scholarly journals Complexity Changes in the US and China’s Stock Markets: Differences, Causes, and Wider Social Implications

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 75
Author(s):  
Jianbo Gao ◽  
Yunfei Hou ◽  
Fangli Fan ◽  
Feiyan Liu
Keyword(s):  
Stock Market ◽  
Time Scales ◽  
Stock Markets ◽  
High Frequency ◽  
Large Scale ◽  
Low Frequency ◽  
Stock Index ◽  
Chinese Market ◽  
Index Data ◽  
The Us

How different are the emerging and the well-developed stock markets in terms of efficiency? To gain insights into this question, we compared an important emerging market, the Chinese stock market, and the largest and the most developed market, the US stock market. Specifically, we computed the Lempel–Ziv complexity (LZ) and the permutation entropy (PE) from two composite stock indices, the Shanghai stock exchange composite index (SSE) and the Dow Jones industrial average (DJIA), for both low-frequency (daily) and high-frequency (minute-to-minute)stock index data. We found that the US market is basically fully random and consistent with efficient market hypothesis (EMH), irrespective of whether low- or high-frequency stock index data are used. The Chinese market is also largely consistent with the EMH when low-frequency data are used. However, a completely different picture emerges when the high-frequency stock index data are used, irrespective of whether the LZ or PE is computed. In particular, the PE decreases substantially in two significant time windows, each encompassing a rapid market rise and then a few gigantic stock crashes. To gain further insights into the causes of the difference in the complexity changes in the two markets, we computed the Hurst parameter H from the high-frequency stock index data of the two markets and examined their temporal variations. We found that in stark contrast with the US market, whose H is always close to 1/2, which indicates fully random behavior, for the Chinese market, H deviates from 1/2 significantly for time scales up to about 10 min within a day, and varies systemically similar to the PE for time scales from about 10 min to a day. This opens the door for large-scale collective behavior to occur in the Chinese market, including herding behavior and large-scale manipulation as a result of inside information.

scholarly journals Government expenditures in the support of technological innovations and impact on stock market and real economy: the empirical evidence from the US and Germany

Equilibrium ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. 717-734
Author(s):  
Jana Kotlebova ◽  
Peter Arendas ◽  
Bozena Chovancova
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Industry 4.0 ◽  
Value Added ◽  
Fiscal Sustainability ◽  
Stock Index ◽  
Government Expenditures ◽  
Annual Data ◽  
Real Economy ◽  
The Us

Research background: The current changes in the global stock markets are related to the next wave of the industrial revolution ?Industry 4.0?. It is expected that the Industry 4.0 will lead to an acceleration of the innovation process and growth of volumes of tailor-made products. The stock markets started to react to the upcoming technological changes over the last decade, which are reflected by the changes in the composition of the major stock indices where the technological sector started to grow in importance. But innovations are not only connected with the specialized technological sector, but they are also of direct concern to the whole spectrum of economic entities. Besides the private investments that are usually allocated via the stock market, also the public sector investments play an important role. Purpose of the article: The aim of this paper is to investigate the relationship between government expenditures on research and development (R&D) and stock markets (and GDP) in the US and in Germany. Methods: We use the tools of descriptive analysis as well as correlation and regression methods of estimation. Findings & Value added: Our research confirms that the collection of data on R&D on annual basis for Germany and the US is insufficient for analytical and systemic management purposes. The real effects of investments in the R&D are time lagged. The regression analysis of annual data confirms only the statistical importance of patent applications as well as interest rate and stock index as independent variables in explanation of variability of real economy growth during the 1985?2017 period. Our model did not prove the significance of government expenditures. We can explain it, among others, by the fact that governments do not pay sufficient attention to the challenges yet, which are associated with the Industry 4.0, especially in the US, where the government expenditures in R&D gradually decrease. The governments in both economies try to increase their support, but fiscal sustainability is a limiting factor.

Do Lower Foreign Flows and Higher Domestic Flows Reduce Indian Equity Market Volatility?

2021 ◽  
pp. 097226292199098
Author(s):  
Vaibhav Aggarwal ◽  
Adesh Doifode ◽  
Mrityunjay Kumar Tiwary
Keyword(s):  
Stock Market ◽  
Mutual Fund ◽  
Stock Markets ◽  
Large Scale ◽  
Structural Changes ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Equity Investments ◽  
Net Equity ◽  
Indian Stock Market

This study examines the relationship that both domestic and foreign institutional net equity flows have with the India stock markets. The motivation behind is the study to examine whether increased net equity investments from domestic institutional investors has reduced the influence of foreign equity flows on the Indian stock market volatility. Our results indicate that only during periods in which domestic equity inflows surpass foreign flows by a significant margin, as seen during 2015–2018, is the Indian stock market volatility not significantly influenced by foreign equity investments. However, during periods of re-emergence of strong foreign net inflows, the Indian market volatility is still being impacted significantly, as has been observed since 2019. Furthermore, we find that both large-scale net buying and net selling by domestic funds increased the stock market volatility as observed during 2015–2018 and COVID-impacted year 2020 respectively. The implications of this study are multi-fold. First, the regulators should discuss with industry bodies before enforcing major structural changes like reconstituting of mutual fund investment mandate in 2017 which forced domestic funds to quickly change portfolio allocation amongst large-cap, mid-cap and small-cap stocks resulting in higher stock market volatility. Second, adequate investor educational and awareness programmes need to be conducted regularly for retail investors to minimize herd behaviour of investing during market rise and heavy redemptions at times of fall. Third, the economic policies should be stable and forward-looking to ensure foreign investors remain attracted to the Indian stock markets at all times.

scholarly journals Optimizing the Frequency Capping: A Robust and Reliable Methodology to Define the Number of Ads to Maximize ROAS

2021 ◽  
Vol 11 (15) ◽  
pp. 6688
Author(s):  
Jesús Romero Leguina ◽  
Ángel Cuevas Rumin ◽  
Rubén Cuevas Rumin
Keyword(s):  
High Frequency ◽  
Large Scale ◽  
Low Frequency ◽  
Digital Marketing ◽  
Optimal Frequency ◽  
Novel Technique ◽  
Maximum Threshold

The goal of digital marketing is to connect advertisers with users that are interested in their products. This means serving ads to users, and it could lead to a user receiving hundreds of impressions of the same ad. Consequently, advertisers can define a maximum threshold to the number of impressions a user can receive, referred to as Frequency Cap. However, low frequency caps mean many users are not engaging with the advertiser. By contrast, with high frequency caps, users may receive many ads leading to annoyance and wasting budget. We build a robust and reliable methodology to define the number of ads that should be delivered to different users to maximize the ROAS and reduce the possibility that users get annoyed with the ads’ brand. The methodology uses a novel technique to find the optimal frequency capping based on the number of non-clicked impressions rather than the traditional number of received impressions. This methodology is validated using simulations and large-scale datasets obtained from real ad campaigns data. To sum up, our work proves that it is feasible to address the frequency capping optimization as a business problem, and we provide a framework that can be used to configure efficient frequency capping values.

scholarly journals Spillovers of Stock Markets among the BRICS: New Evidence in Time and Frequency Domains before the Outbreak of COVID-19 Pandemic

2021 ◽  
Vol 14 (3) ◽  
pp. 112
Author(s):  
Kai Shi
Keyword(s):  
Stock Market ◽  
Frequency Band ◽  
Stock Markets ◽  
Emerging Market ◽  
Low Frequency ◽  
Systematic Risk ◽  
Error Variance ◽  
Frequency Bands ◽  
Volatility Spillovers ◽  
Risk Sources

We attempted to comprehensively decode the connectedness among the abbreviation of five emerging market countries (BRICS) stock markets between 1 August 2002 and 31 December 2019 not only in time domain but also in frequency domain. A continuously varying spillover index based on forecasting error variance decomposition within a generalized abbreviation of vector-autoregression (VAR) framework was computed. With the help of spectral representation, heterogeneous frequency responses to shocks were separated into frequency-specific spillovers in five different frequency bands to reveal differentiated linkages among BRICS markets. Rolling sample analyses were introduced to allow for multiple changes during the sample period. It is found that return spillovers dominated by the high frequency band (within 1 week) part declined with the drop of frequencies, while volatility spillovers dominated by the low frequency band (above 1 quarter) part grew with the decline in frequencies; the dynamics of spillovers were influenced by crucial systematic risk events, and some similarities implied in the spillover dynamics in different frequency bands were found. From the perspective of identifying systematic risk sources, China’s stock market and Russia’s stock market, respectively, played an influential role for return spillover and volatility spillover across BRICS markets.

scholarly journals Which global stock indices trigger stronger contagion risk in the Vietnamese stock market? Evidence using a bivariate analysis

2013 ◽  
Vol 60 (4) ◽  
pp. 473-497 ◽  
Author(s):  
Kuan-Min Wang ◽  
Hung-Cheng Lai
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Correlation Coefficients ◽  
Bivariate Analysis ◽  
Contagion Effects ◽  
Daily Data ◽  
The Us ◽  
Egarch Model ◽  
Bivariate Egarch Model ◽  
Contagion Risk

This paper extends recent investigations into risk contagion effects on stock markets to the Vietnamese stock market. Daily data spanning October 9, 2006 to May 3, 2012 are sourced to empirically validate the contagion effects between stock markets in Vietnam, and China, Japan, Singapore, and the US. To facilitate the validation of contagion effects with market-related coefficients, this paper constructs a bivariate EGARCH model of dynamic conditional correlation coefficients. Using the correlation contagion test and Dungey et al.?s (2005) contagion test, we find contagion effects between the Vietnamese and four other stock markets, namely Japan, Singapore, China, and the US. Second, we show that the Japanese stock market causes stronger contagion risk in the Vietnamese stock market compared to the stock markets of China, Singapore, and the US. Finally, we show that the Chinese and US stock markets cause weaker contagion effects in the Vietnamese stock market because of stronger interdependence effects between the former two markets.

scholarly journals Influence of world stock markets on the development of the stock market in Ukraine

2021 ◽  
Vol 18 (4) ◽  
pp. 223-240
Author(s):  
Inna Shkolnyk ◽  
Serhiy Frolov ◽  
Volodymyr Orlov ◽  
Viktoriia Dziuba ◽  
Yevgen Balatskyi
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Mutual Influence ◽  
Stock Exchanges ◽  
World Federation ◽  
Pairwise Correlation ◽  
Stock Market Performance ◽  
The Us ◽  
The World ◽  
Global Stock Markets

Viewing the development of the stock market in Ukraine, the economy, which world financial organizations characterize as small and open, is largely determined by the trends formed by the global stock markets and leading stock exchanges. Therefore, the study aims to analyze Ukraine’s stock market, the world stock market, stock markets in the regions, and to assess their mutual influence. The study uses the data of the World Federation of Exchanges and National Securities and Stock Market Commission (Ukraine) from 2015 to 2020. Stock market performance forecasts are built using triple exponential smoothing. Based on pairwise correlation coefficients, the existence of a significant dependence in the development of the world stock market on the development of the American stock market was determined. Regarding the Ukrainian stock exchanges, only SE “PFTS” demonstrated its dependence on the US stock market. The results of the regression model based on an exponentially smoothed series of trading volumes in all markets showed that variations in the volume of trading on the world stock market are due to the situation on the US stock markets. Trading volume dynamics on Ukrainian stock exchanges such as SE “PFTS” and SE “Perspektiva” is almost 50% determined by the development of stock markets in the American region. Although Ukraine is geographically located in Europe, the results show a lack of significant links and the impacts of stock markets in this region on the major Ukrainian stock exchanges and the stock market as a whole.

scholarly journals Spillovers to Renewable Energy Stocks in the US and Europe: Are They Different?

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3162 ◽  
Author(s):  
Tiantian Liu ◽  
Shigeyuki Hamori
Keyword(s):  
Renewable Energy ◽  
Stock Markets ◽  
Low Frequency ◽  
Investment Horizon ◽  
Stock Market Index ◽  
Energy Investment ◽  
Time Frequency ◽  
Volatility Spillovers ◽  
Financial Variables ◽  
The Us

This paper examines the spillovers of return and volatility transmitted from fossil energies (crude oil and natural gas) and several important financial variables (stock market index, bonds, and the volatility index) to renewable stock markets in the US and Europe under the time-frequency domain frameworks. The total spillovers of return and volatility from all variables to renewable stock markets in the US are higher than those in Europe. Stock markets transmit the highest return spillovers to renewable energy stocks, which far exceed the spillovers from fossil energy to renewable energy stocks in both regions. In addition, both return and volatility spillovers could be enhanced, possibly due to specific events or sudden changes in prices. In particular, extreme events such as the Brexit referendum in 2016 influenced mostly the volatility spillovers across European markets. Moreover, the spillovers of return and volatility are contingent on frequency, and most return spillovers are concentrated at the high frequency, whereas most volatility spillovers are concentrated at the low frequency. These results remind investors that it is necessary to consider the investment horizon when making their financial decisions on renewable energy investment.

Dynamic relation between global Islamic and conventional sectoral stock and bonds indexes

2020 ◽  
Vol 07 (02) ◽  
pp. 2050006
Author(s):  
Sukriye Tuysuz
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Stock Index ◽  
High Frequency Part ◽  
Dynamic Relation ◽  
The Relationship

This paper examines the relationship between 10 Global sectoral conventional and Islamic assets. For each sector, a conventional, an Islamic stock index and a bond are retained. The analyzed relations are done by taking into account diverse investment horizons by using MODWT and GARCH-DCC-type models. Our results indicate that adding bond indexes into a portfolio composed with conventional stock or Islamic stock is efficient. As for the correlations between conventional and Islamic sectoral indexes, they depend on the sector. Relations between returns of securities are quite similar to the relations between high-frequency part of these series and are very volatile at low frequency.

scholarly journals Opportunity or Crisis? Volatility and Leverage Effect in the World’s Largest Economies

2019 ◽  
Vol 9 (4) ◽  
pp. 1
Author(s):  
Amalendu Bhunia ◽  
Devrim Yaman
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Market Volatility ◽  
Future Market ◽  
Market Returns ◽  
Stock Market Returns ◽  
The Us ◽  
The World ◽  
The Impact ◽  
The Relationship

This paper examines the relationship between asset volatility and leverage for the three largest economies (based on purchasing power parity) in the world; US, China, and India. Collectively, these economies represent Int$56,269 billion of economic power, making it important to understand the relationship among these economies that provide valuable investment opportunities for investors. We focus on a volatile period in economic history starting in 1997 when the Asian financial crisis began. Using autoregressive models, we find that Chinese stock markets have the highest volatility among the three stock markets while the US stock market has the highest average returns. The Chinese market is less efficient than the US and Indian stock markets since the impact of new information takes longer to be reflected in stock prices. Our results show that the unconditional correlation among these stock markets is significant and positive although the correlation values are low in magnitude. We also find that past market volatility is a good indicator of future market volatility in our sample. The results show that positive stock market returns result in lower volatility compared to negative stock market returns. These results demonstrate that the largest economies of the world are highly integrated and investors should consider volatility and leverage besides returns when investing in these countries.

scholarly journals Interdependence and Risk Comparison of Slovak, Hungarian and Polish Stock Markets: Policy and Managerial Implications

2019 ◽  
Vol 69 (2) ◽  
pp. 273-287 ◽  
Author(s):  
Florin Aliu ◽  
Besnik Krasniqi ◽  
Adriana Knapkova ◽  
Fisnik Aliu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Markets ◽  
Stock Exchange ◽  
Weighted Average ◽  
Stock Index ◽  
Risk Level ◽  
Average Return ◽  
Monetary System ◽  
Managerial Implications

Risk captured through the volatility of stock markets stands as the essential concern for financial investors. The financial crisis of 2008 demonstrated that stock markets are highly integrated. Slovakia, Hungary and Poland went through identical centralist economic arrangement, but nowadays operate under diverse stock markets, monetary system and tax structure. The study aims to measure the risk level of the Slovak Stock Market (SAX index), Budapest Stock Exchange (BUX index) and Poland Stock Market (WIG20 index) based on the portfolio diversification model. Results of the study provide information on the diversification benefits generated when SAX, BUX and WIG20 join their stock markets. The study considers that each stock index represents an independent portfolio. Portfolios are built to stand on the available companies that are listed on each stock index from 2007 till 2017. The results of the study show that BUX generates the lowest risk and highest weighted average return. In contrast, SAX is the riskiest portfolio but generates the lowest weighted average return. The results find that the stock prices of BUX have larger positive correlation than the stock prices of SAX. Moreover, the highest diversification benefits are realized when Portfolio SAX joins Portfolio BUX and the lowest diversification benefits are achieved when SAX joins WIG20.

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