Sector Stock Research Articles

Analysis of Changes in Food and Beverage Sector Stock Prices on the Indonesia Stock Exchange

Analysis of Changes in Food and Beverage Sector Stock Prices on the Indonesia Stock Exchange

Which is leading: Renewable or brown energy assets?

This study examines the relationship between crude oil, a proxy for brown energy, and several renewable energy stock sector indices (e.g., solar energy, wind energy, bioenergy, and geothermal energy) over various investment horizons. Using daily data from October 15, 2010, to February 23, 2022, we apply a combination of methods involving co-integration, wavelet coherency, and wavelet-based Granger causality. The results show that the relationship between crude oil and renewable energy indices is non-linear and somewhat multifaceted. Firstly, there are sectorial differences in the intensity of the relationships. Notably, the relationship intensity between the wind and crude oil is lower than that involving geothermal energy or bioenergy. Secondly, the relationship evolves with time. For example, the COVID-19 outbreak seems to have increased the relationship between crude oil and renewable energy markets, notably for solar, bioenergy, and geothermal. Thirdly, the relationship varies across scales. When controlling for the VIX (volatility index), a proxy of the sentiment of market participants, and EPU (economic policy uncertainty index), the relationship seems strong in the long term but weak in the short term. This result is confirmed using a Granger causality test on the wavelet-decomposed series. These findings have important implications for long-term investors, short-term speculators, and policymakers regarding the co-movement between brown and renewable energy markets.

Going green: Do green bonds act as a hedge and safe haven for stock sector risk?

The paper documents the hedging and safe haven benefits of green bonds in equities using the dynamic conditional correlation approach. Green bonds act as a strong hedge for eleven stock sectors in the USA. Additionally, they demonstrate a strong safe haven property with high-emission sectors for the entire study period and with all sectors except financials during the COVID-19 period. Interestingly, this hedging and safe haven benefit of green bonds is agnostic of the environmental disclosure score of a firm. Hence, investors can add green bonds to hedge their equity portfolios regardless of the environmental consciousness of their portfolio firms.

Oil prices and sectoral stock returns in the BRICS-T countries: A time-varying approach

This paper investigates how exchange rates and oil prices have affected sectoral stock returns in the BRICS-T countries over the period from 2 January 2001 to 22 March 2021. Following the estimation of a benchmark linear model, Bai and Perron (2003) tests are carried out in each case to identify structural breaks, and then a state-space model with time-varying parameters is also estimated. The analysis shows that oil prices have a significant, positive effect on the energy sectors of all BRICS-T countries except India; a negative one on the industrial sectors of all countries except Turkey; a negative one on the financial sectors of Brazil, Russia, India, and South Africa; a negative one on the transportation sectors of India and Turkey and a positive one on that of Russia; finally, the most significant effect is on the chemicals sector, though it varies across countries. The subsamples and time-varying estimates indicate that exchange rate returns have a larger influence than oil price returns. Because energy-dependent sectors are vulnerable to global volatility, appropriate energy regulations should be implemented to reduce risk.

Pengukuran Kinerja Portofolio Investasi Dengan Menggunakan Indeks Sharpe Pada Emiten Sektor Transportasi Dan Logistik Yang Terdaftar Di Bursa Efek Indonesia Sebelum Dan Selama Masa Pandemic Covid - 19

The term portfolio is often used to provide an understanding of a set of investments owned by investors in various types of investment instruments such as bonds, mutual funds, stocks, properties, and so on. To measure whether the portfolio carried out by investors has provided an optimal level of return, investors need to measure the performance of their placed investments. The Transportation & Logistics sector is expected to experience a negative impact due to the COVID-19 pandemic. The main problem in this study lies in measuring portfolio performance for companies that are included in the Transportation & Logistics Sector where portfolio performance before the Covid pandemic will be compared with portfolio performance during the Covid pandemic. The purpose of this study is to test and analyze whether the increase or decrease in the Transportation and Logistics Sector stock index is caused by differences in portfolio performance before and during the Covid pandemic. The research method used in this case is a quantitative descriptive method. Data analysis methods in this study include normality test followed by paired sample t-test (if normally distributed) and Wilcoxon test (if not normally distributed). The results showed that there were no significant differences regarding the performance of stock portfolios for companies listed in the Transportation and Logistics Sector during the COVID-19 pandemic when compared to the stock portfolio performance in the period before the COVID-19 pandemic.

Economic and financial ratios: Relevant for stock selection in power and energy sector?

The study shows the effect of Macro-Economic Factors such as Interest Rate (INT) and Gross Domestic Product (GDP), Inflation (INF), Financial Ratios such as EPS, NPM, ROC, DE, ATR, CR, ITR, EV on Power and Energy stock prices. The period of the study is from 2011-2021. S&P BSE Energy Index consists of 26 companies, but the study has been conducted on 21 companies due to the unavailability of data in the continuous form. While in S & P BSE Power Index, 14 companies are listed, out of which data of 4 companies was not available in continuous form, thus the final power index sample is locked with 10 companies. The results are important for investors to select the stocks of Power and Energy sectors by considering the resultant effective factors of the study. The Random Effect (RE) Model of Panel Regression was applied which shows that Inflation, Interest Rate, GDP, EPS, ATR and CR have significant effect on Stock Prices. The Research Paper is based on the stocks of Power and energy sector which empowers an investor in analyzing the stock by considering these ratios and accordingly they may include the stock in their portfolio and diversify the risk. The study hasn’t covered other economic factors and financial ratios. Other factors and ratios may be considered to know the association with stock return.

Greek Banking Sector Stock Reaction to ECB’s Monetary Policy Interventions

Reacting to extreme uncertainty conditions caused by the global financial crisis, the European Central Bank implemented countercyclical strategy, combining conventional and non-traditional monetary policy tools to stabilize financial markets and euro area economies. We study the impact of the euro area monetary authority policy interventions on equity returns of four systemic Greek banks for the period January 2007 to August 2018. In the first step, we collect and classify interventions to several categories. Then, an event study analysis is carried out to evaluate cumulative abnormal returns. In the second step, a panel regression analysis is performed to identify Cumulative Abnormal Return (CAR) determinants. Our results suggest that expansionary conventional monetary policy interventions significantly affect equity returns of Greek banking institutions, assisting the regional banking equity stability. On the other hand, the harmful consequences of Greek debt crisis limited the effectiveness of non-standard measures.

Best Alpha for Forecasting Stock using Brown's Weighted Exponential Moving Average

Stocks are a very profitable type of investment, one of the most profitable stock sectors is mining. PT. Aneka Tambang Tbk (ANTM) is the largest mining company in Indonesia engaged in metals and minerals. The problem that occurs in stock investment is the fluctuation of stock prices. Stock price fluctuations occur because of the process of buying and selling shares, rumors about the company, or certain policies made by the government that can affect the company. These rapid stock price fluctuations are very dangerous for investors because these fluctuations are not always directly proportional to the company's fundamental conditions. Therefore we need a system that can perform forecasting to monitor stock price fluctuations that occur. The author uses the Brown's Weighted Exponential Moving Average (B-WEMA) method for forecasting. The data used are 241 daily PT Aneka Tambang Tbk (ANTM) in the Covid-19 era, starting January 1, 2020 - December 31, 2020. The experiment was carried out into 3 scenarios, namely using 3 months, 6 months, and 12 months data. Alpha tested is 0.1 to 0.9 (1 digit behind the comma) while the number of moving averages is 3, 5, 10, 20, 50, 100, and 200 adjusted to the amount of data. From the experiment it was found that the best forecasting was obtained in scenario 2 with the best manual alpha value having a MAPE error value of 2.84% using an alpha of 0.9 and a moving average of 100.

Modeling the Mexican Confidence and Mexican Stock Sectors with PLS-SEM

The study aims to model the evolution of stock prices and consumer/enterprise confidence index using the PLS-SEM path method to approach their behavior through different types of constructs. Furthermore, different construct sectors are analyzed using the most important enterprises of the Mexican Stock Index, modeling as explanatory variables confidence and macroeconomic single and non-single constructs. The PLS-SEM path model has arrived as a modeling option to be used in financial and economic sciences due to its numerous virtues in statistical assumptions. This research concludes that the Mexican Stock Market can be studied with constructs, improving flexibility and exploratory using different validity measures.

Dynamic frequency volatility spillovers and connectedness between strategic commodity and stock markets: US-based sectoral analysis

This study examines the time-varying frequency spillovers and connectedness between U.S. sector stock markets and both crude oil and gold and their implications on portfolio management. Using the methodology of Diebold and Yilmaz (2012) and Baruník and Křehlík (2018), the results show that oil, gold, financials, utilities, communications services, consumer staples (at short term), and health care are net receivers of spillovers in the system, and the remaining sectors are net contributors regardless of the frequencies. The extent of spillover reduces when both gold and oil assets are added to sector portfolio. Furthermore, the connectedness among markets is heterogeneous and depends on time horizons and market conditions. Oil offers a higher hedging effectiveness in both the short and long term as well as over different turbulent periods compared to oil. The hedging effectiveness is higher in the long term compared to the short term.

The Impact of Low-Carbon Pilot City Policy on Corporate Green Technology Innovation in a Sustainable Development Context—Evidence from Chinese Listed Companies

The low-carbon pilot city policy is an important initiative to explore the path of a win-win situation for both the economy and the environment. Since 2010, China has established 87 low-carbon pilot cities. This policy implementation aims to encourage green technology innovation among listed companies, thereby achieving sustainable corporate growth through the promotion of energy efficiency and renewable energy. This paper aims to unveil the relationship between low-carbon pilot city policies and green technology innovation. This paper explores the impact of policy implementation based on patent data of Chinese listed companies from 2007 to 2019. Empirical results show that the policy can promote green technology innovation among listed companies in the pilot cities. This finding still holds in the parallel trend test and the PSM-Multi-period DID test. Second, the policy has a greater effect on the green-technology innovation of non-state enterprises and can promote more green technology innovation activities of enterprises in the eastern region compared with other areas. Furthermore, in terms of different stock sectors, the low-carbon pilot city policy can significantly promote GEM-affiliated enterprises’ green technology innovation activities. Finally, listed companies with a high degree of digital transformation are more active in green technology innovation in the context of low-carbon pilot city policy.

Effect of Macroeconomic Factors on the Construction Sector Sharia Stock Price Index for the 2019-2021 Period

This study aims to examine the effect of macroeconomics, namely the effect of Exchange Rate, Inflation, BI-7 Day Repo Rate, and Gold Price on the Construction Sector Stock Price Index listed on the Jakarta Islamic Index for the 2019-2021 period during the Covid-19 pandemic. The population used in this study are all those who are included in the building construction sector. The sample in this study is one of the construction companies listed in the Jakarta Islamic Index. The method of analysis in this study uses multiple linear regression analysis. The data collection technique in this research is purposive sampling. Purposive sampling is a technique in determining the research sample by using various considerations or based on the objectives of the researcher. The results of this study indicate that partially and simultaneously Exchange Rate Variable, Inflation, Interest Rate BI-7 Day Repo Rate, and Gold Price have a significant effect on the Construction Sector Stock Price Index. Variables Exchange Rate, Inflation, BI-7 Day Repo Rate, and Gold Price have a significant influence on the dependent variable, namely the Stock Price Index. It can be proven that all independent variables jointly have a significant effect, it can be proven.

Uncertainty and US stock market dynamics

This study investigates the long-term and dynamic relationship between US sector stock returns and risk factors, focusing on uncertainty. Uncertainty risk factors include volatility indices associated with the equity (VIX), fixed-income (TYVIX), oil (OVX), and foreign exchange (EUVIX) markets. The cointegration analysis shows that VIX, TYVIX, OVX, and EUVIX are collectively driving forces of US sector indices in the long run. The causality testing results reveal that uncertainty about long-term interest rates, as proxied by TYVIX, exerts a significant effect on US stock market performance across sectors. Interestingly, in determining the variability of sector stock returns, we find that shocks to the uncertainty of crude oil prices and the exchange rate play a more important role than shocks to their levels. Risk diversification opportunities are identified.

Nexus between Twitter-based sentiment and tourism sector performance amid COVID-19 pandemic

The outbreak of the COVID-19 pandemic and the steps taken to contain its spread resulted in a decline in tourism sector stock prices. Using linear and quantile regressions, we examine the impact of Twitter-based investor sentiment for COVID-19 and Twitter-based sentiment towards uncertainty on the performance of tourism stocks. The findings indicate a heterogenous effect of tweets and Twitter economic uncertainty on tourism sector equity returns with a major impact on the lower quantiles.

Dampak pandemi Covid-19 terhadap portofolio saham bisnis logistik transportasi laut di Indonesia

The Covid-19 pandemic has had an impact on the global economy, particularly in the logistics industry, which includes Indonesia. The goal of this study is to identify the portfolio performance of the top five Indonesian companies in the logistics sector, particularly sea transportation, and analyze the impact before and during the pandemic using a Monte Carlo simulation of the optimal risk asset portfolio two years before the Covid-19 pandemic and two years during the pandemic. Covid-19. Before (during) the Covid-19 pandemic, the optimal portfolio weight value of BULL.JK 4,99% (0,05%), PSSI.JK 92,01% (34,65%), SMDR.JK 1,40% (31,12%), TMAS.JK 1,29% (30,42%) dan WINS.JK 0,31% (3,77%). Based on the sharpe ratio, the performance of the transportation sector stock portfolio provided a high return compared to risk during the Covid-19 pandemic, from 41.71% to 297.59%, with a level of volatility that tends to be stable before and after the Covid-19 pandemic, from 41.65% to 44.56%. Based on these findings, it can be concluded that the logistics sector in sea transportation can improve performance during the Covid-19 pandemic and is worthy of investment as an investor consideration.

Tail Dependency and Risk Spillover between Oil Market and Chinese Sectoral Stock Markets—An Assessment of the 2013 Refined Oil Pricing Reform

The Chinese refined oil pricing reform in 2013 has brought its refined oil price to be more aligned with the international oil price, helping to mitigate prior distorted pricing mechanisms. Its impact on the correlation, tail risks, and spillover effects between the international crude oil market and Chinese sectoral stock markets warrants empirical assessments. Time-varying copula models and conditional VaR (CoVaR) are employed to examine the correlation between the international oil market and Chinese sectoral stock indexes before and after the 2013 pricing reform, as well as the tail risk and spillover effects of the extreme and moderate oil markets. The results show that: (1) the correlation between the oil market and all 11 Chinese stock sectors is positive both before and after the reform, but the correlation is weaker after the reform than before; (2) The downside tail risk of the extreme and moderate oil markets to most Chinese stock market sectors, and the upside tail risk of the moderate oil market to most stock sectors are lower after the reform; (3) Tail risk spillover effects of extreme oil market on all sectors exist before and after the reform; (4) The upside tail risk spillover effects of moderate oil market exist in most sectors before the reform, but they almost all disappear after the reform. The downside risk spillover effects of the moderate oil market do not exist before or after the reform. The findings provide valuable references for portfolio management and future policy update.

Quantiles dependence and dynamic connectedness between distributed ledger technology andsectoral stocks: enhancing the supply chain and investment decisions with digital platforms.

Distributed Ledger Technology (DLT) is highly applicable in various fields, especially the supply chain in many sectors. Against limited empirical evidence, this paper analyzes the relations between the Kensho Distributed Ledger Technology Index and stock indices of 12 sectors, including communication services, consumer discretionary, consumer staples, energy, health care, financials, industrials, information technology, materials, utilities, and real estate, and ESG by employing the quantile coherency and dynamic connectedness techniques. Our results reveal that the quantile coherency between the DLT stock index and the sectoral stock indices in almost all cases is significant and positive. The positive co-movement tends to be stronger in the longer terms and as we move from the lower to the higher quantiles, implying that they are more strongly connected in the long term and during the bearish market condition. Moreover, the dynamic connectedness indicates that the DLT stocks and the sectoral stocks are highly connected, with the former being a net transmitter of spillover shocks. The spillovers are also time-varying, and the results significantly corroborate those of the quantiles coherency methods. Among other relevant implications, DLT can be an important factor in the development and enhancement of these sectors.

Frequency connectedness and spillovers among oil and Islamic sector stock markets: Portfolio hedging implications

Using the asymmetric Baba-Engle-Kraft-Kroner (BEKK)-GARCH model and the frequency spillover methodology by Baruník and Křehlík (2018), this paper examines spillovers and portfolio management between crude oil and US Islamic sector stocks. The results show significant time-varying spillovers between oil and Islamic sectors. The short-term spillovers are stronger than their long-term counterparts. The spillovers intensify during extreme events (global financial crisis and COVID-19 pandemic). The aggregate index, consumer services, raw materials, and manufacturing are net contributors of spillovers in the short term, whereas the remaining sectors are net recipients. In the long-term horizon, we find that consumer goods and finance become net transmitters of spillovers. The raw materials sector becomes a net recipient of spillovers in the long term. Finally, hedging effectiveness is lower in the long term than in the short term during the oil crisis in 2015–2016 and the US presidential election in 2017, US-China trade tension, and the COVID-19 pandemic.

Pandemic uncertainty and sectoral stock returns predictability in South Africa

PurposeIn this paper, the author examines the role of uncertainty due to pandemic on the predictability of sectoral stock returns in South Africa. This is motivated by the ongoing global pandemic, COVID-19, in predicting sector stock returns.Design/methodology/approachThe study considers estimation of dynamic panel data with dynamic common correlated effects estimator and two pair-wise forecast measures, namely Campbell and Thompson (2008) and Clark and West (2007) tests in dealing with the nested predictive models.FindingsThe results show that pandemic uncertainty has a negative and statistically significant effect on the different sector returns, implying that sector stock returns decline as the pandemic outbreak becomes more pronounced. While the single predictor model consistently outperforms the historical average model both for in-sample and out-of-sample, controlling for other macroeconomic variables effect improves the forecast accuracy of infectious diseases uncertainty. These results are consistently robust to both the in-sample and out-of-sample forecast periods, outliers and heterogeneity. These results have implications for portfolio diversification strategies, which we set aside for future research.Originality/valueThe empirical literature is satiated with studies on how news can predict economic and financial variables, however, the role of uncertainty due to infectious diseases in the stock return predictability especially at the sectoral level is less understudied, this is the main contribution of the study.

Analisis Pengaruh Nilai Tukar Rupiah, Inflasi, Tingkat SBI, Dan Return On Asset (ROA) Terhadap Harga Saham Sektor Perbankan

This study aims to analyze the effect of the rupiah exchange rate, inflation, SBI rate, and Return On Assets (ROA) on stock prices of banking sector companies for the 2014-2018 period. The data used in this study is secondary data obtained from published data from Bank Indonesia and the Central Bureau of Statistics which include the rupiah exchange rate, inflation, and SBI rates. Meanwhile, the closing share prices of banking sector companies were obtained from the Indonesia Stock Exchange. The population used is 41 banking sector companies listed on the Indonesia Stock Exchange. The sample selection criteria used a purposive sampling method and obtained a sample of 12 companies. The type of research used in this research is explanatory research which explains the influence of the rupiah exchange rate, inflation and SBI rates on stock prices.
 The results of the analysis of the rupiah exchange rate, inflation and the SBI rate have no simultaneous effect on the dependent variable on stock prices. Partially the rupiah exchange rate variable has no effect on banking sector stock prices and inflation has no effect on banking sector stock prices. Meanwhile, the SBI level partially affects the share price of the banking sector. The variable that has the most dominant effect is the SBI level variable.