Modelling A.I. in Economics

LON:HUW Stock: A High-Growth Company with a Bright Future

Outlook: HELIOS UNDERWRITING PLC is assigned short-term Ba3 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Buy
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (CNN Layer)
Hypothesis Testing : Paired T-Test
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.

HELIOS UNDERWRITING PLC prediction model is evaluated with Modular Neural Network (CNN Layer) and Paired T-Test1,2,3,4 and it is concluded that the LON:HUW stock is predictable in the short/long term. CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications.5 According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Buy

Graph 33

Key Points

  1. How useful are statistical predictions?
  2. How do you pick a stock?
  3. Can neural networks predict stock market?

LON:HUW Stock Price Forecast

We consider HELIOS UNDERWRITING PLC Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of LON:HUW stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4

Sample Set: Neural Network
Time series to forecast: 8 Weeks

According to price forecasts, the dominant strategy among neural network is: Buy

F(Paired T-Test)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 (CNN Layer)) X S(n):→ 8 Weeks i = 1 n a i

n:Time series to forecast

p:Price signals of LON:HUW stock

j:Nash equilibria (Neural Network)

k:Dominated move of LON:HUW stock holders

a:Best response for LON:HUW target price

CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications.5 A paired t-test is a statistical test that compares the means of two paired samples. In a paired t-test, each data point in one sample is paired with a data point in the other sample. The pairs are typically related in some way, such as before and after measurements, or measurements from the same subject under different conditions. The paired t-test is a parametric test, which means that it assumes that the data is normally distributed. The paired t-test is also a dependent samples test, which means that the data points in each pair are correlated.6,7


For further technical information as per how our model work we invite you to visit the article below: 

How do AC Investment Research machine learning (predictive) algorithms actually work?

LON:HUW 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%

Financial Data Adjustments for Modular Neural Network (CNN Layer) based LON:HUW Stock Prediction Model

  1. Alternatively, the entity may base the assessment on both types of information, ie qualitative factors that are not captured through the internal ratings process and a specific internal rating category at the reporting date, taking into consideration the credit risk characteristics at initial recognition, if both types of information are relevant.
  2. The assessment of whether lifetime expected credit losses should be recognised is based on significant increases in the likelihood or risk of a default occurring since initial recognition (irrespective of whether a financial instrument has been repriced to reflect an increase in credit risk) instead of on evidence of a financial asset being credit-impaired at the reporting date or an actual default occurring. Generally, there will be a significant increase in credit risk before a financial asset becomes credit-impaired or an actual default occurs.
  3. When designating risk components as hedged items, an entity considers whether the risk components are explicitly specified in a contract (contractually specified risk components) or whether they are implicit in the fair value or the cash flows of an item of which they are a part (noncontractually specified risk components). Non-contractually specified risk components can relate to items that are not a contract (for example, forecast transactions) or contracts that do not explicitly specify the component (for example, a firm commitment that includes only one single price instead of a pricing formula that references different underlyings)
  4. At the date of initial application, an entity shall determine whether the treatment in paragraph 5.7.7 would create or enlarge an accounting mismatch in profit or loss on the basis of the facts and circumstances that exist at the date of initial application. This Standard shall be applied retrospectively on the basis of that determination.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.


Rating Short-Term Long-Term Senior
Income StatementB2Baa2
Balance SheetBaa2B1
Leverage RatiosB3B2
Cash FlowBaa2Ba2
Rates of Return and ProfitabilityCaa2Caa2

*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?


  1. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
  2. J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
  3. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
  4. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
  5. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
  6. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
  7. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
Frequently Asked QuestionsQ: What is the prediction methodology for LON:HUW stock?
A: LON:HUW stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Paired T-Test
Q: Is LON:HUW stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:HUW Stock.
Q: Is HELIOS UNDERWRITING PLC stock a good investment?
A: The consensus rating for HELIOS UNDERWRITING PLC is Buy and is assigned short-term Ba3 & long-term Ba3 estimated rating.
Q: What is the consensus rating of LON:HUW stock?
A: The consensus rating for LON:HUW is Buy.
Q: What is the prediction period for LON:HUW stock?
A: The prediction period for LON:HUW is 8 Weeks

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