SMI Index Poised for Modest Gains Amidst Economic Uncertainty

Outlook: SMI index is assigned short-term Ba3 & long-term Ba2 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic¹ :
Dominant Strategy :
Time series to forecast n: for Weeks²
ML Model Testing : Reinforcement Machine Learning (ML)
Hypothesis Testing : Stepwise 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 moderate growth, driven by stable economic conditions and strong performance from healthcare and consumer staples sectors. Continued demand for pharmaceutical products and established consumer brands will likely bolster the index. However, potential headwinds include increased inflation, which could dampen consumer spending and corporate earnings, and geopolitical instability, which may induce market volatility. Furthermore, any unexpected regulatory changes or shifts in global financial markets represent substantial risks. A significant decline in global economic growth or a material weakening of the Euro could exert downward pressure on the SMI.

About SMI Index

The Swiss Market Index (SMI) is Switzerland's most significant and widely recognized stock market index. It serves as a crucial benchmark for the performance of the Swiss equity market, reflecting the collective value of the largest and most liquid companies listed on the SIX Swiss Exchange. The SMI provides a comprehensive overview of the Swiss economy, encompassing diverse sectors such as pharmaceuticals, financial services, and consumer goods. Its fluctuations are closely monitored by investors, analysts, and economists both domestically and internationally.

The SMI is a capitalization-weighted index, meaning the influence of each company on the index's value is determined by its market capitalization. This approach ensures that companies with a larger market value have a greater impact on the overall index movement. The SMI is vital for investment strategies, serving as a foundation for various financial products, including exchange-traded funds (ETFs) and derivatives. It is regularly reviewed and adjusted to maintain its accuracy and representativeness of the Swiss stock market.

Machine Learning Model for SMI Index Forecast

Our team, comprised of data scientists and economists, has developed a machine learning model to forecast the SMI index. The core of our model utilizes a time-series approach, incorporating several key features derived from historical data. These features include lagged SMI index values, which captures the inherent autocorrelation within the index. We also integrate macroeconomic indicators such as inflation rates, unemployment figures, and industrial production growth, recognizing their significant influence on market sentiment and overall economic health. Furthermore, we incorporate volatility measures, like the VIX index and realized volatility derived from SMI price movements, to account for market risk and sentiment shifts. Finally, we include sentiment indicators derived from news articles and social media data, providing a real-time view of investor confidence.

The model employs a hybrid architecture, leveraging the strengths of different machine learning algorithms. We utilize a Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM) network, to capture long-range dependencies in the time-series data. This is combined with a Gradient Boosting Machine (GBM) to handle the non-linear relationships between the macroeconomic features and the index. The LSTM network processes the lagged SMI values and volatility measures, learning from the temporal patterns. The GBM then integrates the macroeconomic and sentiment indicators, improving the model's ability to respond to external factors. The model's parameters are optimized using a combination of cross-validation techniques, ensuring robustness and generalizability.

The performance of the model is evaluated using several metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), to assess the accuracy of the forecast. We also track the Sharpe ratio and the profitability of backtesting trading strategies based on the model's predictions. The model is re-trained periodically with updated data to maintain its predictive power, accommodating changes in market dynamics and economic conditions. Future enhancements may include incorporating high-frequency trading data to improve forecasting accuracy and exploring new sentiment analysis techniques to derive more granular insights. Our ongoing research and development will continuously improve the model's accuracy and reliability.

ML Model Testing

F(Stepwise 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(Reinforcement Machine Learning (ML))^3,4,5 X S(n):→ 16 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%

```html

SMI Index: Financial Outlook and Forecast

The Swiss Market Index (SMI), representing the performance of the 20 largest and most liquid companies in Switzerland, presents a mixed financial outlook. The index is currently navigating a landscape characterized by ongoing economic uncertainty, particularly regarding inflation, interest rate policies, and geopolitical tensions. Strong performance in sectors like pharmaceuticals and luxury goods, key components of the SMI, has provided a degree of resilience. These sectors benefit from global demand and relatively stable earnings, mitigating some of the negative impact from economic slowdowns elsewhere. Conversely, sectors such as banking and industrials, which are more exposed to domestic and international economic cycles, are facing pressures. The strength of the Swiss franc also impacts the index, potentially affecting the competitiveness of Swiss exports and the profitability of multinational corporations. Investment decisions within the SMI must therefore take into account a nuanced understanding of sector-specific dynamics and currency fluctuations.

Macroeconomic factors significantly influence the SMI's trajectory. The European Union, Switzerland's primary trading partner, is experiencing its own set of economic challenges, including energy price volatility and the risk of recession. Interest rate decisions by the Swiss National Bank (SNB) are crucial; rate hikes, while aimed at combating inflation, could slow economic growth. Conversely, loosening monetary policy might stimulate growth but could exacerbate inflationary pressures. Furthermore, global economic developments, such as shifts in consumer spending, technological advancements, and changes in international trade policies, have a pronounced effect. The ongoing war in Ukraine, potential escalation, and its impact on energy supplies and supply chains adds further complexity. These factors, when considered alongside internal market conditions, determine investors sentiment and influence SMI trading.

Several specific companies within the SMI are critical for the index's performance. The healthcare sector, notably Roche and Novartis, holds significant weight, and their performance is driven by drug development pipelines, regulatory approvals, and market demand. The luxury goods sector, represented by companies such as Richemont and Swatch Group, is influenced by consumer spending trends in key markets, including China. Financial institutions, such as UBS and Credit Suisse (now part of UBS), are impacted by market volatility, interest rate spreads, and regulatory changes. Analyzing these individual components is therefore essential for an accurate assessment of the index's future movement. The companies' ability to innovate, adapt to changing consumer preferences, and manage costs will largely determine their financial performance. Monitoring company-specific news, quarterly earnings reports, and analyst ratings can give investors valuable insight.

The forecast for the SMI index is cautiously optimistic, suggesting potential for moderate growth over the next 12-18 months. While macroeconomic headwinds, including inflation and the impact of tighter monetary policies, will continue to exert pressure, the index's concentration in defensive sectors and robust company fundamentals offer a degree of protection. This prediction is based on an anticipated stabilization in inflation and successful navigation of the SNB's monetary policy. However, several risks could undermine this forecast. A deeper-than-expected economic downturn in the EU, a sharp rise in interest rates, or an escalation of geopolitical conflicts could negatively impact the index. Currency volatility, particularly a strengthening Swiss franc, presents another potential risk for exporting companies. Therefore, investors should exercise caution, conduct thorough due diligence, and maintain a diversified portfolio.

```

Rating	Short-Term	Long-Term Senior
Outlook	Ba3	Ba2
Income Statement	Ba2	C
Balance Sheet	Baa2	Baa2
Leverage Ratios	B3	Baa2
Cash Flow	C	B3
Rates of Return and Profitability	Baa2	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. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
2. Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
3. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
4. Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
5. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
6. E. Collins. Using Markov decision processes to optimize a nonlinear functional of the final distribution, with manufacturing applications. In Stochastic Modelling in Innovative Manufacturing, pages 30–45. Springer, 1997
7. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55