ANALYSIS OF THE EFFECT OF MACROPRUDENTIAL AND MACROPRUDENTIAL INDICATORS ON THE STOCK PRICE INDEX OF ISLAMIC BANKS IN INDONESIA USING THE ERROR CORRECTION MODEL

The experience of various crises that have occurred, including the impact of the Covid-19 pandemic, presents a challenge to implement macroprudential policies to ensure the financial system survives and continues to carry out its function in driving the economy. The existing macroprudential policies tend to be individual and focus on prudent banking and other financial institutions. Economic fluctuations that occur on the macro side will greatly impact, either directly or indirectly, the stock price index, as well as the company's internal indicators which are considered to have a major influence on the decisions of investors and potential investors to take action on the stock exchange. The type of research used in this research is quantitative research. The nature of this research is descriptive with a quantitative approach. The data collection technique in this research is Literature Study. The test carried out in this study is the multiple linear regression analysis test (multiple linear regression method), this study uses the ECM model to obtain the best model which includes the classical assumption test. The results of this study based on the partial short-term relationship test, it can be concluded that the Exchange Rate, Inflation, and TPF in the short term have no significant effect on the PNBS Stock Price Index. Meanwhile, short-term CAR has a significant positive effect on the PNBS Stock Price Index. Based on the results of the partial long-term relationship test, it can be concluded that in the long term, the Exchange Rate has a significant negative effect and TPF and CAR have a significant positive effect on the PNBS Stock Price Index while Inflation has no significant effect on the PNBS Stock Price Index. Based on the output results of the simultaneous short-term and long-term F test, it shows that all independent variables simultaneously have a significant effect on the PNBS Stock Price Index in the short term. Based on the provisions of the MUI DSN through the issued fatwas related to the Sharia capital market and Sharia shares, it is explained that Sharia stock investment to invest according to the perspective of Sharia economic law is allowed. [ FROM AUTHOR] Copyright of Jurnal Syntax Admiration is the property of Ridwan Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Predicting Stock Price Movement for the Inefficient Market: Case of Hanoi Stock Exchange (HNX)

If the market is efficient, with stock prices accurately reflecting the true risk of an investment, then the issue becomes simpler. While this is true, investors may have a window of opportunity to discover a successful investing strategy if the market is inefficient. The primary goal of this research is to use the Support Vector Machine (SVM) algorithm to predict daily cycles of price increases for the ten largest-cap companies trading on the Hanoi Stock Exchange (HNX) over the Covid-19 timeframe (January 1st, 2019, to December 1st, 2022). Study how the model performs when trained and tested with a moving window. The outcome was an impressive average accuracy of 81.68 percent for the predicting model. © 2023 IEEE.

Impacts of the COVID-19 pandemic on the relationship between the economic factors and stock prices of the transport sector in the stock exchange of Thailand

The COVID-19 pandemic has affected economic sectors and investment and has particularly affected the stock pricing factors for the transport sector (TRANS). This study compares the relationship of the different factors, i.e., the volume, exchange rate (EXR), consumer price index (CPI), and the oil price on the stock prices of the TRANS on the Stock Exchange of Thailand (SET) between the pre-COVID-19 and during COVID-19 periods. Time series data between January 2017 and December 2019 were used for the pre-COVID-19 period, and data between January 2020 and September 2022 were used for the during COVID-19 period. The effects of the independent variables were considered by estimating the long-run relationship using the autoregressive distributed lag (ARDL) and Granger causality from those variables to each stock price. The results found that the COVID-19 pandemic caused different effects on the volume, EX, CPI, and the oil price on the stock prices of the transport sector in the SET. Thus, investors should closely monitor the situations caused by natural disasters and the change of the EXR and oil price to reduce any problems that may arise. © 2023 AESS Publications. All Rights Reserved.

Market Warnings: Learning from the Short-Term Impact of COVID-19 on Stock Market Constituents

Purpose: The paper investigated the short-term impact of the lockdown announcement due to COVID-19 on various industries in India using firms' stock returns and credit ratings. Design/Methodology: The paper used event study methodology to analyze abnormal returns on stocks and credit rating changes of firms following the lockdown to understand the impact on the debt servicing of firms. Findings: The paper found a heterogeneous impact of lockdown on various industries. Pharmaceuticals, chemicals, FMCGs, and telecom sectors saw positive abnormal returns, while textiles, financial services, construction, services, cement, and automobile sectors were the worst affected. The paper also found that smaller companies were more susceptible to the effects of such lockdowns. Indian subsidiaries of foreign MNCs and Central Government-owned firms fared better than privately-owned domestic firms. The debt servicing ability of firms was unimpacted due to the debt relief package announced to mitigate the impact of the lockdown. Practical Implications: The paper's findings have implications for investors and managers who can make informed decisions in advance to reduce the risk to their investment if such a black swan event is expected. The paper's findings could help policymakers identify sectors that require immediate support due to the disruption from such an event. Originality: The paper is unique in investigating the impact of the lockdown due to COVID-19 on companies across different industries, with different ownership groups and sizes. We have not come across such a detailed study investigating the impact of COVID-19 on various industries in India. © 2023, Associated Management Consultants Pvt. Ltd.. All rights reserved.

On the effects of Covid-19 pandemic on stock prices: an imminent global threat.

With the advent of the global COVID-19 pandemic, various world economies have been adversely affected. Occurrences such as plummeting equities, crippling global economic activities and surge in market volatility amongst others have become prevalent across the world. Assessing the economic impact of this crisis becomes expedient from a policy standpoint as the crisis unfolds with extreme speed. To this end, we employ the Autoregressive Distributed Lag approach to examine possible relationship between the pandemic and stock prices for global and some selected countries, namely China, France, Italy and the United States. We find evidence that the effect of COVID-19 observed cases on stock prices is rather varying across countries and limited, the spillover effects orchestrated through the recent oil and financial market volatility cannot be overlooked. Given the speed of occurrences, there is need for governments all over the world to be more proactive in striving for a breakthrough over the virus otherwise, in the coming weeks the global economy may receive a surge of the pandemic.

Stock Price Forecasting of IBEX35 Companies in the Petroleum, Electricity, and Gas Industries

In recent years, time series forecasting has become an essential tool for stock market analysts to make informed decisions regarding stock prices. The present research makes use of various exponential smoothing forecasting methods. These include exponential smoothing with multiplicative errors and additive trend (MAN), exponential smoothing with multiplicative errors (MNN), and simple exponential smoothing with additive errors (ANN) for the forecasting of the stock prices of six different companies in the petroleum, electricity, and gas industries that are listed in the IBEX35 index. The database employed for this research contained the IBEX35 index values and stock closing prices from 3 January 2000 to 30 December 2022. The models trained with this data were employed in order to forecast the index value and the closing prices of the stocks under study from 2 January 2023 to 24 March 2023. The results obtained confirmed that although none of the proposed models outperformed the rest for all the companies, it is possible to calculate forecasting models able to predict a 95% confidence interval about real stock closing values and where the index will be in the following three months.

Can the Narrow and Broad Money Supply Gap Be Used as an Investment Indicator for the Stock Market?

This study uses the golden cross and death cross formed by the gap between the narrow and broad money growth rates as threshold variables to estimate the threshold model and test the causal relationship between money supply and stock prices in eight emerging market economies (EMEs) in Asia;the sample periods are from January 2000 to December 2020. The results show a high-positive, bi-directional relationship between the money supply and stock prices in the golden cross regime. On the other hand, the money supply has a negative, one-way causality on stock prices in the death cross regime. We also conducted a robustness test during the COVID-19 spread, and the result shows that the mechanism still applies, but the effectiveness is reduced. Thus, our contribution is discovering the golden cross and death cross information formed by narrow and broad money, informing stock market investment.

Re-examining oil and BRICS' stock markets: new evidence from wavelet and MGARCH-DCC

This study examines how the relationship between oil and stock market return of BRICS behaves at different investment horizons. Using data ranging from 2006 to 2020, the wavelet and MGARCH-DCC found that the stock markets' return of Russia, Brazil, and South Africa are comparatively more correlated with oil price return across the investment horizons and more volatile particularly during the Covid-19 period. However, the stock markets' return of China and India is less correlated with oil price return and less volatile. It is also revealed that oil price return leads the BRICS' stock markets' return and both are positively correlated.

The impact of COVID-19 on the crash risk of registered new shares in China

After the outbreak of COVID-19, with the stock market fluctuating and sluggish, the registration system reform of the securities issuance is steadily advanced in China. Based on this, the paper uses difference-in-differences (DID) model to investigate the impact of COVID-19 on the stock price crash risk of IPOs. By comparing registration-based and approval-based new shares, the empirical results of this paper show that due to the COVID-19 pandemic, the stock price crash risk of registered new shares has increased significantly. Moreover, with higher investor turnover rate and higher institutional shareholding ratio, the stock price crash risk of registered new shares affected by the pandemic is higher.

Predictive Analytics of Stock Market as a Time Series

This paper presents a new methodology and a comparative study using past stock market data that can help businesses take investing or divesting decisions in critical situations in the future. These may be like the COVID-19 pandemic, where market volatility is extremely high, thus creating an urgent need for better decision support systems to minimise loss and ensure better profits. The results of the study are based on the comparison of different configurations of ARIMAX, Prophet, LSTM and Bidirectional LSTM Models trained on historical NSE data. By understanding the correlation and variations in the data processing and model training parameters, we have successfully proposed a LSTM neural network model training and optimising method which could successfully help businesses take both long and short term profitable decisions before and after big financial and market crises with a respective accuracy of 98.60 percent and 96.97 percent. © 2022 IEEE.

Twitter sentiment and stock market: a COVID-19 analysis

Purpose: Using textual analysis the authors study the relationship between social media sentiments and stock markets during the COVID-19 pandemic. Design/methodology/approach: The study analysis is based on a sample of 1,616,007 tweets over the period January to June 2021 for seven countries. The authors process the tweets via the VADER analyzer thereby producing both positive and negative sentiment measures. Findings: Particularly, the authors prove that higher positivism is associated with a short-term increase in stock prices. On the other side, negativism relates inversely to stock prices with long-term impact, in the case of English-spoken countries. Notably, the study results remain robust to the inclusion of various control variables, including virtual fear and Google vaccine indexes. Finally, the authors prove that positivism is associated with higher returns and lower volatility in the short-run, while negativism is linked with lower returns in the short run. Practical implications: The study analysis also has significant policy implications for researchers, investors and policymakers. First, researchers can employ our measures to quantify market sentiments and expand their research arsenal to incorporate social media trends, thus providing better explanatory power. Second, during times of severe uncertainty such as in a pandemic period, investors could beneficially take into account our textual measures and empirical results when using asset pricing models or constructing their portfolios. Third, the finding that the stock market is heavily governed by sentimental behaviors, especially during crisis periods, implies that policymakers including central banks, governments and capital market commissions must consider these sentiments before exerting their policies. In this regard, governments can effectively develop policy tools and approaches to manage recovery from the pandemic, which translates to greater long-term economic resilience. Moreover, central banks should accordingly adjust their monetary policy measures in order to stabilize financial markets, and by extension, to stop the pandemic from turning into a renewed financial crisis. For example, asset purchase program is considered the main instrument of this kind of intervention. Originality/value: The authors confirm that this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. The paper should be of interest to readers in the areas of finance. © 2023, Emerald Publishing Limited.

Does a Government Response to COVID-19 Hurt the Stock Price of an Energy Enterprise?

This research examines the shock of a government response to COVID-19 on the stock prices of 30 international energy enterprises spanning from January 1, 2020 to December 31, 2020. Overall, the empirical results denote that a government response stringency index, containment and health index, and economic support index all have a statistically significant negative impact on their stock prices. The negative impact from the containment and health index is especially the greatest, implying that a government's stringent responses have great negative effect on the stock prices of most energy enterprises.

A new look at stock price-exchange rate nexus: Analysis of COVID-19 pandemic waves in advanced and emerging economies.

This study takes a new look at the stock price-exchange rate nexus and attempts contributions to the extant studies in a number of intuitive ways. First, we analyze the reverse relationships given the theory-backed two-way causality between the two variables. We reassess the nexus across the First, Second and Third Waves of the COVID-19 pandemic, as well as comparison between advanced and emerging economies. Third, we adopt a panel modeling approach that simultaneously takes nonstationarity, cross sectional dependence, and asymmetry into account. The data analyses show that the relationship is statistically negative for the two nexuses. The magnitudes were higher during the crisis (the COVID-19 pandemic) although the relationship broke down during the Second Wave as the Delta variant surged. We identify relevant investment and policy implications of the findings.

Evaluation of the Improvements of Transformer Prediction with Convolution

Proposed and developed 5 years ago, Transformer has been a prevailing machine learning method and is widely used to solve various kinds of practical problems [1]. According to relevant works, Transformer has performed well in both natural language processing and computer vision tasks, so we would like to test its effectiveness in prediction, specifically, time series prediction. Over the past two years, COVID-19 is no doubt one of the major factors that influences the changes in the stock prices, and the medical industry should be among the most significantly affected, which would provide an ideal sample for us to study transformer on time series prediction. In this paper, we not only construct a machine learning model using Transformer to predict the stock prices of one medical company but also add a convolution layer to try to optimize the predictions. The comparison of the outcome from the two models suggests that the convolution layer could improve the performance of the naive transformer in several ways. © 2022 IEEE.

Do SEC filings indicate any trends? Evidence from the sentiment distribution of forms 10-K and 10-Q with FinBERT

Purpose: This study quantified companies' views on the COVID-19 pandemic with sentiment analysis of US public companies' disclosures. The study aims to provide timely insights to shareholders, investors and consumers by exploring sentiment trends and changes in the industry and the relationship with stock price indices. Design/methodology/approach: From more than 50,000 Form 10-K and Form 10-Q published between 2020 and 2021, over one million texts related to the COVID-19 pandemic were extracted. Applying the FinBERT fine-tuned for this study, the texts were classified into positive, negative and neutral sentiments. The correlations between sentiment trends, differences in sentiment distribution by industry and stock price indices were investigated by statistically testing the changes and distribution of quantified sentiments. Findings: First, there were quantitative changes in texts related to the COVID-19 pandemic in the US companies' disclosures. In addition, the changes in the trend of positive and negative sentiments were found. Second, industry patterns of positive and negative sentiment changes were similar, but no similarities were found in neutral sentiments. Third, in analyzing the relationship between the representative US stock indices and the sentiment trends, the results indicated a positive relationship with positive sentiments and a negative relationship with negative sentiments. Originality/value: Performing sentiment analysis on formal documents like Securities and Exchange Commission (SEC) filings, this study was differentiated from previous studies by revealing the quantitative changes of sentiment implied in the documents and the trend over time. Moreover, an appropriate data preprocessing procedure and analysis method were presented for the time-series analysis of the SEC filings. © 2022, Emerald Publishing Limited.

The Role of Coal and Palm Oil Prices in Indonesia Stock Performance: An ARDL Approach

Indonesia is one of the main exporters of coal and palm oil, implying the source of energy in futures. However, these commodities could have a greater impact on national economy, determining stock market performance. The movement of international trade related to these commodities also contributes to pricing creation in global market. This research aims to investigate the effect of coal and palm oil prices on Indonesia stock performance by a stock market index price. The Autoregressive Distributed Lag (ARDL) was employed in this study to gain the long and short-run results. The research observations lasted between September and December 2021. The research novelty lies in exploring the impact of coal and palm oil price movements after the COVID-19 pandemic in 2020 on the Indonesia Stock Market performance. It can be concluded that these effects are short-t and long-term or both. The findings show long-and short-run effects of palm oil prices on the stock market performance. However, Coal has only a long-run effect on the stock performance. The implication of this study is the consideration of these commodities by policymakers and practitioners for policy in developing the stock market performance. The research limitations and recommendations are also discussed. © 2022, City University of Hong Kong Press. All rights reserved.

The Graph Convolutional Networks Framework for Predicting Pandemic Impact on Stock Prices

Covid-19 has dealt an unprecedented hit to the global economy and all industries, with varying degrees of decline from retail to real estate. This volatility is most evident in stock prices. Previous stock price forecasting methods typically used historical data for each stock as a separate input into the system. This paper proposes an attention-based parallel graph convolutional network framework, which consists of two parallel GCNs. The first GCN takes stock features as input, and the second GCN takes other industry features as input, and sets an attention model to reflect the pairwise interactions between networks. Experimental results on selected stock data show that the model outperforms both the LSTM model and the GCN model in accuracy and F1 score. © 2022 IEEE.

Fund style drift and stock price crash risk – analysis of the mediating effect based on corporate financial risk

PurposeThis paper aims to identify the direct impact of fund style drift on the risk of stock price collapse and the intermediary mechanism of financial risk, so as to better protect the interests of minority investors.Design/methodology/approachThis paper takes all the non-financial companies on the Chinese Growth Enterprise Market from 2011 to 2020 as study object and selects securities investment funds of their top ten circulation stocks to study the relationship between fund style drift and stock price crash risk.FindingsFund style drift is likely to add stock price crash risk. Financial risk is positively correlated with stock price crash risk. Fund style drift affects stock price crash risk via the mediating effect of financial risk, and fund style drift and financial risk have a marked impact on the stock price crash risk of non-state enterprises, yet a non-significant impact on that of state-owned enterprises.Originality/valueThis paper links fund style drift with stock price crash risk in an exploratory manner and enriches the study perspectives of relationship between institutional investors' behaviors and stock price crash risk, thus enjoying certain academic value. On the one hand, it furnishes a new approach to the academic frontier issue concerning financial risk and stock price crash risk, and proves that financial risk is positively correlated with stock price crash risk. On the other hand, it regards financial risk as a mediating variable of fund style drift for stock price crash risk and further explores different influencing mechanism of institutional investors' behaviors.

Speculation and clarification announcements on stock price fluctuations: Why are rumours plausible and hard to clarify?

During the COVID-19 pandemic, stock markets were fragile and sensitive to downside news regardless of whether the news was true. In China, stock rumours are increasingly rampant, affecting the sound development of the capital market. By manually gathering a sample of rumours about Chinese A-share firms, this paper studies the effects of stock market rumours and the corresponding rumour clarifications on stock returns. The study suggests that rumours rely on the information environment to persuade the market through the media effect. In terms of information disclosure, for firms that previously disclosed "negative news”, stock prices would experience abnormal drops when negative rumours appear. In terms of the media effect, rumours released by leading media cause even more significant abnormal fluctuations in stock prices. Further study shows that positive rumours significantly cause an abnormal rise in state-owned enterprises' stock prices, while negative rumours significantly cause an abnormal decline in small and medium enterprise board (SME) and growth enterprise market board (GEM) stock prices. From the perspective of the effect of clarification announcements in restraining stock price fluctuations, clear and timely clarifications are recommended. © 2023 Elsevier Inc.

COVID-19, a blessing in disguise for the Tech sector: Evidence from stock price crash risk.

In this paper we document that although COVID-19 has brought uncertainties to the overall economy, the Technology (tech) sector is the systematic beneficiary of the pandemic. Using a quasi-natural setup, we find a significant notion that the Stock Price Crash Risk (SPCR) of firms within the Tech sector decreases during the COVID-19 pandemic compared to the recent past and firms belonging to other sectors. Our analyses further reveal that firms in the Tech sector with stronger external monitoring and better information environment receive an even greater advantage from the pandemic. Overall, our study suggests that the higher systemic dependency on the Tech sector during the COVID-19 outbreak results in an economic benefit for this sector.