IDX Composite Poised for Steady Growth Amidst Economic Optimism

Outlook: IDX Composite index is assigned short-term B1 & long-term Ba2 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic¹ :
Dominant Strategy :
Time series to forecast n: for Weeks²
ML Model Testing : Statistical Inference (ML)
Hypothesis Testing : ElasticNet Regression
Surveillance : Major exchange and OTC

¹Short-term revised.

²Time series is updated based on short-term trends.

Key Points

The IDX Composite index is poised for a period of moderate growth, driven by positive investor sentiment and sustained domestic economic activity. The consumer sector and financial institutions are anticipated to be key drivers, exhibiting robust performance, while commodity-linked sectors might experience volatility due to global price fluctuations. However, this optimistic outlook faces risks, including potential inflation pressures and the impact of global economic slowdowns, particularly from major trading partners. Furthermore, geopolitical instability could exert downward pressure, impacting market confidence and potentially hindering the overall gains.

About IDX Composite Index

The IDX Composite Index serves as a comprehensive benchmark for the performance of all stocks listed on the Indonesia Stock Exchange (IDX). It reflects the overall market sentiment and provides a broad measure of the Indonesian equity market's health. This index is a capitalization-weighted index, meaning the companies with larger market capitalizations have a greater influence on its value. Daily fluctuations in the IDX Composite are closely monitored by investors, analysts, and policymakers as an indicator of economic activity and investor confidence within Indonesia.

The IDX Composite is crucial for evaluating the performance of investment portfolios, particularly those focused on the Indonesian market. Its composition is periodically reviewed, including the criteria for inclusion and exclusion, to ensure that it accurately represents the Indonesian stock market's dynamics. It provides a readily accessible overview of market trends, enabling informed decisions regarding investment strategies and risk assessment within the Indonesian financial landscape. The index's widespread use makes it an essential tool for understanding and navigating the Indonesian stock market.

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IDX Composite Index Forecast Model

Our team of data scientists and economists has developed a machine learning model to forecast the IDX Composite Index. This model incorporates a diverse range of macroeconomic and financial indicators to predict future index movements. The primary goal is to provide accurate and timely forecasts to assist investors and stakeholders in making informed decisions. The model's architecture utilizes a combination of techniques, including time series analysis (specifically, ARIMA models) to capture the index's historical patterns and machine learning algorithms (such as Random Forests and Gradient Boosting) to leverage the predictive power of external factors. These factors encompass inflation rates, interest rates, GDP growth, commodity prices (particularly oil and gold), currency exchange rates (specifically the Rupiah/USD), and global economic sentiment indicators.

The model's training process involves utilizing historical data spanning several years. Data preprocessing is a crucial step, which includes handling missing values, normalizing data, and feature engineering to enhance predictive accuracy. The model's performance is rigorously evaluated using a hold-out validation set and cross-validation techniques, enabling us to assess its generalization capabilities and avoid overfitting. Evaluation metrics include Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the R-squared value, ensuring a comprehensive performance assessment. The model outputs forecasts for the IDX Composite Index on different time horizons, ranging from short-term (e.g., daily) to medium-term (e.g., monthly) predictions, and provides confidence intervals, which reflect the level of uncertainty associated with the forecasts.

To enhance the model's practical application, we provide regular model updates with fresh data and periodic re-training to adapt to changing market conditions. A crucial component of our methodology is the incorporation of expert judgment and qualitative analysis. The forecasts will be interpreted in conjunction with current events, market trends, and potential risk factors. Moreover, we aim to provide user-friendly visualizations and comprehensive reports to communicate model outputs and key insights. The model serves as a tool to assist in investment analysis and portfolio construction and is not a definitive predictor of market movements.

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ML Model Testing

F(ElasticNet Regression)^6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Statistical Inference (ML))^3,4,5 X S(n):→ 1 Year $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of IDX Composite index

j:Nash equilibria (Neural Network)

k:Dominated move of IDX Composite index holders

a:Best response for IDX Composite target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do KappaSignal algorithms actually work?

IDX Composite Index Forecast Strategic Interaction Table

Strategic Interaction Table Legend:

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Grey to Black): *Technical Analysis%

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IDX Composite Index: Financial Outlook and Forecast

The Indonesian Stock Exchange (IDX) Composite Index is poised for a period of moderate growth, driven by a confluence of factors. Indonesia's strong macroeconomic fundamentals, including a stable currency, manageable inflation, and robust GDP growth, provide a solid foundation. The country's natural resources, particularly in commodities like coal, palm oil, and nickel, continue to be a significant driver of export revenue. Furthermore, government initiatives aimed at infrastructure development and attracting foreign investment are expected to bolster economic activity. The digital economy is also a growing sector, with increasing mobile penetration and e-commerce adoption contributing to a vibrant market landscape. Indonesia's young and growing population presents a large consumer base, fostering domestic demand that will further fuel the index's performance.

Several sectors are expected to outperform the broader IDX Composite. The banking sector is anticipated to benefit from increased lending activity and rising interest rates, although the pace of these gains may be tempered by the need to maintain asset quality. The telecommunications sector is projected to witness expansion as Indonesia improves its digital infrastructure and the demand for data and connectivity increases. Moreover, the consumer discretionary and staples sectors are anticipated to remain resilient, supported by the nation's robust consumer spending. In addition, there may be significant opportunities within the renewable energy and electric vehicle manufacturing space as the government implements initiatives for sustainable development. The mining and infrastructure sectors are set to contribute significantly towards the index, provided global commodity prices and government policies are in favor of them.

However, several external factors could influence the IDX Composite Index's performance. Global economic conditions, including slower growth in major economies like China and the United States, could impact Indonesia's exports and investment inflows. Rising global interest rates, as central banks combat inflation, could make Indonesian assets less attractive to foreign investors. Fluctuations in global commodity prices, particularly for key Indonesian exports, pose a risk. Furthermore, geopolitical tensions, such as those affecting global trade and supply chains, can create uncertainty and potentially disrupt economic activity. Investors need to carefully monitor these external risks and their potential impact on the Indonesian economy. Changes in government regulations and policy implementation speed are also crucial, as abrupt changes could dampen investment sentiment and hinder sector-specific growth.

Overall, the forecast for the IDX Composite Index is **positive**, though with caveats. The index is expected to exhibit moderate growth over the next 12-18 months, reflecting the resilience of the Indonesian economy. This forecast is predicated on the continuation of favorable domestic conditions and the stabilization of global economic trends. However, the primary risk to this positive outlook is a sharper-than-anticipated global economic slowdown, which could significantly reduce export demand and investment inflows. Other key risks include unfavorable movements in currency exchange rates, and commodity prices. Investors should maintain a diversified portfolio, monitor macroeconomic indicators, and be prepared to adapt to evolving market conditions to navigate the risks and capitalize on the opportunities presented by the IDX Composite Index.

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Rating	Short-Term	Long-Term Senior
Outlook	B1	Ba2
Income Statement	C	Ba2
Balance Sheet	Ba1	B1
Leverage Ratios	Ba3	Baa2
Cash Flow	B2	C
Rates of Return and Profitability	B1	Baa2

*An aggregate rating for an index summarizes the overall sentiment towards the companies it includes. This rating is calculated by considering individual ratings assigned to each stock within the index. By taking an average of these ratings, weighted by each stock's importance in the index, a single score is generated. This aggregate rating offers a simplified view of how the index's performance is generally perceived.
How does neural network examine financial reports and understand financial state of the company?

References

1. Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.
2. Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
3. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
4. Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.
5. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
6. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
7. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44