## Summary

Complex networks in stock market and stock price volatility pattern prediction are the important issues in stock price research. Previous studies have used historical information regarding a single stock to predict the future trend of the stock's price, seldom considering comovement among stocks in the same market. In this study, in order to extract the information about relation stocks for prediction, we try to combine the complex network method with machine learning to predict stock price patterns.** We evaluate BUTN LIMITED prediction models with Modular Neural Network (Market Direction Analysis) and Beta ^{1,2,3,4} and conclude that the BTN stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold BTN stock.**

## Key Points

- Market Outlook
- Game Theory
- What is neural prediction?

## BTN Target Price Prediction Modeling Methodology

We consider BUTN LIMITED Stock Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of BTN 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(Beta)

^{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(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+6 month) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of BTN 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?

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

**Sample Set:**Neural Network

**Stock/Index:**BTN BUTN LIMITED

**Time series to forecast n: 29 Nov 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold BTN 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 BUTN LIMITED

- The underlying pool must contain one or more instruments that have contractual cash flows that are solely payments of principal and interest on the principal amount outstanding
- If such a mismatch would be created or enlarged, the entity is required to present all changes in fair value (including the effects of changes in the credit risk of the liability) in profit or loss. If such a mismatch would not be created or enlarged, the entity is required to present the effects of changes in the liability's credit risk in other comprehensive income.
- Credit risk analysis is a multifactor and holistic analysis; whether a specific factor is relevant, and its weight compared to other factors, will depend on the type of product, characteristics of the financial instruments and the borrower as well as the geographical region. An entity shall consider reasonable and supportable information that is available without undue cost or effort and that is relevant for the particular financial instrument being assessed. However, some factors or indicators may not be identifiable on an individual financial instrument level. In such a case, the factors or indicators should be assessed for appropriate portfolios, groups of portfolios or portions of a portfolio of financial instruments to determine whether the requirement in paragraph 5.5.3 for the recognition of lifetime expected credit losses has been met.
- The significance of a change in the credit risk since initial recognition depends on the risk of a default occurring as at initial recognition. Thus, a given change, in absolute terms, in the risk of a default occurring will be more significant for a financial instrument with a lower initial risk of a default occurring compared to a financial instrument with a higher initial risk of a default occurring.

*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

BUTN LIMITED assigned short-term Baa2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Beta ^{1,2,3,4} and conclude that the BTN stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold BTN stock.**

### Financial State Forecast for BTN BUTN LIMITED Stock Options & Futures

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

Outlook* | Baa2 | Ba3 |

Operational Risk | 80 | 69 |

Market Risk | 71 | 73 |

Technical Analysis | 82 | 76 |

Fundamental Analysis | 71 | 74 |

Risk Unsystematic | 72 | 33 |

### Prediction Confidence Score

## References

- Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
- Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
- Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
- LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
- S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010

## Frequently Asked Questions

Q: What is the prediction methodology for BTN stock?A: BTN stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Beta

Q: Is BTN stock a buy or sell?

A: The dominant strategy among neural network is to Hold BTN Stock.

Q: Is BUTN LIMITED stock a good investment?

A: The consensus rating for BUTN LIMITED is Hold and assigned short-term Baa2 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of BTN stock?

A: The consensus rating for BTN is Hold.

Q: What is the prediction period for BTN stock?

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