**Outlook:**Ceridian HCM Holding Inc. is assigned short-term Ba3 & long-term Ba2 estimated rating.

**AUC Score :**

**Short-Term Revised**

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

**Time series to forecast n:** for

^{2}

**Methodology :**Inductive Learning (ML)

**Hypothesis Testing :**Logistic Regression

**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.

## Summary

Ceridian HCM Holding Inc. prediction model is evaluated with Inductive Learning (ML) and Logistic Regression^{1,2,3,4}and it is concluded that the CDAY:TSX stock is predictable in the short/long term. Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.

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

## Key Points

- What is prediction in deep learning?
- What is neural prediction?
- Can machine learning predict?

## CDAY:TSX Target Price Prediction Modeling Methodology

We consider Ceridian HCM Holding Inc. Decision Process with Inductive Learning (ML) where A is the set of discrete actions of CDAY: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(Logistic Regression)

^{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(Inductive Learning (ML)) X S(n):→ 6 Month $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of CDAY:TSX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Inductive Learning (ML)

Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.### Logistic Regression

In statistics, logistic regression is a type of regression analysis used when the dependent variable is categorical. Logistic regression is a probability model that predicts the probability of an event occurring based on a set of independent variables. In logistic regression, the dependent variable is represented as a binary variable, such as "yes" or "no," "true" or "false," or "sick" or "healthy." The independent variables can be continuous or categorical 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?

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

**Sample Set:**Neural Network

**Stock/Index:**CDAY:TSX Ceridian HCM Holding Inc.

**Time series to forecast:**6 Month

**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 Inductive Learning (ML) based CDAY:TSX Stock Prediction Model

- An embedded prepayment option in an interest-only or principal-only strip is closely related to the host contract provided the host contract (i) initially resulted from separating the right to receive contractual cash flows of a financial instrument that, in and of itself, did not contain an embedded derivative, and (ii) does not contain any terms not present in the original host debt contract.
- For the purpose of applying the requirement in paragraph 6.5.12 in order to determine whether the hedged future cash flows are expected to occur, an entity shall assume that the interest rate benchmark on which the hedged cash flows (contractually or non-contractually specified) are based is not altered as a result of interest rate benchmark reform.
- For some types of fair value hedges, the objective of the hedge is not primarily to offset the fair value change of the hedged item but instead to transform the cash flows of the hedged item. For example, an entity hedges the fair value interest rate risk of a fixed-rate debt instrument using an interest rate swap. The entity's hedge objective is to transform the fixed-interest cash flows into floating interest cash flows. This objective is reflected in the accounting for the hedging relationship by accruing the net interest accrual on the interest rate swap in profit or loss. In the case of a hedge of a net position (for example, a net position of a fixed-rate asset and a fixed-rate liability), this net interest accrual must be presented in a separate line item in the statement of profit or loss and other comprehensive income. This is to avoid the grossing up of a single instrument's net gains or losses into offsetting gross amounts and recognising them in different line items (for example, this avoids grossing up a net interest receipt on a single interest rate swap into gross interest revenue and gross interest expense).
- 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.

### CDAY:TSX Ceridian HCM Holding Inc. Financial Analysis*

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

Outlook* | Ba3 | Ba2 |

Income Statement | Ba3 | Baa2 |

Balance Sheet | Baa2 | B2 |

Leverage Ratios | Ba3 | Baa2 |

Cash Flow | C | B1 |

Rates of Return and Profitability | B1 | B3 |

*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

Ceridian HCM Holding Inc. is assigned short-term Ba3 & long-term Ba2 estimated rating. Ceridian HCM Holding Inc. prediction model is evaluated with Inductive Learning (ML) and Logistic Regression^{1,2,3,4} and it is concluded that the CDAY:TSX stock is predictable in the short/long term. ** According to price forecasts for 6 Month period, the dominant strategy among neural network is: Buy**

### Prediction Confidence Score

## References

- Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
- Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
- M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
- Miller A. 2002. Subset Selection in Regression. New York: CRC Press
- V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
- A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
- K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006

## Frequently Asked Questions

Q: What is the prediction methodology for CDAY:TSX stock?A: CDAY:TSX stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Logistic Regression

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

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

Q: Is Ceridian HCM Holding Inc. stock a good investment?

A: The consensus rating for Ceridian HCM Holding Inc. is Buy and is assigned short-term Ba3 & long-term Ba2 estimated rating.

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

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

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

A: The prediction period for CDAY:TSX is 6 Month

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