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.

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.

The Impact of Monetary and Fiscal Policies on the Stock Market in the United States (U.S); Evidence from Linear ARDL Framework

2020 ◽  
Vol 11 (1) ◽  
pp. 132-132
Author(s):  
Aref Emamian
Keyword(s):  
United States ◽  
Stock Market ◽  
The United States ◽  
Real Effective Exchange Rate ◽  
Annual Data ◽  
Fiscal Policies ◽  
Long Run ◽  
The Us ◽  
Short Run ◽  
The Impact

This study examines the impact of monetary and fiscal policies on the stock market in the United States (US), were used. By employing the method of Autoregressive Distributed Lags (ARDL) developed by Pesaran et al. (2001). Annual data from the Federal Reserve, World Bank, and International Monetary Fund, from 1986 to 2017 pertaining to the American economy, the results show that both policies play a significant role in the stock market. We find a significant positive effect of real Gross Domestic Product and the interest rate on the US stock market in the long run and significant negative relationship effect of Consumer Price Index (CPI) and broad money on the US stock market both in the short run and long run. On the other hand, this study only could support the significant positive impact of tax revenue and significant negative impact of real effective exchange rate on the US stock market in the short run while in the long run are insignificant. Keywords: ARDL, monetary policy, fiscal policy, stock market, United States

An empirical study on big data stock volatility forecasting algorithm based on multivariate hybrid criterion fuzzy model

2021 ◽  
pp. 002072092098399
Author(s):  
Zhang Xiao-Wen ◽  
Zeng Min
Keyword(s):  
Big Data ◽  
Stock Market ◽  
Fuzzy Model ◽  
Reference Value ◽  
Developed Countries ◽  
Theoretical Research ◽  
Prediction Algorithm ◽  
Real Economy ◽  
Stock Volatility ◽  
The Us

The fluctuation of the stock market has always been a matter of great concern to investors. People always hope to judge the trend of the stock market through the trend of the K line, so as to obtain the price difference through trading, Therefore, it is a theoretical research concerned by the academic circles to carry out empirical research through big data stock volatility prediction algorithm, so as to establish a model to predict the trend of the stock market. After decades of development, China's stock market has gradually matured in continuous exploration. However, compared with the stock market in developed countries, there are still imperfections. For example, the market value of China's stock market does not improve well with economic growth. Year-on-year growth and the development of the real economy. By studying the historical data from 2002 to 2017, we use the Multivariate Mixed Criterion Fuzzy Model (MMCFM) to predict the price changes in the stock market, and obtain the market in China through error statistical analysis. (SSE) is more unstable than the US stock market. Therefore, Multivariate Mixing Criterion (MMC) can be used as a reference indicator to visually measure market maturity. In this paper, we establish a multivariate mixed criteria fuzzy model, and use big data to predict the stock volatility. The algorithm verifies the reliability and accuracy of the model, which has a good reference value for investors.

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 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.

Measuring Dependence Between the USA and the Asian Economies: A Time-varying Copula Approach

2019 ◽  
Vol 20 (4) ◽  
pp. 962-980 ◽  
Author(s):  
Shegorika Rajwani ◽  
Dilip Kumar
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Financial Markets ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Tail Dependence ◽  
Time Varying ◽  
The Us ◽  
Asian Economies ◽  
Copula Approach

During the past few years, many of the financial markets have gone through devastating effects due to the crisis in one or the other economy of the world. The recent global financial crisis has triggered dramatic movements in various stock markets which may arise from interdependence or contagion between the markets. This article attempts to measure the contagion between the equity markets of Asia and the US stock market. The countries considered in the Asian group are China, India, Indonesia, South Korea, Taiwan, Hong Kong, Malaysia and Japan. Most of the Asian economies have experienced drastic higher volatility and uncertainty in the financial markets. If the markets are contagious, then the investors will be unable to reap benefits through international diversification of the portfolio. In such a case, the policymakers will further frame policies so that they can insulate themselves from inflicting heavy damage from various crises. To achieve our goal, we make use of the time-varying copula approach which helps us to study the joint behaviour of the series based on their marginal distribution. Time-varying copula approach can also capture the non-linear dependence in the series and exhibits a rich pattern of tail behaviour. Our findings support the contagion between the Asian stock markets and the US stock market during the global financial crisis. This article also highlights that the increased tail dependence is an important factor for the contagion between the Asian stock markets and the US market.

Intensity of shock transmission amid US-BRICS markets

2015 ◽  
Vol 10 (3) ◽  
pp. 311-328 ◽  
Author(s):  
P. Lakshmi ◽  
S. Visalakshmi ◽  
Kavitha Shanmugam
Keyword(s):  
Stock Market ◽  
Granger Causality ◽  
Stock Markets ◽  
Subprime Mortgage ◽  
Content Type ◽  
Brics Countries ◽  
Long Run ◽  
The Us ◽  
The Usa ◽  
Short Run

Purpose – The purpose of this paper is to analyze the intensity of transmission of shocks from USA to BRICS countries in the long-run and short-run deviations and swiftness of recovery during US subprime mortgage crisis. This analysis enables the authors to explore the evolving patterns of relationships between these markets and examine whether their co-movements altered either in response to international shocks that originated in advanced markets like USA or due to their domestic fluctuations. Design/methodology/approach – Employing data of daily stock market indices (open and close) of BRICS countries for the period January 2, 2001 to May 31, 2012, this paper examines the interactions and characteristics of price movements of BRICS with US market by applying co-integration tests, vector error correction model and Granger causality relationship. The daily stock market indices data are derived from respective stock exchange web sites. Findings – The results exhibit that both long-run co-integration relationships and short-run Granger causality relationships exist between the stock markets of US-BRICS. Furthermore, this nexus is amplified in the short-run during 2007-2009, when the subprime mortgage financial crisis in the USA cropped up. This finding lends support to the prominence of developed (US) market links in the proliferation of persistent co-movements of BRICS stock markets. Research limitations/implications – The findings imply an increasing degree of global market integration due to quick dissemination of global shocks originating from developed market like USA, and swift recovery which can be attributed to the increased resilience, consistent with the moderated level of domestically driven risk in the BRICS markets. In spite of their similarities, long-run and short-run interdependences with the US stock market exhibit differences among the BRICS. This can be attributed to the regional heterogeneity in long-run risk and return co-movements with the USA. Practical implications – Changes from the US index easily affect these stock markets in the short-run, which implies that the US index may act as a leading indicator for investing funds in BRICS markets. Originality/value – This study would enable the authors to understand whether BRICS economies actually remain resilient to adverse developments in USA and could serve as alternative investment destinations for global portfolio diversification.

Effects of political crises on cointegration between ASEAN-5 stock markets and the US stock market

2009 ◽  
Vol 1 (3) ◽  
pp. 334 ◽  
Author(s):  
Reza Tajaddini ◽  
Zamri Ahmad ◽  
Tajul Ariffin Masron
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Political Crises ◽  
The Us
Sign in / Sign up

Export Citation Format

Share Document