**Outlook:**Cactus Acquisition Corp. 1 Limited Class A Ordinary Share assigned short-term Baa2 & long-term B1 forecasted stock rating.

**Dominant Strategy :**Hold

**Time series to forecast n: 17 Dec 2022**for (n+3 month)

**Methodology :**Ensemble Learning (ML)

## Abstract

Stock price forecasting is a popular and important topic in financial and academic studies. Share market is an volatile place for predicting since there are no significant rules to estimate or predict the price of a share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc. are used to predict the price in tie share market but none of these methods are proved as a consistently acceptable prediction tool. In this paper, we implemented a Random Forest approach to predict stock market prices. (De Faria, E.L., Albuquerque, M.P., Gonzalez, J.L., Cavalcante, J.T.P. and Albuquerque, M.P., 2009. Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods. Expert Systems with Applications, 36(10), pp.12506-12509.)** We evaluate Cactus Acquisition Corp. 1 Limited Class A Ordinary Share prediction models with Ensemble Learning (ML) and Chi-Square ^{1,2,3,4} and conclude that the CCTS stock is predictable in the short/long term. **

**According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold**

## Key Points

- Why do we need predictive models?
- What is statistical models in machine learning?
- How can neural networks improve predictions?

## CCTS Target Price Prediction Modeling Methodology

We consider Cactus Acquisition Corp. 1 Limited Class A Ordinary Share Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of CCTS 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(Chi-Square)

^{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(Ensemble Learning (ML)) X S(n):→ (n+3 month) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of CCTS 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?

## CCTS Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**CCTS Cactus Acquisition Corp. 1 Limited Class A Ordinary Share

**Time series to forecast n: 17 Dec 2022**for (n+3 month)

**According to price forecasts for (n+3 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%**

## Adjusted IFRS* Prediction Methods for Cactus Acquisition Corp. 1 Limited Class A Ordinary Share

- If a financial asset contains a contractual term that could change the timing or amount of contractual cash flows (for example, if the asset can be prepaid before maturity or its term can be extended), the entity must determine whether the contractual cash flows that could arise over the life of the instrument due to that contractual term are solely payments of principal and interest on the principal amount outstanding. To make this determination, the entity must assess the contractual cash flows that could arise both before, and after, the change in contractual cash flows. The entity may also need to assess the nature of any contingent event (ie the trigger) that would change the timing or amount of the contractual cash flows. While the nature of the contingent event in itself is not a determinative factor in assessing whether the contractual cash flows are solely payments of principal and interest, it may be an indicator. For example, compare a financial instrument with an interest rate that is reset to a higher rate if the debtor misses a particular number of payments to a financial instrument with an interest rate that is reset to a higher rate if a specified equity index reaches a particular level. It is more likely in the former case that the contractual cash flows over the life of the instrument will be solely payments of principal and interest on the principal amount outstanding because of the relationship between missed payments and an increase in credit risk. (See also paragraph B4.1.18.)
- The definition of a derivative refers to non-financial variables that are not specific to a party to the contract. These include an index of earthquake losses in a particular region and an index of temperatures in a particular city. Non-financial variables specific to a party to the contract include the occurrence or non-occurrence of a fire that damages or destroys an asset of a party to the contract. A change in the fair value of a non-financial asset is specific to the owner if the fair value reflects not only changes in market prices for such assets (a financial variable) but also the condition of the specific non-financial asset held (a non-financial variable). For example, if a guarantee of the residual value of a specific car exposes the guarantor to the risk of changes in the car's physical condition, the change in that residual value is specific to the owner of the car.
- 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.
- Contractual cash flows that are solely payments of principal and interest on the principal amount outstanding are consistent with a basic lending arrangement. In a basic lending arrangement, consideration for the time value of money (see paragraphs B4.1.9A–B4.1.9E) and credit risk are typically the most significant elements of interest. However, in such an arrangement, interest can also include consideration for other basic lending risks (for example, liquidity risk) and costs (for example, administrative costs) associated with holding the financial asset for a particular period of time. In addition, interest can include a profit margin that is consistent with a basic lending arrangement. In extreme economic circumstances, interest can be negative if, for example, the holder of a financial asset either explicitly or implicitly pays for the deposit of its money for a particular period of time (and that fee exceeds the consideration that the holder receives for the time value of money, credit risk and other basic lending risks and costs).

*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

Cactus Acquisition Corp. 1 Limited Class A Ordinary Share assigned short-term Baa2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Ensemble Learning (ML) with Chi-Square ^{1,2,3,4} and conclude that the CCTS stock is predictable in the short/long term.**

**According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold**

### Financial State Forecast for CCTS Cactus Acquisition Corp. 1 Limited Class A Ordinary Share Options & Futures

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

Outlook* | Baa2 | B1 |

Operational Risk | 69 | 57 |

Market Risk | 86 | 72 |

Technical Analysis | 75 | 53 |

Fundamental Analysis | 88 | 30 |

Risk Unsystematic | 69 | 85 |

### Prediction Confidence Score

## References

- M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
- R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., When to Sell and When to Hold FTNT Stock. AC Investment Research Journal, 101(3).
- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.

## Frequently Asked Questions

Q: What is the prediction methodology for CCTS stock?A: CCTS stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Chi-Square

Q: Is CCTS stock a buy or sell?

A: The dominant strategy among neural network is to Hold CCTS Stock.

Q: Is Cactus Acquisition Corp. 1 Limited Class A Ordinary Share stock a good investment?

A: The consensus rating for Cactus Acquisition Corp. 1 Limited Class A Ordinary Share is Hold and assigned short-term Baa2 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of CCTS stock?

A: The consensus rating for CCTS is Hold.

Q: What is the prediction period for CCTS stock?

A: The prediction period for CCTS is (n+3 month)

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