VN30 Index: Analysts Bullish Amidst Market Optimism.

Outlook: VN 30 index is assigned short-term B2 & long-term B3 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 (Emotional Trigger/Responses Analysis)
Hypothesis Testing : Wilcoxon Sign-Rank 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 experience a period of moderate growth, fueled by increased investor confidence and a recovering economy. Positive sentiment surrounding key sectors, such as technology and consumer goods, is expected to contribute to this upward trajectory. However, this positive outlook is accompanied by risks. Global economic uncertainties, including potential interest rate hikes and inflationary pressures, could exert downward pressure on the index. Furthermore, increased volatility stemming from geopolitical instability or unexpected domestic policy changes poses a significant threat. Therefore, while a positive trajectory is likely, investors should remain vigilant, carefully monitoring external factors and considering hedging strategies to mitigate potential losses. Furthermore, potential downside risks include fluctuations in commodity prices and the emergence of unforeseen domestic market disruptions, which could destabilize the anticipated growth pattern.

About VN 30 Index

The VN30 Index is a market capitalization-weighted index comprising 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 stringent criteria, including market capitalization, trading liquidity, and foreign ownership room. The index serves as a benchmark for the overall performance of the Vietnamese stock market and a crucial tool for investors seeking exposure to the country's leading businesses. It is reviewed and rebalanced quarterly to ensure that the constituents reflect the most dynamic and representative segment of the market.

As a barometer of the Vietnamese economy, the VN30 Index is widely used by institutional and retail investors, as well as financial institutions and fund managers. It is often referenced in macroeconomic analysis and used to track the health and direction of the Vietnamese stock market. Furthermore, derivatives products such as futures contracts are based on the VN30 Index, increasing its importance for hedging and speculation purposes within the broader financial ecosystem. Its performance is closely watched as a significant indicator of investor sentiment and the growth potential of the Vietnamese economy.

Machine Learning Model for VN30 Index Forecast

Our team proposes a comprehensive machine learning model for forecasting the VN30 index. The model will leverage a diverse set of features, encompassing both time-series data and macroeconomic indicators. Time-series features will include historical index values, trading volumes, volatility measures (e.g., realized volatility, implied volatility from derivatives markets), and technical indicators (e.g., moving averages, Relative Strength Index, MACD). Macroeconomic variables will incorporate Vietnam's GDP growth, inflation rates, interest rates, exchange rates (VND/USD), industrial production indices, consumer confidence indices, and foreign investment flows (FDI). These macroeconomic indicators will be sourced from reputable sources such as the General Statistics Office of Vietnam, the State Bank of Vietnam, and international financial institutions. The inclusion of these factors is crucial, because they often contribute to price movements.

The core of the model will be a hybrid approach, incorporating Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, and ensemble methods like Gradient Boosting Machines (GBM) and Random Forests. LSTM networks are well-suited for capturing sequential dependencies inherent in time-series data, allowing the model to recognize patterns and trends over time. GBM and Random Forests will be employed to model the non-linear relationships between the macroeconomic variables and the index, providing robustness and reducing overfitting. Feature engineering will be a critical step, including data cleaning, handling missing values through imputation, and feature scaling. We will also consider feature transformations, such as logarithmic transformations, to stabilize variance and better capture non-linear relationships.

Model training will involve a rigorous process of data splitting (training, validation, and testing sets), hyperparameter tuning, and cross-validation to ensure model generalizability and avoid overfitting. The model's performance will be evaluated using a combination of metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), to assess the magnitude of prediction errors. We will also evaluate the model's directional accuracy by calculating the percentage of correctly predicted price movements. Finally, we will perform backtesting on historical data to assess the model's performance in various market conditions. The model will be continuously monitored, retrained periodically, and updated with new data to ensure its accuracy and relevance in the dynamic market environment.

ML Model Testing

F(Wilcoxon Sign-Rank 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(Modular Neural Network (Emotional Trigger/Responses Analysis))^3,4,5 X S(n):→ 1 Year $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

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: 

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, representing the top 30 largest and most liquid companies listed on the Ho Chi Minh Stock Exchange (HOSE), reflects the overall health and investor sentiment within the Vietnamese stock market. The index's financial outlook is currently influenced by a confluence of factors, including Vietnam's robust economic growth trajectory, driven by strong domestic consumption, increasing foreign direct investment (FDI), and a thriving manufacturing sector. The country's strategic shift towards a more diversified economy, away from over-reliance on specific industries, is also creating promising investment opportunities within the VN30. Furthermore, government policies promoting market development, infrastructure projects, and regulatory reforms are contributing to a favorable investment environment and are expected to support the index's performance. However, external factors, such as fluctuations in global commodity prices and evolving trade dynamics, must be considered in the overall financial outlook.

Several key sectors within the VN30 are expected to be major contributors to its financial performance. The banking sector, representing a significant weight within the index, is poised to benefit from rising credit demand and the increasing financial inclusion of Vietnam's growing middle class. The real estate sector is expected to continue its expansion, driven by urbanization and infrastructure development, though it faces challenges related to regulatory changes and the potential for price corrections. The consumer discretionary and consumer staples sectors are also likely to experience healthy growth, reflecting the rising disposable incomes of Vietnamese consumers and their increasing spending on goods and services. Manufacturing and industrial sectors should continue to provide robust performance supported by favorable conditions of trade agreements and increasing inflows of FDI.

Analysing long-term trends, the forecast for the VN30 index remains positive, supported by structural tailwinds and the government's commitment to sustainable economic growth. Continued FDI inflows, particularly from countries seeking to diversify their supply chains, are expected to boost the index's performance. The Vietnamese government's focus on digital transformation and technological innovation are expected to drive the development of new sectors within the VN30, further diversifying and strengthening the index. Moreover, efforts to improve corporate governance and transparency within listed companies are expected to enhance investor confidence and attract additional capital flows into the market. Finally, the long-term growth of Vietnam's stock market, its rising number of new investors and its increasing presence in the world market, is also expected to benefit VN30 and its component.

The overall outlook for the VN30 index is positive, with an anticipated period of sustained growth, although it will not be without its challenges. We project a positive trend of the index over the next three to five years. This prediction is predicated on Vietnam's continued economic expansion, strategic reforms, and robust investor confidence. However, several risks could potentially undermine this positive trajectory. These include global economic slowdowns, geopolitical instability, fluctuations in global trade and investment, and sector-specific headwinds. Furthermore, unforeseen domestic policy changes or unexpected shifts in market sentiment could negatively affect the VN30's performance. Investors should carefully consider these risks and diversify their portfolios to mitigate potential losses.

Rating	Short-Term	Long-Term Senior
Outlook	B2	B3
Income Statement	Baa2	B3
Balance Sheet	Caa2	C
Leverage Ratios	Ba1	C
Cash Flow	C	B2
Rates of Return and Profitability	C	Caa2

*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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