**Outlook:**AECI LD assigned short-term B2 & long-term Ba3 forecasted stock rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 14 Dec 2022**for (n+4 weeks)

**Methodology :**Modular Neural Network (Market Volatility Analysis)

## Abstract

Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend.(Bi, Q., Yan, H., Chen, C. and Su, Q., 2020, August. An integrated machine learning framework for stock price prediction. In China Conference on Information Retrieval (pp. 99-110). Springer, Cham.)** We evaluate AECI LD prediction models with Modular Neural Network (Market Volatility Analysis) and Polynomial Regression ^{1,2,3,4} and conclude that the LON:87FZ stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell**

## Key Points

- Market Risk
- How useful are statistical predictions?
- Stock Rating

## LON:87FZ Target Price Prediction Modeling Methodology

We consider AECI LD Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of LON:87FZ 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(Polynomial 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(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+4 weeks) $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:87FZ 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?

## LON:87FZ Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:87FZ AECI LD

**Time series to forecast n: 14 Dec 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) 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 AECI LD

- A contractually specified inflation risk component of the cash flows of a recognised inflation-linked bond (assuming that there is no requirement to account for an embedded derivative separately) is separately identifiable and reliably measurable, as long as other cash flows of the instrument are not affected by the inflation risk component.
- For the avoidance of doubt, the effects of replacing the original counterparty with a clearing counterparty and making the associated changes as described in paragraph 6.5.6 shall be reflected in the measurement of the hedging instrument and therefore in the assessment of hedge effectiveness and the measurement of hedge effectiveness
- For lifetime expected credit losses, an entity shall estimate the risk of a default occurring on the financial instrument during its expected life. 12-month expected credit losses are a portion of the lifetime expected credit losses and represent the lifetime cash shortfalls that will result if a default occurs in the 12 months after the reporting date (or a shorter period if the expected life of a financial instrument is less than 12 months), weighted by the probability of that default occurring. Thus, 12-month expected credit losses are neither the lifetime expected credit losses that an entity will incur on financial instruments that it predicts will default in the next 12 months nor the cash shortfalls that are predicted over the next 12 months.
- The following are examples of when the objective of the entity's business model may be achieved by both collecting contractual cash flows and selling financial assets. This list of examples is not exhaustive. Furthermore, the examples are not intended to describe all the factors that may be relevant to the assessment of the entity's business model nor specify the relative importance of the factors.

*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

AECI LD assigned short-term B2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Polynomial Regression ^{1,2,3,4} and conclude that the LON:87FZ stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell**

### Financial State Forecast for LON:87FZ AECI LD Options & Futures

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

Outlook* | B2 | Ba3 |

Operational Risk | 52 | 71 |

Market Risk | 47 | 58 |

Technical Analysis | 63 | 37 |

Fundamental Analysis | 55 | 88 |

Risk Unsystematic | 55 | 57 |

### Prediction Confidence Score

## References

- V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
- Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
- Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
- S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
- L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
- Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
- 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

## Frequently Asked Questions

Q: What is the prediction methodology for LON:87FZ stock?A: LON:87FZ stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Polynomial Regression

Q: Is LON:87FZ stock a buy or sell?

A: The dominant strategy among neural network is to Sell LON:87FZ Stock.

Q: Is AECI LD stock a good investment?

A: The consensus rating for AECI LD is Sell and assigned short-term B2 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of LON:87FZ stock?

A: The consensus rating for LON:87FZ is Sell.

Q: What is the prediction period for LON:87FZ stock?

A: The prediction period for LON:87FZ is (n+4 weeks)