**Outlook:**The Central and Eastern Europe Fund Inc. (The) Common Stock assigned short-term B1 & long-term Ba3 forecasted stock rating.

**Dominant Strategy :**Wait until speculative trend diminishes

**Time series to forecast n: 17 Dec 2022**for (n+16 weeks)

**Methodology :**Reinforcement Machine Learning (ML)

## Abstract

Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems.(Patel, J., Shah, S., Thakkar, P. and Kotecha, K., 2015. Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1), pp.259-268.)** We evaluate The Central and Eastern Europe Fund Inc. (The) Common Stock prediction models with Reinforcement Machine Learning (ML) and ElasticNet Regression ^{1,2,3,4} and conclude that the CEE stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes**

## Key Points

- Trading Signals
- Trust metric by Neural Network
- Can statistics predict the future?

## CEE Target Price Prediction Modeling Methodology

We consider The Central and Eastern Europe Fund Inc. (The) Common Stock Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of CEE 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(ElasticNet 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(Reinforcement Machine Learning (ML)) X S(n):→ (n+16 weeks) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of CEE 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?

## CEE Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**CEE The Central and Eastern Europe Fund Inc. (The) Common Stock

**Time series to forecast n: 17 Dec 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes**

**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 The Central and Eastern Europe Fund Inc. (The) Common Stock

- A regular way purchase or sale gives rise to a fixed price commitment between trade date and settlement date that meets the definition of a derivative. However, because of the short duration of the commitment it is not recognised as a derivative financial instrument. Instead, this Standard provides for special accounting for such regular way contracts (see paragraphs 3.1.2 and B3.1.3–B3.1.6).
- An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods only if it is possible to do so without the use of hindsight. If an entity restates prior periods, the restated financial statements must reflect all the requirements in this Standard for the affected financial instruments. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
- It would not be acceptable to designate only some of the financial assets and financial liabilities giving rise to the inconsistency as at fair value through profit or loss if to do so would not eliminate or significantly reduce the inconsistency and would therefore not result in more relevant information. However, it would be acceptable to designate only some of a number of similar financial assets or similar financial liabilities if doing so achieves a significant reduction (and possibly a greater reduction than other allowable designations) in the inconsistency. For example, assume an entity has a number of similar financial liabilities that sum to CU100 and a number of similar financial assets that sum to CU50 but are measured on a different basis. The entity may significantly reduce the measurement inconsistency by designating at initial recognition all of the assets but only some of the liabilities (for example, individual liabilities with a combined total of CU45) as at fair value through profit or loss. However, because designation as at fair value through profit or loss can be applied only to the whole of a financial instrument, the entity in this example must designate one or more liabilities in their entirety. It could not designate either a component of a liability (eg changes in value attributable to only one risk, such as changes in a benchmark interest rate) or a proportion (ie percentage) of a liability.
- An entity shall assess at the inception of the hedging relationship, and on an ongoing basis, whether a hedging relationship meets the hedge effectiveness requirements. At a minimum, an entity shall perform the ongoing assessment at each reporting date or upon a significant change in the circumstances affecting the hedge effectiveness requirements, whichever comes first. The assessment relates to expectations about hedge effectiveness and is therefore only forward-looking.

*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

The Central and Eastern Europe Fund Inc. (The) Common Stock assigned short-term B1 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Reinforcement Machine Learning (ML) with ElasticNet Regression ^{1,2,3,4} and conclude that the CEE stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes**

### Financial State Forecast for CEE The Central and Eastern Europe Fund Inc. (The) Common Stock Options & Futures

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

Outlook* | B1 | Ba3 |

Operational Risk | 87 | 78 |

Market Risk | 38 | 42 |

Technical Analysis | 45 | 77 |

Fundamental Analysis | 68 | 61 |

Risk Unsystematic | 60 | 69 |

### Prediction Confidence Score

## References

- E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
- Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
- Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
- R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
- Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
- L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
- S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013

## Frequently Asked Questions

Q: What is the prediction methodology for CEE stock?A: CEE stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and ElasticNet Regression

Q: Is CEE stock a buy or sell?

A: The dominant strategy among neural network is to Wait until speculative trend diminishes CEE Stock.

Q: Is The Central and Eastern Europe Fund Inc. (The) Common Stock stock a good investment?

A: The consensus rating for The Central and Eastern Europe Fund Inc. (The) Common Stock is Wait until speculative trend diminishes and assigned short-term B1 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of CEE stock?

A: The consensus rating for CEE is Wait until speculative trend diminishes.

Q: What is the prediction period for CEE stock?

A: The prediction period for CEE is (n+16 weeks)