A Agilent Technologies Inc. Common Stock

Outlook: Agilent Technologies Inc. Common Stock assigned short-term Ba3 & long-term Caa1 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 17 Dec 2022 for (n+4 weeks)
Methodology : Modular Neural Network (Market News Sentiment Analysis)

Abstract

Nowadays, the stock market's prediction is a topic that attracted researchers in the world. Stock market prediction is a process that requires a comprehensive understanding of the data stock movement and analysis it accurately. Therefore, it needs intelligent methods to deal with this task to ensure that the prediction is as correct as possible, which will return profitable benefits to investors. The main goal of this article is the employment of effective machine learning techniques to build a strong model for stock market prediction.(Kompella, S. and Chakravarthy Chilukuri, K.C.C., 2020. Stock market prediction using machine learning methods. International Journal of Computer Engineering and Technology, 10(3), p.2019.) We evaluate Agilent Technologies Inc. Common Stock prediction models with Modular Neural Network (Market News Sentiment Analysis) and Independent T-Test^1,2,3,4 and conclude that the A stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. Why do we need predictive models?
2. Buy, Sell and Hold Signals
3. What are the most successful trading algorithms?

A Target Price Prediction Modeling Methodology

We consider Agilent Technologies Inc. Common Stock Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of 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(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(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+4 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of 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?

A Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: A Agilent Technologies Inc. Common Stock
Time series to forecast n: 17 Dec 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) 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 (Grey to Black): *Technical Analysis%

Adjusted IFRS* Prediction Methods for Agilent Technologies Inc. Common Stock

1. There is a rebuttable presumption that unless inflation risk is contractually specified, it is not separately identifiable and reliably measurable and hence cannot be designated as a risk component of a financial instrument. However, in limited cases, it is possible to identify a risk component for inflation risk that is separately identifiable and reliably measurable because of the particular circumstances of the inflation environment and the relevant debt market
2. For example, when the critical terms (such as the nominal amount, maturity and underlying) of the hedging instrument and the hedged item match or are closely aligned, it might be possible for an entity to conclude on the basis of a qualitative assessment of those critical terms that the hedging instrument and the hedged item have values that will generally move in the opposite direction because of the same risk and hence that an economic relationship exists between the hedged item and the hedging instrument (see paragraphs B6.4.4–B6.4.6).
3. An entity shall assess at the inception of the hedging relationship, and on an ongoing basis, whether a hedging relationship meets the hedge effectiveness requirements. At a minimum, an entity shall perform the ongoing assessment at each reporting date or upon a significant change in the circumstances affecting the hedge effectiveness requirements, whichever comes first. The assessment relates to expectations about hedge effectiveness and is therefore only forward-looking.
4. When measuring hedge ineffectiveness, an entity shall consider the time value of money. Consequently, the entity determines the value of the hedged item on a present value basis and therefore the change in the value of the hedged item also includes the effect of the time value of money.

*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

Agilent Technologies Inc. Common Stock assigned short-term Ba3 & long-term Caa1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Independent T-Test^1,2,3,4 and conclude that the A stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

Financial State Forecast for A Agilent Technologies Inc. Common Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	Caa1
Operational Risk	66	35
Market Risk	58	42
Technical Analysis	52	31
Fundamental Analysis	66	66
Risk Unsystematic	85	31

Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 608 signals.

References

1. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
2. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
3. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
4. Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678
5. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
6. 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
7. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 3111–19. San Diego, CA: Neural Inf. Process. Syst. Found.

Frequently Asked Questions

Q: What is the prediction methodology for A stock?
A: A stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Independent T-Test
Q: Is A stock a buy or sell?
A: The dominant strategy among neural network is to Buy A Stock.
Q: Is Agilent Technologies Inc. Common Stock stock a good investment?
A: The consensus rating for Agilent Technologies Inc. Common Stock is Buy and assigned short-term Ba3 & long-term Caa1 forecasted stock rating.
Q: What is the consensus rating of A stock?
A: The consensus rating for A is Buy.
Q: What is the prediction period for A stock?
A: The prediction period for A is (n+4 weeks)

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