Overnight and Daytime Stock-Return Dynamites on the London Stock Exchange: The Impacts of “Big Bang” and the 1987 Stock-Market Crash

1995 ◽  
Vol 13 (4) ◽  
pp. 365-378 ◽  
Author(s):  
Ronald W. Masulis ◽  
Victor K. Ng
Keyword(s):  
Stock Market ◽  
Stock Return ◽  
Stock Exchange ◽  
Big Bang ◽  
London Stock Exchange ◽  
Stock Market Crash

Iran’s ebullient stock market is heading for a crash

2020 ◽  
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Real Estate Market ◽  
Social Consequences ◽  
Stock Market Crash ◽  
Content Type ◽  
High Inflation ◽  
Online Trading ◽  
The Government ◽  
Media Channels

Significance This was despite a sharp economic contraction, high inflation and currency devaluation. The government has played a significant role in promoting equities growth, with state or quasi-state organisations dominating the market. Impacts A stock market crash would have devastating economic and social consequences. With many reluctant to leave their houses because of COVID-19, the growth in online trading technology will accelerate. There will be a boom in firms providing stock market advice through social media channels for a small one-off or monthly fee. Diversion of funds from the forex market to the stock exchange will temporarily help ward off a new currency crisis. With richer Iranians seeking to transfer additional assets abroad, there could be a new boost for the Turkish real estate market.

scholarly journals Detection of structural regimes and analyzing the impact of crude oil market on Canadian stock market: Markov regime-switching approach

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammadreza Mahmoudi ◽  
Hana Ghaneei
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Stock Return ◽  
Stock Exchange ◽  
Markov Switching ◽  
Stock Index ◽  
Var Model ◽  
Content Type ◽  
Oil Market ◽  
The Impact

Purpose This study aims to analyze the impact of the crude oil market on the Toronto Stock Exchange Index (TSX). Design/methodology/approach The focus is on detecting nonlinear relationship based on monthly data from 1970 to 2021 using Markov-switching vector auto regression (VAR) model. Findings The results indicate that TSX return contains two regimes: positive return (Regime 1), when growth rate of stock index is positive; and negative return (Regime 2), when growth rate of stock index is negative. Moreover, Regime 1 is more volatile than Regime 2. The findings also show the crude oil market has a negative effect on the stock market in Regime 1, while it has a positive effect on the stock market in Regime 2. In addition, the authors can see this effect in Regime 1 more significantly in comparison to Regime 2. Furthermore, two-period lag of oil price decreases stock return in Regime 1, while it increases stock return in Regime 2. Originality/value This study aims to address the effect of oil market fluctuation on TSX index using Markov-switching approach and capture the nonlinearities between them. To the best of the author’s knowledge, this is the first study to assess the effect of the oil market on TSX in different regimes using Markov-switching VAR model. Because Canada is the sixth-largest producer and exporter of oil in the world as well as the TSX as the Canada’s main stock exchange is the tenth-largest stock exchange in the world by market capitalization, this paper’s framework to analyze a nonlinear relationship between oil market and the stock market of Canada helps stock market players like policymakers, institutional investors and private investors to get a better understanding of the real world.

scholarly journals Efficiency and Multifractality Analysis of the Chinese Stock Market: Evidence from Stock Indices before and after the 2015 Stock Market Crash

2019 ◽  
Vol 11 (6) ◽  
pp. 1699 ◽  
Author(s):  
Chenyu Han ◽  
Yiming Wang ◽  
Yingying Xu
Keyword(s):  
Stock Market ◽  
Market Efficiency ◽  
Stock Exchange ◽  
Practical Significance ◽  
Fluctuation Analysis ◽  
Chinese Stock Market ◽  
Stock Market Crash ◽  
Gaussian Probability Density Function ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Daily Return

This paper examines the daily return series of four main indices, including Shanghai Stock Exchange Composite Index (SSE), Shenzhen Stock Exchange Component Index (SZSE), Shanghai Shenzhen 300 Index (SHSE-SZSE300), and CSI Smallcap 500 index (CSI500) in Chinese stock market from 2000 to 2018 by multifractal detrended fluctuation analysis (MF-DFA). The series of the daily return of the indices exhibit significant multifractal properties on the whole time scale and SZSE has the highest multifractal properties among the four indices, indicating the lowest market efficiency. The multifractal properties of four indices are due to long-range correlation and fat-tail characteristics of the non-Gaussian probability density function, and these two factors have different effects on the multifractality of four indices. This paper aims to compare the multifractility degrees of the four indices in three sub-samples divided by the 2015 stock market crash and to discuss its effects on efficiency of the Shanghai and Shenzhen stock market in each sub-sample. Meanwhile, we study the effect of the 2015 stock market crash on market efficiency from the statistical and fractal perspectives, which has theoretical and practical significance in the application of Effective Market Hypothesis (EMH) in China’s stock market, and it thereby affects the healthy and sustainability of the market. The results also provide important implications for further study on the dynamic mechanism and efficiency in stock market and they are relevant to portfolio managers and policy makers in a number of ways to maintain the sustainable development of China’s capital market and economy.

scholarly journals Watch Your Neighbor: A Volatility Spillover in ASEAN-5 Stock Exchange

2019 ◽  
Vol 10 (1) ◽  
pp. 59-65
Author(s):  
Sumani Sumani ◽  
Siti Saadah
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Stock Return ◽  
Stock Exchange ◽  
Selection Process ◽  
Asset Price ◽  
Price Volatility ◽  
The Philippines ◽  
Volatility Spillover ◽  
Asean Countries

In the process of financial markets integration, this research was conducted to investigate the phenomenon of the transmission of stock return volatility among stock market in five ASEAN countries. Those were Indonesia, Singapore, Malaysia, Philippines, and Thailand. The research was important because when the interdependence of financial markets had increased, changes in asset prices in the market were not only influenced by the shock in the market but also by its response to asset price volatility that occurred in other countries. Information about the volatility spillover between markets was important for investors to the portfolio selection process. Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) method was used on a daily time series stock return data obtained by accessing www.bloomberg.com. The result indicates that the shock in Singapore, Malaysia, Thailand, and the Philippines stock market will be transmitted to the Indonesia Stock Market with an asymmetric pattern. It has increased intensity after the implementation of the ASEAN Economic Community in December 2015.

scholarly journals ANALISIS EFFICIENT MARKET HYPOTHESIS PADA BURSA EFEK INDONESIA TERHADAP PASAR SAHAM ASEAN

2017 ◽  
Vol 2 (2) ◽  
pp. 128
Author(s):  
Astriyani Prima Kartika ◽  
Jubaedah Jubaedah ◽  
Fitri Yetti
Keyword(s):  
Stock Market ◽  
Hypothesis Testing ◽  
Stock Return ◽  
Sampling Method ◽  
Stock Exchange ◽  
Efficient Market Hypothesis ◽  
Descriptive Statistics ◽  
Test Results ◽  
Exchange Effect ◽  
Significance Level

This research aims to analyze the performance of Indonesia Stock Exchange to ASEAN stock market during period 2012-2016. The technique of determining the sample using purposive sampling method and 6 countries as sample are Indonesia, Singapore, Malaysia, Vietnam and Philippines. Hypothesis testing in this study using Descriptive Statistics Analysis, Test Run and Kolomogorov Smirnov with a significance level of 0.05. Test results show that: (1) Indonesia Stock Exchange has the highest efficient rating in ASEAN stock market (2) ASEAN Stock Exchange effect on Indonesia Stock Exchange. (3) The Indonesia Stock Exchange has a stock return pattern that fluctuates normally in the ASEAN stock market.div>

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