**Outlook:**USCOM LIMITED assigned short-term Baa2 & long-term Ba3 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 12 Dec 2022**for (n+8 weeks)

**Methodology :**Active Learning (ML)

## Abstract

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators.(Devadoss, A.V. and Ligori, T.A.A., 2013. Stock prediction using artificial neural networks. International Journal of Data Mining Techniques and Applications, 2(1), pp.283-291.)** We evaluate USCOM LIMITED prediction models with Active Learning (ML) and Spearman Correlation ^{1,2,3,4} and conclude that the UCM 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

- What is the use of Markov decision process?
- Probability Distribution
- Trading Interaction

## UCM Target Price Prediction Modeling Methodology

We consider USCOM LIMITED Decision Process with Active Learning (ML) where A is the set of discrete actions of UCM 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(Spearman Correlation)

^{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(Active Learning (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 UCM 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?

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

**Sample Set:**Neural Network

**Stock/Index:**UCM USCOM LIMITED

**Time series to forecast n: 12 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 USCOM LIMITED

- Paragraph 4.1.1(a) requires an entity to classify financial assets on the basis of the entity's business model for managing the financial assets, unless paragraph 4.1.5 applies. An entity assesses whether its financial assets meet the condition in paragraph 4.1.2(a) or the condition in paragraph 4.1.2A(a) on the basis of the business model as determined by the entity's key management personnel (as defined in IAS 24 Related Party Disclosures).
- Annual Improvements to IFRS Standards 2018–2020, issued in May 2020, added paragraphs 7.2.35 and B3.3.6A and amended paragraph B3.3.6. An entity shall apply that amendment for annual reporting periods beginning on or after 1 January 2022. Earlier application is permitted. If an entity applies the amendment for an earlier period, it shall disclose that fact.
- In some cases, the qualitative and non-statistical quantitative information available may be sufficient to determine that a financial instrument has met the criterion for the recognition of a loss allowance at an amount equal to lifetime expected credit losses. That is, the information does not need to flow through a statistical model or credit ratings process in order to determine whether there has been a significant increase in the credit risk of the financial instrument. In other cases, an entity may need to consider other information, including information from its statistical models or credit ratings processes.
- For the purpose of applying the requirements in paragraphs 6.4.1(c)(i) and B6.4.4–B6.4.6, an entity shall assume that the interest rate benchmark on which the hedged cash flows and/or the hedged risk (contractually or noncontractually specified) are based, or the interest rate benchmark on which the cash flows of the hedging instrument are based, is not altered as a result of interest rate benchmark reform.

*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

USCOM LIMITED assigned short-term Baa2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Active Learning (ML) with Spearman Correlation ^{1,2,3,4} and conclude that the UCM 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 UCM USCOM LIMITED Options & Futures

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

Outlook* | Baa2 | Ba3 |

Operational Risk | 73 | 38 |

Market Risk | 74 | 83 |

Technical Analysis | 82 | 69 |

Fundamental Analysis | 70 | 64 |

Risk Unsystematic | 71 | 72 |

### Prediction Confidence Score

## References

- Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678
- Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
- Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
- Lai TL, Robbins H. 1985. Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6:4–22
- Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
- Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
- Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322

## Frequently Asked Questions

Q: What is the prediction methodology for UCM stock?A: UCM stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Spearman Correlation

Q: Is UCM stock a buy or sell?

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

Q: Is USCOM LIMITED stock a good investment?

A: The consensus rating for USCOM LIMITED is Buy and assigned short-term Baa2 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of UCM stock?

A: The consensus rating for UCM is Buy.

Q: What is the prediction period for UCM stock?

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