**Outlook:**THALASSA HOLDINGS LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 28 Jan 2023**for (n+1 year)

**Methodology :**Statistical Inference (ML)

## Abstract

THALASSA HOLDINGS LIMITED prediction model is evaluated with Statistical Inference (ML) and Statistical Hypothesis Testing^{1,2,3,4}and it is concluded that the LON:THAL stock is predictable in the short/long term.

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy**

## Key Points

- Is now good time to invest?
- What is the use of Markov decision process?
- Fundemental Analysis with Algorithmic Trading

## LON:THAL Target Price Prediction Modeling Methodology

We consider THALASSA HOLDINGS LIMITED Decision Process with Statistical Inference (ML) where A is the set of discrete actions of LON:THAL 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(Statistical Hypothesis Testing)

^{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(Statistical Inference (ML)) X S(n):→ (n+1 year) $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:THAL 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:THAL Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**LON:THAL THALASSA HOLDINGS LIMITED

**Time series to forecast n: 28 Jan 2023**for (n+1 year)

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy**

**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 THALASSA HOLDINGS LIMITED

- In applying the effective interest method, an entity identifies fees that are an integral part of the effective interest rate of a financial instrument. The description of fees for financial services may not be indicative of the nature and substance of the services provided. Fees that are an integral part of the effective interest rate of a financial instrument are treated as an adjustment to the effective interest rate, unless the financial instrument is measured at fair value, with the change in fair value being recognised in profit or loss. In those cases, the fees are recognised as revenue or expense when the instrument is initially recognised.
- Conversely, if the critical terms of the hedging instrument and the hedged item are not closely aligned, there is an increased level of uncertainty about the extent of offset. Consequently, the hedge effectiveness during the term of the hedging relationship is more difficult to predict. In such a situation it might only be possible for an entity to conclude on the basis of a quantitative assessment that an economic relationship exists between the hedged item and the hedging instrument (see paragraphs B6.4.4–B6.4.6). In some situations a quantitative assessment might also be needed to assess whether the hedge ratio used for designating the hedging relationship meets the hedge effectiveness requirements (see paragraphs B6.4.9–B6.4.11). An entity can use the same or different methods for those two different purposes.
- If a collar, in the form of a purchased call and written put, prevents a transferred asset from being derecognised and the entity measures the asset at fair value, it continues to measure the asset at fair value. The associated liability is measured at (i) the sum of the call exercise price and fair value of the put option less the time value of the call option, if the call option is in or at the money, or (ii) the sum of the fair value of the asset and the fair value of the put option less the time value of the call option if the call option is out of the money. The adjustment to the associated liability ensures that the net carrying amount of the asset and the associated liability is the fair value of the options held and written by the entity. For example, assume an entity transfers a financial asset that is measured at fair value while simultaneously purchasing a call with an exercise price of CU120 and writing a put with an exercise price of CU80. Assume also that the fair value of the asset is CU100 at the date of the transfer. The time value of the put and call are CU1 and CU5 respectively. In this case, the entity recognises an asset of CU100 (the fair value of the asset) and a liability of CU96 [(CU100 + CU1) – CU5]. This gives a net asset value of CU4, which is the fair value of the options held and written by the entity.
- This Standard does not specify a method for assessing whether a hedging relationship meets the hedge effectiveness requirements. However, an entity shall use a method that captures the relevant characteristics of the hedging relationship including the sources of hedge ineffectiveness. Depending on those factors, the method can be a qualitative or a quantitative assessment.

*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

THALASSA HOLDINGS LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. THALASSA HOLDINGS LIMITED prediction model is evaluated with Statistical Inference (ML) and Statistical Hypothesis Testing^{1,2,3,4} and it is concluded that the LON:THAL stock is predictable in the short/long term. ** According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy**

### LON:THAL THALASSA HOLDINGS LIMITED Financial Analysis*

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

Outlook* | Ba1 | Ba1 |

Income Statement | B2 | Baa2 |

Balance Sheet | Baa2 | B2 |

Leverage Ratios | C | B1 |

Cash Flow | C | Baa2 |

Rates of Return and Profitability | Caa2 | B1 |

*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

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## Frequently Asked Questions

Q: What is the prediction methodology for LON:THAL stock?A: LON:THAL stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Statistical Hypothesis Testing

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

A: The dominant strategy among neural network is to Buy LON:THAL Stock.

Q: Is THALASSA HOLDINGS LIMITED stock a good investment?

A: The consensus rating for THALASSA HOLDINGS LIMITED is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.

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

A: The consensus rating for LON:THAL is Buy.

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

A: The prediction period for LON:THAL is (n+1 year)