LON:AXL ARROW EXPLORATION CORP.

Outlook: ARROW EXPLORATION CORP. assigned short-term B1 & long-term Baa2 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 12 Dec 2022 for (n+16 weeks)
Methodology : Modular Neural Network (Financial Sentiment Analysis)

Abstract

Stocks are possibly the most popular financial instrument invented for building wealth and are the centerpiece of any investment portfolio. The advances in trading technology has opened up the markets so that nowadays nearly anybody can own stocks. From last few decades, there seen explosive increase in the average person's interest for stock market. In a financially explosive market, as the stock market, it is important to have a very accurate prediction of a future trend. Because of the financial crisis and recording profits, it is compulsory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires progressive algorithms of machine learning with help of Artificial Intelligence (AI).(Chen, S. and He, H., 2018, October. Stock prediction using convolutional neural network. In IOP Conference series: materials science and engineering (Vol. 435, No. 1, p. 012026). IOP Publishing.) We evaluate ARROW EXPLORATION CORP. prediction models with Modular Neural Network (Financial Sentiment Analysis) and Linear Regression^1,2,3,4 and conclude that the LON:AXL stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. What is neural prediction?
2. Market Risk
3. Can stock prices be predicted?

LON:AXL Target Price Prediction Modeling Methodology

We consider ARROW EXPLORATION CORP. Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of LON:AXL 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(Linear Regression)^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 (Financial Sentiment Analysis)) X S(n):→ (n+16 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON:AXL 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:AXL Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:AXL ARROW EXPLORATION CORP.
Time series to forecast n: 12 Dec 2022 for (n+16 weeks)

According to price forecasts for (n+16 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 ARROW EXPLORATION CORP.

1. When determining whether the recognition of lifetime expected credit losses is required, an entity shall consider reasonable and supportable information that is available without undue cost or effort and that may affect the credit risk on a financial instrument in accordance with paragraph 5.5.17(c). An entity need not undertake an exhaustive search for information when determining whether credit risk has increased significantly since initial recognition.
2. In some circumstances, the renegotiation or modification of the contractual cash flows of a financial asset can lead to the derecognition of the existing financial asset in accordance with this Standard. When the modification of a financial asset results in the derecognition of the existing financial asset and the subsequent recognition of the modified financial asset, the modified asset is considered a 'new' financial asset for the purposes of this Standard.
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. If such a mismatch would be created or enlarged, the entity is required to present all changes in fair value (including the effects of changes in the credit risk of the liability) in profit or loss. If such a mismatch would not be created or enlarged, the entity is required to present the effects of changes in the liability's credit risk in other comprehensive income.

*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

ARROW EXPLORATION CORP. assigned short-term B1 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Linear Regression^1,2,3,4 and conclude that the LON:AXL stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

Financial State Forecast for LON:AXL ARROW EXPLORATION CORP. Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	Baa2
Operational Risk	70	84
Market Risk	66	75
Technical Analysis	63	89
Fundamental Analysis	34	60
Risk Unsystematic	73	56

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 579 signals.

References

1. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
2. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
3. M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016
4. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
5. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
6. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
7. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002

Frequently Asked Questions

Q: What is the prediction methodology for LON:AXL stock?
A: LON:AXL stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Linear Regression
Q: Is LON:AXL stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:AXL Stock.
Q: Is ARROW EXPLORATION CORP. stock a good investment?
A: The consensus rating for ARROW EXPLORATION CORP. is Buy and assigned short-term B1 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of LON:AXL stock?
A: The consensus rating for LON:AXL is Buy.
Q: What is the prediction period for LON:AXL stock?
A: The prediction period for LON:AXL is (n+16 weeks)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login