OPEN Opendoor Technologies Inc Common Stock

Outlook: Opendoor Technologies Inc Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 24 May 2023 for (n+4 weeks)
Methodology : Statistical Inference (ML)

Abstract

Opendoor Technologies Inc Common Stock prediction model is evaluated with Statistical Inference (ML) and ElasticNet Regression^1,2,3,4 and it is concluded that the OPEN stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. Market Signals
2. Market Outlook
3. Short/Long Term Stocks

OPEN Target Price Prediction Modeling Methodology

We consider Opendoor Technologies Inc Common Stock Decision Process with Statistical Inference (ML) where A is the set of discrete actions of OPEN 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(ElasticNet Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Statistical Inference (ML)) X S(n):→ (n+4 weeks) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of OPEN 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?

OPEN Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: OPEN Opendoor Technologies Inc Common Stock
Time series to forecast n: 24 May 2023 for (n+4 weeks)

According to price forecasts for (n+4 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%

IFRS Reconciliation Adjustments for Opendoor Technologies Inc Common Stock

1. For the purposes of measuring expected credit losses, the estimate of expected cash shortfalls shall reflect the cash flows expected from collateral and other credit enhancements that are part of the contractual terms and are not recognised separately by the entity. The estimate of expected cash shortfalls on a collateralised financial instrument reflects the amount and timing of cash flows that are expected from foreclosure on the collateral less the costs of obtaining and selling the collateral, irrespective of whether foreclosure is probable (ie the estimate of expected cash flows considers the probability of a foreclosure and the cash flows that would result from it). Consequently, any cash flows that are expected from the realisation of the collateral beyond the contractual maturity of the contract should be included in this analysis. Any collateral obtained as a result of foreclosure is not recognised as an asset that is separate from the collateralised financial instrument unless it meets the relevant recognition criteria for an asset in this or other Standards.
2. An entity shall apply Annual Improvements to IFRS Standards 2018–2020 to financial liabilities that are modified or exchanged on or after the beginning of the annual reporting period in which the entity first applies the amendment.
3. 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.
4. If changes are made in addition to those changes required by interest rate benchmark reform to the financial asset or financial liability designated in a hedging relationship (as described in paragraphs 5.4.6–5.4.8) or to the designation of the hedging relationship (as required by paragraph 6.9.1), an entity shall first apply the applicable requirements in this Standard to determine if those additional changes result in the discontinuation of hedge accounting. If the additional changes do not result in the discontinuation of hedge accounting, an entity shall amend the formal designation of the hedging relationship as specified in paragraph 6.9.1.

*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

Opendoor Technologies Inc Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Opendoor Technologies Inc Common Stock prediction model is evaluated with Statistical Inference (ML) and ElasticNet Regression^1,2,3,4 and it is concluded that the OPEN stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

OPEN Opendoor Technologies Inc Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	C	Ba1
Balance Sheet	Baa2	Caa2
Leverage Ratios	Baa2	Baa2
Cash Flow	C	C
Rates of Return and Profitability	C	B2

*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

Trust metric by Neural Network: 75 out of 100 with 621 signals.

References

1. Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
2. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
3. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
4. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
5. Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
6. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
7. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017

Frequently Asked Questions

Q: What is the prediction methodology for OPEN stock?
A: OPEN stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and ElasticNet Regression
Q: Is OPEN stock a buy or sell?
A: The dominant strategy among neural network is to Buy OPEN Stock.
Q: Is Opendoor Technologies Inc Common Stock stock a good investment?
A: The consensus rating for Opendoor Technologies Inc Common Stock is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of OPEN stock?
A: The consensus rating for OPEN is Buy.
Q: What is the prediction period for OPEN stock?
A: The prediction period for OPEN is (n+4 weeks)

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