**Outlook:**TortoiseEcofin Acquisition Corp. III Class A Ordinary Shares assigned short-term B2 & long-term B2 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 11 Dec 2022**for (n+4 weeks)

**Methodology :**Modular Neural Network (Speculative Sentiment Analysis)

## Abstract

A speculator on a Stock Market, aside from having money to spare, needs at least one other thing — a means of producing accurate and understandable predictions ahead of others in the Market, so that a tactical and price advantage can be gained. This work demonstrates that it is possible to predict one such Market to a high degree of accuracy. (Huang, Y., Capretz, L.F. and Ho, D., 2021, December. Machine learning for stock prediction based on fundamental analysis. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 01-10). IEEE.)** We evaluate TortoiseEcofin Acquisition Corp. III Class A Ordinary Shares prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Wilcoxon Sign-Rank Test ^{1,2,3,4} and conclude that the TRTL stock is predictable in the short/long term. **

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

## Key Points

- Nash Equilibria
- Understanding Buy, Sell, and Hold Ratings
- Nash Equilibria

## TRTL Target Price Prediction Modeling Methodology

We consider TortoiseEcofin Acquisition Corp. III Class A Ordinary Shares Decision Process with Modular Neural Network (Speculative Sentiment Analysis) where A is the set of discrete actions of TRTL 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(Wilcoxon Sign-Rank 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 (Speculative Sentiment Analysis)) X S(n):→ (n+4 weeks) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of TRTL 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?

## TRTL Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**TRTL TortoiseEcofin Acquisition Corp. III Class A Ordinary Shares

**Time series to forecast n: 11 Dec 2022**for (n+4 weeks)

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

## Adjusted IFRS* Prediction Methods for TortoiseEcofin Acquisition Corp. III Class A Ordinary Shares

- Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.
- If the underlyings are not the same but are economically related, there can be situations in which the values of the hedging instrument and the hedged item move in the same direction, for example, because the price differential between the two related underlyings changes while the underlyings themselves do not move significantly. That is still consistent with an economic relationship between the hedging instrument and the hedged item if the values of the hedging instrument and the hedged item are still expected to typically move in the opposite direction when the underlyings move.
- For example, an entity may use this condition to designate financial liabilities as at fair value through profit or loss if it meets the principle in paragraph 4.2.2(b) and the entity has financial assets and financial liabilities that share one or more risks and those risks are managed and evaluated on a fair value basis in accordance with a documented policy of asset and liability management. An example could be an entity that has issued 'structured products' containing multiple embedded derivatives and manages the resulting risks on a fair value basis using a mix of derivative and non-derivative financial instruments
- When designating risk components as hedged items, an entity considers whether the risk components are explicitly specified in a contract (contractually specified risk components) or whether they are implicit in the fair value or the cash flows of an item of which they are a part (noncontractually specified risk components). Non-contractually specified risk components can relate to items that are not a contract (for example, forecast transactions) or contracts that do not explicitly specify the component (for example, a firm commitment that includes only one single price instead of a pricing formula that references different underlyings)

*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

TortoiseEcofin Acquisition Corp. III Class A Ordinary Shares assigned short-term B2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Wilcoxon Sign-Rank Test ^{1,2,3,4} and conclude that the TRTL stock is predictable in the short/long term.**

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

### Financial State Forecast for TRTL TortoiseEcofin Acquisition Corp. III Class A Ordinary Shares Options & Futures

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

Outlook* | B2 | B2 |

Operational Risk | 80 | 43 |

Market Risk | 34 | 58 |

Technical Analysis | 57 | 38 |

Fundamental Analysis | 46 | 41 |

Risk Unsystematic | 63 | 65 |

### Prediction Confidence Score

## References

- Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
- Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
- Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
- Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
- Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
- V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
- Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505

## Frequently Asked Questions

Q: What is the prediction methodology for TRTL stock?A: TRTL stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Wilcoxon Sign-Rank Test

Q: Is TRTL stock a buy or sell?

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

Q: Is TortoiseEcofin Acquisition Corp. III Class A Ordinary Shares stock a good investment?

A: The consensus rating for TortoiseEcofin Acquisition Corp. III Class A Ordinary Shares is Buy and assigned short-term B2 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of TRTL stock?

A: The consensus rating for TRTL is Buy.

Q: What is the prediction period for TRTL stock?

A: The prediction period for TRTL is (n+4 weeks)