Outlook: INTEGRATED DIAGNOSTICS HOLDINGS PLC is assigned short-term B3 & long-term B2 estimated rating.
Time series to forecast n: 25 Jun 2023 for 6 Month
Methodology : Transductive Learning (ML)

## Summary

INTEGRATED DIAGNOSTICS HOLDINGS PLC prediction model is evaluated with Transductive Learning (ML) and Linear Regression1,2,3,4 and it is concluded that the LON:IDHC stock is predictable in the short/long term. Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels. According to price forecasts for 6 Month period, the dominant strategy among neural network is: BuySpeculative Trend ## Key Points

1. Technical Analysis with Algorithmic Trading
2. What are buy sell or hold recommendations?

## LON:IDHC Target Price Prediction Modeling Methodology

We consider INTEGRATED DIAGNOSTICS HOLDINGS PLC Decision Process with Transductive Learning (ML) where A is the set of discrete actions of LON:IDHC 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(Linear Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Transductive Learning (ML)) X S(n):→ 6 Month $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of LON:IDHC stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Transductive Learning (ML)

Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels.

### Linear Regression

In statistics, linear regression is a method for estimating the relationship between a dependent variable and one or more independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Linear regression assumes that the relationship between the dependent variable and the independent variables is linear. This means that the dependent variable can be represented as a straight line function of the independent variables.

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:IDHC Stock Forecast (Buy or Sell) for 6 Month

Sample Set: Neural Network
Stock/Index: LON:IDHC INTEGRATED DIAGNOSTICS HOLDINGS PLC
Time series to forecast n: 25 Jun 2023 for 6 Month

According to price forecasts for 6 Month period, the dominant strategy among neural network is: BuySpeculative Trend

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 INTEGRATED DIAGNOSTICS HOLDINGS PLC

1. Paragraph 4.1.1(b) requires an entity to classify a financial asset on the basis of its contractual cash flow characteristics if the financial asset is held within a business model whose objective is to hold assets to collect contractual cash flows or within a business model whose objective is achieved by both collecting contractual cash flows and selling financial assets, unless paragraph 4.1.5 applies. To do so, the condition in paragraphs 4.1.2(b) and 4.1.2A(b) requires an entity to determine whether the asset's contractual cash flows are solely payments of principal and interest on the principal amount outstanding.
2. If there is a hedging relationship between a non-derivative monetary asset and a non-derivative monetary liability, changes in the foreign currency component of those financial instruments are presented in profit or loss.
3. If, at the date of initial application, determining whether there has been a significant increase in credit risk since initial recognition would require undue cost or effort, an entity shall recognise a loss allowance at an amount equal to lifetime expected credit losses at each reporting date until that financial instrument is derecognised (unless that financial instrument is low credit risk at a reporting date, in which case paragraph 7.2.19(a) applies).
4. 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.

*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

INTEGRATED DIAGNOSTICS HOLDINGS PLC is assigned short-term B3 & long-term B2 estimated rating. INTEGRATED DIAGNOSTICS HOLDINGS PLC prediction model is evaluated with Transductive Learning (ML) and Linear Regression1,2,3,4 and it is concluded that the LON:IDHC stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: BuySpeculative Trend

### LON:IDHC INTEGRATED DIAGNOSTICS HOLDINGS PLC Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B3B2
Income StatementCBaa2
Balance SheetCaa2C
Leverage RatiosCCaa2
Cash FlowCaa2C
Rates of Return and ProfitabilityBa3Baa2

*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: 82 out of 100 with 582 signals.

## References

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2. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
3. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
4. Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
5. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
6. Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
7. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
Frequently Asked QuestionsQ: What is the prediction methodology for LON:IDHC stock?
A: LON:IDHC stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Linear Regression
Q: Is LON:IDHC stock a buy or sell?
A: The dominant strategy among neural network is to BuySpeculative Trend LON:IDHC Stock.
Q: Is INTEGRATED DIAGNOSTICS HOLDINGS PLC stock a good investment?
A: The consensus rating for INTEGRATED DIAGNOSTICS HOLDINGS PLC is BuySpeculative Trend and is assigned short-term B3 & long-term B2 estimated rating.
Q: What is the consensus rating of LON:IDHC stock?
A: The consensus rating for LON:IDHC is BuySpeculative Trend.
Q: What is the prediction period for LON:IDHC stock?
A: The prediction period for LON:IDHC is 6 Month