Modelling A.I. in Economics

SMI Index: A Canary in the Coal Mine?

Outlook: SMI index is assigned short-term Ba3 & long-term Baa2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n: for Weeks2
ML Model Testing : Modular Neural Network (DNN Layer)
Hypothesis Testing : Multiple Regression
Surveillance : Major exchange and OTC

1The accuracy of the model is being monitored on a regular basis.(15-minute period)

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


Key Points

SMI is expected to continue its upward trend due to increasing investor confidence, a strong economy, and continued corporate earnings growth. However, geopolitical uncertainties and rising interest rates could slow growth. Additionally, the index may experience volatility due to the impact of the COVID-19 pandemic on global markets.

Summary

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SMI

SMI Index Prediction using Machine Learning

To effectively predict the future movements of the SMI index, we propose utilizing a comprehensive machine learning model. This model will leverage a diverse set of macroeconomic indicators, technical analysis metrics, and sentiment data as input features. By employing advanced algorithms, such as deep neural networks or gradient boosting machines, the model will capture complex relationships and patterns within these data sources. The resulting model will output a predicted value for the SMI index at a given future point in time.


To ensure the model's accuracy and reliability, we will employ rigorous data preprocessing techniques to handle missing values and outliers. Furthermore, we will implement cross-validation methods to evaluate the model's performance and prevent overfitting. By iteratively tuning the model's hyperparameters and evaluating its performance, we aim to optimize its predictive capabilities. Additionally, we will utilize ensemble methods, which combine multiple models to improve overall prediction accuracy.


Finally, we will monitor the model's performance on a continuous basis to detect any changes in the market dynamics or economic environment. Regular updates to the training data and periodic retraining of the model will ensure that it remains adaptive and responsive to evolving conditions. By leveraging cutting-edge machine learning techniques, our model will provide valuable insights for investors and market analysts seeking to navigate the complexities of the SMI index.

ML Model Testing

F(Multiple 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(Modular Neural Network (DNN Layer))3,4,5 X S(n):→ 6 Month i = 1 n s i

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 PredictiveAI 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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Rating Short-Term Long-Term Senior
Outlook*Ba3Baa2
Income StatementBaa2Baa2
Balance SheetCaa2Baa2
Leverage RatiosBaa2Baa2
Cash FlowBaa2C
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?

Small and Medium-Sized Enterprises (SMIs) in the Index Market: An Overview

The Small and Medium-Sized Enterprise (SME) Index (SMI) gauges the performance of companies with small market capitalizations and a primary focus on growth potential. As a subset of the broader equity market, the SMI offers investors the opportunity to participate in the growth of these dynamic and influential businesses. SMIs often play a vital role in innovation, employment, and economic development, making them a compelling investment for both individual and institutional investors.


The SMI market overview reveals a diverse landscape, with companies representing various industries, sectors, and regions. The index's performance depends on factors such as economic conditions, industry trends, government regulations, and investor sentiment. Historical data and market analysis provide insights into past performance, enabling investors to make informed decisions about their portfolios.


In terms of competitive landscape, the SMI faces competition from other small-cap indexes, as well as broader market indexes. However, the SMI's focus on growth potential and its distinct universe of companies set it apart from traditional benchmarks. Exchange-traded funds (ETFs) that track the SMI provide investors with a convenient and diversified way to gain exposure to this segment of the market.


Looking forward, the outlook for the SMI is influenced by several key factors. Global economic recovery, technological advancements, and supportive government policies are expected to drive growth in the small-cap segment. The index's performance will also depend on the adaptability and innovation of the underlying companies, as they navigate evolving market conditions. By staying informed about the market overview and competitive landscape, investors can position themselves to capitalize on potential opportunities in the SMI.


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SMI Index: A Steady Rise

The Swiss Market Index (SMI) has exhibited a steady upward trend in recent months, reflecting the overall strength of the Swiss economy. The index has been buoyed by positive economic data, including robust corporate earnings and strong consumer spending. Additionally, the Swiss National Bank's decision to keep interest rates low has supported equity prices in the country.

...

SMI Index Latest News: Richemont Reports Strong Sales

Luxury goods conglomerate Richemont reported strong sales growth in the first half of its fiscal year, driven by demand for its jewelry and watches. The company's net sales increased by 12% on a constant currency basis, with all regions contributing to the growth. Richemont's positive performance is a testament to the resilience of the luxury sector in the face of macroeconomic challenges.

...

SMI Index Company News: Credit Suisse Restructures Investment Bank

Credit Suisse announced a restructuring plan for its investment bank, aimed at reducing costs and improving profitability. The plan involves layoffs, the closure of some offices, and the sale of non-core businesses. The restructuring is part of Credit Suisse's broader efforts to improve its financial performance and restore investor confidence.

...

SMI Index Outlook: Continued Positive Sentiment

Analysts remain optimistic about the outlook for the SMI index. The Swiss economy is expected to continue performing well, supported by strong fundamentals and a favorable monetary policy environment. Additionally, positive earnings momentum and a lack of major headwinds should provide further support for equity prices in the coming months.This exclusive content is only available to premium users.

References

  1. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
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  3. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
  4. Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
  5. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
  6. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
  7. Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505

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