Outlook: HAMBRO PERKS ACQUISITION COMPANY LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 21 Jan 2023 for (n+6 month)
Methodology : Modular Neural Network (Market Volatility Analysis)

## Abstract

HAMBRO PERKS ACQUISITION COMPANY LIMITED prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Pearson Correlation1,2,3,4 and it is concluded that the LON:HPA1 stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

## Key Points

1. Technical Analysis with Algorithmic Trading
2. Can statistics predict the future?
3. Decision Making

## LON:HPA1 Target Price Prediction Modeling Methodology

We consider HAMBRO PERKS ACQUISITION COMPANY LIMITED Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of LON:HPA1 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+6 month) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of LON:HPA1 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:HPA1 Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:HPA1 HAMBRO PERKS ACQUISITION COMPANY LIMITED
Time series to forecast n: 21 Jan 2023 for (n+6 month)

According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

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 HAMBRO PERKS ACQUISITION COMPANY LIMITED

1. If a variable-rate financial liability bears interest of (for example) three-month LIBOR minus 20 basis points (with a floor at zero basis points), an entity can designate as the hedged item the change in the cash flows of that entire liability (ie three-month LIBOR minus 20 basis points—including the floor) that is attributable to changes in LIBOR. Hence, as long as the three-month LIBOR forward curve for the remaining life of that liability does not fall below 20 basis points, the hedged item has the same cash flow variability as a liability that bears interest at three-month LIBOR with a zero or positive spread. However, if the three-month LIBOR forward curve for the remaining life of that liability (or a part of it) falls below 20 basis points, the hedged item has a lower cash flow variability than a liability that bears interest at threemonth LIBOR with a zero or positive spread.
2. For the purposes of applying the requirements in paragraphs 5.7.7 and 5.7.8, an accounting mismatch is not caused solely by the measurement method that an entity uses to determine the effects of changes in a liability's credit risk. An accounting mismatch in profit or loss would arise only when the effects of changes in the liability's credit risk (as defined in IFRS 7) are expected to be offset by changes in the fair value of another financial instrument. A mismatch that arises solely as a result of the measurement method (ie because an entity does not isolate changes in a liability's credit risk from some other changes in its fair value) does not affect the determination required by paragraphs 5.7.7 and 5.7.8. For example, an entity may not isolate changes in a liability's credit risk from changes in liquidity risk. If the entity presents the combined effect of both factors in other comprehensive income, a mismatch may occur because changes in liquidity risk may be included in the fair value measurement of the entity's financial assets and the entire fair value change of those assets is presented in profit or loss. However, such a mismatch is caused by measurement imprecision, not the offsetting relationship described in paragraph B5.7.6 and, therefore, does not affect the determination required by paragraphs 5.7.7 and 5.7.8.
3. An entity's business model is determined at a level that reflects how groups of financial assets are managed together to achieve a particular business objective. The entity's business model does not depend on management's intentions for an individual instrument. Accordingly, this condition is not an instrument-by-instrument approach to classification and should be determined on a higher level of aggregation. However, a single entity may have more than one business model for managing its financial instruments. Consequently, classification need not be determined at the reporting entity level. For example, an entity may hold a portfolio of investments that it manages in order to collect contractual cash flows and another portfolio of investments that it manages in order to trade to realise fair value changes. Similarly, in some circumstances, it may be appropriate to separate a portfolio of financial assets into subportfolios in order to reflect the level at which an entity manages those financial assets. For example, that may be the case if an entity originates or purchases a portfolio of mortgage loans and manages some of the loans with an objective of collecting contractual cash flows and manages the other loans with an objective of selling them.
4. An entity shall apply this Standard for annual periods beginning on or after 1 January 2018. Earlier application is permitted. If an entity elects to apply this Standard early, it must disclose that fact and apply all of the requirements in this Standard at the same time (but see also paragraphs 7.1.2, 7.2.21 and 7.3.2). It shall also, at the same time, apply the amendments in Appendix C.

*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

HAMBRO PERKS ACQUISITION COMPANY LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. HAMBRO PERKS ACQUISITION COMPANY LIMITED prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Pearson Correlation1,2,3,4 and it is concluded that the LON:HPA1 stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

### LON:HPA1 HAMBRO PERKS ACQUISITION COMPANY LIMITED Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCC
Balance SheetCBa3
Leverage RatiosBaa2Caa2
Cash FlowBa1Caa2
Rates of Return and ProfitabilityCBa3

*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

Trust metric by Neural Network: 91 out of 100 with 576 signals.

## References

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2. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
3. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
4. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
5. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Short/Long Term Stocks: FOX Stock Forecast. AC Investment Research Journal, 101(3).
6. Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
7. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:HPA1 stock?
A: LON:HPA1 stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Pearson Correlation
Q: Is LON:HPA1 stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:HPA1 Stock.
Q: Is HAMBRO PERKS ACQUISITION COMPANY LIMITED stock a good investment?
A: The consensus rating for HAMBRO PERKS ACQUISITION COMPANY LIMITED is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:HPA1 stock?
A: The consensus rating for LON:HPA1 is Hold.
Q: What is the prediction period for LON:HPA1 stock?
A: The prediction period for LON:HPA1 is (n+6 month)