News - Comment. Money & Markets. Investment - The next big stock market craze on the horizon: space

2019 ◽  
Vol 14 (12) ◽  
pp. 15-15
Author(s):  
C. Chambers
Keyword(s):  
Stock Market ◽  
Money Markets

scholarly journals The Relationship between Macroeconomic Factors and Stock Market Indices Performances in Indian Stock Market

2018 ◽  
Vol 7 (4.36) ◽  
pp. 592
Author(s):  
D. Kinslin ◽  
V. P. Velmurugan
Keyword(s):  
Stock Market ◽  
Capital Market ◽  
Significant Finding ◽  
Macroeconomic Factors ◽  
Money Markets ◽  
Macroeconomic Variables ◽  
Indian Economy ◽  
Financing Costs ◽  
Indian Stock Market ◽  
The Relationship

This investigation endeavors in observationally testing the connection between macroeconomic variables and the exhibitions of two noteworthy Indian security advertise lists of BSE-sensex and NSE-clever. The yearly information of a few macroeconomic elements of FIIs net venture, trade rates, oil value, financing costs, swelling rates and gold rates from 1995-96 to 2014-15 are thought about and it attempts to uncover the most impact of these elements on the 'Stock files exhibitions' of the Indian securities exchange. In compatibility of this, the connection investigation and various relapse examination was utilized to contemplate the connection between the two chose security advertise files exhibitions and the six chose macroeconomic elements from the Indian economy. The significant finding is that macroeconomic elements impact securities exchange lists exhibitions in India. It is suggested that the usage of appropriate monetary approaches will be useful to money markets files and it will result in required development in the Indian capital market.   

Estimating the Long-Run Determinant of the Efficiency of the Stock Market in India

2017 ◽  
Vol 13 (1-2) ◽  
pp. 70-80 ◽  
Author(s):  
Amit Kumar Singh ◽  
Neha Nainwal
Keyword(s):  
Stock Market ◽  
Positive Impact ◽  
Ordinary Least Squares ◽  
Money Markets ◽  
Speed Of Adjustment ◽  
Goods And Services ◽  
Long Run ◽  
The Government ◽  
Market Capitalisation ◽  
The Impact

One of the prominent views is that development in a stock market has a positive impact on economic growth. The role of the stock market becomes important as it leads to capital formation in an economy which is used for producing goods and services in it, leading to growth in the real sector. However, it is only possible if the stock market is efficient enough to mobilise saving from a deficit spender unit to a surplus spender unit. Therefore, our study proposes to estimate the determinant of stock efficiency with the help of a fully modified ordinary least-squares model. The result of the analysis indicates that although both the risk-free interest rate and market capitalisation have a positive and significant impact on stock return, the impact of market capitalisation is larger. In terms of dynamic analysis, the error correction model shows that the speed of adjustment is around 50 per cent or time taken for re-establishing the long-run equilibrium is about two years. As market capitalisation is one of the important determinants of the efficiency of a stock market, the government should bring new reforms in the capital and money markets so that new financial innovations can be introduced in the market. Simultaneously, the regulation should be made to provide higher protection to the investor which further helps them to increase their confidence in the market.

scholarly journals Dynamic spillovers between stock and money markets in Nigeria: A VARMA-GARCH approach

2019 ◽  
Vol 11 (2) ◽  
pp. 255-283
Author(s):  
Afees A. Salisu ◽  
Kazeem O. Isah ◽  
Alberto Assandri
Keyword(s):  
Stock Market ◽  
Money Market ◽  
Conditional Variance ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Forecast Performance ◽  
Money Markets ◽  
Alternative Approach ◽  
Multivariate Volatility ◽  
Over Time

This study examines probable dynamic spillover transmissions between the Nigerian stock and money markets using the multivariate volatility framework that simultaneously accounts for both returns and shock spillovers. Based on relevant pre-tests, the VARMA-CCC-GARCH framework is selected and consequently employed to model the spillovers. The study finds significant cross-market return and shock spillovers between the two markets. Thus, a shock to one market is more likely to spill over to the other market. It is also observed that shocks have persistent effects on stock market volatility but transitory effects on money market volatility. In other words, shocks to the money market die out over time while shocks to stock market tend to persist over time. In addition, including lagged own shocks and lagged own conditional variance when forecasting the future volatility of both return series may enhance their forecast performance. An alternative approach proposed by Diebold and Yilmaz (2012) is also employed for robustness and the results are consistent with those obtained from the VARMA-CCC-GARCH model.

Predicting Stock Market Performance

2020 ◽  
pp. 1-10
Author(s):  
Thomas Plieger ◽  
Thomas Grünhage ◽  
Éilish Duke ◽  
Martin Reuter
Keyword(s):  
Stock Market ◽  
Risk Taking ◽  
Life Stress ◽  
Price Volatility ◽  
Individual Characteristics ◽  
Self Report ◽  
Financial Behavior ◽  
Market Simulation ◽  
Stock Market Performance ◽  
Risk Taking Behavior

Abstract. Gender and personality traits influence risk proneness in the context of financial decisions. However, most studies on this topic have relied on either self-report data or on artificial measures of financial risk-taking behavior. Our study aimed to identify relevant trading behaviors and personal characteristics related to trading success. N = 108 Caucasians took part in a three-week stock market simulation paradigm, in which they traded shares of eight fictional companies that differed in issue price, volatility, and outcome. Participants also completed questionnaires measuring personality, risk-taking behavior, and life stress. Our model showed that being male and scoring high on self-directedness led to more risky financial behavior, which in turn positively predicted success in the stock market simulation. The total model explained 39% of the variance in trading success, indicating a role for other factors in influencing trading behavior. Future studies should try to enrich our model to get a more accurate impression of the associations between individual characteristics and financially successful behavior in context of stock trading.

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