AC Investment Research

LON:INSG INSIG AI PLC Research Report

Outlook: INSIG AI PLC assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 29 Dec 2022 for (n+8 weeks)
Methodology : Multi-Instance Learning (ML)

Abstract

Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one.(Pahwa, K. and Agarwal, N., 2019, February. Stock market analysis using supervised machine learning. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (pp. 197-200). IEEE.) We evaluate INSIG AI PLC prediction models with Multi-Instance Learning (ML) and Polynomial Regression1,2,3,4 and conclude that the LON:INSG stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

Key Points

  1. Investment Risk
  2. Trading Signals
  3. What are the most successful trading algorithms?

LON:INSG Target Price Prediction Modeling Methodology

We consider INSIG AI PLC Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of LON:INSG 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= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Multi-Instance Learning (ML)) X S(n):→ (n+8 weeks) R = r 1 r 2 r 3

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:INSG INSIG AI PLC
Time series to forecast n: 29 Dec 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

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 INSIG AI PLC

  1. An entity may use practical expedients when measuring expected credit losses if they are consistent with the principles in paragraph 5.5.17. An example of a practical expedient is the calculation of the expected credit losses on trade receivables using a provision matrix. The entity would use its historical credit loss experience (adjusted as appropriate in accordance with paragraphs B5.5.51–B5.5.52) for trade receivables to estimate the 12-month expected credit losses or the lifetime expected credit losses on the financial assets as relevant. A provision matrix might, for example, specify fixed provision rates depending on the number of days that a trade receivable is past due (for example, 1 per cent if not past due, 2 per cent if less than 30 days past due, 3 per cent if more than 30 days but less than 90 days past due, 20 per cent if 90–180 days past due etc). Depending on the diversity of its customer base, the entity would use appropriate groupings if its historical credit loss experience shows significantly different loss patterns for different customer segments. Examples of criteria that might be used to group assets include geographical region, product type, customer rating, collateral or trade credit insurance and type of customer (such as wholesale or retail)
  2. When rebalancing a hedging relationship, an entity shall update its analysis of the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its (remaining) term (see paragraph B6.4.2). The documentation of the hedging relationship shall be updated accordingly.
  3. IFRS 16, issued in January 2016, amended paragraphs 2.1, 5.5.15, B4.3.8, B5.5.34 and B5.5.46. An entity shall apply those amendments when it applies IFRS 16.
  4. At the date of initial application, an entity shall use reasonable and supportable information that is available without undue cost or effort to determine the credit risk at the date that a financial instrument was initially recognised (or for loan commitments and financial guarantee contracts at the date that the entity became a party to the irrevocable commitment in accordance with paragraph 5.5.6) and compare that to the credit risk at the date of initial application of this Standard.

*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

INSIG AI PLC assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Multi-Instance Learning (ML) with Polynomial Regression1,2,3,4 and conclude that the LON:INSG stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

LON:INSG INSIG AI PLC Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCB3
Balance SheetBaa2B3
Leverage RatiosCC
Cash FlowB1Baa2
Rates of Return and ProfitabilityCB3

*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: 80 out of 100 with 484 signals.

References

  1. Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
  2. J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
  3. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
  4. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
  5. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
  6. Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]
  7. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
Frequently Asked QuestionsQ: What is the prediction methodology for LON:INSG stock?
A: LON:INSG stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Polynomial Regression
Q: Is LON:INSG stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:INSG Stock.
Q: Is INSIG AI PLC stock a good investment?
A: The consensus rating for INSIG AI PLC is Sell and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:INSG stock?
A: The consensus rating for LON:INSG is Sell.
Q: What is the prediction period for LON:INSG stock?
A: The prediction period for LON:INSG is (n+8 weeks)

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