**Outlook:**High Tide Inc. is assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 20 Apr 2023**for (n+3 month)

**Methodology :**Modular Neural Network (Social Media Sentiment Analysis)

## Abstract

High Tide Inc. prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Linear Regression^{1,2,3,4}and it is concluded that the HITI:TSXV stock is predictable in the short/long term.

**According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy**

## Key Points

- What is prediction in deep learning?
- What is prediction model?
- Reaction Function

## HITI:TSXV Target Price Prediction Modeling Methodology

We consider High Tide Inc. Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of HITI: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(Linear 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(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+3 month) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of HITI: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?

## HITI:TSXV Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**HITI:TSXV High Tide Inc.

**Time series to forecast n: 20 Apr 2023**for (n+3 month)

**According to price forecasts for (n+3 month) 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%**

## IFRS Reconciliation Adjustments for High Tide Inc.

- For the avoidance of doubt, the effects of replacing the original counterparty with a clearing counterparty and making the associated changes as described in paragraph 6.5.6 shall be reflected in the measurement of the hedging instrument and therefore in the assessment of hedge effectiveness and the measurement of hedge effectiveness
- If the group of items does not have any offsetting risk positions (for example, a group of foreign currency expenses that affect different line items in the statement of profit or loss and other comprehensive income that are hedged for foreign currency risk) then the reclassified hedging instrument gains or losses shall be apportioned to the line items affected by the hedged items. This apportionment shall be done on a systematic and rational basis and shall not result in the grossing up of the net gains or losses arising from a single hedging instrument.
- An entity that first applies these amendments after it first applies this Standard shall apply paragraphs 7.2.32–7.2.34. The entity shall also apply the other transition requirements in this Standard necessary for applying these amendments. For that purpose, references to the date of initial application shall be read as referring to the beginning of the reporting period in which an entity first applies these amendments (date of initial application of these amendments).
- 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.

*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.

## Conclusions

High Tide Inc. is assigned short-term Ba1 & long-term Ba1 estimated rating. High Tide Inc. prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Linear Regression^{1,2,3,4} and it is concluded that the HITI:TSXV stock is predictable in the short/long term. ** According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy**

### HITI:TSXV High Tide Inc. Financial Analysis*

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

Outlook* | Ba1 | Ba1 |

Income Statement | Caa2 | Baa2 |

Balance Sheet | C | Baa2 |

Leverage Ratios | Baa2 | Caa2 |

Cash Flow | Baa2 | Caa2 |

Rates of Return and Profitability | Baa2 | Baa2 |

*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?

### Prediction Confidence Score

## References

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- Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
- Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
- D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
- Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
- S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010

## Frequently Asked Questions

Q: What is the prediction methodology for HITI:TSXV stock?A: HITI:TSXV stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Linear Regression

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

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

Q: Is High Tide Inc. stock a good investment?

A: The consensus rating for High Tide Inc. is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.

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

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

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

A: The prediction period for HITI:TSXV is (n+3 month)