**Outlook:**MEGAPORT LIMITED assigned short-term B3 & long-term B2 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 06 Dec 2022**for (n+1 year)

**Methodology :**Active Learning (ML)

## Abstract

The aim of this study is to evaluate the effectiveness of using external indicators, such as commodity prices and currency exchange rates, in predicting movements. The performance of each technique is evaluated using different domain specific metrics. A comprehensive evaluation procedure is described, involving the use of trading simulations to assess the practical value of predictive models, and comparison with simple benchmarks that respond to underlying market growth.(Shah, V.H., 2007. Machine learning techniques for stock prediction. Foundations of Machine Learning| Spring, 1(1), pp.6-12.)** We evaluate MEGAPORT LIMITED prediction models with Active Learning (ML) and Sign Test ^{1,2,3,4} and conclude that the MP1 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 Buy MP1 stock.**

## Key Points

- Nash Equilibria
- Trading Interaction
- Understanding Buy, Sell, and Hold Ratings

## MP1 Target Price Prediction Modeling Methodology

We consider MEGAPORT LIMITED Decision Process with Active Learning (ML) where A is the set of discrete actions of MP1 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(Sign Test)

^{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+1 year) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of MP1 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?

## MP1 Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**MP1 MEGAPORT LIMITED

**Time series to forecast n: 06 Dec 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy MP1 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 MEGAPORT LIMITED

- An entity can also designate only changes in the cash flows or fair value of a hedged item above or below a specified price or other variable (a 'one-sided risk'). The intrinsic value of a purchased option hedging instrument (assuming that it has the same principal terms as the designated risk), but not its time value, reflects a one-sided risk in a hedged item. For example, an entity can designate the variability of future cash flow outcomes resulting from a price increase of a forecast commodity purchase. In such a situation, the entity designates only cash flow losses that result from an increase in the price above the specified level. The hedged risk does not include the time value of a purchased option, because the time value is not a component of the forecast transaction that affects profit or loss.
- When rebalancing a hedging relationship, an entity shall update its analysis of the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its (remaining) term (see paragraph B6.4.2). The documentation of the hedging relationship shall be updated accordingly.
- Paragraph 6.3.4 permits an entity to designate as hedged items aggregated exposures that are a combination of an exposure and a derivative. When designating such a hedged item, an entity assesses whether the aggregated exposure combines an exposure with a derivative so that it creates a different aggregated exposure that is managed as one exposure for a particular risk (or risks). In that case, the entity may designate the hedged item on the basis of the aggregated exposure
- An example of a fair value hedge is a hedge of exposure to changes in the fair value of a fixed-rate debt instrument arising from changes in interest rates. Such a hedge could be entered into by the issuer or by the holder.

*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

MEGAPORT LIMITED assigned short-term B3 & long-term B2 forecasted stock rating.** We evaluate the prediction models Active Learning (ML) with Sign Test ^{1,2,3,4} and conclude that the MP1 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 Buy MP1 stock.**

### Financial State Forecast for MP1 MEGAPORT LIMITED Options & Futures

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

Outlook* | B3 | B2 |

Operational Risk | 44 | 54 |

Market Risk | 56 | 35 |

Technical Analysis | 53 | 70 |

Fundamental Analysis | 57 | 48 |

Risk Unsystematic | 34 | 45 |

### Prediction Confidence Score

## References

- Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
- Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.
- White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
- J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
- R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
- S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014
- Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93

## Frequently Asked Questions

Q: What is the prediction methodology for MP1 stock?A: MP1 stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Sign Test

Q: Is MP1 stock a buy or sell?

A: The dominant strategy among neural network is to Buy MP1 Stock.

Q: Is MEGAPORT LIMITED stock a good investment?

A: The consensus rating for MEGAPORT LIMITED is Buy and assigned short-term B3 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of MP1 stock?

A: The consensus rating for MP1 is Buy.

Q: What is the prediction period for MP1 stock?

A: The prediction period for MP1 is (n+1 year)