**Outlook:**ARENA REIT. assigned short-term B2 & long-term B1 forecasted stock rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 15 Dec 2022**for (n+6 month)

**Methodology :**Active Learning (ML)

## Abstract

Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one.(Vadlamudi, S., 2017. Stock Market Prediction using Machine Learning: A Systematic Literature Review. American Journal of Trade and Policy, 4(3), pp.123-128.)** We evaluate ARENA REIT. prediction models with Active Learning (ML) and Lasso Regression ^{1,2,3,4} and conclude that the ARF stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell**

## Key Points

- Should I buy stocks now or wait amid such uncertainty?
- Is now good time to invest?
- Market Signals

## ARF Target Price Prediction Modeling Methodology

We consider ARENA REIT. Decision Process with Active Learning (ML) where A is the set of discrete actions of ARF 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(Lasso Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Active Learning (ML)) X S(n):→ (n+6 month) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of ARF 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?

## ARF Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**ARF ARENA REIT.

**Time series to forecast n: 15 Dec 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell**

**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 ARENA REIT.

- Conversely, if the critical terms of the hedging instrument and the hedged item are not closely aligned, there is an increased level of uncertainty about the extent of offset. Consequently, the hedge effectiveness during the term of the hedging relationship is more difficult to predict. In such a situation it might only be possible for an entity to conclude on the basis of a quantitative assessment that an economic relationship exists between the hedged item and the hedging instrument (see paragraphs B6.4.4–B6.4.6). In some situations a quantitative assessment might also be needed to assess whether the hedge ratio used for designating the hedging relationship meets the hedge effectiveness requirements (see paragraphs B6.4.9–B6.4.11). An entity can use the same or different methods for those two different purposes.
- Compared to a business model whose objective is to hold financial assets to collect contractual cash flows, this business model will typically involve greater frequency and value of sales. This is because selling financial assets is integral to achieving the business model's objective instead of being only incidental to it. However, there is no threshold for the frequency or value of sales that must occur in this business model because both collecting contractual cash flows and selling financial assets are integral to achieving its objective.
- For the purpose of applying the requirements in paragraphs 6.4.1(c)(i) and B6.4.4–B6.4.6, an entity shall assume that the interest rate benchmark on which the hedged cash flows and/or the hedged risk (contractually or noncontractually specified) are based, or the interest rate benchmark on which the cash flows of the hedging instrument are based, is not altered as a result of interest rate benchmark reform.
- To calculate the change in the value of the hedged item for the purpose of measuring hedge ineffectiveness, an entity may use a derivative that would have terms that match the critical terms of the hedged item (this is commonly referred to as a 'hypothetical derivative'), and, for example for a hedge of a forecast transaction, would be calibrated using the hedged price (or rate) level. For example, if the hedge was for a two-sided risk at the current market level, the hypothetical derivative would represent a hypothetical forward contract that is calibrated to a value of nil at the time of designation of the hedging relationship. If the hedge was for example for a one-sided risk, the hypothetical derivative would represent the intrinsic value of a hypothetical option that at the time of designation of the hedging relationship is at the money if the hedged price level is the current market level, or out of the money if the hedged price level is above (or, for a hedge of a long position, below) the current market level. Using a hypothetical derivative is one possible way of calculating the change in the value of the hedged item. The hypothetical derivative replicates the hedged item and hence results in the same outcome as if that change in value was determined by a different approach. Hence, using a 'hypothetical derivative' is not a method in its own right but a mathematical expedient that can only be used to calculate the value of the hedged item. Consequently, a 'hypothetical derivative' cannot be used to include features in the value of the hedged item that only exist in the hedging instrument (but not in the hedged item). An example is debt denominated in a foreign currency (irrespective of whether it is fixed-rate or variable-rate debt). When using a hypothetical derivative to calculate the change in the value of such debt or the present value of the cumulative change in its cash flows, the hypothetical derivative cannot simply impute a charge for exchanging different currencies even though actual derivatives under which different currencies are exchanged might include such a charge (for example, cross-currency interest rate swaps).

*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

ARENA REIT. assigned short-term B2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Active Learning (ML) with Lasso Regression ^{1,2,3,4} and conclude that the ARF stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell**

### Financial State Forecast for ARF ARENA REIT. Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B2 | B1 |

Operational Risk | 90 | 63 |

Market Risk | 42 | 88 |

Technical Analysis | 42 | 43 |

Fundamental Analysis | 53 | 68 |

Risk Unsystematic | 60 | 38 |

### Prediction Confidence Score

## References

- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
- Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
- Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
- Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
- Miller A. 2002. Subset Selection in Regression. New York: CRC Press
- Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Is FFBC Stock Buy or Sell?(Stock Forecast). AC Investment Research Journal, 101(3).

## Frequently Asked Questions

Q: What is the prediction methodology for ARF stock?A: ARF stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Lasso Regression

Q: Is ARF stock a buy or sell?

A: The dominant strategy among neural network is to Sell ARF Stock.

Q: Is ARENA REIT. stock a good investment?

A: The consensus rating for ARENA REIT. is Sell and assigned short-term B2 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of ARF stock?

A: The consensus rating for ARF is Sell.

Q: What is the prediction period for ARF stock?

A: The prediction period for ARF is (n+6 month)