IBEX 35 Index Anticipated to Maintain Steady Growth Trajectory

Outlook: IBEX 35 index is assigned short-term B2 & long-term B2 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 (News Feed Sentiment Analysis)
Hypothesis Testing : Spearman Correlation
Surveillance : Major exchange and OTC

¹Short-term revised.

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

Key Points

The IBEX 35 index is projected to experience moderate growth, driven by improved domestic economic performance and potential tailwinds from the European Union. However, this positive outlook is tempered by several risks. Geopolitical instability, particularly concerning the ongoing conflict and its impact on energy markets, could trigger market volatility and dampen investor sentiment. Furthermore, the index is susceptible to fluctuations due to changes in global interest rates and inflation, which could affect corporate earnings and consumer spending. A sudden slowdown in the global economy, especially in major trading partners, represents another significant threat, potentially hindering the index's upward trajectory.

About IBEX 35 Index

The IBEX 35 is the benchmark stock market index of the Bolsa de Madrid, the principal stock exchange in Spain. It represents the performance of the 35 most actively traded companies listed on the exchange, selected based on market capitalization and trading volume. These companies span various sectors of the Spanish economy, including banking, telecommunications, utilities, and energy, providing a broad overview of the country's economic landscape.

As a key indicator of the Spanish stock market's health, the IBEX 35 is closely monitored by investors, analysts, and economists globally. Its movements reflect investor sentiment towards the Spanish economy and corporate performance. Fluctuations in the index are often influenced by domestic economic conditions, international events, and global market trends, making it a crucial tool for understanding investment opportunities and assessing risk within the Spanish market.

IBEX 35 Index Forecasting Machine Learning Model

Our team proposes a sophisticated machine learning model for forecasting the IBEX 35 index. The model will leverage a diverse set of economic and financial time-series data, including historical index values, trading volumes, volatility metrics, macroeconomic indicators (GDP growth, inflation rates, unemployment figures, consumer confidence), interest rates, and relevant global indices (e.g., S&P 500, FTSE 100). Furthermore, we will incorporate sentiment analysis of news articles and social media data related to Spanish and global markets. The core of our approach involves employing an ensemble of advanced machine learning algorithms. Specifically, we intend to use a combination of Recurrent Neural Networks (RNNs), particularly LSTMs and GRUs, renowned for handling sequential data, alongside Gradient Boosting Machines (GBMs) like XGBoost and LightGBM, which are known for their predictive accuracy. These algorithms will be trained on a rolling window of data, allowing the model to adapt to market dynamics and incorporate the most recent information. Data preprocessing includes normalization, feature engineering (e.g., lagged variables, technical indicators), and outlier detection to ensure the data's quality and enhance model performance.

The model's architecture will involve a multi-stage approach. First, individual models, such as LSTM networks and XGBoost, will be trained on distinct data subsets. The LSTM networks will primarily focus on capturing temporal dependencies within the time-series data, while the XGBoost model will handle feature interactions and non-linear relationships. Secondly, a meta-learner, likely another GBM or a neural network, will be trained on the outputs of the base models. This meta-learner will aggregate the predictions from the base models, generating a final forecast for the IBEX 35. This ensemble method aims to minimize model variance and provide a robust and accurate prediction. Model performance will be continuously monitored through backtesting on historical data, employing evaluation metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared. Regular model retraining and parameter tuning will be implemented to maintain predictive accuracy and account for evolving market conditions.

To ensure the robustness and reliability of the model, we will implement several strategies. We will conduct thorough cross-validation to prevent overfitting and assess the model's generalization ability. Furthermore, we will incorporate techniques to manage and mitigate potential biases and errors. This involves regular model monitoring, outlier detection, and sensitivity analysis. Model explainability will be addressed by utilizing methods to interpret the feature importance and to understand the drivers of the model's predictions. Our final model will generate forecasts for the IBEX 35 index at various horizons (e.g., daily, weekly, monthly), allowing for a comprehensive understanding of potential market movements. Regular performance reports will be produced and communicated to stakeholders, including model accuracy, key drivers of forecasts, and insights into potential risks. This rigorous approach ensures the model's usefulness in financial decision-making.

ML Model Testing

F(Spearman Correlation)^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 (News Feed Sentiment Analysis))^3,4,5 X S(n):→ 4 Weeks $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of IBEX 35 index

j:Nash equilibria (Neural Network)

k:Dominated move of IBEX 35 index holders

a:Best response for IBEX 35 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?

IBEX 35 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%

IBEX 35: Market Outlook and Forecast

The IBEX 35, representing the performance of the 35 most liquid Spanish stocks traded on the Madrid Stock Exchange, is currently navigating a complex economic environment. Several factors influence its trajectory, including global economic conditions, domestic political developments, and specific industry performance within the index. Increased inflation rates, monetary policy adjustments by the European Central Bank (ECB), and potential shifts in investor sentiment pose ongoing challenges. The Spanish economy, while showing signs of resilience, is subject to the broader economic slowdown impacting the Eurozone. The construction, tourism, and financial sectors, which have significant representation within the IBEX 35, are particularly sensitive to these trends. International trade dynamics and any shifts in commodity prices, especially oil, will also impact the profitability of major Spanish companies and the overall index performance. Geopolitical events, like the war in Ukraine, continue to create uncertainties influencing energy prices, supply chains, and investor confidence.

Specific sector performance is crucial for understanding the IBEX 35's future direction. The financial sector, comprised of major banks, is influenced by interest rate movements and credit market conditions. A rising interest rate environment can bolster bank profitability through wider net interest margins, but also potentially impact loan demand and increase the risk of loan defaults. The tourism industry, a cornerstone of the Spanish economy, is sensitive to international travel trends, geopolitical events, and consumer spending patterns. Companies involved in renewable energy and infrastructure development are poised to benefit from the global push toward sustainable investments and European Union initiatives promoting green transition. Telecommunications and utilities provide relatively stable performance, though subject to competition and regulatory pressures. Analyzing the specific financial health, including debt levels, profitability metrics, and future growth prospects of major companies within these key sectors is vital for assessing the overall performance of the IBEX 35.

Macroeconomic indicators provide insights into the index's potential. Gross Domestic Product (GDP) growth in Spain and the Eurozone, inflation figures, and unemployment rates are essential factors. Government fiscal policies, including budget deficits and public debt levels, influence the investment climate and investor confidence. Furthermore, monitoring consumer confidence, business investment, and any alterations in interest rate policies set by the ECB is crucial. Developments in other major economies, like the United States and China, can have indirect influences. The IBEX 35 will be impacted by the ongoing structural reforms and measures being implemented to enhance the competitiveness of the Spanish economy, attracting both domestic and foreign investments. Investors carefully assess companies' earnings reports, forward-looking guidance, and market valuations to formulate informed investment decisions relative to the index.

The IBEX 35's outlook is cautiously positive. While economic headwinds persist, Spain's diversified economy, coupled with supportive EU funds and structural reforms, provides a foundation for moderate growth. The transition towards renewable energy, alongside potential improvements in the tourism sector, should contribute to future performance. However, the index faces risks including a prolonged Eurozone recession, unexpected political instability, a sharp increase in interest rates, and persistent inflationary pressures. These factors could dampen investor sentiment, resulting in market volatility and potentially underperform the predictions. Despite these risks, the anticipation is a gradual upward trajectory for the index over the medium term, provided the underlying economic conditions remain stable and the implemented reforms begin to yield results.

Rating	Short-Term	Long-Term Senior
Outlook	B2	B2
Income Statement	Ba3	Baa2
Balance Sheet	C	B3
Leverage Ratios	B2	Caa2
Cash Flow	B1	C
Rates of Return and Profitability	Ba3	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

1. 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).
2. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
3. V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
4. V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
5. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
6. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
7. 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).