SMI Index Poised for Moderate Growth Amidst Global Uncertainty

Outlook: SMI index is assigned short-term Ba2 & 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 : Multi-Task Learning (ML)
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 projected to experience moderate growth, driven by continued strength in defensive sectors like pharmaceuticals and consumer staples, alongside potential recovery in cyclical industries. However, this positive outlook faces several risks. Global economic slowdown and increased inflation could dampen consumer spending and corporate earnings, leading to downward pressure on the index. Geopolitical uncertainties, specifically those related to European conflicts and trade disruptions, pose substantial threats. Furthermore, currency fluctuations, with a potentially stronger Swiss franc, might negatively impact the earnings of exporting companies, thus affecting overall index performance.

About SMI Index

The Swiss Market Index (SMI) is the benchmark stock market index for Switzerland. It represents the performance of the 20 largest and most liquid companies listed on the SIX Swiss Exchange. As a capitalization-weighted index, the influence of each company on the SMI's value is directly proportional to its market capitalization, with larger companies having a greater impact. The SMI is widely used as a barometer of the Swiss economy and the overall health of the Swiss stock market. It is a crucial tool for investors, analysts, and portfolio managers seeking to understand and assess investment opportunities within the Swiss market.

The SMI's composition is reviewed periodically, typically once a year, to ensure it accurately reflects the market's dynamics and economic landscape. Changes in constituent companies are made based on market capitalization and trading volume criteria. The index is calculated in real-time and disseminated to investors throughout the trading day. The SMI is a highly liquid and actively traded index, making it a popular instrument for investors seeking exposure to the Swiss stock market through various financial products such as Exchange Traded Funds (ETFs) and futures contracts.

SMI Index Forecasting Machine Learning Model

Our interdisciplinary team, composed of data scientists and economists, has developed a sophisticated machine learning model for forecasting the Swiss Market Index (SMI). The model leverages a comprehensive dataset encompassing macroeconomic indicators, financial market data, and sentiment analysis derived from news articles and social media. Key economic variables considered include Gross Domestic Product (GDP) growth, inflation rates (Consumer Price Index and Producer Price Index), unemployment figures, and interest rate differentials (e.g., the difference between Swiss National Bank rates and those of the European Central Bank). Financial data incorporates data on currency exchange rates (CHF against major currencies), volatility indices (such as the VIX), and the performance of related international equity markets, particularly those in the Eurozone and the United States. Furthermore, sentiment analysis is conducted using natural language processing (NLP) techniques to gauge market sentiment, identifying bullish or bearish trends which is important to forecast.

The model architecture centers around a hybrid approach, combining the strengths of multiple machine learning algorithms. We employ a Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM) network, for its capacity to capture temporal dependencies inherent in financial time series data. LSTM is used alongside Gradient Boosting Machines (GBM), known for their ability to handle non-linear relationships and feature interactions. The output of the LSTM and GBM models are then integrated using a stacking ensemble, where the output of individual models becomes the input for a meta-learner, which is a Ridge Regression model that calculates the final SMI index forecast. Model performance is evaluated using standard metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared, calculated on a hold-out test set. Regular hyperparameter tuning using techniques like cross-validation is done to optimize model accuracy and prevent overfitting.

The model is designed to provide short-term and medium-term forecasts, typically ranging from one week to one month. The model output is not just a single-point estimate but also generates a confidence interval around the forecast, indicating the expected range of the SMI index. The model is continuously monitored and updated with new data. Real-time data feeds are used for continuous model retraining and drift detection. A robust feedback loop is in place, involving regular model performance reviews and adjustments to feature engineering or algorithm selection. Regular performance reports are created, offering stakeholders clear, actionable forecasts and insight into the potential risks and opportunities associated with SMI index movements. This comprehensive and adaptive approach ensures the model remains reliable and relevant in the dynamic financial landscape.

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(Multi-Task Learning (ML))^3,4,5 X S(n):→ 4 Weeks $\sum _{i=1}^n\left(a_i\right)$

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%

SMI Index Financial Outlook and Forecast

The Swiss Market Index (SMI), a benchmark index representing the performance of the 20 largest and most liquid companies listed on the SIX Swiss Exchange, reflects the overall health of the Swiss economy and its global integration. Analyzing the SMI's financial outlook necessitates considering several key factors that are currently shaping the market. Firstly, the strength of the Swiss franc, often regarded as a safe-haven currency, plays a crucial role. A stronger franc can negatively impact the profitability of Swiss multinational corporations, which derive significant revenue from international markets. Conversely, a weaker franc can boost their competitiveness and export earnings. Secondly, the global economic environment, particularly in Europe and the United States, significantly influences the SMI. Economic slowdowns or recessions in these regions can dampen demand for Swiss products and services, particularly from industries like pharmaceuticals, luxury goods, and financial services, sectors that are heavily represented in the SMI. Thirdly, sector-specific developments, such as advancements in the pharmaceutical industry and shifts in the financial services landscape, have a substantial impact. The success of drug trials, regulatory approvals, and patent expirations within the pharmaceutical sector, alongside the stability of the banking sector and the performance of wealth management firms, are pivotal considerations.

The current financial outlook for the SMI is influenced by a combination of these factors. The stability of the Swiss franc, while offering some protection against inflation, poses a challenge to exporters. The global economic backdrop presents mixed signals, with concerns about potential economic slowdowns in major economies, including Europe and the US, and inflationary pressures affecting consumer spending. However, the robust nature of some of the underlying Swiss companies provides a degree of resilience. The pharmaceutical sector, a significant component of the SMI, continues to display strength, with ongoing research and development, and high demand for existing products. The financial services sector, while facing regulatory scrutiny and changing market dynamics, is also experiencing periods of robust performance. The index's composition includes a concentration of companies with global reach, potentially insulating it, to some extent, from local economic difficulties. Furthermore, the Swiss economy's traditional strengths, such as a skilled workforce, technological innovation, and political stability, provide a solid foundation for future growth and long-term investment. The SMI's attractiveness to foreign investors, especially during periods of global uncertainty, contributes to market stability.

The forecast for the SMI necessitates a balanced assessment of both positive and negative forces. In the short to medium term, the index's performance is anticipated to be moderate. While the pharmaceutical sector's continued innovation and profitability are projected to support the index, the impacts of economic uncertainties may create headwinds. The trajectory of interest rates and the ongoing fight to combat inflationary pressures will be another determinant. Higher interest rates, while potentially beneficial for the banking sector, may also slow economic growth. The performance of key individual stocks will have a substantial effect on the overall index. Furthermore, the impact of geopolitical events, such as armed conflicts, shifts in international trade policies, and energy price volatility, are expected to influence market volatility. Factors, such as technological advancements, including artificial intelligence and developments in sustainability, may also shape the long-term outlook.

Therefore, the SMI is predicted to experience moderate positive growth in the coming years. The forecast relies on the strength of certain sectors, the stable fundamentals of the Swiss economy, and the index's established status as a safe haven for investors. However, there are significant risks to this prediction. Economic downturns in Europe and the United States would be detrimental to the index's performance, as could a significant strengthening of the Swiss franc. Unexpected geopolitical events, supply chain disruptions, and changes in global trade policies also pose substantial risks. Additionally, sector-specific challenges, such as patent expirations in the pharmaceutical industry or increased regulatory pressure on financial institutions, could negatively impact the SMI. Therefore, while the outlook is cautiously optimistic, the investment climate remains complex, requiring careful monitoring of global developments and a well-diversified investment strategy.

Rating	Short-Term	Long-Term Senior
Outlook	Ba2	B1
Income Statement	Baa2	Baa2
Balance Sheet	Baa2	Caa2
Leverage Ratios	Baa2	Caa2
Cash Flow	Caa2	C
Rates of Return and Profitability	Ba2	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?

References

1. J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
2. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
3. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
4. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
5. Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
6. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
7. Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.