PSI PSC INSURANCE GROUP LIMITED

PSC INSURANCE GROUP LIMITED Research Report

Summary

Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised learning which is not so adequate for learning problems with long-term goals and delayed rewards. This paper proposes a method of applying reinforcement learning, suitable for modeling and learning various kinds of interactions in real situations, to the problem of stock price prediction. We evaluate PSC INSURANCE GROUP LIMITED prediction models with Active Learning (ML) and Polynomial Regression^1,2,3,4 and conclude that the PSI stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold PSI stock.

Key Points

1. What is prediction in deep learning?
2. Market Risk
3. Can stock prices be predicted?

PSI Target Price Prediction Modeling Methodology

We consider PSC INSURANCE GROUP LIMITED Decision Process with Active Learning (ML) where A is the set of discrete actions of PSI 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(Polynomial Regression)^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(Active Learning (ML)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of PSI 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?

PSI Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: PSI PSC INSURANCE GROUP LIMITED
Time series to forecast n: 30 Nov 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold PSI 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 PSC INSURANCE GROUP LIMITED

1. A portfolio of financial assets that is managed and whose performance is evaluated on a fair value basis (as described in paragraph 4.2.2(b)) is neither held to collect contractual cash flows nor held both to collect contractual cash flows and to sell financial assets. The entity is primarily focused on fair value information and uses that information to assess the assets' performance and to make decisions. In addition, a portfolio of financial assets that meets the definition of held for trading is not held to collect contractual cash flows or held both to collect contractual cash flows and to sell financial assets. For such portfolios, the collection of contractual cash flows is only incidental to achieving the business model's objective. Consequently, such portfolios of financial assets must be measured at fair value through profit or loss.
2. If, at the date of initial application, it is impracticable (as defined in IAS 8) for an entity to assess whether the fair value of a prepayment feature was insignificant in accordance with paragraph B4.1.12(c) on the basis of the facts and circumstances that existed at the initial recognition of the financial asset, an entity shall assess the contractual cash flow characteristics of that financial asset on the basis of the facts and circumstances that existed at the initial recognition of the financial asset without taking into account the exception for prepayment features in paragraph B4.1.12. (See also paragraph 42S of IFRS 7.)
3. IFRS 17, issued in May 2017, amended paragraphs 2.1, B2.1, B2.4, B2.5 and B4.1.30, and added paragraph 3.3.5. Amendments to IFRS 17, issued in June 2020, further amended paragraph 2.1 and added paragraphs 7.2.36‒7.2.42. An entity shall apply those amendments when it applies IFRS 17.
4. The underlying pool must contain one or more instruments that have contractual cash flows that are solely payments of principal and interest on the principal amount outstanding

*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

PSC INSURANCE GROUP LIMITED assigned short-term Ba3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Polynomial Regression^1,2,3,4 and conclude that the PSI stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold PSI stock.

Financial State Forecast for PSI PSC INSURANCE GROUP LIMITED Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	Ba3
Operational Risk	37	84
Market Risk	79	81
Technical Analysis	49	87
Fundamental Analysis	90	41
Risk Unsystematic	67	35

Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 859 signals.

References

1. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
2. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
3. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
4. J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
5. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
6. J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.
7. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994

Frequently Asked Questions

Q: What is the prediction methodology for PSI stock?
A: PSI stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Polynomial Regression
Q: Is PSI stock a buy or sell?
A: The dominant strategy among neural network is to Hold PSI Stock.
Q: Is PSC INSURANCE GROUP LIMITED stock a good investment?
A: The consensus rating for PSC INSURANCE GROUP LIMITED is Hold and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of PSI stock?
A: The consensus rating for PSI is Hold.
Q: What is the prediction period for PSI stock?
A: The prediction period for PSI is (n+1 year)

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