LON:GST GSTECHNOLOGIES LTD

Outlook: GSTECHNOLOGIES LTD is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 28 Feb 2023 for (n+6 month)
Methodology : Reinforcement Machine Learning (ML)

Abstract

GSTECHNOLOGIES LTD prediction model is evaluated with Reinforcement Machine Learning (ML) and Independent T-Test^1,2,3,4 and it is concluded that the LON:GST stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

Key Points

1. Can statistics predict the future?
2. Trading Signals
3. What is a prediction confidence?

LON:GST Target Price Prediction Modeling Methodology

We consider GSTECHNOLOGIES LTD Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of LON:GST 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(Independent T-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(Reinforcement Machine Learning (ML)) X S(n):→ (n+6 month) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of LON:GST 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:GST Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:GST GSTECHNOLOGIES LTD
Time series to forecast n: 28 Feb 2023 for (n+6 month)

According to price forecasts for (n+6 month) 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%

IFRS Reconciliation Adjustments for GSTECHNOLOGIES LTD

1. Paragraph 5.7.5 permits an entity to make an irrevocable election to present in other comprehensive income changes in the fair value of an investment in an equity instrument that is not held for trading. This election is made on an instrument-by-instrument (ie share-by-share) basis. Amounts presented in other comprehensive income shall not be subsequently transferred to profit or loss. However, the entity may transfer the cumulative gain or loss within equity. Dividends on such investments are recognised in profit or loss in accordance with paragraph 5.7.6 unless the dividend clearly represents a recovery of part of the cost of the investment.
2. For the purposes of the transition provisions in paragraphs 7.2.1, 7.2.3–7.2.28 and 7.3.2, the date of initial application is the date when an entity first applies those requirements of this Standard and must be the beginning of a reporting period after the issue of this Standard. Depending on the entity's chosen approach to applying IFRS 9, the transition can involve one or more than one date of initial application for different requirements.
3. Historical information is an important anchor or base from which to measure expected credit losses. However, an entity shall adjust historical data, such as credit loss experience, on the basis of current observable data to reflect the effects of the current conditions and its forecasts of future conditions that did not affect the period on which the historical data is based, and to remove the effects of the conditions in the historical period that are not relevant to the future contractual cash flows. In some cases, the best reasonable and supportable information could be the unadjusted historical information, depending on the nature of the historical information and when it was calculated, compared to circumstances at the reporting date and the characteristics of the financial instrument being considered. Estimates of changes in expected credit losses should reflect, and be directionally consistent with, changes in related observable data from period to period
4. For a discontinued hedging relationship, when the interest rate benchmark on which the hedged future cash flows had been based is changed as required by interest rate benchmark reform, for the purpose of applying paragraph 6.5.12 in order to determine whether the hedged future cash flows are expected to occur, the amount accumulated in the cash flow hedge reserve for that hedging relationship shall be deemed to be based on the alternative benchmark rate on which the hedged future cash flows will be based.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

Conclusions

GSTECHNOLOGIES LTD is assigned short-term Ba1 & long-term Ba1 estimated rating. GSTECHNOLOGIES LTD prediction model is evaluated with Reinforcement Machine Learning (ML) and Independent T-Test^1,2,3,4 and it is concluded that the LON:GST stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

LON:GST GSTECHNOLOGIES LTD Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	Baa2
Balance Sheet	Caa2	Caa2
Leverage Ratios	Baa2	Caa2
Cash Flow	B3	C
Rates of Return and Profitability	Ba2	Caa2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 818 signals.

References

1. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
2. Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678
3. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
4. L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
5. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., MO Stock Price Prediction. AC Investment Research Journal, 101(3).
6. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
7. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.

Frequently Asked Questions

Q: What is the prediction methodology for LON:GST stock?
A: LON:GST stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Independent T-Test
Q: Is LON:GST stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:GST Stock.
Q: Is GSTECHNOLOGIES LTD stock a good investment?
A: The consensus rating for GSTECHNOLOGIES LTD is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:GST stock?
A: The consensus rating for LON:GST is Buy.
Q: What is the prediction period for LON:GST stock?
A: The prediction period for LON:GST is (n+6 month)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login