Dominant Strategy : Hold
Time series to forecast n: 17 Jan 2023 for (n+1 year)
Methodology : Active Learning (ML)
Abstract
CORDIANT DIGITAL INFRASTRUCTURE LIMITED prediction model is evaluated with Active Learning (ML) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the LON:CSRD stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: HoldKey Points
- What is prediction model?
- Operational Risk
- How can neural networks improve predictions?
LON:CSRD Target Price Prediction Modeling Methodology
We consider CORDIANT DIGITAL INFRASTRUCTURE LIMITED Decision Process with Active Learning (ML) where A is the set of discrete actions of LON:CSRD 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= X R(Active Learning (ML)) X S(n):→ (n+1 year)
n:Time series to forecast
p:Price signals of LON:CSRD 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:CSRD Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: LON:CSRD CORDIANT DIGITAL INFRASTRUCTURE LIMITED
Time series to forecast n: 17 Jan 2023 for (n+1 year)
According to price forecasts for (n+1 year) 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%
IFRS Reconciliation Adjustments for CORDIANT DIGITAL INFRASTRUCTURE LIMITED
- An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.
- 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.
- An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.
- An entity that first applies IFRS 17 as amended in June 2020 at the same time it first applies this Standard shall apply paragraphs 7.2.1–7.2.28 instead of paragraphs 7.2.38–7.2.42.
*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
CORDIANT DIGITAL INFRASTRUCTURE LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. CORDIANT DIGITAL INFRASTRUCTURE LIMITED prediction model is evaluated with Active Learning (ML) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the LON:CSRD stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold
LON:CSRD CORDIANT DIGITAL INFRASTRUCTURE LIMITED Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba1 | Ba1 |
Income Statement | B1 | C |
Balance Sheet | Caa2 | C |
Leverage Ratios | Ba1 | Ba2 |
Cash Flow | C | B2 |
Rates of Return and Profitability | C | Baa2 |
*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
References
- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., MO Stock Price Prediction. AC Investment Research Journal, 101(3).
- Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
- Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
- Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
- Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
- Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
- Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
Frequently Asked Questions
Q: What is the prediction methodology for LON:CSRD stock?A: LON:CSRD stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Statistical Hypothesis Testing
Q: Is LON:CSRD stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:CSRD Stock.
Q: Is CORDIANT DIGITAL INFRASTRUCTURE LIMITED stock a good investment?
A: The consensus rating for CORDIANT DIGITAL INFRASTRUCTURE LIMITED is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:CSRD stock?
A: The consensus rating for LON:CSRD is Hold.
Q: What is the prediction period for LON:CSRD stock?
A: The prediction period for LON:CSRD is (n+1 year)
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