LON:CATC CATCO REINSURANCE OPPORTUNITIES FUND LIMITED (Forecast)

CATCO REINSURANCE OPPORTUNITIES FUND LIMITED Research Report

Abstract

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Outlook: CATCO REINSURANCE OPPORTUNITIES FUND LIMITED assigned short-term B3 & long-term Ba1 forecasted stock rating.
Signal: Hold
Time series to forecast n: 06 Dec 2022 for (n+8 weeks)

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Stock market also called as equity market is the aggregation of the sellers and buyers. It is concerned with the domain where the shares of various public listed companies are traded. For predicting the growth of economy, stock market acts as an index. Due to the nonlinear nature, the prediction of the stock market becomes a difficult task. But the application of various machine learning techniques has been becoming a powerful source for the prediction.(Patel, J., Shah, S., Thakkar, P. and Kotecha, K., 2015. Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1), pp.259-268.) We evaluate CATCO REINSURANCE OPPORTUNITIES FUND LIMITED prediction models with Modular Neural Network (DNN Layer) and Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the LON:CATC stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:CATC stock.

Key Points

1. Should I buy stocks now or wait amid such uncertainty?
2. What statistical methods are used to analyze data?
3. Should I buy stocks now or wait amid such uncertainty?

LON:CATC Target Price Prediction Modeling Methodology

We consider CATCO REINSURANCE OPPORTUNITIES FUND LIMITED Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of LON:CATC 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 Rank-Sum Test)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+8 weeks) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of LON:CATC 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:CATC Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: LON:CATC CATCO REINSURANCE OPPORTUNITIES FUND LIMITED
Time series to forecast n: 06 Dec 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:CATC stock.

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 (Yellow to Green): *Technical Analysis%

Adjusted IFRS* Prediction Methods for CATCO REINSURANCE OPPORTUNITIES FUND LIMITED

1. 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.
2. For lifetime expected credit losses, an entity shall estimate the risk of a default occurring on the financial instrument during its expected life. 12-month expected credit losses are a portion of the lifetime expected credit losses and represent the lifetime cash shortfalls that will result if a default occurs in the 12 months after the reporting date (or a shorter period if the expected life of a financial instrument is less than 12 months), weighted by the probability of that default occurring. Thus, 12-month expected credit losses are neither the lifetime expected credit losses that an entity will incur on financial instruments that it predicts will default in the next 12 months nor the cash shortfalls that are predicted over the next 12 months.
3. 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.
4. The change in the value of the hedged item determined using a hypothetical derivative may also be used for the purpose of assessing whether a hedging relationship meets the hedge effectiveness requirements.

*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

CATCO REINSURANCE OPPORTUNITIES FUND LIMITED assigned short-term B3 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the LON:CATC stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:CATC stock.

Financial State Forecast for LON:CATC CATCO REINSURANCE OPPORTUNITIES FUND LIMITED Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Ba1
Operational Risk	40	60
Market Risk	61	82
Technical Analysis	41	73
Fundamental Analysis	65	69
Risk Unsystematic	40	72

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 742 signals.

References

1. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
2. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
3. G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
4. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
5. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
6. J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
7. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press

Frequently Asked Questions

Q: What is the prediction methodology for LON:CATC stock?
A: LON:CATC stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Wilcoxon Rank-Sum Test
Q: Is LON:CATC stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:CATC Stock.
Q: Is CATCO REINSURANCE OPPORTUNITIES FUND LIMITED stock a good investment?
A: The consensus rating for CATCO REINSURANCE OPPORTUNITIES FUND LIMITED is Hold and assigned short-term B3 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of LON:CATC stock?
A: The consensus rating for LON:CATC is Hold.
Q: What is the prediction period for LON:CATC stock?
A: The prediction period for LON:CATC is (n+8 weeks)

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