AUC Score :
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n:
ML Model Testing : Statistical Inference (ML)
Hypothesis Testing : Chi-Square
Surveillance : Major exchange and OTC
1Short-term revised.
2Time series is updated based on short-term trends.
Key Points
BEL 20 index is likely to experience moderate volatility, with the potential for modest gains driven by positive sentiment in the broader European market and anticipated earnings reports from key constituent companies. The index may face headwinds from global economic uncertainties, particularly concerning inflation and interest rate hikes, which could dampen investor enthusiasm and lead to periods of consolidation or slight declines. The biggest risk is a significant downturn triggered by unforeseen geopolitical events or a sharper-than-expected slowdown in the global economy. Furthermore, sector-specific challenges, such as regulatory changes affecting key sectors within the index, could also pose a threat to overall performance.About BEL 20 Index
The BEL 20 is a prominent stock market index representing the performance of the 20 most actively traded and largest companies listed on Euronext Brussels. It serves as a crucial barometer of the economic health and investor sentiment within the Belgian economy. Companies included in the BEL 20 are selected based on market capitalization, liquidity, and their representation of various sectors, offering a diversified view of the Belgian market. The index is reviewed periodically to ensure its composition remains reflective of the evolving financial landscape.
The BEL 20 is widely used by institutional and retail investors as a benchmark for portfolio performance. It allows investors to assess the overall performance of Belgian equities and make informed investment decisions. Additionally, the index serves as the underlying asset for various financial instruments, such as exchange-traded funds (ETFs) and derivatives, providing opportunities for investors to gain exposure to the Belgian stock market and manage risk. Its movements are closely monitored by economists, financial analysts, and the public alike, as they can reflect broader macroeconomic trends within Belgium and Europe.

BEL 20 Index Forecast Model
Our data science and economics team has developed a machine learning model to forecast the performance of the BEL 20 index. The model leverages a comprehensive dataset, including historical index values, macroeconomic indicators (GDP growth, inflation rates, interest rates), and market sentiment data derived from news articles and social media. We have integrated financial ratios from constituent companies, such as price-to-earnings ratios, debt-to-equity ratios, and dividend yields. Time-series analysis techniques, including ARIMA and Exponential Smoothing, are employed to capture temporal patterns and dependencies within the index data. Furthermore, the model incorporates features related to the global economy, such as the performance of major international stock indices (e.g., the S&P 500, FTSE 100), exchange rates, and commodity prices. This multi-faceted approach allows the model to capture a wide range of factors that can influence the BEL 20's fluctuations.
The core of our model utilizes a gradient boosting algorithm, specifically XGBoost, renowned for its predictive accuracy and ability to handle complex relationships in the data. The model undergoes rigorous training and validation using a split-sample approach. The dataset is divided into training, validation, and test sets. The training set is used to train the model, the validation set is used to tune hyperparameters and optimize model performance, and the test set is held back until the final stage to assess its out-of-sample predictive capability. Model evaluation metrics, such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), are computed to quantify forecast accuracy. We utilize cross-validation techniques to provide a robust assessment of model performance. Feature importance analysis is conducted to identify and prioritize the most influential factors driving index movements.
The model's output is a probabilistic forecast of the BEL 20 index, providing not only point predictions but also confidence intervals reflecting the uncertainty associated with the forecast. The model's forecasts are generated on a rolling basis, allowing for timely updates and adaptation to evolving market conditions. The team will provide continuous monitoring, retraining, and refinement of the model to ensure optimal performance. We aim to deliver regular reports that summarize the model's performance, identify key drivers of change, and offer insights into potential market movements. Regular interaction with economists allows for fundamental analysis considerations and real world scenarios for improved accuracy and reliability.
ML Model Testing
n:Time series to forecast
p:Price signals of BEL 20 index
j:Nash equilibria (Neural Network)
k:Dominated move of BEL 20 index holders
a:Best response for BEL 20 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?
BEL 20 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%
Financial Outlook and Forecast for the BEL 20 Index
The BEL 20 index, representing the performance of the 20 most significant companies listed on Euronext Brussels, is poised for a period of moderate growth, underpinned by a cautiously optimistic economic environment. Key sectors contributing to the index's strength include pharmaceuticals, materials, and utilities, reflecting a degree of resilience amidst global economic uncertainties. The Belgian economy, while facing challenges from inflation and rising interest rates, is expected to demonstrate moderate growth, supported by consumer spending and sustained export demand, particularly within the Eurozone. Corporate earnings are projected to exhibit steady growth, although the rate of expansion may be somewhat muted compared to previous periods of strong economic activity. Diversification within the BEL 20, with its exposure to various global markets, provides a buffer against specific regional downturns. Furthermore, ongoing investments in infrastructure and renewable energy projects are expected to provide a boost to certain key players within the index.
Several factors are likely to influence the trajectory of the BEL 20 in the near to medium term. The evolution of interest rates, dictated by the European Central Bank (ECB), will play a critical role. Rising interest rates could potentially dampen consumer spending and business investment, leading to a slowdown in economic growth. Conversely, a sustained period of relatively stable or declining rates would likely encourage economic activity and bolster investor confidence, positively impacting the index's performance. Geopolitical events, such as the ongoing conflict in Ukraine and its broader impact on energy prices and supply chains, also pose a significant consideration. Inflationary pressures, while showing signs of easing, could still pose a challenge, as companies may struggle to pass on increased costs to consumers. The performance of specific sectors, such as financial services and technology, which may be more sensitive to market volatility, would also significantly influence the overall index trend.
The market sentiment surrounding the BEL 20 is expected to be influenced by various factors. Investor confidence is pivotal, and any negative shifts in global sentiment, coupled with unforeseen economic shocks, could quickly undermine market stability. The regulatory landscape, including decisions made by the Belgian government concerning corporate taxation, could also significantly impact companies' profitability and investment decisions, which, in turn, would influence the index performance. Furthermore, the evolving landscape of environmental, social, and governance (ESG) factors is becoming increasingly important to investors, and companies that demonstrate a strong commitment to sustainability may attract greater investor interest. The overall market liquidity and the volume of trading activity on Euronext Brussels will also be crucial determinants of the BEL 20's performance, with increased trading activity generally supporting higher levels of index performance.
The forecast for the BEL 20 index is cautiously positive. We anticipate moderate growth in the upcoming period, but with significant risks. The potential for a sustained decline in economic growth due to unexpected events such as an escalation of geopolitical tension, a rapid rise in interest rates, or a prolonged period of high inflation could curtail performance. Conversely, any favorable shifts, such as a faster-than-expected easing of inflationary pressures or stronger-than-anticipated economic growth within the Eurozone, will support the index's performance. The risk to this forecast would be a sharper-than-expected global economic slowdown, coupled with a decline in investor confidence which could have negative effects on the BEL 20 index.
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook | B2 | B2 |
Income Statement | B2 | B2 |
Balance Sheet | B2 | Ba2 |
Leverage Ratios | B2 | C |
Cash Flow | B3 | Baa2 |
Rates of Return and Profitability | Ba2 | C |
*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
- J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
- Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
- Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
- Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Tesla Stock: Hold for Now, But Watch for Opportunities. AC Investment Research Journal, 220(44).
- Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
- Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
- Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Can Neural Networks Predict Stock Market?. AC Investment Research Journal, 220(44).