APZ ASPEN GROUP

ASPEN GROUP Research Report

Summary

Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. We evaluate ASPEN GROUP prediction models with Modular Neural Network (DNN Layer) and Statistical Hypothesis Testing^1,2,3,4 and conclude that the APZ stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold APZ stock.

Key Points

1. Market Outlook
2. What are the most successful trading algorithms?
3. What is prediction in deep learning?

APZ Target Price Prediction Modeling Methodology

We consider ASPEN GROUP Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of APZ 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(Statistical Hypothesis Testing)^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 (DNN Layer)) X S(n):→ (n+1 year) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of APZ 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?

APZ Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: APZ ASPEN GROUP
Time series to forecast n: 03 Dec 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold APZ 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 ASPEN GROUP

1. 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.
2. A net position is eligible for hedge accounting only if an entity hedges on a net basis for risk management purposes. Whether an entity hedges in this way is a matter of fact (not merely of assertion or documentation). Hence, an entity cannot apply hedge accounting on a net basis solely to achieve a particular accounting outcome if that would not reflect its risk management approach. Net position hedging must form part of an established risk management strategy. Normally this would be approved by key management personnel as defined in IAS 24.
3. If the underlyings are not the same but are economically related, there can be situations in which the values of the hedging instrument and the hedged item move in the same direction, for example, because the price differential between the two related underlyings changes while the underlyings themselves do not move significantly. That is still consistent with an economic relationship between the hedging instrument and the hedged item if the values of the hedging instrument and the hedged item are still expected to typically move in the opposite direction when the underlyings move.
4. If items are hedged together as a group in a cash flow hedge, they might affect different line items in the statement of profit or loss and other comprehensive income. The presentation of hedging gains or losses in that statement depends on the group of items

*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

ASPEN GROUP assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Statistical Hypothesis Testing^1,2,3,4 and conclude that the APZ stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold APZ stock.

Financial State Forecast for APZ ASPEN GROUP Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B2
Operational Risk	85	49
Market Risk	52	63
Technical Analysis	39	55
Fundamental Analysis	51	40
Risk Unsystematic	48	53

Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 642 signals.

References

1. Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
2. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
3. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
4. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
5. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
6. J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
7. V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000

Frequently Asked Questions

Q: What is the prediction methodology for APZ stock?
A: APZ stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Statistical Hypothesis Testing
Q: Is APZ stock a buy or sell?
A: The dominant strategy among neural network is to Hold APZ Stock.
Q: Is ASPEN GROUP stock a good investment?
A: The consensus rating for ASPEN GROUP is Hold and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of APZ stock?
A: The consensus rating for APZ is Hold.
Q: What is the prediction period for APZ stock?
A: The prediction period for APZ is (n+1 year)

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