**Outlook:**Real Brokerage Inc. (The) assigned short-term B1 & long-term Ba3 forecasted stock rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 09 Dec 2022**for (n+1 year)

**Methodology :**Multi-Instance Learning (ML)

## Abstract

This study aims to predict the direction of stock prices by integrating time-varying effective transfer entropy (ETE) and various machine learning algorithms. At first, we explore that the ETE based on 3 and 6 months moving windows can be regarded as the market explanatory variable by analyzing the association between the financial crises and Granger-causal relationships among the stocks.(Vijh, M., Chandola, D., Tikkiwal, V.A. and Kumar, A., 2020. Stock closing price prediction using machine learning techniques. Procedia computer science, 167, pp.599-606.)** We evaluate Real Brokerage Inc. (The) prediction models with Multi-Instance Learning (ML) and Pearson Correlation ^{1,2,3,4} and conclude that the REAX:TSX stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell**

## Key Points

- Nash Equilibria
- Stock Rating
- Can stock prices be predicted?

## REAX:TSX Target Price Prediction Modeling Methodology

We consider Real Brokerage Inc. (The) Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of REAX:TSX 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(Pearson 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(Multi-Instance Learning (ML)) X S(n):→ (n+1 year) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of REAX:TSX 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?

## REAX:TSX Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**REAX:TSX Real Brokerage Inc. (The)

**Time series to forecast n: 09 Dec 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell**

**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 Real Brokerage Inc. (The)

- 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.
- Paragraph 6.3.4 permits an entity to designate as hedged items aggregated exposures that are a combination of an exposure and a derivative. When designating such a hedged item, an entity assesses whether the aggregated exposure combines an exposure with a derivative so that it creates a different aggregated exposure that is managed as one exposure for a particular risk (or risks). In that case, the entity may designate the hedged item on the basis of the aggregated exposure
- The rebuttable presumption in paragraph 5.5.11 is not an absolute indicator that lifetime expected credit losses should be recognised, but is presumed to be the latest point at which lifetime expected credit losses should be recognised even when using forward-looking information (including macroeconomic factors on a portfolio level).
- An entity shall apply this Standard for annual periods beginning on or after 1 January 2018. Earlier application is permitted. If an entity elects to apply this Standard early, it must disclose that fact and apply all of the requirements in this Standard at the same time (but see also paragraphs 7.1.2, 7.2.21 and 7.3.2). It shall also, at the same time, apply the amendments in Appendix C.

*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

Real Brokerage Inc. (The) assigned short-term B1 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Multi-Instance Learning (ML) with Pearson Correlation ^{1,2,3,4} and conclude that the REAX:TSX stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell**

### Financial State Forecast for REAX:TSX Real Brokerage Inc. (The) Options & Futures

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

Outlook* | B1 | Ba3 |

Operational Risk | 44 | 56 |

Market Risk | 65 | 85 |

Technical Analysis | 90 | 45 |

Fundamental Analysis | 68 | 69 |

Risk Unsystematic | 34 | 56 |

### Prediction Confidence Score

## References

- Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
- Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
- V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
- Y. Le Tallec. Robust, risk-sensitive, and data-driven control of Markov decision processes. PhD thesis, Massachusetts Institute of Technology, 2007.
- Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
- Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
- Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.

## Frequently Asked Questions

Q: What is the prediction methodology for REAX:TSX stock?A: REAX:TSX stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Pearson Correlation

Q: Is REAX:TSX stock a buy or sell?

A: The dominant strategy among neural network is to Sell REAX:TSX Stock.

Q: Is Real Brokerage Inc. (The) stock a good investment?

A: The consensus rating for Real Brokerage Inc. (The) is Sell and assigned short-term B1 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of REAX:TSX stock?

A: The consensus rating for REAX:TSX is Sell.

Q: What is the prediction period for REAX:TSX stock?

A: The prediction period for REAX:TSX is (n+1 year)

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