MAXR:TSX Maxar Technologies Inc.

Outlook: Maxar Technologies Inc. assigned short-term B1 & long-term B2 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 08 Dec 2022 for (n+3 month)
Methodology : Transfer Learning (ML)

Abstract

Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors' behavior.(Hushani, P., 2019. Using autoregressive modelling and machine learning for stock market prediction and trading. In Third International Congress on Information and Communication Technology (pp. 767-774). Springer, Singapore.) We evaluate Maxar Technologies Inc. prediction models with Transfer Learning (ML) and Independent T-Test^1,2,3,4 and conclude that the MAXR:TSX stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

Key Points

1. Is Target price a good indicator?
2. What is the best way to predict stock prices?
3. How do predictive algorithms actually work?

MAXR:TSX Target Price Prediction Modeling Methodology

We consider Maxar Technologies Inc. Decision Process with Transfer Learning (ML) where A is the set of discrete actions of MAXR:TSX 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(Independent T-Test)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Transfer Learning (ML)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of MAXR:TSX 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?

MAXR:TSX Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: MAXR:TSX Maxar Technologies Inc.
Time series to forecast n: 08 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

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 Maxar Technologies Inc.

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. The requirement that an economic relationship exists means that the hedging instrument and the hedged item have values that generally move in the opposite direction because of the same risk, which is the hedged risk. Hence, there must be an expectation that the value of the hedging instrument and the value of the hedged item will systematically change in response to movements in either the same underlying or underlyings that are economically related in such a way that they respond in a similar way to the risk that is being hedged (for example, Brent and WTI crude oil).
3. Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.
4. An alternative benchmark rate designated as a non-contractually specified risk component that is not separately identifiable (see paragraphs 6.3.7(a) and B6.3.8) at the date it is designated shall be deemed to have met that requirement at that date, if, and only if, the entity reasonably expects the alternative benchmark rate will be separately identifiable within 24 months. The 24-month period applies to each alternative benchmark rate separately and starts from the date the entity designates the alternative benchmark rate as a non-contractually specified risk component for the first time (ie the 24- month period applies on a rate-by-rate basis).

*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

Maxar Technologies Inc. assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Independent T-Test^1,2,3,4 and conclude that the MAXR:TSX stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

Financial State Forecast for MAXR:TSX Maxar Technologies Inc. Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B2
Operational Risk	46	34
Market Risk	83	76
Technical Analysis	87	48
Fundamental Analysis	44	70
Risk Unsystematic	47	47

Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 740 signals.

References

1. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
2. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
3. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
4. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
5. Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
6. D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
7. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London

Frequently Asked Questions

Q: What is the prediction methodology for MAXR:TSX stock?
A: MAXR:TSX stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Independent T-Test
Q: Is MAXR:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy MAXR:TSX Stock.
Q: Is Maxar Technologies Inc. stock a good investment?
A: The consensus rating for Maxar Technologies Inc. is Buy and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of MAXR:TSX stock?
A: The consensus rating for MAXR:TSX is Buy.
Q: What is the prediction period for MAXR:TSX stock?
A: The prediction period for MAXR:TSX is (n+3 month)