Analyzing the Potential Influence of Shanghai Stock Market Based on Link Prediction Method

2017 ◽  
Vol 5 (5) ◽  
pp. 446-461 ◽  
Author(s):  
Hongxing Yao ◽  
Yunxia Lu
Keyword(s):  
Stock Market ◽  
Link Prediction ◽  
Stock Exchange ◽  
Similarity Index ◽  
Prediction Method ◽  
Local Information ◽  
Market Model ◽  
Potential Influence ◽  
Threshold Method ◽  
Structural Centrality

Abstract In this paper, we analyze the 180 stocks which have the potential influence on the Shanghai Stock Exchange (SSE). First, we use the stock closing prices from January 1, 2005 to June 19, 2015 to calculate logarithmic the correlation coefficient and then build the stock market model by threshold method. Secondly, according to different networks under different thresholds, we find out the potential influence stocks on the basis of local structural centrality. Finally, by comparing the accuracy of similarity index of the local information and path in the link prediction method, we demonstrate that there are best similarity index to predict the probability for nodes connection in the different stock networks.

Evidential link prediction method based on the importance of high-order path index

2021 ◽  
Author(s):  
Jingjing Xia ◽  
Guang Ling ◽  
Qingju Fan ◽  
Fang Wang ◽  
Ming-Feng Ge
Keyword(s):  
Link Prediction ◽  
Similarity Index ◽  
Information Leakage ◽  
Prediction Method ◽  
High Order ◽  
Clustering Coefficient ◽  
Dempster Shafer Theory ◽  
Path Index ◽  
Shafer Theory ◽  
Entire Network

Link prediction, aiming to find missing links in an observed network or predict those links that may occur in the future, has become a basic challenge of network science. Most existing link prediction methods are based on local or global topological attributes of the network such as degree, clustering coefficient, path index, etc. In the process of resource allocation, as the number of connections between the common neighbors of the paired nodes increases, it is easy to leak information through them. To overcome this problem, we proposed a new similarity index named ESHOPI (link prediction based on Dempster–Shafer theory and the importance of higher-order path index), which can prevent information leakage by penalizing ordinary neighbors and considering the information of the entire network and each node at the same time. In addition, high-order paths are used to improve the performance of link prediction by penalizing the longer reachable paths between the seed nodes. The effectiveness of ESHOPI is shown by the experiments on both synthetic and real-world networks.

Link prediction based on the tie connection strength of common neighbor

2019 ◽  
Vol 30 (11) ◽  
pp. 1950089 ◽  
Author(s):  
Yujie Yang ◽  
Jianhua Zhang ◽  
Xuzhen Zhu ◽  
Jinming Ma ◽  
Xin Su
Keyword(s):  
Network Topology ◽  
Link Prediction ◽  
Free Parameter ◽  
Prediction Method ◽  
Large Degree ◽  
Local Information ◽  
Connection Strength ◽  
Common Neighbor ◽  
Multiple Paths ◽  
The Common

Traditional link prediction indices focus on the degree of the common neighbor and consider that the common neighbor with large degree contributes less to the similarity of two unconnected endpoints. Therefore, some of the local information-based methods only restrain the common neighbor with large degree for avoiding the influence dissipation. We find, however, if the large degree common neighbor connects with two unconnected endpoints through multiple paths simultaneously, these paths actually serve as transmission influences instead of dissipation. We regard these paths as the tie connection strength (TCS) of the common neighbor, and larger TCS can promote two unconnected endpoints to link with each other. Meanwhile, we notice that the similarity of node-pairs also relates to the network topology structure. Thus, in order to study the influences of TCS and the network structure on similarity, we introduce a free parameter and propose a novel link prediction method based on the TCS of the common neighbor. The experiment results on 12 real networks suggest that the proposed TCS index can improve the accuracy of link prediction.

Impact of Stock Splits and Rights Issue Announcements on Market Price: Evidence from India

2016 ◽  
Vol 7 (2) ◽  
Author(s):  
Babitha Rohit ◽  
Prakash Pinto ◽  
Shakila B.
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Exchange ◽  
Market Price ◽  
Abnormal Returns ◽  
Market Model ◽  
Stock Splits ◽  
Indian Stock Market ◽  
Rights Issue ◽  
The Impact

The current paper studies the impact of two events i.e stock splits and rights issue announcement on the stock returns of companies listed on the Bombay Stock Exchange. The study consists of a sample of 90 announcements for stock splits and 29 announcements for rights issue during the period 2011-2014. Market model is used to calculate the abnormal returns of securities. Positive Average Abnormal Returns were observed for the two events on the day their announcements, however they are not statistically significant. The study concludes that the Indian stock market is efficient in its semi-strong form.

scholarly journals Assessing the Impact of S&P SL20 Index Construction on Listed Companies in Colombo Stock Exchange (CSE)

2016 ◽  
Vol 8 (7) ◽  
pp. 159
Author(s):  
Upeksha Perera ◽  
Rohana Dissanayake ◽  
Mangalika Jayasundara
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Stock Exchange ◽  
Abnormal Returns ◽  
Market Model ◽  
Stock Market Index ◽  
Index Construction ◽  
Price Series ◽  
Adjusted Model ◽  
The Impact

<p>A stock market index is designed to measure the performance of value of a set of stocks. The set of stock can be entire market of a particular country or a sector. Indices can be used not only to see how the stock market, for instance, has changed over time, but it allows easy comparison between stocks that represent different sectors or even different stocks. An index construction or rebalancing of existing index is a major market event that investor might know before the event take place. The index inclusion reflects a positive situation about the quality, risks and possible future return of the stock. This study examine whether any price and trading volume effects arise from S&amp;P SL 20 index construction. S&amp;P SL 20 index was launched in 26, June 2012, based on 20 blue chip companies in Sri Lanka. The current study employs the standard event study methodology to identify the abnormal returns associated with the launching of the S&amp;P SL 20 index. Three normal return benchmarks, namely the market-adjusted model, mean-adjusted model and the market model have been used for the purpose of finding abnormal returns. Price series and volumes of stocks in S&amp;P SL 20 list (after and before) were considered and those are retrieved from Colombo stock exchange.</p><p>The study finds that the abnormal returns following the launch of the S&amp;P SL 20 index is statistically insignificant.</p>

scholarly journals Assessing Commonality in Liquidity with Principal Component Analysis: The Case of the Warsaw Stock Exchange

2020 ◽  
Vol 13 (12) ◽  
pp. 328
Author(s):  
Joanna Olbryś ◽  
Elżbieta Majewska
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Principal Components ◽  
Central And Eastern Europe ◽  
Stock Exchange ◽  
Principal Component ◽  
Component Analysis ◽  
Market Model ◽  
Latent Factors ◽  
Warsaw Stock Exchange

The studies concerning commonality in liquidity on emerging markets in Central and Eastern Europe are scarce and, in particular, they do not utilize the Principal Component Analysis (PCA) to identify latent factors in liquidity. Therefore, the main aim of this research is to assess commonality in liquidity on the Warsaw Stock Exchange (WSE) with the use of the PCA to extract common components of liquidity across a sample of stocks, and from a set of several liquidity proxies. The robustness tests within the whole sample and sub-periods are provided. The PCA results reveal that common latent factors in liquidity estimates exist on the Polish stock market, and three principal components are sufficient to substitute for the seven liquidity proxies utilized in this research. The regressions using these three principal components of liquidity proxies as latent factors in the market model of liquidity indicate no evidence of co-movements in liquidity on the WSE. The results are homogenous for all investigated periods so no reason has been found to reject the research hypothesis that commonality in liquidity does not exist on the Polish stock market. To the best of the authors’ knowledge, no similar research has been conducted for the WSE thus far.

scholarly journals Stock Market Reaction to Election Cycles: The Nigerian Experience

2018 ◽  
Vol 4 (1) ◽  
pp. 63-76
Author(s):  
Sharlywest Uwabor EBOIGBE ◽  
Kennedy Prince MODUGU
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Prices ◽  
Stock Exchange ◽  
Market Model ◽  
Stock Market Returns ◽  
Strict Adherence ◽  
Political Activities ◽  
Stock Market Reaction ◽  
Petroleum Sector

This study seeks to unravel the relationship between national electoral events and industry’s stock market returns using the various presidential elections in Nigeria. The study adopts the traditional Market Model (MM) and testing with the Cumulative Abnormal Return (CAR) approach on the daily market data from the Nigerian Stock Exchange. Evidences abound that banking and Petroleum sector decreases before and increases after all elections. With the same trend for other sectors such as Conglomerates stock prices which oscillated in the same direction for the1999 and 2003; Brewery took their turn 1999 and 2011 while building sector experienced this event effect in 1999, thereby revealing industry connectivity with political activities. This manifests as their stock returns tend to reduce generally before and increase after election periods. We therefore recommend the depoliticization of public policies through strict adherence to corporate governance codes and strengthening of public institutions. This will put a check on the political manoeuvrings of the economy by boosting investors’ confidence on the market regardless of electoral activities and power swings. More importantly, for those stocks that experiences increase in value after election it is a better time to sell those portfolios and buy these stocks that experience loss in value at a post-election window.                                                             

A new method for predicting future links in temporal networks based on node influence

2021 ◽  
pp. 2150160
Author(s):  
Cong Li ◽  
Xinsheng Ji ◽  
Shuxin Liu ◽  
Haitao Li
Keyword(s):  
Link Prediction ◽  
Statistical Physics ◽  
Network Science ◽  
Similarity Index ◽  
Prediction Method ◽  
Temporal Networks ◽  
Wide Range ◽  
Ranking Score ◽  
Matching Degree ◽  
Definition Of

Link prediction in temporal networks has always been a hot topic in both statistical physics and network science. Most existing works fail to consider the inner relationship between nodes, leading to poor prediction accuracy. Even though a wide range of realistic networks are temporal ones, few existing works investigated the properties of realistic and temporal networks. In this paper, we address the problem of abstracting individual attributes and propose a adaptive link prediction method for temporal networks based on [Formula: see text]-index to predict future links. The matching degree of nodes is first defined considering both the native influence and the secondary influence of local structure. Then a similarity index is designed using a decaying parameter to punish the snapshots with their occurring time. Experimental results on five realistic temporal networks observing consistent gains of 2–9% AUC in comparison to the best baseline in four networks show that our proposed method outperforms several benchmarks under two standard evaluation metrics: AUC and Ranking score. We also investigate the influence of the free parameter and the definition of matching degree on the prediction performance.

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