Decomposition assuming stock market as a dynamic system. DMD is an equation free, data-driven, spatio-temporal algorithm which decomposes a system to modes that have predetermined temporal behaviour associated with them. These modes help us determine how...
IPO (Initial Public Offerings) pricing is a systematic and complicated task that directly determines whether the capital market is able to function in a healthy way. As China's stock market goes through ups and downs, China Securities Regulatory Commission generally adopts a positive strategy. Therefore, to weaken the policy factors, 48 cases of IPO companies dated from January 2014 to February 2014...
This paper presents a machine learning method for event-driven stock prediction, using L1 regularized Logistic regression model. It studies the stock price movement after listed companies make announcements. The model uses specific events extracted from these announcements and combine with financial indicators of listed companies, macro indicators, and technical indicators as dependent variables....
Stock market dynamics is of great importance to researchers from diverse fields. Network Analysis of stock data can play an important role in the study of stock market. In this paper, network based data mining of stock market is done to identify crucial players. Stock market network in United States created based on dynamics of stocks over one year captured as daily time series, is used for the analysis...
This study focuses on how the cumulative excess returns (CER) of corporate bonds in the Japanese market respond to simultaneous publications of current net earnings and management’s net earnings forecast. The estimation results using a regression model generalizing the interaction of the current net earnings and management’s net earnings forecast show that the CER of corporate bonds is influenced...
This study modifies Fukuda Nakamura Caballero Hoshi Kashyap (FN-CHK) method to identify zombie firms by incorporating non-recurring gains and losses. The effectiveness of the proposed method is evaluated by existing unqualified companies and specially-treated companies in Chinese stock market. Based on the proposed method, this study analyzes the development trend, the proportion of zombie firms in...
Based on the characteristics of the Chinese market and the Chinese institutions, this paper intends to use the account error correction data to study the relationship between income management and tax burden. As revealed in the study, the underlying goal of income management for listed companies is the alleged increase in profits. However, most companies do not pay extra taxes, on the contrary, they...
A numerical prediction algorithm called addition-subtraction frequency (ASF) algorithm is presented in this paper to predict potential valley-point dates of stock market. In the paper, we use historical valley-point date data of Shanghai Security Exchange (SSE) Composite Index as the input of ASF algorithm. According to the Royal Swedish Academy of Sciences, the stock prediction in the next three...
Stock price prediction is considered as one of the most challenging and important tasks. It is so complex and uncertainly, so that it is not enough to only use financial indicators for stock price prediction. As we known, patents is not only can protect the companies technologies' development and promote the advance of the core technologies, but can be used as one of the evaluation indicators to estimate...
The Detrending Moving Average (DMA) algorithm can be implemented to estimate the Shannon entropy of a long-range correlated sequence which will be shown to be of particular relevance for its significance in finance. The entropy is written as the sum of two terms corresponding respectively to power-law (ordered) and exponentially (disordered) distributed blocks (clusters). Interestingly, the behaviour...
This paper makes an empirical test on the correlation between financial restatement and audit report lag (ARL) using data of non-financial listed companies in China during the period 2009–2012. The empirical results show that there is a significant positive correlation between financial restatement and ARL. The longer the ARL is, the more likely it is for the company to perform a financial restatement...
IPO secondary offerings have been widely concerned by people. The purpose of this paper is to study the effect of IPO secondary offerings on IPO underpricing in China's capital market. The data about IPO secondary offerings and IPO underpricing in the Shanghai and Shenzhen A stock market listed companies from December 2013 to April 2016 are used for empirical analysis. The study finds that the IPO...
In the view of the fact that the stock market in China continues to expand, the study on momentum effect requires the addition of volume to adjust the relevant indicators. This paper selects the weekly returns of all stocks in the Shanghai Stock Exchange and the Shenzhen Stock Exchange as the sample, and uses the overlapping sampling method to test the momentum effect of Chinese stock market from...
Using the panel data of thirteen provincial cities in China's Jiangsu Province, this paper analyzes the interactive relationship between industrial agglomeration and talent agglomeration in Jiangsu Province on the basis of elaborating the interaction mechanism between them, and draws lessons from European Union's “smart specialization” policy to make targeted recommendations. Our results show that:...
In this work we propose to use Twitter to find companies with a good growth potential that could be good investment options. In order to achieve this we built a sentiment model using the text content of tweets. We make use of hashtags to collect Twitter posts from a broad range of emotions so that our sentiment model can reliably distinguish tweets containing different sentiment expressions. To guarantee...
In recent years, merger and acquisition has become the main means of expanding for Chinese listed companies. Meanwhile, the manager has played an increasingly important role when the company tries to make acquisitions. Based on the viewpoint of behavioral finance, managers are more likely to become overconfident. Therefore, it is very necessary to apply the theory of managerial overconfidence to the...
From the perspective of the credit risk, this paper discusses the factors affecting credit spreads of different credit risk corporate bonds. The study chooses the credit rating as the standard to measure the credit risk of corporate bonds. The sample of daily data covers the period from August 2013 to December 2015 and we use Merton model to analyze the different influencing factors. The empirical...
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