IBEX 35 Index Forecast

Outlook: IBEX 35 index is assigned short-term B1 & 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 (Market News Sentiment Analysis)
Hypothesis Testing : Independent T-Test
Surveillance : Major exchange and OTC

¹Short-term revised.

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

Key Points

This exclusive content is only available to premium users.

About IBEX 35 Index

This exclusive content is only available to premium users.

This exclusive content is only available to premium users.

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(Modular Neural Network (Market News Sentiment Analysis))^3,4,5 X S(n):→ 1 Year $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

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: Financial Outlook and Forecast

The IBEX 35, Spain's benchmark stock market index, is currently navigating a complex financial landscape shaped by both domestic and international influences. On the domestic front, the Spanish economy has demonstrated resilience, supported by robust tourism, a recovering labor market, and the ongoing disbursement of European Union recovery funds. These factors have provided a foundation for corporate earnings growth, particularly in sectors like banking, utilities, and renewable energy, which hold significant weight within the index. However, persistent inflation, albeit moderating, continues to be a concern, impacting consumer spending power and potentially corporate margins. The European Central Bank's monetary policy, particularly interest rate decisions, remains a critical determinant of the overall investment environment, influencing borrowing costs and investor sentiment towards equities.

Internationally, geopolitical tensions, notably the ongoing conflict in Ukraine and broader global supply chain disruptions, continue to cast a shadow over global markets, including the IBEX 35. Fluctuations in energy prices, exacerbated by these geopolitical events, directly affect the profitability of many Spanish companies and contribute to inflationary pressures. Furthermore, the economic performance of key trading partners, especially within the Eurozone, has a significant ripple effect on Spanish exports and corporate revenues. The evolving global economic growth outlook, with varying forecasts from international institutions, adds another layer of uncertainty. Investors are closely watching for signs of a potential global slowdown or recession, which could dampen demand for goods and services produced by IBEX 35 constituents.

Looking ahead, the financial outlook for the IBEX 35 is subject to several key drivers. The continued effective deployment of NextGenerationEU funds is crucial for structural reforms and investment, offering a significant tailwind for sectors focused on digitalization and green transition. The banking sector, a substantial component of the index, is expected to benefit from a stable or slightly rising interest rate environment, supporting net interest margins. However, increased competition and the ongoing need for digital transformation pose challenges. The energy sector faces a dual dynamic: while renewable energy companies are poised for growth driven by policy support and climate goals, traditional energy producers are subject to the volatility of global commodity prices and regulatory shifts.

The overall forecast for the IBEX 35 leans towards a cautiously optimistic trajectory, contingent on the successful management of inflationary pressures and the mitigation of geopolitical risks. A positive prediction hinges on continued economic recovery, stable monetary policy, and the sustained positive impact of EU recovery funds. However, significant risks remain. These include a sharper-than-expected global economic downturn, a resurgence of inflation, escalating geopolitical conflicts leading to further energy shocks, and potential domestic political instability. A materialization of these risks could lead to a downward revision of earnings expectations and a corresponding negative impact on the index's performance.

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

*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. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
2. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
3. 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).
4. Lai TL, Robbins H. 1985. Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6:4–22
5. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
6. V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
7. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85