HAWKWING PLC Forecast & Analysis

Outlook: HAWKWING PLC is assigned short-term B2 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Sell
Time series to forecast n: for Weeks²
Methodology : Statistical Inference (ML)
Hypothesis Testing : ElasticNet Regression
Surveillance : Major exchange and OTC

¹The accuracy of the model is being monitored on a regular basis.(15-minute period)

²Time series is updated based on short-term trends.

Summary

HAWKWING PLC prediction model is evaluated with Statistical Inference (ML) and ElasticNet Regression^1,2,3,4 and it is concluded that the LON:HNG stock is predictable in the short/long term. Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell

Key Points

1. What is statistical models in machine learning?
2. Reaction Function
3. Nash Equilibria

LON:HNG Target Price Prediction Modeling Methodology

We consider HAWKWING PLC Decision Process with Statistical Inference (ML) where A is the set of discrete actions of LON:HNG 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(ElasticNet 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(Statistical Inference (ML)) X S(n):→ 1 Year $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of LON:HNG stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Statistical Inference (ML)

Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.

ElasticNet Regression

Elastic net regression is a type of regression analysis that combines the benefits of ridge regression and lasso regression. It is a regularized regression method that adds a penalty to the least squares objective function in order to reduce the variance of the estimates, induce sparsity in the model, and reduce overfitting. This is done by adding a term to the objective function that is proportional to the sum of the squares of the coefficients and the sum of the absolute values of the coefficients. The penalty terms are controlled by two parameters, called the ridge constant and the lasso constant. Elastic net regression can be used to address the problems of multicollinearity, overfitting, and sensitivity to outliers. It is a more flexible method than ridge regression or lasso regression, and it can often achieve better results.

 

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:HNG Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: LON:HNG HAWKWING PLC
Time series to forecast: 1 Year

According to price forecasts, the dominant strategy among neural network is: Sell

Strategic Interaction Table Legend:

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%

Financial Data Adjustments for Statistical Inference (ML) based LON:HNG Stock Prediction Model

1. For floating-rate financial assets and floating-rate financial liabilities, periodic re-estimation of cash flows to reflect the movements in the market rates of interest alters the effective interest rate. If a floating-rate financial asset or a floating-rate financial liability is recognised initially at an amount equal to the principal receivable or payable on maturity, re-estimating the future interest payments normally has no significant effect on the carrying amount of the asset or the liability.
2. If changes are made in addition to those changes required by interest rate benchmark reform to the financial asset or financial liability designated in a hedging relationship (as described in paragraphs 5.4.6–5.4.8) or to the designation of the hedging relationship (as required by paragraph 6.9.1), an entity shall first apply the applicable requirements in this Standard to determine if those additional changes result in the discontinuation of hedge accounting. If the additional changes do not result in the discontinuation of hedge accounting, an entity shall amend the formal designation of the hedging relationship as specified in paragraph 6.9.1.
3. An entity shall assess separately whether each subgroup meets the requirements in paragraph 6.6.1 to be an eligible hedged item. If any subgroup fails to meet the requirements in paragraph 6.6.1, the entity shall discontinue hedge accounting prospectively for the hedging relationship in its entirety. An entity also shall apply the requirements in paragraphs 6.5.8 and 6.5.11 to account for ineffectiveness related to the hedging relationship in its entirety.
4. This Standard does not specify a method for assessing whether a hedging relationship meets the hedge effectiveness requirements. However, an entity shall use a method that captures the relevant characteristics of the hedging relationship including the sources of hedge ineffectiveness. Depending on those factors, the method can be a qualitative or a quantitative assessment.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

LON:HNG HAWKWING PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B1
Income Statement	Caa2	Ba1
Balance Sheet	C	C
Leverage Ratios	Caa2	Caa2
Cash Flow	B3	Baa2
Rates of Return and Profitability	Baa2	Caa2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Conclusions

HAWKWING PLC is assigned short-term B2 & long-term B1 estimated rating. HAWKWING PLC prediction model is evaluated with Statistical Inference (ML) and ElasticNet Regression^1,2,3,4 and it is concluded that the LON:HNG stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell

Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 651 signals.

References

1. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
2. Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
3. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
4. Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
5. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
6. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
7. Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35

Frequently Asked Questions

Q: What is the prediction methodology for LON:HNG stock?
A: LON:HNG stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and ElasticNet Regression
Q: Is LON:HNG stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:HNG Stock.
Q: Is HAWKWING PLC stock a good investment?
A: The consensus rating for HAWKWING PLC is Sell and is assigned short-term B2 & long-term B1 estimated rating.
Q: What is the consensus rating of LON:HNG stock?
A: The consensus rating for LON:HNG is Sell.
Q: What is the prediction period for LON:HNG stock?
A: The prediction period for LON:HNG is 1 Year

People also ask

⚐ What are the top stocks to invest in right now?
☵ What happens to stocks when they're delisted?