Indonesia Movie Ratings

[Kandy Swartzel]

Theratings are determined by comprehensive evaluations conducted by major credit rating agencies such as Standard & Poor's (S&P), Moody's, and Fitch Ratings. Each of these agencies uses a distinct rating scale and methodology to assess the economic and financial health of Indonesia, taking into account various factors such as economic performance, fiscal policies, political stability, and debt levels.

Indonesia long-term credit ratings, as assigned by major rating agencies, offer a comparative insight into the country's creditworthiness over an extended period. Each rating agency uses its own scale to evaluate the credit risk associated with Indonesia sovereign debt. These scales help investors understand the likelihood that Indonesia will meet its financial obligations.

Attitudes toward China are largely negative across the 24 countries surveyed. A median of two-thirds say they have an unfavorable opinion of the country and a median of just 28% offer positive ratings.

Half or more in each of the North American and European countries surveyed have somewhat or very unfavorable opinions of China. This includes majorities of three-quarters or more in Sweden, the U.S., Canada, the Netherlands and Germany.

In the Asia-Pacific region, views vary widely. While Australia and Japan stand out as the countries with the highest unfavorable ratings (87% each), just a quarter of Indonesian adults offer negative evaluations of China. And in India, where two-thirds have a negative view of China, half of adults offer very unfavorable opinions of the neighboring country (the largest share in any country surveyed).

The sub-Saharan African populations surveyed offer some of the highest ratings of China. Nigeria and Kenya stand out for their shares of favorable views (80% and 72%, respectively), and a plurality of South Africans hold favorable opinions as well.

The shares offering unfavorable evaluations of China also grew in Brazil, Mexico, Argentina and South Africa. In Brazil, the public went from being net favorable toward China in 2019 to net unfavorable in 2023.

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Introduction: The Hamilton Depression Rating Scale (HDRS) is the most widely used depression rating scale worldwide. Reliability of HDRS has been reported mainly from Western countries. The current study tested the reliability of HDRS ratings among psychiatric residents in Indonesia, before and after HDRS training. The hypotheses were that: (i) prior to the training reliability of HDRS ratings is poor; and (ii) HDRS training can improve reliability of HDRS ratings to excellent levels. Furthermore, we explored cultural validity at item level.

Methods: Videotaped HDRS interviews were rated by 30 psychiatric residents before and after 1 day of HDRS training. Based on a gold standard rating, percentage correct ratings and deviation from the standard were calculated.

Results: Correct ratings increased from 83% to 99% at item level and from 70% to 100% for the total rating. The average deviation from the gold standard rating improved from 0.07 to 0.02 at item level and from 2.97 to 0.46 for the total rating.

Discussion: HDRS assessment by psychiatric trainees in Indonesia without prior training is unreliable. A short, evidence-based HDRS training improves reliability to near perfect levels. The outlined training program could serve as a template for HDRS trainings. HDRS items that may be less valid for assessment of depression severity in Indonesia are discussed.

The table below shows the ratings of universities with an active QS Stars subscription that have agreed to publish their results. You can filter the institutions by location, rating, and/or category.

This study aims to analyze the mediating role of earnings management on the effect of specific factors on sukuk ratings. The sample of this research is all sukuk issuing companies listed on the Indonesia Stock Exchange (IDX) which are rated by PT. PEFINDO during 2010-2020. The data was analyzed using Partial Least Square-Structural Equation Modeling (PLS SEM). The results show that liquidity, leverage and activity ratio variables have no effect on earnings management, but firm size does affect earnings management. Furthermore, the findings suggest that leverage, activity ratio, company size, and earnings management all have an impact on the sukuk rating, whilst liquidity does not affect the sukuk rating. Furthermore, earnings management does not mediate the effect of liquidity, leverage, activity ratio and firm size on the sukuk rating. The results of this study can add insight for the company's management and investors. This study can be used as a reference for firm management in raising the sukuk rating and as a factor for investors in deciding whether or not to invest in sukuk.

Sharia bonds, a segment within the realm of Islamic finance, has demonstrated significant growth over the last decade, emerging as one of the fastest-growing sectors in the industry. This study aimed to analyze the effect of liquidity, profitability, leverage, maturity, and auditor's reputation on sharia bonds ratings. The data collection method used is secondary data sourced from the company's financial statements. The population in this study are companies that issue sharia bonds on the Indonesia Stock Exchange. Sampling using the purposive sampling method with 17 with certain criteria. The data were analyzed using SPSS version 22. The analysis used is ordinal logistic regression analysis. The results showed that liquidity and leverage had no effect on the sharia bond's rating. The profitability, maturity, and reputation of auditors negatively affect the rating of sharia bonds. Sharia-compliant bonds, also known as Sukuk, have a substantial impact on the Islamic finance sector and contribute to the advancement of the global Islamic banking and finance industry. These bonds serve as a means for governments, corporations, and institutions to secure capital while adhering to the principles of Sharia law. They attract investors who prioritize investments that align with their religious beliefs and ethical values.

Some developing economies are finally seeing light at the end of the tunnel. Global inflation is receding and global interest rates appear to have peaked, prompting a bond-issuance rush by these economies to refinance their debt before the opportunity vanishes. In early January, Mexico, Indonesia, and several other developing economies easily raised more than $50 billion from bond investors.

These economies, in short, have now been locked out of global capital markets for more than two years. They have issued almost no international bonds during that time, a barren spell of the kind not seen since the global financial crisis (Figure 2B). Not surprisingly, 11 of them have defaulted since 2020, approaching the total of the previous two decades.

The economic effects have been severe: by the end of 2024, people in nearly half of developing economies with weak credit ratings will be poorer on average than they were in 2019, on the eve of the COVID-19 pandemic (Figure 3A). For developing economies with better credit ratings, the comparable share is just 8 percent. Prospects are unlikely to improve anytime soon: developing economies with weak ratings will grow nearly a full percentage point more slowly over 2024-25 than they did in the decade before the pandemic (Figure 3B).

Building fiscal space means broadening government revenue bases and prioritizing public spending. Distortive and wasteful subsidies can be jettisoned, for example. On the monetary side, these economies can help themselves by putting in place credible exchange-rate systems and nurturing central-bank independence. These reforms will need to be complemented by improvements in the quality of domestic institutions, so that a more investment-friendly environment can be established. These policy interventions will not be easy to implement. But they are indispensable to restoring economic stability, attracting much-needed investment, and promoting growth.

Beyond these 28 developing economies, another 31 mostly low-income countries with no credit rating are already in debt distress or high risk of it. That implies roughly one out of every three developing economies is struggling with high debt in an environment of weak growth, steep borrowing costs, and a multitude of downside risks. An additional shock could easily push more of them over the edge. Should that happen, the silent debt crisis would become an increasingly loud one.

This table summarizes the latest bond ratings and appropriate default spreads for different countries. While you can use these numbers as rough estimates of country risk premiums, you may want to modify the premia to reflect the additonal risk of equity markets. To estimate the long term country equity risk premium, I start with a default spread, which I obtain in one of two ways:

(1) I use the local currency sovereign rating (from Moody's: www.moodys.com) and estimate the default spread for that rating (based upon traded country bonds) over a default free government bond rate. For countries without a Moody's rating but with an S&P rating, I use the Moody's equivalent of the S&P rating. To get the default spreads by sovereign rating, I use the CDS spreads and compute the average CDS spread by rating. Using that number as a basis, I extrapolate for those ratings for which I have no CDS spreads.

(2) I start with the CDS spread for the country, if one is available and subtract out the US CDS spread, since my mature market premium is derived from the US market. That difference becomes the country spread. For the few countries that have CDS spreads that are lower than the US, I will get a negative number.

You can add just this default spread to the mature market premium to arrive at the total equity risk premium. I add an additional step. In the short term especially, the equity country risk premium is likely to be greater than the country's default spread. You can estimate an adjusted country risk premium by multiplying the default spread by the relative equity market volatility for that market (Std dev in country equity market/Std dev in country bond). Sicnce government bonds are not available or traded in most countries, I approximate the relative equity market volatility by estimating the standard deviations in two indices, the S&P emerging market equity index (for equities) and an iShares emerging market government bond ETF (for government bonds), and using that ratio for all countries to estimate the additional country risk premium. Finally, I add that country risk premium to my estimate of a mature market equity risk premium, for which I use the implied equity ris premium of the S&P 500.

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