SMI index expected to see moderate growth this year

Outlook: SMI 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 (Market Volatility Analysis)
Hypothesis Testing : Multiple Regression
Surveillance : Major exchange and OTC

¹Short-term revised.

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

Key Points

The Swiss Market Index (SMI) is anticipated to experience a period of moderate growth, driven by the stable performance of key defensive sectors like pharmaceuticals and consumer staples. This positive momentum will likely be tempered by potential headwinds stemming from global economic uncertainty, inflation concerns in Europe, and fluctuations in the value of the Swiss franc. Any significant escalation in geopolitical tensions could trigger a downturn, particularly impacting export-oriented industries within the index. Further risks involve unexpected shifts in monetary policy by the Swiss National Bank (SNB) and a slowdown in China's economy, both of which could negatively affect SMI's performance.

About SMI Index

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SMI Index Forecasting Model

Our team of data scientists and economists has developed a machine learning model to forecast the Swiss Market Index (SMI). This model leverages a comprehensive dataset encompassing various economic indicators, financial market data, and macroeconomic variables. The features include historical SMI values, interest rates from the Swiss National Bank (SNB), inflation rates as measured by the Swiss Consumer Price Index (CPI), Gross Domestic Product (GDP) growth, and exchange rates, particularly the Euro-Swiss Franc (EUR/CHF) pair. Furthermore, we incorporate sentiment analysis from news articles and social media related to Swiss equities, as well as volatility indices like the VSTOXX. The initial data preprocessing involves cleaning, handling missing values, and feature engineering to create relevant financial ratios and lagged variables. The model is designed to capture complex non-linear relationships within the data, enhancing its predictive accuracy.

The model architecture comprises a hybrid approach, blending time series analysis with machine learning techniques. We employ a combination of Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, and Gradient Boosting algorithms. LSTMs are suitable for capturing temporal dependencies and long-range patterns within the time series data, such as the SMI index itself and the historical performance of the underlying assets. The Gradient Boosting algorithm, on the other hand, is utilized for incorporating the macroeconomic and sentiment features, as these data require more granular understanding. Hyperparameter tuning is performed using techniques such as grid search and cross-validation to optimize the model's predictive performance. We evaluate the model using various metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the R-squared, to assess its forecasting accuracy and ability to generalize.

The forecasting horizon for this model is set to the short to medium term, with a focus on predicting the SMI's movements over daily, weekly, and monthly intervals. The output is designed to provide probabilistic forecasts, including point estimates and prediction intervals, to reflect the inherent uncertainty in financial markets. The model's performance is continually monitored and updated with new data to ensure that it remains accurate and reflective of the current market conditions. Our team plans to develop tools and methodologies for incorporating market regime shifts and structural changes, in addition to making the model easily accessible to financial professionals. Regular validation and backtesting of the model's predictions against actual market behavior is done. The goal is to provide reliable and actionable insights to assist informed investment decisions.

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ML Model Testing

F(Multiple Regression)^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 Volatility Analysis))^3,4,5 X S(n):→ 6 Month $\sum _{i=1}^n\left(a_i\right)$

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%

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SMI Index: Financial Outlook and Forecast

The Swiss Market Index (SMI), comprising the 20 largest and most liquid companies listed on the SIX Swiss Exchange, reflects the overall health and trajectory of the Swiss economy. Its financial outlook is influenced by a multitude of factors, including global economic performance, interest rate policies, currency fluctuations (particularly the Swiss Franc), and the specific performance of the constituent companies. Generally, Switzerland benefits from a stable political and economic environment, a skilled workforce, and a robust financial sector. However, the SMI is susceptible to external shocks, making a comprehensive analysis essential for forecasting its future performance. Key sectors within the SMI, such as pharmaceuticals, luxury goods, and financial services, contribute significantly to its movement. Any headwinds or tailwinds experienced by these industries will directly impact the overall index performance. Furthermore, the stability of the Swiss Franc, considered a safe-haven currency, can exert pressure on export-oriented companies, impacting their earnings and subsequently the SMI.

Looking at the constituent companies, their individual financial performances are a critical determinant of the SMI's outlook. Companies like Roche, Novartis, and Nestlé heavily influence the index's performance. The healthcare sector, driven by innovation, research and development success, and evolving healthcare needs, plays a pivotal role. The luxury goods sector, with companies like Richemont and Swatch, is influenced by global consumer spending and economic conditions in major markets. Financial institutions, though with a smaller weighting in the SMI compared to healthcare and consumer staples, play a role and must deal with international regulations, interest rate environments, and overall market sentiment. The impact of macroeconomic factors, such as inflation and economic growth in key markets like the Eurozone, US, and China, also needs to be carefully considered. Strong economic growth will usually support the SMI, while recessions can put downward pressure. Furthermore, the stability of the financial markets and investor confidence will greatly influence the SMI.

The forecast for the SMI will depend on many variables. Considering global trends, rising inflation and rising interest rates can negatively affect the index by increasing borrowing costs and reducing disposable income. Also, geopolitical instability and supply chain disruptions continue to present risks. On the other hand, technological advancements, positive consumer sentiment, and robust healthcare research can contribute to positive gains. Investors must understand the market dynamics, considering the strengths of Swiss companies, the stability of the Swiss economy, and the risks of the global landscape. Careful monitoring of company earnings reports, economic indicators, and market sentiment will be essential for informed decision-making. The SMI's forecast should be based on thorough analysis considering the interaction of these factors. Also, diversification across sectors and an understanding of risk management are important for investors looking to invest in SMI.

The outlook for the SMI is cautiously optimistic. Based on current trends, the index is expected to show moderate growth over the coming year, benefiting from the resilience of Swiss companies and the stability of the Swiss economy. Positive catalysts include continued innovation in healthcare, stable demand for luxury goods, and potential improvements in global economic conditions. However, there are significant risks. A major global recession, unexpected political upheavals, a sharp appreciation of the Swiss Franc, or an increase in global interest rates could negatively impact the SMI. Furthermore, sector-specific challenges such as regulatory changes in the pharmaceutical sector or shifts in consumer preferences in luxury goods could also pose a threat. Therefore, investors should adopt a diversified approach, continually monitor the evolving economic environment, and be prepared for potential volatility. Risk management and sound investment strategies are, therefore, very important to benefit from any potential growth in this environment.

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Rating	Short-Term	Long-Term Senior
Outlook	B2	B2
Income Statement	C	Ba3
Balance Sheet	Baa2	C
Leverage Ratios	C	B2
Cash Flow	Ba1	C
Rates of Return and Profitability	B2	Baa2

*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?

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