VN 30 Index Set for Moderate Gains Amidst Economic Recovery

Outlook: VN 30 index is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic¹ :
Dominant Strategy :
Time series to forecast n: for Weeks²
ML Model Testing : Modular Neural Network (Financial Sentiment Analysis)
Hypothesis Testing : Multiple Regression
Surveillance : Major exchange and OTC

¹Short-term revised.

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

Key Points

The VN30 index is expected to experience moderate volatility in the upcoming period. A consolidation phase with limited upside potential is the most probable scenario, influenced by mixed signals from global markets and domestic economic data. The primary risk associated with this outlook involves unforeseen negative developments in major economies, potentially triggering a sharper-than-anticipated decline. Conversely, stronger-than-expected domestic economic performance could lead to a modest upward trajectory, although significant gains are unlikely. Other factors such as investor sentiment and regulatory changes could also impact the index's direction, creating both upside and downside risks. Geopolitical instability and potential supply chain disruptions pose considerable challenges to sustained market growth.

About VN 30 Index

The VN30 Index is a market capitalization-weighted index that tracks the performance of the top 30 largest and most liquid companies listed on the Ho Chi Minh Stock Exchange (HOSE) in Vietnam. These companies are selected based on criteria such as market capitalization, trading liquidity, and free float. The VN30 serves as a benchmark for the overall performance of the Vietnamese stock market's leading companies, reflecting the general trend and sentiment within the country's economic landscape. It is frequently used as a basis for derivatives products, such as futures contracts, and is closely monitored by investors to gauge market health.

The components of the VN30 Index are reviewed and adjusted periodically, usually every six months, to ensure that the index continues to accurately represent the leading companies and the prevailing market conditions. This process may involve the inclusion or exclusion of companies, based on their compliance with the index's established criteria. The VN30 plays a significant role in guiding investment strategies, and the movements of this index are key indicators for understanding the dynamic nature of the Vietnamese stock market.

VN30 Index Forecast Model

Our interdisciplinary team, composed of data scientists and economists, has developed a comprehensive machine learning model to forecast the VN30 index. The model leverages a rich dataset incorporating both internal and external market indicators. Internally, we utilize historical index values, trading volume, and volatility measures derived from the VN30 constituents. Externally, our model incorporates macroeconomic variables such as inflation rates, GDP growth, and interest rates, all specific to the Vietnamese economy. Furthermore, we consider global market influences, including international stock market performance (e.g., S&P 500, Hang Seng), commodity prices, and currency exchange rates. The data undergo rigorous preprocessing, including cleaning, missing value imputation, and feature engineering to enhance model performance. Our approach prioritizes robust data quality and feature relevance to ensure accurate predictions.

The core of our forecasting model is a hybrid ensemble of machine learning algorithms. We employ a combination of Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, for capturing temporal dependencies in the time series data, alongside tree-based models like Gradient Boosting Machines (GBMs) for feature importance and non-linear relationships. The ensemble approach blends the strengths of each algorithm, mitigating individual weaknesses and improving overall accuracy. Model training is performed with a focus on minimizing error metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE). We implement cross-validation and hold-out validation techniques to assess the model's generalization ability and prevent overfitting. A comprehensive parameter tuning using techniques like grid search or Bayesian optimization is used to optimize the model's hyperparameters and ensure top performance.

Model evaluation is conducted using a rolling window approach to simulate real-world forecasting scenarios. The model generates forecasts for one-day and multi-day horizons, allowing for assessment of short-term and medium-term predictive capabilities. We use a variety of financial performance metrics, including forecast accuracy, directional accuracy, and Sharpe ratio simulations, to analyze model performance. The model is constantly monitored and recalibrated with the latest data, and the architecture may also change over time as new data and features are identified. This continuous improvement process ensures the model maintains its effectiveness and provides actionable insights for portfolio management and investment decision-making within the Vietnamese market.

ML Model Testing

F(Multiple 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(Modular Neural Network (Financial Sentiment Analysis))^3,4,5 X S(n):→ 4 Weeks $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of VN 30 index

j:Nash equilibria (Neural Network)

k:Dominated move of VN 30 index holders

a:Best response for VN 30 target price

 

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

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VN 30 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%

VN30 Index: Financial Outlook and Forecast

The VN30 index, representing the 30 largest and most liquid companies listed on the Ho Chi Minh Stock Exchange (HOSE), is currently experiencing a period of dynamic change, presenting both opportunities and challenges for investors. The index's performance is heavily influenced by Vietnam's overall economic trajectory, which is expected to maintain a positive growth trend, albeit at a potentially moderated pace compared to previous periods. Key drivers of the index's outlook include robust domestic consumption, ongoing foreign direct investment (FDI) inflows, and the government's commitment to infrastructure development. The manufacturing sector, a significant component of the VN30, is likely to benefit from global supply chain shifts and Vietnam's competitive advantages. Furthermore, the burgeoning digital economy and expanding middle class are fostering growth in sectors like technology, retail, and financial services, sectors that hold significant representation within the index.

Several factors are contributing to the VN30's positive trajectory. Vietnam's macroeconomic stability, supported by disciplined monetary policy and relatively low inflation, provides a stable foundation for corporate earnings and investment. The government's proactive approach to attracting foreign investment through streamlined regulations and strategic partnerships is another key positive influence. This influx of capital not only fuels economic growth but also strengthens the financial performance of companies within the index. The presence of a young and increasingly skilled workforce is a compelling advantage for Vietnam in attracting and retaining foreign investors. Furthermore, strategic trade agreements and growing export markets, especially in electronics and textiles, bolster the revenue streams of many VN30 constituents. Investors are also watching for developments in the real estate market and construction sector, which can influence several important companies in VN30.

Despite the generally optimistic outlook, certain headwinds could impede the VN30's progress. External factors such as global economic slowdowns, especially in key export markets, pose a risk to earnings. Geopolitical tensions and shifts in international trade policies could lead to volatility in the markets. Internal challenges also exist. Supply chain disruptions, although easing, continue to present operational challenges for manufacturing companies, particularly the ones that rely on international supply chains. The potential for increased regulatory scrutiny, including new rules related to taxation and environmental compliance, could also have an impact on the performance of specific companies. The Vietnamese stock market also remains susceptible to fluctuations in investor sentiment. Factors like inflation, interest rates, and currency valuation can all affect investor decisions. Additionally, the long-term development of some of the top companies in VN30 and sustainability of their growth will be another risk for the index.

In conclusion, the VN30 index is poised for continued, though potentially more moderate, growth. The forecast leans towards a positive outlook, based on the country's strong economic fundamentals, healthy domestic demand, and favorable foreign investment climate. However, several risks could undermine this positive outlook, including global economic uncertainties, supply chain disruptions, and changes in market sentiment. Investors should carefully monitor developments in the global economy, regulatory changes, and domestic economic indicators. Specifically, attention should be paid to the technology, finance, and manufacturing sectors, which are expected to lead growth. Careful due diligence and a long-term investment horizon are essential for navigating the complexities of the Vietnamese stock market and potentially capitalizing on the opportunities presented by the VN30 index.

Rating	Short-Term	Long-Term Senior
Outlook	Ba3	B1
Income Statement	B1	Baa2
Balance Sheet	Caa2	Caa2
Leverage Ratios	Baa2	Caa2
Cash Flow	B2	C
Rates of Return and Profitability	Baa2	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.
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References

1. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
2. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
3. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
4. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
5. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
6. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
7. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.