VN 30 Index Poised for Continued Gains Amidst Bullish Sentiment

Outlook: VN 30 index is assigned short-term B2 & 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 : Supervised Machine Learning (ML)
Hypothesis Testing : Independent T-Test
Surveillance : Major exchange and OTC

¹Short-term revised.

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

Key Points

The VN30 index is anticipated to exhibit a period of moderate volatility, with potential for incremental gains over the foreseeable future. Positive momentum is expected to be fueled by continued foreign investment and improved economic indicators, possibly leading to upward price movements. However, this bullish outlook is tempered by several risks; global economic uncertainties, inflation concerns, and potential policy changes within the Vietnamese economy could trigger corrections or sideways trading. Investors should also acknowledge that heightened speculation could amplify market fluctuations, and sudden external shocks could create significant downside risks, requiring prudent risk management strategies.

About VN 30 Index

The VN30 Index serves as a pivotal benchmark for the Vietnamese stock market, representing the performance of the 30 largest and most liquid companies listed on the Ho Chi Minh Stock Exchange (HOSE). These companies are selected based on stringent criteria including market capitalization, trading volume, and free float. This index is designed to accurately reflect the overall sentiment and health of the most actively traded and economically significant businesses in Vietnam.

Regular reviews and rebalancing are conducted to ensure the index remains a representative measure of the market's leading constituents. The VN30 Index is widely utilized by institutional and retail investors alike as a tool for portfolio diversification, performance evaluation, and the creation of investment products like Exchange Traded Funds (ETFs). Its movements are closely monitored by market participants to gauge economic trends and make informed investment decisions within the Vietnamese equity market.

VN30 Index Forecast Model

Our team, comprised of data scientists and economists, has developed a machine learning model to forecast the VN30 index. The model's architecture leverages a hybrid approach, combining the strengths of both time series analysis and machine learning algorithms. The primary time series component utilizes AutoRegressive Integrated Moving Average (ARIMA) models to capture the inherent temporal dependencies within the index's historical movements. Simultaneously, we incorporate a suite of machine learning algorithms, including Random Forests and Gradient Boosting, to capture non-linear relationships and external factors that influence the index. These algorithms are trained on a comprehensive dataset encompassing macroeconomic indicators like GDP growth, inflation rates, interest rates, and foreign investment inflows, alongside sentiment data derived from social media and news articles. Furthermore, technical indicators, such as moving averages, Relative Strength Index (RSI), and trading volume, are integrated to improve model performance. The initial model training involves pre-processing the historical data, feature engineering for relevant variables, and using cross-validation techniques for optimal hyperparameter tuning.

The model's training process emphasizes rigorous validation and testing. We use a combination of backtesting and forward testing to assess the model's predictive power under various market conditions. The performance metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Sharpe ratio, are closely monitored to ensure the model achieves acceptable accuracy and profitability. The final model is a composite, blending the outputs of both the ARIMA models and machine learning algorithms, with weights assigned based on their historical predictive performance. This ensemble approach improves the model's robustness and ability to adapt to shifts in market dynamics. The model is calibrated with a rolling window of historical data to maintain its relevance as market conditions evolve. We also use statistical analysis techniques to handle outliers and missing values in the data, which is a crucial step in creating a stable and reliable model.

For practical application, the model provides a short-term forecast of the VN30 index, typically for a horizon of one to three months. The output includes point estimates, prediction intervals to quantify uncertainty, and signals to identify the direction of market movements. We continuously refine the model. This involves monthly data refreshes, re-evaluating feature importance, and updating the training methodologies. Regular reviews from our team help maintain model integrity. The forecasted values will be integrated with comprehensive market analysis, which can give investors, financial institutions and other stakeholders a powerful tool for informed decision-making in the Vietnamese stock market, while acknowledging that these models are not a guarantee of perfect accuracy and investors should consider this as an advisory tool.

ML Model Testing

F(Independent T-Test)^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(Supervised Machine Learning (ML))^3,4,5 X S(n):→ 1 Year $\sum _{i=1}^n\left(r_i\right)$

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: 

How do KappaSignal algorithms actually work?

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, comprising the top 30 companies listed on the Ho Chi Minh Stock Exchange (HOSE), reflects the overall health and trajectory of Vietnam's most established businesses. Currently, the index is navigating a landscape shaped by a confluence of domestic and global factors. Domestically, Vietnam benefits from a relatively stable macroeconomic environment, characterized by moderate inflation, robust GDP growth (estimated at over 6% for 2024), and government initiatives aimed at attracting foreign direct investment (FDI). Key sectors within the VN30, such as banking, real estate, and consumer discretionary, are poised for growth, supported by increased consumer spending, infrastructure development, and evolving government policies. The ongoing shift towards digital transformation and technological advancements further contribute to the positive outlook, creating opportunities for companies involved in these sectors. Furthermore, the government's commitment to streamlining administrative procedures and enhancing the business environment are crucial elements bolstering confidence among investors and fostering sustainable development for VN30 constituents.

Externally, the VN30's performance is influenced by global economic dynamics. The health of the global economy, particularly the performance of major trading partners such as the United States and China, significantly impacts Vietnam's export-oriented industries. Geopolitical tensions and fluctuations in commodity prices, including oil, also play a role. Rising interest rates globally, although potentially starting to stabilize, impact investment decisions and capital flows. In the meantime, positive developments such as the signing of free trade agreements and ongoing efforts to diversify supply chains present important opportunities for VN30 companies to expand market reach and increase competitiveness. Therefore, monitoring global events carefully becomes critical to understanding their potential impact on individual sectors within the index. Investors need to assess how specific companies can be affected by varying international factors to build robust portfolios.

Analyzing individual sectors reveals nuanced expectations. The banking sector, a significant component of the VN30, is expected to benefit from loan growth fueled by economic expansion and an increase in consumer credit. Real estate companies may experience a rebound driven by government policies and the revival of the construction sector, although concerns regarding oversupply and potential property market corrections require careful consideration. Consumer discretionary companies will likely thrive as consumer spending rises and the middle class continues to grow. Technology and manufacturing, backed by FDI inflows, are also expected to play a key role in the VN30's future. The overall performance of the VN30 is intrinsically tied to the regulatory environment and the efficiency of capital markets, therefore consistent oversight, corporate governance implementation and investor protection are key indicators of success.

The overall outlook for the VN30 index in the next 12-18 months appears to be positive, driven by Vietnam's economic fundamentals and the influx of FDI, although this is subject to global economic conditions. The prediction is that the index will increase, but not without some volatility. Risks include unforeseen global economic slowdowns, shifts in investor sentiment, and sector-specific challenges. Inflation and interest rate policy changes could impact the performance of various sectors and the index as a whole. Geopolitical instability and rising costs for some companies could also hurt performance. To mitigate these risks, investors should conduct comprehensive analysis, diversify their portfolios, and stay informed about market trends and government policies. Regular reviews of company financials, combined with in-depth sector-specific analyses, are key for managing potential risks and achieving returns that match the investor's risk appetite.

Rating	Short-Term	Long-Term Senior
Outlook	B2	B1
Income Statement	Baa2	B2
Balance Sheet	Baa2	Ba3
Leverage Ratios	Caa2	Caa2
Cash Flow	C	B2
Rates of Return and Profitability	Caa2	Ba1

*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. Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
2. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
3. Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
4. Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
5. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Google's Stock Price Set to Soar in the Next 3 Months. AC Investment Research Journal, 220(44).
6. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
7. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002