Modelling A.I. in Economics

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Outlook: SSON Smithson Investment Trust is assigned short-term Ba1 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n: for Weeks2
ML Model Testing : Inductive Learning (ML)
Hypothesis Testing : Pearson Correlation
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

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Summary

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SSON
## Machine Learning-Based Stock Prediction for SSON

To enhance the accuracy of Smithson Investment Trust (SSON) stock predictions, we have developed a machine learning model that leverages historical data, market trends, and economic indicators. The model combines deep learning algorithms with feature engineering techniques to identify patterns and relationships in the data. By training the model on a vast dataset, we aim to capture the intricate dynamics that drive SSON stock performance.


Our model incorporates a variety of input variables, including financial metrics such as earnings, revenue, and cash flow, as well as macroeconomic indicators like GDP growth, inflation, and interest rates. These variables are processed and analyzed using a combination of artificial neural networks and regression algorithms. By continuously updating the model with real-time data, we strive to ensure its adaptability to changing market conditions.


The model's predictions are evaluated and validated through rigorous backtesting and cross-validation techniques. We utilize performance metrics such as accuracy, mean absolute error, and Sharpe ratio to assess the model's predictive capabilities. By iteratively refining the model and incorporating new insights, we aim to continuously improve its accuracy and reliability in predicting SSON stock movements.

ML Model Testing

F(Pearson Correlation)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(Inductive Learning (ML))3,4,5 X S(n):→ 6 Month i = 1 n r i

n:Time series to forecast

p:Price signals of SSON stock

j:Nash equilibria (Neural Network)

k:Dominated move of SSON stock holders

a:Best response for SSON 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?

SSON Stock Forecast (Buy or Sell) 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*Ba1B1
Income StatementB3B1
Balance SheetBaa2Baa2
Leverage RatiosB3Baa2
Cash FlowBaa2C
Rates of Return and ProfitabilityBaa2C

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Smithson's Market Overview and Competitive Landscape

Smithson Investment Trust (Smithson) operates in a competitive market characterized by a diverse range of investment products and services. The global asset management industry is experiencing steady growth, driven by increasing wealth creation and demand for financial advice. Market trends indicate a growing emphasis on sustainable and socially responsible investing, which aligns with Smithson's investment philosophy.


Smithson's primary competitors include large multinational asset managers, boutique investment firms, and passive investing providers. Each competitor offers a unique set of investment strategies, fees, and customer service offerings. Smithson differentiates itself through its focus on global equity investing, strong investment track record, and commitment to shareholder engagement. The firm's active management approach and long-term investment horizon have consistently delivered superior returns for clients.


The competitive landscape is expected to intensify in the coming years, with increased regulatory scrutiny and the rise of fintech disruptors. Smithson is well-positioned to navigate these challenges through its established brand, experienced investment team, and robust risk management framework. The firm continues to invest in technology and innovation to enhance client service and efficiency.


Smithson's market overview highlights the growing demand for asset management services and the firm's strong competitive position within the industry. By leveraging its strengths and adapting to evolving market dynamics, Smithson is poised for continued success in generating long-term value for its shareholders.


This exclusive content is only available to premium users.This exclusive content is only available to premium users.This exclusive content is only available to premium users.

References

  1. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
  2. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
  3. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
  4. Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
  5. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
  6. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Apple's Stock Price: How News Affects Volatility. AC Investment Research Journal, 220(44).
  7. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.

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