**Outlook:**Champion Iron Limited is assigned short-term Ba3 & long-term B2 estimated rating.

**AUC Score :**

**Short-Term Revised**

^{1}:**Dominant Strategy :**Buy

**Time series to forecast n:** for

^{2}

**Methodology :**Statistical Inference (ML)

**Hypothesis Testing :**Sign Test

**Surveillance :**Major exchange and OTC

^{1}The accuracy of the model is being monitored on a regular basis.(15-minute period)

^{2}Time series is updated based on short-term trends.

## Abstract

Champion Iron Limited prediction model is evaluated with Statistical Inference (ML) and Sign Test^{1,2,3,4}and it is concluded that the CIA:TSX stock is predictable in the short/long term. Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.

**According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Buy**

## Key Points

- Game Theory
- How do you pick a stock?
- Can neural networks predict stock market?

## CIA:TSX Target Price Prediction Modeling Methodology

We consider Champion Iron Limited Decision Process with Statistical Inference (ML) where A is the set of discrete actions of CIA:TSX 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(Sign Test)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Statistical Inference (ML)) X S(n):→ 4 Weeks $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

p:Price signals of CIA:TSX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Statistical Inference (ML)

Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.### Sign Test

The sign test is a non-parametric hypothesis test that is used to compare two paired samples. In a paired sample, each data point in one sample is paired with a data point in the other sample. The pairs are typically related in some way, such as before and after measurements, or measurements from the same subject under different conditions. The sign test is a non-parametric test, which means that it does not assume that the data is normally distributed. The sign test is also a dependent samples test, which means that the data points in each pair are correlated.

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?

## CIA:TSX Stock Forecast (Buy or Sell)

**Sample Set:**Neural Network

**Stock/Index:**CIA:TSX Champion Iron Limited

**Time series to forecast:**4 Weeks

**According to price forecasts, the dominant strategy among neural network is: Buy**

Strategic Interaction Table Legend:

**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%**

### Financial Data Adjustments for Statistical Inference (ML) based CIA:TSX Stock Prediction Model

- Measurement of a financial asset or financial liability and classification of recognised changes in its value are determined by the item's classification and whether the item is part of a designated hedging relationship. Those requirements can create a measurement or recognition inconsistency (sometimes referred to as an 'accounting mismatch') when, for example, in the absence of designation as at fair value through profit or loss, a financial asset would be classified as subsequently measured at fair value through profit or loss and a liability the entity considers related would be subsequently measured at amortised cost (with changes in fair value not recognised). In such circumstances, an entity may conclude that its financial statements would provide more relevant information if both the asset and the liability were measured as at fair value through profit or loss.
- A net position is eligible for hedge accounting only if an entity hedges on a net basis for risk management purposes. Whether an entity hedges in this way is a matter of fact (not merely of assertion or documentation). Hence, an entity cannot apply hedge accounting on a net basis solely to achieve a particular accounting outcome if that would not reflect its risk management approach. Net position hedging must form part of an established risk management strategy. Normally this would be approved by key management personnel as defined in IAS 24.
- The following are examples of when the objective of the entity's business model may be achieved by both collecting contractual cash flows and selling financial assets. This list of examples is not exhaustive. Furthermore, the examples are not intended to describe all the factors that may be relevant to the assessment of the entity's business model nor specify the relative importance of the factors.
- If a financial instrument that was previously recognised as a financial asset is measured at fair value through profit or loss and its fair value decreases below zero, it is a financial liability measured in accordance with paragraph 4.2.1. However, hybrid contracts with hosts that are assets within the scope of this Standard are always measured in accordance with paragraph 4.3.2.

*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.

### CIA:TSX Champion Iron Limited Financial Analysis*

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba3 | B2 |

Income Statement | Caa2 | C |

Balance Sheet | Baa2 | C |

Leverage Ratios | Caa2 | C |

Cash Flow | Ba1 | Baa2 |

Rates of Return and Profitability | Baa2 | B2 |

*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?

## Conclusions

Champion Iron Limited is assigned short-term Ba3 & long-term B2 estimated rating. Champion Iron Limited prediction model is evaluated with Statistical Inference (ML) and Sign Test^{1,2,3,4} and it is concluded that the CIA:TSX stock is predictable in the short/long term. ** According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Buy**

### Prediction Confidence Score

## References

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- 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
- Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
- Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
- K. Boda, J. Filar, Y. Lin, and L. Spanjers. Stochastic target hitting time and the problem of early retirement. Automatic Control, IEEE Transactions on, 49(3):409–419, 2004
- Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
- E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.

## Frequently Asked Questions

Q: What is the prediction methodology for CIA:TSX stock?A: CIA:TSX stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Sign Test

Q: Is CIA:TSX stock a buy or sell?

A: The dominant strategy among neural network is to Buy CIA:TSX Stock.

Q: Is Champion Iron Limited stock a good investment?

A: The consensus rating for Champion Iron Limited is Buy and is assigned short-term Ba3 & long-term B2 estimated rating.

Q: What is the consensus rating of CIA:TSX stock?

A: The consensus rating for CIA:TSX is Buy.

Q: What is the prediction period for CIA:TSX stock?

A: The prediction period for CIA:TSX is 4 Weeks

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