scholarly journals Do negative events really have deteriorating effects on stock performance? A comparative study on Tesla (US) and Nio (China)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi Xuan Lim ◽  
Consilz Tan
Keyword(s):  
Stock Market ◽  
Cognitive Biases ◽  
Time Series Data ◽  
Negative Impact ◽  
Series Data ◽  
Stock Performance ◽  
Content Type ◽  
Short Run ◽  
Remedial Actions ◽  
Processing Errors

PurposeBoth investors and the stock markets are believed to behave in a perfectly rational manner, where investors focus on utility maximization and are not subjected to cognitive biases or any information processing errors. However, it has been discovered that the sentiment of the social mood has a significant impact on the stock market. This study aims to analyze how did the protest event of Tesla happened in April 2021 have a significant effect on the company's stock performance as well as its competitors, Nio, under the competitive effect.Design/methodology/approachThe research is based on time series data collected from Tesla and Nio by employing 10 days, 15 days and 20 days anticipation and adjustment period for the event study. This study employed a text sentiment analysis to identify the polarity of the sentiment of the protest event using the Microsoft Azure machine learning tool which utilizes MPQA subjective lexicon.FindingsThe findings provide further evidence to show that a company-specific negative event has deteriorating effects on its stock performance, while having an opposite effect on its competitors.Research limitations/implicationsThe paper argues that negative sentiments through social media word of mouth (SWOM) affect the stock market not just in the short run but potentially in the longer run. Such negative sentiments might create a snowball effect which causes the market to further scrutinize a company's operations and possibly lose confidence in the company.Originality/valueThis study explores how the Tesla's protest event at Shanghai Auto Show 2021 has a significant impact on Tesla's stock performance and prolonged negative impact although Tesla implemented immediate remedial actions. The remedial actions were not accepted positively and induced a wave of negative news which had a more persistent effect.

scholarly journals Stock market development and economic growth in Tanzania: an ARDL and bound testing approach

2020 ◽  
Vol 5 (3) ◽  
pp. 187-206
Author(s):  
Saganga Mussa Kapaya
Keyword(s):  
Economic Growth ◽  
Stock Market ◽  
Time Series Data ◽  
Market Development ◽  
Series Data ◽  
Content Type ◽  
Stock Market Development ◽  
Testing Procedures ◽  
Long Run ◽  
Short Run

Purpose The purpose of this paper is to contribute to empirical evidence by recognizing the importance of stock markets in the financial system and consequently its causality to economic growth and vice versa. Design/methodology/approach The study used the autoregressive distribute lag model (ARDL) with bound testing procedures, the sample covered quarterly time-series data from 2001q1 to 2019q2 in Tanzania. Findings The results suggest that stock market development have both negative and positive causality for both short-run dynamics and long-run relationship with economic growth. Economic growth is found to only cause and relate negatively to liquidity both in the short-run and in the long-run. The results show predominantly a unidirectional causality flow from stock market development to economic growth and finds partial causality flow from economic growth to stock market development, as represented by stock market turnover which proxied liquidity. Originality/value The use of quarterly data to reflect more realistically the dynamics of the variables because yearly data may sometimes cover-up specific dynamics that may be useful for prediction and policy planning. The study uses indices to capture general aspects within the stock market against economic growth as an intuitive way to aggregate the stock market development effects.

Dynamic prediction of Indian stock market: an artificial neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Goel ◽  
Narinder Pal Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Institutional Investors ◽  
Time Series Data ◽  
Gradient Algorithm ◽  
Series Data ◽  
Content Type ◽  
Artificial Neural ◽  
Indian Stock Market

Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

Has the low level of economic growth spurred informal sector activities in Uganda? An empirical analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Stephen Esaku
Keyword(s):  
Economic Growth ◽  
Informal Sector ◽  
Shadow Economy ◽  
Time Series Data ◽  
Series Data ◽  
Content Type ◽  
Low Level ◽  
Revenue Collection ◽  
Long Run ◽  
Short Run

PurposeIn this paper, the authors examine how economic growth shapes the shadow economy in the long and short run.Design/methodology/approachUsing annual time series data from Uganda, drawn from various data sources, covering the period from 1991 to 2017, the authors apply the ARDL modeling approach to cointegration.FindingsThis paper finds that an increase in economic growth significantly reduces the size of the shadow economy, in both the long and short run, all else equal. However, the long-run relationship between the shadow economy and growth is non-linear. The results suggest that the rise of the shadow economy could partially be attributed to the slow and sluggish rate of economic growth.Practical implicationsThese findings imply that addressing informality requires addressing underlying factors of underdevelopment since improvements in economic growth also translate into a reduction in the size of the shadow economy in the short and long run.Originality/valueThese findings reveal that the low level of economic growth is an issue because it spurs informal sector activities in the short run. However, as the economy improves, it becomes an incentive for individuals to operate in the informal sector. Additionally, tackling shadow activities in the short run could help improve tax revenue collection.

scholarly journals The long-Run and Short-Run Analysis between Stock Market Index and Macroeconomic Variables in Jordan: Bounds Tests Approach

2019 ◽  
Vol 12 (4) ◽  
pp. 50
Author(s):  
Raed Walid Al-Smadi ◽  
Muthana Mohammad Omoush
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Stock Market Index ◽  
Macroeconomic Variables ◽  
Market Index ◽  
Long Run ◽  
Equilibrium Relationship ◽  
Short Run

This paper investigates the long-run and short-run relationship between stock market index and the macroeconomic variables in Jordan. Annual time series data for the 1978–2017 periods and the ARDL bounding test are used. The results identify long-run equilibrium relationship between stock market index and the macroeconomic variables in Jordan. Jordanian policy makers have to pay more attention to the current regulation in the Amman Stock Exchange(ASE) and manage it well, thus ultimately helping financial development.

A Sectoral Analysis of the Role of Stock Market Development on Economic Growth: Empirical Evidence from Indian Economy

2017 ◽  
Vol 18 (4) ◽  
pp. 911-923 ◽  
Author(s):  
Madhu Sehrawat ◽  
A.K. Giri
Keyword(s):  
Economic Growth ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Trade Openness ◽  
Series Data ◽  
Causality Test ◽  
Long Run ◽  
Short Run

The present study examines the relationship between Indian stock market and economic growth from a sectoral perspective using quarterly time-series data from 2003:Q4 to 2014:Q4. The results of the autoregressive distributed lag (ARDL) approach bounds test confirm the existence of a cointegrating relationship between sector-specific gross domestic product (GDP) and sector-specific stock indices. The empirical results reveal that sector-specific economic growth are significantly influenced by changes in the respective sector-specific stock price indices in the long run as well as in the short run. Apart from that, the control variables, such as trade openness and inflation, act as the instrument variables in explaining the variations in the sector-specific GDP of the economy. The results of Granger causality test demonstrate unidirectional long-run as well as short-run causality running from sector specific stock prices to respective sector GDP. The findings suggest that economic growth of the country is sensitive to respective sub-sector stock market investments. The findings highlight the reasons for cyclical and counter-cyclical business phase for the overall economy.

The effects of political, economic and financial components of country risk on housing prices in South Africa

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paul-Francois Muzindutsi ◽  
Sanelisiwe Jamile ◽  
Nqubeko Zibani ◽  
Adefemi A. Obalade
Keyword(s):  
Risk Factors ◽  
South Africa ◽  
Housing Market ◽  
Time Series Data ◽  
Housing Prices ◽  
Political Risk ◽  
Country Risk ◽  
Series Data ◽  
Content Type ◽  
Short Run

Purpose The housing market in South Africa has the potential to drive economic growth and attract foreign investment, but it can be affected by various risk factors. This paper aims to conduct an empirical analysis of the effect of country risk components on the housing market in South Africa. Design/methodology/approach Linear and nonlinear autoregressive distributed lag (ARDL) models were used to evaluate the effects of the economic, financial and political risk factors of country risk on the prices of different segments of houses based on 276 monthly time-series data from January1995 to December 2015. Findings First, the results established that the three housing indices were more sensitive to political risk in the long run. Second, short run results showed that the three housing indices were largely influenced by their own preceding adjustments in the short run albeit minimal influences from political risk. Third, large housing segments indicated a higher magnitude of the country risk effect in South Africa. Originality/value This paper concluded that the response of housing prices to changes in the country risk components differed across the three segments of the housing market in South Africa. Consequently, this study presented the first comparison of the reactions of different housing segments to different components country risk.

Determinants of environmental degradation in Saudi Arabia: exploring the unexplored

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammad Tahir ◽  
Arshad Hayat ◽  
Umar Burki
Keyword(s):  
Saudi Arabia ◽  
Environmental Degradation ◽  
Time Series Data ◽  
Trade Openness ◽  
Negative Relationship ◽  
Series Data ◽  
Content Type ◽  
Distributed Lag ◽  
Long Run ◽  
Short Run

Purpose Environmental degradation is recognized as a serious problem globally, and hence, Saudi Arabia is no exception. This paper aims to focus on the economy of Saudi Arabia to identify the determinants of environmental degradation. Design/methodology/approach Time series data spanning from 1971 to 2014 is used and analyzed using the recently developed autoregressive distributed lag modeling approach. Findings The obtained results reflected that natural resources, per person income and urbanization, have impacted environmental degradation both positively and significantly in the long run. Similarly, an insignificant negative relationship is established between trade openness and environmental degradation. Moreover, energy consumption has positively but insignificantly affected environmental degradation. In the short run, only per capita income has positively influenced environmental degradation while the rest of the variables have lost either significance levels or their direction of relationship has reversed. Originality/value As this is a pioneering study on the economy of Saudi Arabia, therefore, the authors assume that policymakers will find the findings of the current study very useful while formulating and implementing policies to control environmental degradation.

Asymmetric effects of oil price changes on the Nigerian exchange rate

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Patrick Onodje ◽  
Temitope Ahmdalat Oke ◽  
Oluwatimilehin Aina ◽  
Nazeer Ahmed
Keyword(s):  
Exchange Rate ◽  
Time Series Data ◽  
Oil Price ◽  
Series Data ◽  
Price Changes ◽  
Content Type ◽  
Asymmetric Effects ◽  
Wald Tests ◽  
Long Run ◽  
Short Run

Purpose The purpose of this paper is to examine the effect of crude oil prices on the Nigerian exchange rate with emphasis on discriminating between the effects of positive and negative changes in oil price on exchange rate. Design/methodology/approach The authors used monthly time series data from 1996:1 to 2019:6 and adopted two oil price measures, namely, Brent crude and West Texas Intermediary prices. For analysis, the authors used stepwise least squares to estimate a non-linear ARDL (NARDL) model and Wald tests to determine cointegration and the presence of asymmetric effects. Findings The findings showed that positive and negative Brent crude price changes significantly affect exchange rates differently in nominal terms, both in the long-run and short-run. However, the differences were purely in terms of effect size because the exchange rate decreased for both negative and positive oil price changes. Originality/value Whilst empirical research on asymmetries in the effect of oil price on exchange rate abounds, little evidence exists in Nigeria’s case. Although some studies previously tested for asymmetric oil price effects on the Nigerian currency, the approach used did not estimate long and short-run effects or test of long-run and short-run asymmetries. This paper fills this methodological gap using monthly using the NARDL approach. The NARDL approach provided the advantage of estimating effects for the long-run and short-run and testing for asymmetries in both time spans.

Globalization, role of institutions and economic performance in Indian economy

2019 ◽  
Vol 11 (1) ◽  
pp. 82-100 ◽  
Author(s):  
Madhu Sehrawat ◽  
A.K. Giri
Keyword(s):  
Economic Growth ◽  
Economic Performance ◽  
Time Series Data ◽  
Empirical Support ◽  
Institutional Quality ◽  
Series Data ◽  
Content Type ◽  
Indian Economy ◽  
Integration Test ◽  
Short Run

PurposeUsing time series data for the period 1982-2016, this study aims to explore the effect of globalization, institutional quality on economic performance for Indian economy by endogenizing financial development.Design/methodology/approachThe stationarity properties of the variables are tested by Saikkonen and Lütkepohl unit root test, and the co-integration test proposed by Bayer–Hanck (2013) is used to check the long- and short-run relationship among the variables. The robustness is established by autoregressive distributed lag approach (ARDL), and the Granger causality test is used to assess the causal relationship among the variables.FindingsThe empirical findings indicate the existence of the co-integrating relationship among the variables, and the ARDL estimates reveal that both globalization and institutional quality act as important key drivers for India’s economic performance. However, the institutional quality does not affect the short-run economic growth.Research limitations/implicationsThe study finds that institutional quality and globalization index are crucial to accelerate economic performance. Therefore, policy efforts should be focused on the improvement of these indicators by offering protection of property rights, reduction in government corruption, reducing political instability, price stability and stable macroeconomic environment. This study recommends that policy should be geared toward development of financial sector, promotion of financial integration, which will create the environment for the efficient allocation of credit.Originality/valueThis study provides empirical support for the proposition that both globalization and institutional quality matter for India’s emerging economic growth by taking account of the structural break.

scholarly journals The Determinant of Indonesian Stock Returns’ Volatility: Evidence from Islamic and Conventional Stock Market

2021 ◽  
Vol 6 (3) ◽  
pp. 277-296
Author(s):  
Septiana Indarwati ◽  
Agus Widarjono
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Money Supply ◽  
Time Series Data ◽  
Negative Impact ◽  
Stock Exchange ◽  
Series Data ◽  
Macroeconomic Variables ◽  
Distributed Lag ◽  
Macroeconomic Indicators

Islamic stock market is apparently different from the conventional stock market due to the prohibition of unlawful goods and excessive risk-taking behavior. This study explores the extent to which the Indonesian Islamic and conventional stock returns' volatility responds to the macroeconomic indicators. This study employs Jakarta Islamic Index (JII) and Indonesian Stock Exchange (IDX) and uses monthly time-series data covering 2001: M1 - 2019: M12. The volatility of stock returns is measured using Generalized Autoregressive Conditional Heteroskedasticity (GARCH). By employing the Autoregressive Distributed Lag Model (ARDL), the results validate the evidence of the long-run relationship between the stock market's volatility and macroeconomic variables. A rising in money supply and an economic upturn reduce the volatility of conventional stock returns but only an expansionary money supply diminishes the volatility of Islamic stock returns. Conversely, high inflation and sharp depreciation of the Rupiah boost the stock returns' volatility. The results further show an interesting finding that the Islamic stock market's volatility is more responsive to changes in macroeconomic indicators than the volatility of their counterpart conventional stock market. Policymakers should take strict rules during the worst economic conditions to minimize the negative impact of the instability of macroeconomic variables.

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