BNP Paribas Research Report

Summary

Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction. We evaluate BNP Paribas prediction models with Deductive Inference (ML) and Pearson Correlation1,2,3,4 and conclude that the BNP.PA 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 BNP.PA stock.

Key Points

1. What is a prediction confidence?
2. How do you decide buy or sell a stock?
3. Short/Long Term Stocks

BNP.PA Target Price Prediction Modeling Methodology

We consider BNP Paribas Stock Decision Process with Deductive Inference (ML) where A is the set of discrete actions of BNP.PA 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 Î³ ∈ [0, 1] is a move factor for expectation.1,2,3,4

F(Pearson Correlation)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Deductive Inference (ML)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of BNP.PA 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?

BNP.PA Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: BNP.PA BNP Paribas
Time series to forecast n: 18 Nov 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold BNP.PA 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 BNP Paribas

1. An entity shall apply the impairment requirements in Section 5.5 retrospectively in accordance with IAS 8 subject to paragraphs 7.2.15 and 7.2.18–7.2.20.
2. If the holder cannot assess the conditions in paragraph B4.1.21 at initial recognition, the tranche must be measured at fair value through profit or loss. If the underlying pool of instruments can change after initial recognition in such a way that the pool may not meet the conditions in paragraphs B4.1.23–B4.1.24, the tranche does not meet the conditions in paragraph B4.1.21 and must be measured at fair value through profit or loss. However, if the underlying pool includes instruments that are collateralised by assets that do not meet the conditions in paragraphs B4.1.23–B4.1.24, the ability to take possession of such assets shall be disregarded for the purposes of applying this paragraph unless the entity acquired the tranche with the intention of controlling the collateral.
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. 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.

*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

BNP Paribas assigned short-term B2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Pearson Correlation1,2,3,4 and conclude that the BNP.PA 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 BNP.PA stock.

Financial State Forecast for BNP.PA BNP Paribas Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2Ba2
Operational Risk 4753
Market Risk4258
Technical Analysis5182
Fundamental Analysis8388
Risk Unsystematic6155

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 507 signals.

References

1. V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
2. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
3. D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
4. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
5. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
6. J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
7. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
Frequently Asked QuestionsQ: What is the prediction methodology for BNP.PA stock?
A: BNP.PA stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Pearson Correlation
Q: Is BNP.PA stock a buy or sell?
A: The dominant strategy among neural network is to Hold BNP.PA Stock.
Q: Is BNP Paribas stock a good investment?
A: The consensus rating for BNP Paribas is Hold and assigned short-term B2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of BNP.PA stock?
A: The consensus rating for BNP.PA is Hold.
Q: What is the prediction period for BNP.PA stock?
A: The prediction period for BNP.PA is (n+4 weeks)