Confidence Index Research Articles

Understanding consumer confidence evolution in response to adverse shocks and how marketing contributes to business s success in economic downturns

Purpose- Firstly, this paper examines how current measurements of consumer confidence might be used to forecast future household spending by using economic variable (i.e. unemployment rate, national adjusted income per capita) data in China, with the help of statistic tools (i.e. Python and Pandas) for the job of data cleaning and analyzing. Secondly, I will focus on the ways in which businesses alter their marketing mix in the face of economic hardship to survive by understanding the importance of utilizing effective marketing strategies during economic downturns. Methodology- This paper examines the predictive performance of consumer confidence index on change in consumption growth by constructing three different OLS regression models and by integrating several existing proposals for effective marketing strategies for businesses in times of low consumer confidence to help business managers to make wise and effective response to economic downturns. Findings- It is shown both 1 year lagged consumer confidence and 3 years lagged consumer confidence are good predictors for current change in consumption patterns, whereas 2 years lagged consumer confidence shows negative correlation with consumption change, and consumer confidence have their own predictive power regardless of the macroeconomic variables. Conclusion- Therefore, this paper contributes to the existing literature by providing empirical findings that consumer confidence cannot always fulfill the role of anticipating consumption change and by further providing concrete policy recommendations for consumer confidence’ revival during recessions in an effort to fill the gap. Keywords: Consumer confidence, marketing, consumption, consumer behavior, forecasting. JEL Codes: B16, B22, E21, E27, M21, M31

Do monetary policy credibility and disagreements in inflation and interest rate expectations affect business confidence? Evidence from an inflation targeting developing country

PurposeBusiness confidence is crucial to firm decisions, but it is deeply related to professional forecasters' expectations. Since Brazil is an important inflation targeting country, this paper investigates whether monetary policy credibility and disagreements in inflation and interest rate expectations relate to business confidence in Brazil. The study considers the aggregate business confidence index and the business confidence indexes for 11 industrial sectors in Brazil.Design/methodology/approachThe authors run ordinary least squares and generalized method of moments regressions to assess the direct effects of disagreements in expectation and monetary policy credibility on business confidence. The authors also make use of Wald test of parameter equality to observe whether there are “offsetting effects” of monetary credibility in mitigating the effects of both disagreements in expectations on business confidence. Besides, the authors run quantile regressions to analyze the effect of the main explanatory variables of interest on business confidence in contexts where business confidence is low (pessimistic) or high (optimistic).FindingsDisagreements in inflation expectations reduce business confidence, monetary policy credibility improves business confidence and credibility mitigates the adverse effects of disagreements in expectations on business confidence. The sectors most sensitive to monetary policy credibility are Rubber, Motor Vehicles, Metallurgy, Metal Products and Cellulose. The findings also suggest the effect of disagreement in inflation expectations on business confidence decreases as confidence increases, and the effect of monetary policy credibility on business confidence increases as entrepreneurs are more optimistic.Originality/valueWhile there is evidence that monetary policy credibility is beneficial to the economy, there are no studies on the effects of disagreements in inflation and interest rate expectations on business confidence (at the aggregate and sectoral levels). Besides, there are no studies that have investigated whether monetary policy credibility can mitigate the effects of disagreements in inflation and interest rate expectations on business confidence (at the aggregate and sectoral levels). Therefore, there are gaps to be filled in the literature addressing business confidence, monetary policy credibility and disagreements in expectations. These issues are particularly important to inflation targeting developing countries.

Ultrasonic effects with different vibration positions on gas tungsten arc wire additive manufactured aluminum nanocomposite

Ultrasonically assisted gas tungsten arc based wire additive manufacturing (UA-GTAAM) of TiB2 nanoparticle-reinforced AA7075 aluminum metal matrix nanocomposite (MMNC) was conducted with the UA probe directly immersed in the molten pool. This work focuses on the UA effects with different probe positions on the UA-melt interactions and the resulting microstructure. In situ high-speed imaging reveals melt pool surface ripples induced by UA. More refined surface ripples, corresponding to a faster melt flow, are observed with the UA probe immersed deeper into the melt. This condition also leads to a lighter etched color under optical microscope, which is related to a more homogeneous microstructure with less eutectic phase at grain boundaries. Electron backscatter diffraction (EBSD) analysis was employed for grain orientation and nanoparticle distributions, where the lower Confidence Index (CI) served as an indicator for intragranular nanoparticle segregation. The EBSD results show a strong (110) texture in the conventional WAAM builds. High-resolution backscattered electron (BSE) imaging reveals nanoparticles tend to segregate into preferred (001) and (111) grains. UA suppresses the epitaxial growth of (110) grains and promotes a more randomly orientated microstructure. Accordingly, more (001) and (111) grains are available to accommodate nanoparticles and promote a more homogeneous dispersion.

Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons.

The complex process of liver graft assessment is one point for improvement in liver transplantation. The main objective of this study is to develop a tool that supports the surgeon who is responsible for liver donation in the decision-making process whether to accept a graft or not using the initial variables available to it. Liver graft samples candidate for liver transplantation after donor brain death were studied. All of them were evaluated "in situ" for transplantation, and those discarded after the "in situ" evaluation were considered as no transplantable liver grafts, while those grafts transplanted after "in situ" evaluation were considered as transplantable liver grafts. First, a single-center, retrospective and cohort study identifying the risk factors associated with the no transplantable group was performed. Then, a prediction model decision support system based on machine learning, and using a tree ensemble boosting classifier that is capable of helping to decide whether to accept or decline a donor liver graft, was developed. A total of 350 liver grafts that were evaluated for liver transplantation were studied. Steatosis was the most frequent reason for classifying grafts as no transplantable, and the main risk factors identified in the univariant study were age, dyslipidemia, personal medical history, personal surgical history, bilirubinemia, and the result of previous liver ultrasound (p < 0.05). When studying the developed model, we observe that the best performance reordering in terms of accuracy corresponds to 76.29% with an area under the curve of 0.79. Furthermore, the model provides a classification together with a confidence index of reliability, for most cases in our data, with the probability of success in the prediction being above 0.85. The tool presented in this study obtains a high accuracy in predicting whether a liver graft will be transplanted or deemed non-transplantable based on the initial variables assigned to it. The inherent capacity for improvement in the system causes the rate of correct predictions to increase as new data are entered. Therefore, we believe it is a tool that can help optimize the graft pool for liver transplantation.

Detecting mechanical property anomalies along railway earthworks by Bayesian appraisal of MASW data

The techniques traditionally used to estimate the mechanical properties of railway earthworks (RE) are costly and of low performance. There is a great need for the development of non-destructive methods, which would allow a fast and efficient diagnosis of RE. Seismic surface-wave (SW) methods, also known as multi-channel analysis of surface waves (MASW), were rescaled here for there systematic implementation on RE. A test site was chosen along the North European High Speed Line (HSL) for its well-known correspondence between discontinuity in mechanical properties and anomalous maintenance forces. With the proposed acquisition and processing strategy, a contrast in SW propagation velocity was correlated with the change in shear modulus of the soil layers beneath the track, previously evaluated in the laboratory. Bayesian inversion of the SW data also allowed to integrate the strong a priori knowledge available on these HSL lines and to provide quantitative results with a confidence index to help end-users in their decisions.

Peer-to-Peer Lending as a Determinant of Federal Housing Administration-Insured Mortgages to Meet Sustainable Development Goals

In this paper, we investigate the influential factors of Federal Housing Administration (FHA) mortgage loans, focusing our research interest on peer-to-peer (P2P) lending, the most successful FinTech lending model. We consider P2P lending an alternative source of financing that marginal borrowers use to pay the increased mortgage down payment, making them eligible to receive a mortgage from conventional banks. In other words, we examine whether and to what extent P2P lending has a positive impact on the FHA loans volume by providing the ability to circumvent the loan-to-value (LTV) cap policy. As a result, P2P lending can be seen as a means for ”rationed” borrowers to have access to the market by reducing inequalities and promoting financial inclusion, thus achieving Sustainable Development Goals (SDGs). We employ hand-collected data from FHA mortgages, P2P loans, and other economic factors from all 50 U.S. states during 2007–2017 and use panel data techniques for this purpose. Research shows that P2P lending, GDP per capita, population growth, broad money growth rate, interest rate, unemployment rate, new housing units, and consumer confidence Index produce effects on FHA loans. We show that P2P lending, a nonconventional determinant, is causally associated with a significant increase in the count and volume of FHA loans, implying that P2P lending has a positive impact on them. The ability of P2P to bypass mortgage supply constraints (tightened LTV caps) by providing small loans to borrowers to meet the increased down payment requirements is very important to policy-makers, as it shows that constraining the volume of mortgage loans may be not achieved. Macroprudential tools designed to control credit growth may prove ineffective, as the use of alternative forms of lending helps circumvent them and ultimately leads to excessive household leverage with all the risks that it poses to the financial system.

EXAMINATION OF THE RELATIONSHIP BETWEEN FOREIGN INVESTORS AND BIST-100 RETURN INDEX

Purpose- The aim of this study is to analyze the relationship of foreign portfolio investments, which are gaining importance in Turkey, with the Borsa Istanbul (BIST)-100 return index. Method- The relationship between foreign investor portfolio sizes and BIST-100 return index was examined based on quarterly periods covering the years 2010 and 2021. While the data on foreign portfolio sizes and the number of foreign investors were obtained from the central registry, data on the BIST-100 return index were obtained from the official website of the Central Bank. In the study, first of all, it was tested by correlation analysis whether there was a significant correlation between the data. If there is a relationship between the data, the direction of the relationship was investigated using the Granger Causality test method developed by Granger (1969), and econometric methods such as unit root test and stationarity analysis were used to make sense of the findings. Findings- The causality direction from BIST-100 index to Foreign portfolio value was determined by reaching the one-way causality relationship that the stock returns (BIST-100 return index) are the cause of the foreign investor portfolio value. Therefore, in this research, it is concluded that the performance of the stock market in Turkey acts independently from the portfolio values of foreign investors. Conclusion- Considering the dynamics and structural features of the financial markets in Turkey, the impact of the sectoral and company-level investments of foreign investors may differ according to the general stock returns. Therefore, although these results do not definitively show that there is no effect of foreign investors, they may reveal different dimensions of the effect of foreign investors, as will be discussed in this research. Keywords: Foreign investments, Borsa Istanbul, Capital Markets, Confidence Index, Granger Causality JEL Codes: G10, G11, G15

Linkage of Non-Fungible (NFT) Tokens to Measures of Uncertainty: Does Fear Bode Well for NFT Holders?

The study investigates the relationship between the returns of Non-Fungible Tokens (NFT) and its categories; and fear indices during times of crisis. The fear indices considered are Global Fear Index (GFI), Global Economic Policy Uncertainty Index (GEPU), Twitter based Economic Uncertainty Index (TEU), Global Consumer Confidence Index (CCI), Infectious Diseases Equity Market Volatility Index (IDEMV) and Crypto Volatility Index (CVI). Employing Granger Causality Test, Autoregressive Distributed Lag technique and ARDL Bounds test on data for the period starting 1st February 2020 and ending 28th February 2022, it is found that short run association exists between TEU, CVI and NFT returns. Further, GFI leads NFT Art returns while TEU leads NFT Metaverse returns by lag 5 and lag 2 respectively. No association between fear metrics and NFT Collectible, NFT Game and NFT utility is observed. No long run association in found between NFT returns and fear indices except TEU which influences NFT returns. It is concluded that NFT, NFT Art and NFT Metaverse returns have positive association to at least one fear index during times of turmoil, especially for the short run.

What drives US consumer confidence? The asymmetric effects of economic uncertainty

AbstractThis paper empirically investigates the asymmetric effects of different economic variables including uncertainty caused by government policies on the two main US consumer confidence indices. This paper further investigates whether COVID‐19 has any significant effect on the relationship between US consumer confidence indices and their determinants. The empirical investigation is conducted by means of non‐linear asymmetric autoregressive distributed lag (NARDL) tests, so that the asymmetric effect of uncertainty and other determinants on consumer confidence may be studied. The paper applies monthly data from January 2010 to December 2021. Results indicate that the COVID‐19 pandemic did not alter the stability of the long‐term relationship between the consumer confidence indices and their determinants. Uncertainty imposed by government policies plays a significant role before and, more prominently, during the pandemic. The increased effect of the uncertainty may be due to the jump in economic uncertainty during the COVID era. Our work may help to foster consumer confidence in terms of macroeconomic policy variables. The results can also expand the scope of investors' decision making as it provides an in‐depth understanding of the drivers of consumers' confidence in the US economy.

Effect of E-WoHealth on Perceived Body Image, Self Confidence, and Body Mass Index (BMI) among Female Adolescents

Low acceptance of body image can lead to behavioral changes and disorders such as depression, excessive desire to lose weight and dieting, eating disorders, as well as low self-esteem. This study aims to analyze the effect of using E-WoHealth on perceived body image, self-confidence, and Body Mass Index (BMI) among female adolescents. This study has obtained ethical approval issued by the Faculty of Public Health, Hasanuddin University No.105/UN4.14.1/YP.01.02/2023 3.01.2023. This was a Quasi-Experimental quantitative study with the R&amp;D (Research and Development) approach and two-group Pre-test-Post-test design. The current study was conducted at State SHS 4 of Palopo City from February 20 to March 30, 2023. A sample size consisted of 108 students, who were selected through a total sampling technique and were assigned into 2 groups, namely the intervention group and the control group consisted of 54 respondents, respectively. The study design developed and validated learning media called Education Woman Health, later abbreviated as E-WoHealth, as a website containing animated videos, text videos and e-booklets to be applied as educational media. The study findings indicated a significant increase in the post-test perceptions of respondents with a p-value of 0.000 &lt;0.05, which means there are differences in perceptions of female adolescents after being given education using E-WoHealth. Furthermore, there was a significant difference between the intervention group and the control group with a p-value 0.000 &lt;0.05. It can be concluded that there was an effect of using web-based E-WoHealth on perceived body image, self-confidence and body mass index among female adolescents. Animated videos and e-booklets can be used as alternative educational medium to increase adolescents' perceptions for self-assessment to create self-confidence and a positive body image.

Prognostic value of blood urea nitrogen to serum albumin ratio for acute kidney injury and in-hospital mortality in intensive care unit patients with intracerebral haemorrhage: a retrospective cohort study using the MIMIC-IV database

ObjectiveWe sought to evaluate the prognostic ability of blood urea nitrogen to serum albumin ratio (BAR) for acute kidney injury (AKI) and in-hospital mortality in patients with intracerebral haemorrhage (ICH)...

Model of trusted interaction of agents in decentralized cyber-physical environmentv

Objective. The purpose of the work is to increase the efficiency of the functioning of agents of a cyber-physical system in the presence of agents with incorrect or malicious behavior. The goal is achieved by establishing a trusted interaction between agents and quick detection of malicious impact.Method. Trusted interaction is carried out using distributed ledger technology and agent confidence indicators. The novelty of the proposed solution lies in the fact that information about the actions of agents is stored and aggregated using smart contracts at specified time intervals. Each agent keeps a local copy of the agent interaction chain. If several agents interact with each other, then they exchange information stored in their copies of the ledger.Result. To test the proposed method, we implemented it in C++. For the experiments, the scenario of collective perception by agents of a decentralized cyber-physical environment in a specialized simulation program Contiki Cooja was used.Conclusion. The method implemented using the solutions proposed in this work showed higher efficiency than the method based on the dynamic calculation of the confidence index. The proposed solutions can be applied not only in cyber-physical systems, but also in any other systems with decentralized control.

Measuring Economic Uncertainty Synchrony with Cross-Sample Entropy Under Common External Factors: The Case of Chile

In this paper, we measured the uncertainty synchrony level of Chilean business economic perception and consumer economic perception, both affected by common external factors reflected in the Global Economy Perception Index (GEPI), unemployment, inflation, interest rate, Monthly Economic Activity (MEAI) and the Economic Policy Uncertainty (EPUI) indexes. We propose using the Composite Multiscale Partial Cross-Sample Entropy (CMPCSE), which quantifies the intrinsic similarity of both time series affected by a common external factor. Uncertainty is measured through the Business Confidence Index (BCI) and Consumer Perception Index (CPI). BCI time series provide useful information about industry, commerce, and the finance, mining, construction and agricultural sectors, the global economic and general business situation. CPI time series measure consumer perception regarding the state of the economy, with consumers evaluating their economic situation and expectations. Results showed a high level of synchronization between business and consumer perceptions in the indexes due to different factors. The most influential in the long term corresponded to unemployment, interest rates, and inflation, EPUI and MEAI, generating uncertainty over a longer period. In addition, the GEPI was found to have an immediate effect on synchronization and high dependence on global uncertainty. Therefore, results could be useful for decision-making related to public policies based on microeconomic indicators of the construction and natural resource sectors, for example.

Forecasting consumer confidence through semantic network analysis of online news

This research studies the impact of online news on social and economic consumer perceptions through semantic network analysis. Using over 1.8 million online articles on Italian media covering four years, we calculate the semantic importance of specific economic-related keywords to see if words appearing in the articles could anticipate consumers’ judgments about the economic situation and the Consumer Confidence Index. We use an innovative approach to analyze big textual data, combining methods and tools of text mining and social network analysis. Results show a strong predictive power for the judgments about the current households and national situation. Our indicator offers a complementary approach to estimating consumer confidence, lessening the limitations of traditional survey-based methods.

Stock Valuation and Business Performance of Property Industry Post COVID-19 Era (Case Study of PT B)

In 2019 the pandemic Covid-19 caused sharp decline on Indonesian economy, yet in 2021 the economic condition started to recover, indicated by 7% increase in Gross Domestic Product and Consumer Confidence Index. One sector influenced by strong economic recovery is property industry, furthermore government gave stimulus to boost property industry growth. PT B is a leading property developer having various projects and land bank across Indonesia. Post Covid-19 era, the financial performance of PT B showed a good condition, the evidence is the increase in company profitability and decline in the debt. The fundamentals contrast with the stock price as the price declined even in the recovery phase. This condition encourages the writer to do the research on PT B stock valuation and whether the share is classified as overvalued or undervalued. The research will utilize secondary such as financial reports, annual reports, public expose from idx.co.id, and other literature sources. The methodology to process the data is Discounted Cash Flow valuation method to find the actual intrinsic value and value-added intrinsic value, to find the fair value using Net Asset Value valuation method. The added value is a proposal to developing housing projects for Generation Z with goal to increase the company share price. The result is the stock fair value to be IDR 1,312 – 2,554. Finally, the author will determine if the stock of PT B is undervalued or overvalued based on the research results.

Predictive estimation of the Visceral Adiposity Index (VAI) and Dysfunctional Adiposity Index (DAI) for metabolic syndrome in a working population

To evaluate metabolic syndrome using three methodsproposed by recognizedinternational institutions, and the visceraladiposity (VAI)anddysfunctionaladiposity (DAI)indices for prediction and prevalence estimation in working populations. Cross-sectional study in workers from different Spanish autonomous communities who underwent a health examination between January 2019 and September 2021 at four occupational risk prevention services. Metabolic syndrome was evaluated according to criteria from the National Cholesterol Education Program Adult Treatment Panel III (NCEP/ATP-III), the International Diabetes Federation (IDF) and the Joint Interim Statement (JIS). VAI and DAI values were calculated using their specific formulas and their predictive capacity was measured using ROC curves. The SPSS 27.0 program was used, with statistical significance level set at p< 0.05. 418 343 workers were included, mostly men (58.8%), average age between 30 and 49 years (58.0%), social class III, mostly manual workers (75.9%) and nonsmokers (66.9%). The prevalence of metabolic syndrome differed depending on the criteria used, being higher in men with IDF and JIS, and in women with ATPIII. For the three definitions of metabolic syndrome, the values of the area under the curve were > 0.8 (>80%). The highest VAI was obtained with the JIS, and the highest DAI with the ATPIII. The highest confidence index was for ATPIII and JIS. The VAI and ICD adiposity indices show high predictive capacity in metabolic syndrome with all three criteria used and can be useful for prevention activities in occupational health.

A Denoising Autoencoder for Improved Kikuchi Pattern Quality and Indexing in Electron Backscatter Diffraction

The rapid collection and indexing of electron diffraction patterns as produced via electron backscatter diffraction (EBSD) has enabled crystallographic orientation and structural determination, as well as additional property-determining strain and dislocation density information with increasing speed, resolution, and efficiency. Pattern indexing quality is reliant on the noise of the collected electron diffraction patterns, which is often convoluted by sample preparation and data collection parameters. EBSD acquisition is sensitive to many factors and thus can result in low confidence index (CI), poor image quality (IQ), and improper minimization of fit, which can result in noisy datasets and misrepresent the microstructure. In an attempt to enable both higher speed EBSD data collection and enable greater orientation fit accuracy with noisy datasets, an image denoising autoencoder was implemented to improve pattern quality. We show that EBSD data processed through the autoencoder results in a higher CI, IQ, and a more accurate degree of fit. In addition, using denoised datasets in HR-EBSD cross correlative strain analysis can result in reduced phantom strain from erroneous calculations due to the increased indexing accuracy and improved correspondence between collected and simulated patterns.

SLEEP-SEE-THROUGH: Explainable Deep Learning for Sleep Event Detection and Quantification From Wearable Somnography.

Evidence is rapidly accumulating that multifactorial nocturnal monitoring, through the coupling of wearable devices and deep learning, may be disruptive for early diagnosis and assessment of sleep disorders. In this work, optical, differential air-pressure and acceleration signals, acquired by a chest-worn sensor, are elaborated into five somnographic-like signals, which are then used to feed a deep network. This addresses a three-fold classification problem to predict the overall signal quality (normal, corrupted), three breathing-related patterns (normal, apnea, irregular) and three sleep-related patterns (normal, snoring, noise). In order to promote explainability, the developed architecture generates additional information in the form of qualitative (saliency maps) and quantitative (confidence indices) data, which helps to improve the interpretation of the predictions. Twenty healthy subjects enrolled in this study were monitored overnight for approximately ten hours during sleep. Somnographic-like signals were manually labeled according to the three class sets to build the training dataset. Both record- and subject-wise analyses were performed to evaluate the prediction performance and the coherence of the results. The network was accurate (0.96) in distinguishing normal from corrupted signals. Breathing patterns were predicted with higher accuracy (0.93) than sleep patterns (0.76). The prediction of irregular breathing was less accurate (0.88) than that of apnea (0.97). In the sleep pattern set, the distinction between snoring (0.73) and noise events (0.61) was less effective. The confidence index associated with the prediction allowed us to elucidate ambiguous predictions better. The saliency map analysis provided useful insights to relate predictions to the input signal content. While preliminary, this work supported the recent perspective on the use of deep learning to detect particular sleep events in multiple somnographic signals, thus representing a step towards bringing the use of AI-based tools for sleep disorder detection incrementally closer to clinical translation.

Business activity and business confidence: a new volatility transmission relationship

PurposeThe purpose of this study is to examine the transmission of volatility between business confidence index and stock market indices in Greece. The country remains the riskiest project in European Union (EU) and previous studies fail to reach an accurate conclusion regarding the direction of this transmission.Design/methodology/approachThe study covers the period from January 2013 to August 2022 in monthly basis where important economic events occur. Considering that these economic events derive strong volatility moments, the authors adopt a new methodology that measures the transmission of volatility with higher precision. This is the generalized spillover analysis by Diebold and Yilmaz (2009, 2012).FindingsThe results indicate that Business Confidence Index (BCI) is the main receiver of volatility spillovers in Greece under all aspects of the used methodology. The specificity of the results shows that business activity through a green growth model is what drives investor confidence and then their activities.Originality/valueAlthough a handful of studies have considered the transmission of volatility between BCI and stock market indices, this study contributes in several ways. This study focuses on one country (Greece), avoiding the dispersion of the results from the examination of the relationship in several countries. The used country remains the riskiest project in EU even nowadays, while other studies fail to confirm the main direction of volatility spillovers from business confidence to stock returns. This study covers a period that is ignored by previous studies and includes important economic events. In addition, considering that these economic events derive strong volatility moments, a new methodology is adopted in this field of research that measures the transmission of volatility with higher accuracy.

Relationship between consumer confidence and stock market index in Vietnam: Research impacts of the passion of Covid-19

The article, studies the relationship between consumer confidence and the stock market index in Vietnam's stock market in the context of the Covid-19 pandemic shock, data collected from 2012Q1 to 2021Q4 from Nielsen data sources. Consumer confidence is measured by the consumer confidence index (CCI), and the stock market index (SMI). The authors use the P.VAR model to solve the set goals. Research results show that there is a strong positive relationship between consumer confidence and the stock market index in Vietnam in the period from 2012 to 2021. In addition, shocks of the past CCI and SMI value also affect current CCI and SMI values. This shows that when consumer confidence is in a positive direction, the SMI will also be in a positive direction and vice versa. In addition, the results showed that Covid 19 did not significantly affect the above relationship. From the research results, the authors propose governance implications to attract more investors to participate in the stock market, contributing to promoting capital in the circulating economy more effectively