LON:BP.A BP PLC

Outlook: BP PLC assigned short-term B2 & long-term B2 forecasted stock rating.
Dominant Strategy : Wait until speculative trend diminishes
Time series to forecast n: 07 Dec 2022 for (n+1 year)
Methodology : Multi-Instance Learning (ML)

Abstract

Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing.(Reddy, V.K.S., 2018. Stock market prediction using machine learning. International Research Journal of Engineering and Technology (IRJET), 5(10), pp.1033-1035.) We evaluate BP PLC prediction models with Multi-Instance Learning (ML) and Paired T-Test^1,2,3,4 and conclude that the LON:BP.A stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:BP.A stock.

Key Points

1. Can machine learning predict?
2. Nash Equilibria
3. What is the best way to predict stock prices?

LON:BP.A Target Price Prediction Modeling Methodology

We consider BP PLC Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of LON:BP.A 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Multi-Instance Learning (ML)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of LON:BP.A 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:BP.A Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:BP.A BP PLC
Time series to forecast n: 07 Dec 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:BP.A 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 BP PLC

1. A contractually specified inflation risk component of the cash flows of a recognised inflation-linked bond (assuming that there is no requirement to account for an embedded derivative separately) is separately identifiable and reliably measurable, as long as other cash flows of the instrument are not affected by the inflation risk component.
2. If the group of items does not have any offsetting risk positions (for example, a group of foreign currency expenses that affect different line items in the statement of profit or loss and other comprehensive income that are hedged for foreign currency risk) then the reclassified hedging instrument gains or losses shall be apportioned to the line items affected by the hedged items. This apportionment shall be done on a systematic and rational basis and shall not result in the grossing up of the net gains or losses arising from a single hedging instrument.
3. If the underlyings are not the same but are economically related, there can be situations in which the values of the hedging instrument and the hedged item move in the same direction, for example, because the price differential between the two related underlyings changes while the underlyings themselves do not move significantly. That is still consistent with an economic relationship between the hedging instrument and the hedged item if the values of the hedging instrument and the hedged item are still expected to typically move in the opposite direction when the underlyings move.
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

BP PLC assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Multi-Instance Learning (ML) with Paired T-Test^1,2,3,4 and conclude that the LON:BP.A stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:BP.A stock.

Financial State Forecast for LON:BP.A BP PLC Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B2
Operational Risk	51	67
Market Risk	62	46
Technical Analysis	34	66
Fundamental Analysis	80	34
Risk Unsystematic	55	60

Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 677 signals.

References

1. Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
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. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
4. Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
5. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., When to Sell and When to Hold FTNT Stock. AC Investment Research Journal, 101(3).
6. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
7. Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press

Frequently Asked Questions

Q: What is the prediction methodology for LON:BP.A stock?
A: LON:BP.A stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Paired T-Test
Q: Is LON:BP.A stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:BP.A Stock.
Q: Is BP PLC stock a good investment?
A: The consensus rating for BP PLC is Wait until speculative trend diminishes and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:BP.A stock?
A: The consensus rating for LON:BP.A is Wait until speculative trend diminishes.
Q: What is the prediction period for LON:BP.A stock?
A: The prediction period for LON:BP.A is (n+1 year)

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