Modelling A.I. in Economics

LON:TTG TT ELECTRONICS PLC

Outlook: TT ELECTRONICS PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 30 Mar 2023 for (n+16 weeks)
Methodology : Modular Neural Network (Social Media Sentiment Analysis)

Abstract

TT ELECTRONICS PLC prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Stepwise Regression1,2,3,4 and it is concluded that the LON:TTG stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

Key Points

  1. Probability Distribution
  2. Is now good time to invest?
  3. Can neural networks predict stock market?

LON:TTG Target Price Prediction Modeling Methodology

We consider TT ELECTRONICS PLC Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of LON:TTG 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


F(Stepwise Regression)5,6,7= 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 (Social Media Sentiment Analysis)) X S(n):→ (n+16 weeks) i = 1 n a i

n:Time series to forecast

p:Price signals of LON:TTG stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

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:TTG Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:TTG TT ELECTRONICS PLC
Time series to forecast n: 30 Mar 2023 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

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%

IFRS Reconciliation Adjustments for TT ELECTRONICS PLC

  1. Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.
  2. If a put option written by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at fair value, the associated liability is measured at the option exercise price plus the time value of the option. The measurement of the asset at fair value is limited to the lower of the fair value and the option exercise price because the entity has no right to increases in the fair value of the transferred asset above the exercise price of the option. This ensures that the net carrying amount of the asset and the associated liability is the fair value of the put option obligation. For example, if the fair value of the underlying asset is CU120, the option exercise price is CU100 and the time value of the option is CU5, the carrying amount of the associated liability is CU105 (CU100 + CU5) and the carrying amount of the asset is CU100 (in this case the option exercise price).
  3. All investments in equity instruments and contracts on those instruments must be measured at fair value. However, in limited circumstances, cost may be an appropriate estimate of fair value. That may be the case if insufficient more recent information is available to measure fair value, or if there is a wide range of possible fair value measurements and cost represents the best estimate of fair value within that range.
  4. IFRS 16, issued in January 2016, amended paragraphs 2.1, 5.5.15, B4.3.8, B5.5.34 and B5.5.46. An entity shall apply those amendments when it applies IFRS 16.

*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.

Conclusions

TT ELECTRONICS PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. TT ELECTRONICS PLC prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Stepwise Regression1,2,3,4 and it is concluded that the LON:TTG stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

LON:TTG TT ELECTRONICS PLC Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa3B1
Balance SheetBa2B1
Leverage RatiosB1B3
Cash FlowBaa2C
Rates of Return and ProfitabilityBa3B3

*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?

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 674 signals.

References

  1. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can stock prices be predicted?(SMI Index Stock Forecast). AC Investment Research Journal, 101(3).
  2. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., How do you decide buy or sell a stock?(SAIC Stock Forecast). AC Investment Research Journal, 101(3).
  3. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
  4. D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
  5. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
  6. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
  7. 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.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:TTG stock?
A: LON:TTG stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Stepwise Regression
Q: Is LON:TTG stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:TTG Stock.
Q: Is TT ELECTRONICS PLC stock a good investment?
A: The consensus rating for TT ELECTRONICS PLC is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:TTG stock?
A: The consensus rating for LON:TTG is Sell.
Q: What is the prediction period for LON:TTG stock?
A: The prediction period for LON:TTG is (n+16 weeks)



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