LON:FTSV FORESIGHT SOLAR & TECHNOLOGY VCT PLC

Outlook: FORESIGHT SOLAR & TECHNOLOGY VCT PLC assigned short-term Ba2 & long-term B1 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 17 Dec 2022 for (n+3 month)
Methodology : Reinforcement Machine Learning (ML)

Abstract

The stock market is very volatile and non-stationary and generates huge volumes of data in every second. In this article, the existing machine learning algorithms are analyzed for stock market forecasting and also a new pattern-finding algorithm for forecasting stock trend is developed. Three approaches can be used to solve the problem: fundamental analysis, technical analysis, and the machine learning. Experimental analysis done in this article shows that the machine learning could be useful for investors to make profitable decisions.(Rao, P.S., Srinivas, K. and Mohan, A.K., 2020. A survey on stock market prediction using machine learning techniques. In ICDSMLA 2019 (pp. 923-931). Springer, Singapore.) We evaluate FORESIGHT SOLAR & TECHNOLOGY VCT PLC prediction models with Reinforcement Machine Learning (ML) and Statistical Hypothesis Testing^1,2,3,4 and conclude that the LON:FTSV 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

1. Probability Distribution
2. Reaction Function
3. Trust metric by Neural Network

LON:FTSV Target Price Prediction Modeling Methodology

We consider FORESIGHT SOLAR & TECHNOLOGY VCT PLC Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of LON:FTSV 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Reinforcement Machine Learning (ML)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of LON:FTSV 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:FTSV Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: LON:FTSV FORESIGHT SOLAR & TECHNOLOGY VCT PLC
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 FORESIGHT SOLAR & TECHNOLOGY VCT PLC

1. For example, when the critical terms (such as the nominal amount, maturity and underlying) of the hedging instrument and the hedged item match or are closely aligned, it might be possible for an entity to conclude on the basis of a qualitative assessment of those critical terms that the hedging instrument and the hedged item have values that will generally move in the opposite direction because of the same risk and hence that an economic relationship exists between the hedged item and the hedging instrument (see paragraphs B6.4.4–B6.4.6).
2. In addition to those hedging relationships specified in paragraph 6.9.1, an entity shall apply the requirements in paragraphs 6.9.11 and 6.9.12 to new hedging relationships in which an alternative benchmark rate is designated as a non-contractually specified risk component (see paragraphs 6.3.7(a) and B6.3.8) when, because of interest rate benchmark reform, that risk component is not separately identifiable at the date it is designated.
3. If subsequently an entity reasonably expects that the alternative benchmark rate will not be separately identifiable within 24 months from the date the entity designated it as a non-contractually specified risk component for the first time, the entity shall cease applying the requirement in paragraph 6.9.11 to that alternative benchmark rate and discontinue hedge accounting prospectively from the date of that reassessment for all hedging relationships in which the alternative benchmark rate was designated as a noncontractually specified risk component.
4. For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the contract.

*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

FORESIGHT SOLAR & TECHNOLOGY VCT PLC assigned short-term Ba2 & long-term B1 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Statistical Hypothesis Testing^1,2,3,4 and conclude that the LON:FTSV 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 LON:FTSV FORESIGHT SOLAR & TECHNOLOGY VCT PLC Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba2	B1
Operational Risk	73	79
Market Risk	74	55
Technical Analysis	89	39
Fundamental Analysis	80	50
Risk Unsystematic	32	62

Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 731 signals.

References

1. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
2. 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
3. R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
4. 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
5. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
6. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
7. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.

Frequently Asked Questions

Q: What is the prediction methodology for LON:FTSV stock?
A: LON:FTSV stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Statistical Hypothesis Testing
Q: Is LON:FTSV stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:FTSV Stock.
Q: Is FORESIGHT SOLAR & TECHNOLOGY VCT PLC stock a good investment?
A: The consensus rating for FORESIGHT SOLAR & TECHNOLOGY VCT PLC is Hold and assigned short-term Ba2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:FTSV stock?
A: The consensus rating for LON:FTSV is Hold.
Q: What is the prediction period for LON:FTSV stock?
A: The prediction period for LON:FTSV is (n+3 month)

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