**Outlook:**PACIFIC HORIZON INVESTMENT TRUST PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 23 May 2023**for (n+6 month)

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

## Abstract

PACIFIC HORIZON INVESTMENT TRUST PLC prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Paired T-Test^{1,2,3,4}and it is concluded that the LON:PHI 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

- Which neural network is best for prediction?
- Buy, Sell and Hold Signals
- Prediction Modeling

## LON:PHI Target Price Prediction Modeling Methodology

We consider PACIFIC HORIZON INVESTMENT TRUST PLC Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of LON:PHI 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(Paired T-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(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+6 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

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

**Sample Set:**Neural Network

**Stock/Index:**LON:PHI PACIFIC HORIZON INVESTMENT TRUST PLC

**Time series to forecast n: 23 May 2023**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%**

## IFRS Reconciliation Adjustments for PACIFIC HORIZON INVESTMENT TRUST PLC

- An entity must look through until it can identify the underlying pool of instruments that are creating (instead of passing through) the cash flows. This is the underlying pool of financial instruments.
- 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.
- IFRS 17, issued in May 2017, amended paragraphs 2.1, B2.1, B2.4, B2.5 and B4.1.30, and added paragraph 3.3.5. Amendments to IFRS 17, issued in June 2020, further amended paragraph 2.1 and added paragraphs 7.2.36‒7.2.42. An entity shall apply those amendments when it applies IFRS 17.
- Conversely, if changes in the extent of offset indicate that the fluctuation is around a hedge ratio that is different from the hedge ratio that is currently used for that hedging relationship, or that there is a trend leading away from that hedge ratio, hedge ineffectiveness can be reduced by adjusting the hedge ratio, whereas retaining the hedge ratio would increasingly produce hedge ineffectiveness. Hence, in such circumstances, an entity must evaluate whether the hedging relationship reflects an imbalance between the weightings of the hedged item and the hedging instrument that would create hedge ineffectiveness (irrespective of whether recognised or not) that could result in an accounting outcome that would be inconsistent with the purpose of hedge accounting. If the hedge ratio is adjusted, it also affects the measurement and recognition of hedge ineffectiveness because, on rebalancing, the hedge ineffectiveness of the hedging relationship must be determined and recognised immediately before adjusting the hedging relationship in accordance with paragraph B6.5.8.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

## Conclusions

PACIFIC HORIZON INVESTMENT TRUST PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. PACIFIC HORIZON INVESTMENT TRUST PLC prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Paired T-Test^{1,2,3,4} and it is concluded that the LON:PHI 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**

### LON:PHI PACIFIC HORIZON INVESTMENT TRUST PLC Financial Analysis*

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

Outlook* | Ba1 | Ba1 |

Income Statement | B3 | Ba3 |

Balance Sheet | Ba1 | B3 |

Leverage Ratios | B2 | C |

Cash Flow | Caa2 | C |

Rates of Return and Profitability | Baa2 | Baa2 |

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.

How does neural network examine financial reports and understand financial state of the company?

### Prediction Confidence Score

## References

- Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
- Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
- Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
- Artis, M. J. W. Zhang (1990), "BVAR forecasts for the G-7," International Journal of Forecasting, 6, 349–362.
- Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79

## Frequently Asked Questions

Q: What is the prediction methodology for LON:PHI stock?A: LON:PHI stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Paired T-Test

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

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

Q: Is PACIFIC HORIZON INVESTMENT TRUST PLC stock a good investment?

A: The consensus rating for PACIFIC HORIZON INVESTMENT TRUST PLC is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.

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

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

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

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

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