**Outlook:**CASTINGS PLC assigned short-term Ba1 & long-term Ba3 forecasted stock rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 11 Dec 2022**for (n+6 month)

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

## Abstract

Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction.(Bi, Q., Goodman, K.E., Kaminsky, J. and Lessler, J., 2019. What is machine learning? A primer for the epidemiologist. American journal of epidemiology, 188(12), pp.2222-2239.)** We evaluate CASTINGS PLC prediction models with Modular Neural Network (Market Direction Analysis) and Pearson Correlation ^{1,2,3,4} and conclude that the LON:CGS 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

- Stock Rating
- Stock Rating
- Market Outlook

## LON:CGS Target Price Prediction Modeling Methodology

We consider CASTINGS PLC Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of LON:CGS 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(Pearson Correlation)

^{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) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:CGS 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:CGS Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**LON:CGS CASTINGS PLC

**Time series to forecast n: 11 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 CASTINGS PLC

- Expected credit losses are a probability-weighted estimate of credit losses (ie the present value of all cash shortfalls) over the expected life of the financial instrument. A cash shortfall is the difference between the cash flows that are due to an entity in accordance with the contract and the cash flows that the entity expects to receive. Because expected credit losses consider the amount and timing of payments, a credit loss arises even if the entity expects to be paid in full but later than when contractually due.
- An alternative benchmark rate designated as a non-contractually specified risk component that is not separately identifiable (see paragraphs 6.3.7(a) and B6.3.8) at the date it is designated shall be deemed to have met that requirement at that date, if, and only if, the entity reasonably expects the alternative benchmark rate will be separately identifiable within 24 months. The 24-month period applies to each alternative benchmark rate separately and starts from the date the entity designates the alternative benchmark rate as a non-contractually specified risk component for the first time (ie the 24- month period applies on a rate-by-rate basis).
- 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.
- For floating-rate financial assets and floating-rate financial liabilities, periodic re-estimation of cash flows to reflect the movements in the market rates of interest alters the effective interest rate. If a floating-rate financial asset or a floating-rate financial liability is recognised initially at an amount equal to the principal receivable or payable on maturity, re-estimating the future interest payments normally has no significant effect on the carrying amount of the asset or the liability.

*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

CASTINGS PLC assigned short-term Ba1 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Pearson Correlation ^{1,2,3,4} and conclude that the LON:CGS 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 LON:CGS CASTINGS PLC Options & Futures

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

Outlook* | Ba1 | Ba3 |

Operational Risk | 90 | 76 |

Market Risk | 87 | 82 |

Technical Analysis | 80 | 73 |

Fundamental Analysis | 63 | 54 |

Risk Unsystematic | 31 | 38 |

### Prediction Confidence Score

## References

- S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
- S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
- 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.
- Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
- Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press
- Harris ZS. 1954. Distributional structure. Word 10:146–62
- Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70

## Frequently Asked Questions

Q: What is the prediction methodology for LON:CGS stock?A: LON:CGS stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Pearson Correlation

Q: Is LON:CGS stock a buy or sell?

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

Q: Is CASTINGS PLC stock a good investment?

A: The consensus rating for CASTINGS PLC is Sell and assigned short-term Ba1 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of LON:CGS stock?

A: The consensus rating for LON:CGS is Sell.

Q: What is the prediction period for LON:CGS stock?

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