Real Brokerage Inc. (The) Research Report

## Summary

The presented paper modeled and predicted stock returns using LSTM. The historical data of stock market were transformed into 30-days-long sequences with 10 learning features and 7-day earning rate labeling. The model was fitted by training on 1200000 sequences and tested using the other 350000 sequences. We evaluate Real Brokerage Inc. (The) prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Pearson Correlation1,2,3,4 and conclude that the REAX:TSX stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold REAX:TSX stock.

## Key Points

1. How do you decide buy or sell a stock?
2. Can stock prices be predicted?
3. What is prediction in deep learning?

## REAX:TSX Target Price Prediction Modeling Methodology

We consider Real Brokerage Inc. (The) Stock Decision Process with Modular Neural Network (Speculative Sentiment Analysis) 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

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+8 weeks)

Sample Set: Neural Network
Stock/Index: REAX:TSX Real Brokerage Inc. (The)
Time series to forecast n: 22 Nov 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold REAX:TSX 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 Real Brokerage Inc. (The)

1. An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.
2. Such designation may be used whether paragraph 4.3.3 requires the embedded derivatives to be separated from the host contract or prohibits such separation. However, paragraph 4.3.5 would not justify designating the hybrid contract as at fair value through profit or loss in the cases set out in paragraph 4.3.5(a) and (b) because doing so would not reduce complexity or increase reliability.
3. An entity shall apply this Standard retrospectively, in accordance with IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors, except as specified in paragraphs 7.2.4–7.2.26 and 7.2.28. This Standard shall not be applied to items that have already been derecognised at the date of initial application.
4. If such a mismatch would be created or enlarged, the entity is required to present all changes in fair value (including the effects of changes in the credit risk of the liability) in profit or loss. If such a mismatch would not be created or enlarged, the entity is required to present the effects of changes in the liability's credit risk in other comprehensive income.

*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 B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Pearson Correlation1,2,3,4 and conclude that the REAX:TSX stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold REAX:TSX stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 7847
Market Risk3883
Technical Analysis3165
Fundamental Analysis5745
Risk Unsystematic5949

### Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 709 signals.

## References

1. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
2. V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
3. S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
4. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
5. 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
6. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
7. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
Frequently Asked QuestionsQ: What is the prediction methodology for REAX:TSX stock?
A: REAX:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Pearson Correlation
Q: Is REAX:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Hold REAX:TSX Stock.
Q: Is Real Brokerage Inc. (The) stock a good investment?
A: The consensus rating for Real Brokerage Inc. (The) is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of REAX:TSX stock?
A: The consensus rating for REAX:TSX is Hold.
Q: What is the prediction period for REAX:TSX stock?
A: The prediction period for REAX:TSX is (n+8 weeks)

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