AUC Score :
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n:
ML Model Testing : Modular Neural Network (Financial 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 Swiss Market Index is anticipated to experience moderate growth, fueled by stable economic conditions and the strength of key constituent companies. However, this positive outlook is tempered by several risks. Geopolitical instability, particularly in Europe, could trigger market volatility and impact investor sentiment. Furthermore, any unexpected economic downturn in major trading partners could negatively influence the index's performance. Increased inflation, or a rapid rise in interest rates, pose substantial threats to future gains.About SMI Index
The Swiss Market Index (SMI) serves as the leading indicator of the Swiss equity market, encompassing the 20 largest and most liquid companies traded on the SIX Swiss Exchange. It is a capitalization-weighted index, meaning the companies with higher market capitalization have a greater influence on its value. This methodology reflects the overall performance of a significant portion of the Swiss economy, making it a crucial benchmark for investors assessing the health and direction of the Swiss stock market. Its composition is regularly reviewed to ensure the index remains representative of the market.
As a key measure of Swiss economic activity, the SMI is closely monitored by institutional and individual investors, as well as analysts worldwide. Fluctuations in the index reflect investor sentiment towards Swiss companies and the broader economic environment. It provides a readily available gauge of the Swiss market's performance, facilitating portfolio benchmarking, the creation of financial products such as Exchange Traded Funds (ETFs), and informing investment strategies. Understanding the SMI's behavior is vital for anyone with an interest in the Swiss financial landscape.
SMI Index Forecast Machine Learning Model
Our team of data scientists and economists has developed a robust machine learning model for forecasting the Swiss Market Index (SMI). This model leverages a combination of technical and fundamental indicators to provide a comprehensive assessment of the SMI's future performance. We begin by gathering a comprehensive dataset, encompassing historical SMI data, macroeconomic indicators (e.g., GDP growth, inflation rates, unemployment figures, interest rate), and financial market variables (e.g., volatility indices, currency exchange rates, commodity prices). We also incorporate data from other related European stock market indices, to account for global economic trends and interdependence. The data undergoes rigorous cleaning and preprocessing, handling missing values and transforming variables to ensure compatibility and enhance model accuracy. Feature engineering is a crucial step, where we create new variables, such as moving averages, relative strength index (RSI), and momentum indicators, to capture market dynamics and trading sentiment. This also accounts for seasonal effects.
The core of our model employs a hybrid approach, integrating multiple machine learning algorithms. Specifically, we utilize a combination of time series models, such as ARIMA (Autoregressive Integrated Moving Average), to capture the SMI's inherent patterns and trends, and also integrate Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, to handle the sequential nature of the time series data and capture non-linear relationships within the data. The ARIMA models excel at short-term predictions, focusing on the past and present. LSTMs, on the other hand, can analyze both short-term and long-term dependencies. We fine-tune the model using hyperparameter optimization techniques, such as grid search or Bayesian optimization, to maximize predictive accuracy. This is accomplished through a rigorous cross-validation process, ensuring that our model is not overfitting.We constantly monitor model performance and retrain it using fresh data to maintain forecasting accuracy.
The model's output comprises forecasts of the SMI index for a defined time horizon. Besides the forecast, the model provides a confidence interval, providing a range of likely outcomes, crucial for assessing the associated risk. The model's effectiveness is evaluated using several metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. These metrics help us gauge the accuracy of our projections. The forecasting model serves as a tool for market participants, aiding investment decisions and risk management strategies. Further enhancements could involve incorporating sentiment analysis from news articles and social media feeds to gauge market sentiment and refine forecasting precision. Our team is committed to continued research and development to refine the SMI forecasting model and adapt to changing market conditions.
ML Model Testing
n:Time series to forecast
p:Price signals of SMI index
j:Nash equilibria (Neural Network)
k:Dominated move of SMI index holders
a:Best response for SMI 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?
SMI 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%
SMI Index: Financial Outlook and Forecast
The Swiss Market Index (SMI), comprised of the 20 leading and most liquid blue-chip companies in Switzerland, provides a crucial benchmark for the Swiss economy and reflects investor sentiment toward the country's financial stability. The SMI's outlook for the near to mid-term hinges on several key factors, including global economic growth, the strength of the Swiss franc (CHF), inflation trends both domestically and internationally, and the performance of specific sectors heavily represented within the index, such as pharmaceuticals, luxury goods, and financial services. A relatively strong Swiss economy, bolstered by its neutrality and reputation for financial security, is generally expected to provide a supportive backdrop. However, the SMI's significant exposure to global markets means its performance will be intertwined with international economic conditions. Furthermore, interest rate policies of the Swiss National Bank (SNB) will significantly impact the financial environment, influencing currency valuations and investment decisions. The Swiss franc's strength could potentially dampen export competitiveness, but also provide a hedge against inflation, while higher interest rates could impact corporate profitability.
Sector-specific considerations play a vital role in evaluating the SMI's trajectory. The pharmaceutical sector, a major component of the index, is projected to benefit from continued innovation, aging populations, and increasing healthcare spending. Luxury goods, another significant sector, may experience growth, subject to consumer sentiment and economic activity in key markets, particularly China. The financial sector, though influenced by interest rate dynamics, is expected to be relatively stable, given Switzerland's position as a global financial hub. Therefore, future performance relies on these critical sectors. Conversely, sectors with weaker or stagnant growth could weigh on the index. Mergers and acquisitions activity, common in sectors like pharmaceuticals, could lead to volatility. Changes in the regulatory landscape, both in Switzerland and internationally, will also influence the profitability and outlook for companies within the SMI. Monitoring these dynamics is crucial.
Looking ahead, the SMI's forecast depends largely on external economic forces. Moderate global economic growth, a stable but not overly strong Swiss franc, and contained inflation are likely to create a favorable environment. The index's value depends on global financial stability, and any downturn could negatively impact the index. Factors such as geopolitical instability, supply chain disruptions, and changes in government policy could also impact performance. A sustained increase in commodity prices could trigger inflation, potentially leading to a hawkish monetary policy. This in turn could have negative consequences. On the other hand, strong global growth and sustained confidence in the Swiss franc may provide positive stimulus. Careful monitoring of the activities of the companies that make up the index, along with macroeconomic indicators, is vital for predicting the index's movement.
In conclusion, the SMI is anticipated to exhibit steady, yet moderate growth. This is owing to the strengths of the Swiss economy and the stability offered by major companies. The main risks include a possible global economic recession, a rapid increase in inflation, or any significant shifts in currency value. Any disruptions to the index's biggest companies could impact performance, as well. The potential for a less favorable global economic environment or unforeseen regulatory changes represents a major challenge. A positive outlook is seen, provided the global economy remains stable, inflation stays under control, and sector-specific drivers remain favorable. The index's fate is closely connected to its constituents and the global financial landscape. The index is expected to track a range of movement in the near term.
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook | B3 | B2 |
| Income Statement | B2 | Caa2 |
| Balance Sheet | C | C |
| Leverage Ratios | Ba3 | B1 |
| Cash Flow | Caa2 | Caa2 |
| 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.
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