BLDP:TSX Ballard Power Systems Inc.

Buy

Hold

Sell

Speculative

Outlook: Ballard Power Systems Inc. assigned short-term Ba3 & long-term B2 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 06 Dec 2022 for (n+6 month)

Abstract

Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modeling of finance time series importantly guide investors' decisions and trades. In addition, in a dynamic environment such as the stock market, the nonlinearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this paper proposes an intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices.(Leung, C.K.S., MacKinnon, R.K. and Wang, Y., 2014, July. A machine learning approach for stock price prediction. In Proceedings of the 18th International Database Engineering & Applications Symposium (pp. 274-277).) We evaluate Ballard Power Systems Inc. prediction models with Multi-Task Learning (ML) and Sign Test^1,2,3,4 and conclude that the BLDP:TSX stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy BLDP:TSX stock.

Key Points

1. Market Signals
2. Decision Making
3. What is prediction model?

BLDP:TSX Target Price Prediction Modeling Methodology

We consider Ballard Power Systems Inc. Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of BLDP: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(Sign 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(Multi-Task Learning (ML)) X S(n):→ (n+6 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of BLDP: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?

BLDP:TSX Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: BLDP:TSX Ballard Power Systems Inc.
Time series to forecast n: 06 Dec 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy BLDP:TSX 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 Ballard Power Systems Inc.

1. Annual Improvements to IFRSs 2010–2012 Cycle, issued in December 2013, amended paragraphs 4.2.1 and 5.7.5 as a consequential amendment derived from the amendment to IFRS 3. An entity shall apply that amendment prospectively to business combinations to which the amendment to IFRS 3 applies.
2. A hedge of a firm commitment (for example, a hedge of the change in fuel price relating to an unrecognised contractual commitment by an electric utility to purchase fuel at a fixed price) is a hedge of an exposure to a change in fair value. Accordingly, such a hedge is a fair value hedge. However, in accordance with paragraph 6.5.4, a hedge of the foreign currency risk of a firm commitment could alternatively be accounted for as a cash flow hedge.
3. Credit risk analysis is a multifactor and holistic analysis; whether a specific factor is relevant, and its weight compared to other factors, will depend on the type of product, characteristics of the financial instruments and the borrower as well as the geographical region. An entity shall consider reasonable and supportable information that is available without undue cost or effort and that is relevant for the particular financial instrument being assessed. However, some factors or indicators may not be identifiable on an individual financial instrument level. In such a case, the factors or indicators should be assessed for appropriate portfolios, groups of portfolios or portions of a portfolio of financial instruments to determine whether the requirement in paragraph 5.5.3 for the recognition of lifetime expected credit losses has been met.
4. For the purposes of applying the requirement in paragraph 5.7.7(a), credit risk is different from asset-specific performance risk. Asset-specific performance risk is not related to the risk that an entity will fail to discharge a particular obligation but instead it is related to the risk that a single asset or a group of assets will perform poorly (or not at all).

*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

Ballard Power Systems Inc. assigned short-term Ba3 & long-term B2 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Sign Test^1,2,3,4 and conclude that the BLDP:TSX stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy BLDP:TSX stock.

Financial State Forecast for BLDP:TSX Ballard Power Systems Inc. Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B2
Operational Risk	46	33
Market Risk	85	38
Technical Analysis	87	56
Fundamental Analysis	60	66
Risk Unsystematic	56	61

Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 656 signals.

References

1. Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
2. Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505
3. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Tempur Sealy Stock Forecast & Analysis. AC Investment Research Journal, 101(3).
4. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
5. L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
6. Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
7. 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.

Frequently Asked Questions

Q: What is the prediction methodology for BLDP:TSX stock?
A: BLDP:TSX stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Sign Test
Q: Is BLDP:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy BLDP:TSX Stock.
Q: Is Ballard Power Systems Inc. stock a good investment?
A: The consensus rating for Ballard Power Systems Inc. is Buy and assigned short-term Ba3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of BLDP:TSX stock?
A: The consensus rating for BLDP:TSX is Buy.
Q: What is the prediction period for BLDP:TSX stock?
A: The prediction period for BLDP:TSX is (n+6 month)

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