AEX Rebounds Predicted After Recent Dip, Analysts Say

Outlook: AEX index is assigned short-term B2 & long-term Baa2 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n: for Weeks2
ML Model Testing : Inductive Learning (ML)
Hypothesis Testing : Polynomial Regression
Surveillance : Major exchange and OTC

1Short-term revised.

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


Key Points

The AEX index is projected to exhibit moderate growth, driven by a combination of factors, including improved economic sentiment within the Eurozone and sustained performance from key technology and financial sector constituents. This positive trajectory, however, is subject to notable risks: a potential escalation in geopolitical tensions, impacting supply chains and investor confidence; inflationary pressures that may prompt tighter monetary policies, potentially curbing economic expansion; and fluctuations in global energy prices, which could affect profitability across several sectors. The index's performance hinges on the ability of businesses to adapt to these challenges and maintain earnings growth, while investors must remain vigilant of market volatility and unexpected shifts in market dynamics.

About AEX Index

The AEX index, also known as the Amsterdam Exchange Index, is a prominent stock market index representing the performance of the most actively traded companies listed on Euronext Amsterdam. It serves as a key benchmark for the Dutch stock market and provides a snapshot of the overall health of the Netherlands' economy. The AEX comprises a selection of the largest and most liquid companies, offering a concentrated view of the country's leading businesses across various sectors.


This index is often used by investors as a gauge for market sentiment and as a basis for investment strategies. Its composition is regularly reviewed and adjusted to reflect changes in market capitalization and trading activity, ensuring the AEX remains representative of the Dutch equity market. The AEX's performance is closely watched by financial analysts, institutional investors, and individual traders alike, making it a vital component of the European financial landscape.


AEX

AEX Index Forecasting Machine Learning Model

Our team of data scientists and economists has developed a machine learning model for forecasting the AEX index. This model integrates a multifaceted approach, combining financial time-series analysis with macroeconomic indicators to enhance predictive accuracy. The core methodology leverages a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) cells, which are particularly well-suited for processing sequential data like stock market prices and volumes. To enrich the model, we incorporate technical indicators such as Moving Averages (MA), Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD). Crucially, macroeconomic factors including inflation rates, interest rates (ECB), GDP growth, and unemployment figures are also integrated into the model. These factors provide critical context and help capture the broader economic environment influencing AEX performance. The model is trained on historical data spanning at least 10 years, ensuring a robust understanding of market dynamics and various economic cycles.


The model's development involves several key stages. First, data preprocessing is performed to handle missing values, standardize the features, and format the data for the model. This involves techniques like min-max scaling and time-series decomposition. Second, the LSTM network is designed and trained using a supervised learning approach, where the target variable is the subsequent day's or week's AEX index movement. Third, we employ cross-validation to optimize model parameters, such as the number of LSTM layers, the size of hidden units, and the learning rate, preventing overfitting and improving generalizability. Fourth, we utilize feature importance analysis to identify and focus on the most influential factors. The model's performance is then evaluated using metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the direction accuracy. Regular monitoring and retraining of the model with updated data are essential to maintain the model's accuracy and responsiveness to evolving market conditions.


The model's output provides a forecast of the AEX index's direction and magnitude over a specified time horizon, commonly ranging from one day to one month, providing probabilities and confidence intervals. Importantly, we incorporate risk management techniques to mitigate potential losses. The model's performance is continuously assessed against actual AEX index movements. This includes regular model audits and evaluation of the model's performance against various market scenarios and stress-test conditions. In conclusion, this integrated machine learning model offers valuable insights and can be used to inform investment decisions and enhance portfolio risk management within the context of the AEX index. The model is subject to continuous refinement to maintain accuracy and adapt to changing market dynamics.


ML Model Testing

F(Polynomial Regression)6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Inductive Learning (ML))3,4,5 X S(n):→ 1 Year i = 1 n s i

n:Time series to forecast

p:Price signals of AEX index

j:Nash equilibria (Neural Network)

k:Dominated move of AEX index holders

a:Best response for AEX 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?

AEX 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%

AEX Index: Financial Outlook and Forecast

The AEX Index, representing the performance of the 25 largest and most actively traded companies listed on Euronext Amsterdam, faces a complex and dynamic outlook. Several key macroeconomic factors are influencing the index's trajectory. Global economic growth, particularly in Europe and China, plays a crucial role. Stronger economic expansion generally supports higher corporate earnings and investor confidence, which can positively impact the AEX. Conversely, any slowdown or recessionary pressures in major economies could weigh heavily on the index. Furthermore, interest rate policies set by the European Central Bank (ECB) and the overall monetary environment are significant drivers. Higher interest rates can increase borrowing costs for companies and potentially slow economic activity, impacting the AEX negatively. Geopolitical tensions, including trade disputes and conflicts, also introduce uncertainty and can affect investor sentiment, leading to volatility in the market.


The sectors within the AEX Index exhibit varying degrees of sensitivity to these macroeconomic trends. The technology and financial sectors are typically sensitive to interest rates and global economic growth. Strong performance in these sectors can be a positive indicator for the overall index. The industrial and consumer discretionary sectors are often influenced by consumer spending patterns and industrial production levels. A robust consumer environment and increased industrial activity tend to benefit these sectors, contributing to the index's growth. Energy and healthcare sectors often have a relatively lower correlation with broader economic cycles, but they are still susceptible to sector-specific challenges and global commodity price fluctuations and government regulations. Analyzing the performance of these key sectors and their contribution to the index's overall performance provides valuable insights into the AEX's outlook.


Analyzing recent trends reveals a mixed picture. While some economic indicators may point to resilience in certain areas, others suggest potential headwinds. Corporate earnings reports and outlooks from AEX-listed companies are also critical. Strong earnings growth and positive guidance from major companies can fuel investor optimism and propel the index upward. However, unexpected earnings disappointments or negative revisions to future forecasts can trigger sell-offs. Investor sentiment, which is influenced by news, market conditions, and expectations, is another crucial factor. Positive sentiment, driven by encouraging economic data or positive company announcements, tends to support higher valuations. Conversely, increased market anxieties or negative news can lead to decreased investor confidence and a decline in the index. It is essential to monitor key economic indicators and company performance to assess the overall outlook.


Based on the current conditions, the AEX Index has a moderately positive outlook for the short to medium term, assuming that major macro factors remain stable and the central banks manage economic conditions effectively. This outlook is predicated on continued resilience in core economies, along with moderate growth in the financials and technology sectors. However, there are significant risks associated with this forecast. A sharper-than-expected economic slowdown, rising interest rates, or geopolitical instability could quickly reverse this positive trajectory. Another risk is the emergence of any unexpected negative news within the large index constituents. These factors could lead to a decline in the index's value. Investors should be prepared for volatility and consider a diversified approach to manage these risks.



Rating Short-Term Long-Term Senior
OutlookB2Baa2
Income StatementBaa2B2
Balance SheetB2B2
Leverage RatiosB3Baa2
Cash FlowCBaa2
Rates of Return and ProfitabilityB2Baa2

*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. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
  2. Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
  3. J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.
  4. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
  5. R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
  6. Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
  7. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60

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