AKL AUCKLAND REAL ESTATE TRUST

Outlook: AUCKLAND REAL ESTATE TRUST assigned short-term B2 & long-term B2 forecasted stock rating.
Dominant Strategy : Wait until speculative trend diminishes
Time series to forecast n: 13 Dec 2022 for (n+3 month)
Methodology : Supervised Machine Learning (ML)

Abstract

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted.(Choudhry, R. and Garg, K., 2008. A hybrid machine learning system for stock market forecasting. International Journal of Computer and Information Engineering, 2(3), pp.689-692.) We evaluate AUCKLAND REAL ESTATE TRUST prediction models with Supervised Machine Learning (ML) and Chi-Square^1,2,3,4 and conclude that the AKL stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

Key Points

1. Buy, Sell and Hold Signals
2. How do you know when a stock will go up or down?
3. Trading Signals

AKL Target Price Prediction Modeling Methodology

We consider AUCKLAND REAL ESTATE TRUST Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of AKL 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(Chi-Square)^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(Supervised Machine Learning (ML)) X S(n):→ (n+3 month) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of AKL 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?

AKL Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: AKL AUCKLAND REAL ESTATE TRUST
Time series to forecast n: 13 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

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 AUCKLAND REAL ESTATE TRUST

1. For the purposes of applying the requirement in paragraph 5.7.7(a), credit risk is different from asset-specific performance risk. Asset-specific performance risk is not related to the risk that an entity will fail to discharge a particular obligation but instead it is related to the risk that a single asset or a group of assets will perform poorly (or not at all).
2. There are two types of components of nominal amounts that can be designated as the hedged item in a hedging relationship: a component that is a proportion of an entire item or a layer component. The type of component changes the accounting outcome. An entity shall designate the component for accounting purposes consistently with its risk management objective.
3. Rebalancing does not apply if the risk management objective for a hedging relationship has changed. Instead, hedge accounting for that hedging relationship shall be discontinued (despite that an entity might designate a new hedging relationship that involves the hedging instrument or hedged item of the previous hedging relationship as described in paragraph B6.5.28).
4. Paragraph 4.1.1(b) requires an entity to classify a financial asset on the basis of its contractual cash flow characteristics if the financial asset is held within a business model whose objective is to hold assets to collect contractual cash flows or within a business model whose objective is achieved by both collecting contractual cash flows and selling financial assets, unless paragraph 4.1.5 applies. To do so, the condition in paragraphs 4.1.2(b) and 4.1.2A(b) requires an entity to determine whether the asset's contractual cash flows are solely payments of principal and interest on the principal amount outstanding.

*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

AUCKLAND REAL ESTATE TRUST assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Chi-Square^1,2,3,4 and conclude that the AKL stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

Financial State Forecast for AKL AUCKLAND REAL ESTATE TRUST Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B2
Operational Risk	61	30
Market Risk	75	41
Technical Analysis	37	32
Fundamental Analysis	54	69
Risk Unsystematic	45	90

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 856 signals.

References

1. T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
2. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
3. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
4. D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
5. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
6. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
7. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.

Frequently Asked Questions

Q: What is the prediction methodology for AKL stock?
A: AKL stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Chi-Square
Q: Is AKL stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes AKL Stock.
Q: Is AUCKLAND REAL ESTATE TRUST stock a good investment?
A: The consensus rating for AUCKLAND REAL ESTATE TRUST is Wait until speculative trend diminishes and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of AKL stock?
A: The consensus rating for AKL is Wait until speculative trend diminishes.
Q: What is the prediction period for AKL stock?
A: The prediction period for AKL is (n+3 month)