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**Outlook:**Buffalo Coal Corp. assigned short-term B2 & long-term B1 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 06 Dec 2022**for (n+4 weeks)

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## Abstract

Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. (Patil, P., Wu, C.S.M., Potika, K. and Orang, M., 2020, January. Stock market prediction using ensemble of graph theory, machine learning and deep learning models. In Proceedings of the 3rd International Conference on Software Engineering and Information Management (pp. 85-92).)** We evaluate Buffalo Coal Corp. prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Wilcoxon Sign-Rank Test ^{1,2,3,4} and conclude that the BUF:TSXV stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy BUF:TSXV stock.**

## Key Points

- How do you know when a stock will go up or down?
- What is Markov decision process in reinforcement learning?
- What is prediction in deep learning?

## BUF:TSXV Target Price Prediction Modeling Methodology

We consider Buffalo Coal Corp. Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of BUF:TSXV 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(Wilcoxon Sign-Rank 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(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of BUF:TSXV 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?

## BUF:TSXV Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**BUF:TSXV Buffalo Coal Corp.

**Time series to forecast n: 06 Dec 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy BUF:TSXV stock.**

**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 (Yellow to Green): *Technical Analysis%**

## Adjusted IFRS* Prediction Methods for Buffalo Coal Corp.

- Rebalancing refers to the adjustments made to the designated quantities of the hedged item or the hedging instrument of an already existing hedging relationship for the purpose of maintaining a hedge ratio that complies with the hedge effectiveness requirements. Changes to designated quantities of a hedged item or of a hedging instrument for a different purpose do not constitute rebalancing for the purpose of this Standard
- An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight and the restated financial statements reflect all the requirements in this Standard. 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.
- 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.
- IFRS 16, issued in January 2016, amended paragraphs 2.1, 5.5.15, B4.3.8, B5.5.34 and B5.5.46. An entity shall apply those amendments when it applies IFRS 16.

*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

Buffalo Coal Corp. assigned short-term B2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Wilcoxon Sign-Rank Test ^{1,2,3,4} and conclude that the BUF:TSXV stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy BUF:TSXV stock.**

### Financial State Forecast for BUF:TSXV Buffalo Coal Corp. Options & Futures

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

Outlook* | B2 | B1 |

Operational Risk | 86 | 90 |

Market Risk | 37 | 35 |

Technical Analysis | 31 | 61 |

Fundamental Analysis | 50 | 58 |

Risk Unsystematic | 68 | 42 |

### Prediction Confidence Score

## References

- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., What are buy sell or hold recommendations?(AIRC Stock Forecast). AC Investment Research Journal, 101(3).
- Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
- Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.
- Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
- Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
- Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
- V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014

## Frequently Asked Questions

Q: What is the prediction methodology for BUF:TSXV stock?A: BUF:TSXV stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Wilcoxon Sign-Rank Test

Q: Is BUF:TSXV stock a buy or sell?

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

Q: Is Buffalo Coal Corp. stock a good investment?

A: The consensus rating for Buffalo Coal Corp. is Buy and assigned short-term B2 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of BUF:TSXV stock?

A: The consensus rating for BUF:TSXV is Buy.

Q: What is the prediction period for BUF:TSXV stock?

A: The prediction period for BUF:TSXV is (n+4 weeks)