**Outlook:**CD PRIVATE EQUITY FUND I assigned short-term Ba1 & long-term B1 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 17 Dec 2022**for (n+8 weeks)

**Methodology :**Deductive Inference (ML)

## Abstract

In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. (Kadole, A., 2020. A Machine Learning Model for Stock Price Prediction using Neural Network.)** We evaluate CD PRIVATE EQUITY FUND I prediction models with Deductive Inference (ML) and Sign Test ^{1,2,3,4} and conclude that the CD1 stock is predictable in the short/long term. **

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

## Key Points

- Prediction Modeling
- What are buy sell or hold recommendations?
- Technical Analysis with Algorithmic Trading

## CD1 Target Price Prediction Modeling Methodology

We consider CD PRIVATE EQUITY FUND I Decision Process with Deductive Inference (ML) where A is the set of discrete actions of CD1 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(Deductive Inference (ML)) X S(n):→ (n+8 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of CD1 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?

## CD1 Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**CD1 CD PRIVATE EQUITY FUND I

**Time series to forecast n: 17 Dec 2022**for (n+8 weeks)

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

**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%**

## Adjusted IFRS* Prediction Methods for CD PRIVATE EQUITY FUND I

- The expected credit losses on a loan commitment shall be discounted using the effective interest rate, or an approximation thereof, that will be applied when recognising the financial asset resulting from the loan commitment. This is because for the purpose of applying the impairment requirements, a financial asset that is recognised following a draw down on a loan commitment shall be treated as a continuation of that commitment instead of as a new financial instrument. The expected credit losses on the financial asset shall therefore be measured considering the initial credit risk of the loan commitment from the date that the entity became a party to the irrevocable commitment.
- The assessment of whether lifetime expected credit losses should be recognised is based on significant increases in the likelihood or risk of a default occurring since initial recognition (irrespective of whether a financial instrument has been repriced to reflect an increase in credit risk) instead of on evidence of a financial asset being credit-impaired at the reporting date or an actual default occurring. Generally, there will be a significant increase in credit risk before a financial asset becomes credit-impaired or an actual default occurs.
- In accordance with paragraph 4.1.3(a), principal is the fair value of the financial asset at initial recognition. However that principal amount may change over the life of the financial asset (for example, if there are repayments of principal).
- As noted in paragraph B4.3.1, when an entity becomes a party to a hybrid contract with a host that is not an asset within the scope of this Standard and with one or more embedded derivatives, paragraph 4.3.3 requires the entity to identify any such embedded derivative, assess whether it is required to be separated from the host contract and, for those that are required to be separated, measure the derivatives at fair value at initial recognition and subsequently. These requirements can be more complex, or result in less reliable measures, than measuring the entire instrument at fair value through profit or loss. For that reason this Standard permits the entire hybrid contract to be designated as at fair value through profit or loss.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

CD PRIVATE EQUITY FUND I assigned short-term Ba1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Deductive Inference (ML) with Sign Test ^{1,2,3,4} and conclude that the CD1 stock is predictable in the short/long term.**

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

### Financial State Forecast for CD1 CD PRIVATE EQUITY FUND I Options & Futures

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

Outlook* | Ba1 | B1 |

Operational Risk | 75 | 74 |

Market Risk | 74 | 41 |

Technical Analysis | 45 | 63 |

Fundamental Analysis | 73 | 61 |

Risk Unsystematic | 87 | 49 |

### Prediction Confidence Score

## References

- Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
- Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
- Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
- Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. 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.
- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
- 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

## Frequently Asked Questions

Q: What is the prediction methodology for CD1 stock?A: CD1 stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Sign Test

Q: Is CD1 stock a buy or sell?

A: The dominant strategy among neural network is to Buy CD1 Stock.

Q: Is CD PRIVATE EQUITY FUND I stock a good investment?

A: The consensus rating for CD PRIVATE EQUITY FUND I is Buy and assigned short-term Ba1 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of CD1 stock?

A: The consensus rating for CD1 is Buy.

Q: What is the prediction period for CD1 stock?

A: The prediction period for CD1 is (n+8 weeks)

- Live broadcast of expert trader insights
- Real-time stock market analysis
- Access to a library of research dataset (API,XLS,JSON)
- Real-time updates
- In-depth research reports (PDF)