Stock Price Movement Associated with Temporary Trading Suspensions: Bear Market Versus Bull Market

1976 ◽  
Vol 11 (4) ◽  
pp. 577 ◽  
Author(s):  
Michael H. Hopewell ◽  
Arthur L. Schwartz
Keyword(s):  
Stock Price ◽  
Price Movement ◽  
Bear Market ◽  
Bull Market

scholarly journals Dynamic Evolution of Securities Market Network Structure under Acute Fluctuation Circumstances

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Haifei Liu ◽  
Tingqiang Chen ◽  
Zuhan Hu
Keyword(s):  
Stock Market ◽  
Network Structure ◽  
Topological Structure ◽  
Securities Market ◽  
Structure Stability ◽  
Dynamic Evolution ◽  
Bear Market ◽  
Bull Market ◽  
Market Transaction ◽  
Measuring Network

This empirical research applies cointegration in the traditional measurement method first to build directed weighted networks in the context of stock market. Then, this method is used to design the indicators and the value simulation for measuring network fluctuation and studying the dynamic evolution mechanism of stock market transaction networks as affected by price fluctuations. Finally, the topological structure and robustness of the network are evaluated. The results show that network structure stability is strong in the bull market stage and weak in the bear market stage. And the convergence rate of the dynamic evolution of network fluctuation is higher in the bull market stage than in the bear market stage.

scholarly journals Asymmetric Reactions of China’s Stock Market to Short-Term Interest Rates

2016 ◽  
Vol 8 (5) ◽  
pp. 260 ◽  
Author(s):  
Fang Fang ◽  
Weijia Dong ◽  
Xin Lv
Keyword(s):  
Stock Market ◽  
Interest Rates ◽  
Stock Returns ◽  
Regime Switching ◽  
Developed Countries ◽  
Short Term ◽  
Bear Market ◽  
Bull Market ◽  
Negative Effect ◽  
Markov Regime Switching Model

This paper investigates how China’s stock market reacts to short-term interest rates, as represented by the Shanghai Interbank Offered Rate (Shibor). We adopt the Markov Regime Switching model to divide China’s stock market into Medium, Bull and Bear market; and then examine how Shibor influences market returns and risk in different market regimes. We find that short-term interest rates have a significant negative effect on stock returns in Medium and Bull market, but could not affect stock returns in Bear market. In addition, different maturities of Shibor have different effects on stock returns. Furthermore, we find that the short-term interest rates have a negative effect on market risk in Bull market, but a positive effect in Bear market. Our findings show that China’s market is quite peculiar and distinctive from the U.S. market or other developed countries’ markets in many ways.

scholarly journals The Geometric Brownian Motion of Indosat Telecommunications Daily Stock Price During the Covid-19 Pandemic in Indonesia

2021 ◽  
Vol 2084 (1) ◽  
pp. 012012
Author(s):  
Tiara Shofi Edriani ◽  
Udjianna Sekteria Pasaribu ◽  
Yuli Sri Afrianti ◽  
Ni Nyoman Wahyu Astute
Keyword(s):  
Brownian Motion ◽  
Stock Price ◽  
Service Providers ◽  
Main Idea ◽  
Geometric Brownian Motion ◽  
Government Policies ◽  
Percentage Error ◽  
Price Movement ◽  
Data Movement ◽  
Total Data

Abstract One of the major telecommunication and network service providers in Indonesia is PT Indosat Tbk. During the coronavirus (COVID-19) pandemic, the daily stock price of that company was influenced by government policies. This study addresses stock data movement from February 5, 2020 to February 5, 2021, resulted in 243 data, using the Geometric Brownian motion (GBM). The stochastic process realization of this stock price fluctuates and increases exponentially, especially in the 40 latest data. Because of this situation, the realization is transformed into log 10 and calculated its return. As a result, weak stationary in variance is obtained. Furthermore, only data from December 7, 2020 to February 5, 2021 fulfill the GBM assumption of stock price return, as R t 1 * , t 1 * = 1 , 2 , 3 , … , 40 . The main idea of this study is adding datum one by one as much as 10% – 15% of the total data R t 1 * , starting from December 4, 2020 backwards. Following this procedure, and based on the 3% < p-value < 10%, the study shows that its datum can be included in R t 1 * , so t 1 * = − 4. − 3 , − 2 , … , 40 and form five other data groups, R t 2 * , … , R t 6 * . Considering Mean Absolute Percentage Error (MAPE) and amount of data from each group, R t 6 * is selected for modelling. Thus, GBM succeeded in representing the stock price movement of the second most popular Indonesian telecommunication company during COVID-19 pandemic.

scholarly journals Forecasting stock price movement: new evidence from a novel hybrid deep learning model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Zhao ◽  
Zhonglu Chen
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Hybrid Model ◽  
Stock Price ◽  
Linear Time ◽  
Price Movement ◽  
Content Type ◽  
Non Linear ◽  
New Evidence ◽  
Deep Learning Model

PurposeThis study explores whether a new machine learning method can more accurately predict the movement of stock prices.Design/methodology/approachThis study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.FindingsThe hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.Originality/valueThis study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

Stock price movement around the merger announcements: insider trading or market anticipation?

2014 ◽  
Vol 40 (8) ◽  
pp. 821-843 ◽  
Author(s):  
Pawan Jain ◽  
Mark A. Sunderman
Keyword(s):  
Insider Trading ◽  
Stock Price ◽  
Policy Implications ◽  
Securities Market ◽  
Price Movement ◽  
Content Type ◽  
Different Types ◽  
Time Periods ◽  
Merger Announcement ◽  
Merger Announcements

Purpose – The purpose of this paper is to examine the stock price movements for existence of informed trading prior to a merger announcement for the companies listed on the emerging markets of India for the period from 1996 to 2010. Design/methodology/approach – This study applies several event study methodologies and regression analyses to analyze the stock price movement surrounding a merger announcement. The paper divides mergers in two different types: industry merger cases and non-industry merger cases and in two different time periods: recession and boom. Findings – The results show that the information held only by insiders’ works its way into prices. The paper finds strong evidence of insider trading in the case of industry mergers and mergers during recessions. Practical implications – The results from this study have immediate policy implications for India and other developing markets as the paper provides the type of mergers and time periods when merger announcements are more susceptible to insider trading. Originality/value – The paper extends the literature on mergers and insider trading by analyzing firms trading on a developing capital market, which, unlike the developed markets, is characterized by inadequate disclosure and a weaker enforcement of securities regulations. The results support this notion and recommend Indian securities market regulators to tighten the lax regulations. In addition, the author document the divergence in price reaction to the merger announcements for different types of mergers: industry mergers and non-industry mergers, as well as for mergers during different market conditions: recession vs booming capital markets.

Dynamic Business Network Analysis for Correlated Stock Price Movement Prediction

2015 ◽  
Vol 30 (2) ◽  
pp. 26-33 ◽  
Author(s):  
Wenping Zhang ◽  
Chunping Li ◽  
Yunming Ye ◽  
Wenjie Li ◽  
Eric W.T. Ngai
Keyword(s):  
Network Analysis ◽  
Stock Price ◽  
Business Network ◽  
Price Movement ◽  
Movement Prediction
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