Modelling A.I. in Economics

LON:TM1 Target Price Prediction

TECHNOLOGY MINERALS PLC Research Report

Summary

This study aims to predict the direction of stock prices by integrating time-varying effective transfer entropy (ETE) and various machine learning algorithms. At first, we explore that the ETE based on 3 and 6 months moving windows can be regarded as the market explanatory variable by analyzing the association between the financial crises and Granger-causal relationships among the stocks. We evaluate TECHNOLOGY MINERALS PLC prediction models with Active Learning (ML) and Paired T-Test1,2,3,4 and conclude that the LON:TM1 stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:TM1 stock.

Key Points

  1. What are the most successful trading algorithms?
  2. Trading Signals
  3. Stock Rating

LON:TM1 Target Price Prediction Modeling Methodology

We consider TECHNOLOGY MINERALS PLC Stock Decision Process with Active Learning (ML) where A is the set of discrete actions of LON:TM1 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(Paired T-Test)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(Active Learning (ML)) X S(n):→ (n+4 weeks) e x rx

n:Time series to forecast

p:Price signals of LON:TM1 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:TM1 Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:TM1 TECHNOLOGY MINERALS PLC
Time series to forecast n: 19 Nov 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:TM1 stock.

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 (Yellow to Green): *Technical Analysis%

Adjusted IFRS* Prediction Methods for TECHNOLOGY MINERALS PLC

  1. However, an entity is not required to separately recognise interest revenue or impairment gains or losses for a financial asset measured at fair value through profit or loss. Consequently, when an entity reclassifies a financial asset out of the fair value through profit or loss measurement category, the effective interest rate is determined on the basis of the fair value of the asset at the reclassification date. In addition, for the purposes of applying Section 5.5 to the financial asset from the reclassification date, the date of the reclassification is treated as the date of initial recognition.
  2. If a guarantee provided by an entity to pay for default losses on a transferred asset prevents the transferred asset from being derecognised to the extent of the continuing involvement, the transferred asset at the date of the transfer is measured at the lower of (i) the carrying amount of the asset and (ii) the maximum amount of the consideration received in the transfer that the entity could be required to repay ('the guarantee amount'). The associated liability is initially measured at the guarantee amount plus the fair value of the guarantee (which is normally the consideration received for the guarantee). Subsequently, the initial fair value of the guarantee is recognised in profit or loss when (or as) the obligation is satisfied (in accordance with the principles of IFRS 15) and the carrying value of the asset is reduced by any loss allowance.
  3. Adjusting the hedge ratio by decreasing the volume of the hedging instrument does not affect how the changes in the value of the hedged item are measured. The measurement of the changes in the fair value of the hedging instrument related to the volume that continues to be designated also remains unaffected. However, from the date of rebalancing, the volume by which the hedging instrument was decreased is no longer part of the hedging relationship. For example, if an entity originally hedged the price risk of a commodity using a derivative volume of 100 tonnes as the hedging instrument and reduces that volume by 10 tonnes on rebalancing, a nominal amount of 90 tonnes of the hedging instrument volume would remain (see paragraph B6.5.16 for the consequences for the derivative volume (ie the 10 tonnes) that is no longer a part of the hedging relationship).
  4. The accounting for the time value of options in accordance with paragraph 6.5.15 applies only to the extent that the time value relates to the hedged item (aligned time value). The time value of an option relates to the hedged item if the critical terms of the option (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the option and the hedged item are not fully aligned, an entity shall determine the aligned time value, ie how much of the time value included in the premium (actual time value) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.15). An entity determines the aligned time value using the valuation of the option that would have critical terms that perfectly match the hedged item.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

TECHNOLOGY MINERALS PLC assigned short-term Ba1 & long-term B2 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Paired T-Test1,2,3,4 and conclude that the LON:TM1 stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:TM1 stock.

Financial State Forecast for LON:TM1 TECHNOLOGY MINERALS PLC Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba1B2
Operational Risk 8159
Market Risk4848
Technical Analysis5588
Fundamental Analysis8439
Risk Unsystematic8940

Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 513 signals.

References

  1. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
  2. Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
  3. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
  4. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
  5. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
  6. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
  7. Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
Frequently Asked QuestionsQ: What is the prediction methodology for LON:TM1 stock?
A: LON:TM1 stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Paired T-Test
Q: Is LON:TM1 stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:TM1 Stock.
Q: Is TECHNOLOGY MINERALS PLC stock a good investment?
A: The consensus rating for TECHNOLOGY MINERALS PLC is Hold and assigned short-term Ba1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:TM1 stock?
A: The consensus rating for LON:TM1 is Hold.
Q: What is the prediction period for LON:TM1 stock?
A: The prediction period for LON:TM1 is (n+4 weeks)

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