Outlook: Drive Shack Inc. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 19 Feb 2023 for (n+1 year)
Methodology : Modular Neural Network (CNN Layer)

## Abstract

Drive Shack Inc. prediction model is evaluated with Modular Neural Network (CNN Layer) and Independent T-Test1,2,3,4 and it is concluded that the DS 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

1. How do you pick a stock?
2. What are the most successful trading algorithms?
3. Short/Long Term Stocks

## DS Target Price Prediction Modeling Methodology

We consider Drive Shack Inc. Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of DS 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(Independent T-Test)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 (CNN Layer)) X S(n):→ (n+1 year) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of DS 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?

## DS Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: DS Drive Shack Inc.
Time series to forecast n: 19 Feb 2023 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%

## IFRS Reconciliation Adjustments for Drive Shack Inc.

1. The assessment of whether lifetime expected credit losses should be recognised is based on significant increases in the likelihood or risk of a default occurring since initial recognition (irrespective of whether a financial instrument has been repriced to reflect an increase in credit risk) instead of on evidence of a financial asset being credit-impaired at the reporting date or an actual default occurring. Generally, there will be a significant increase in credit risk before a financial asset becomes credit-impaired or an actual default occurs.
2. An entity can rebut this presumption. However, it can do so only when it has reasonable and supportable information available that demonstrates that even if contractual payments become more than 30 days past due, this does not represent a significant increase in the credit risk of a financial instrument. For example when non-payment was an administrative oversight, instead of resulting from financial difficulty of the borrower, or the entity has access to historical evidence that demonstrates that there is no correlation between significant increases in the risk of a default occurring and financial assets on which payments are more than 30 days past due, but that evidence does identify such a correlation when payments are more than 60 days past due.
3. When using historical credit loss experience in estimating expected credit losses, it is important that information about historical credit loss rates is applied to groups that are defined in a manner that is consistent with the groups for which the historical credit loss rates were observed. Consequently, the method used shall enable each group of financial assets to be associated with information about past credit loss experience in groups of financial assets with similar risk characteristics and with relevant observable data that reflects current conditions.
4. In accordance with paragraph 4.1.3(a), principal is the fair value of the financial asset at initial recognition. However that principal amount may change over the life of the financial asset (for example, if there are repayments of principal).

*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

Drive Shack Inc. is assigned short-term Ba1 & long-term Ba1 estimated rating. Drive Shack Inc. prediction model is evaluated with Modular Neural Network (CNN Layer) and Independent T-Test1,2,3,4 and it is concluded that the DS 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

### DS Drive Shack Inc. Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa1Ba2
Balance SheetBaa2Baa2
Leverage RatiosCB2
Cash FlowBaa2Ba3
Rates of Return and ProfitabilityBa3Ba1

*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: 72 out of 100 with 839 signals.

## References

1. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
2. D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
3. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
4. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
5. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
6. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
7. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
Frequently Asked QuestionsQ: What is the prediction methodology for DS stock?
A: DS stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Independent T-Test
Q: Is DS stock a buy or sell?
A: The dominant strategy among neural network is to Sell DS Stock.
Q: Is Drive Shack Inc. stock a good investment?
A: The consensus rating for Drive Shack Inc. is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of DS stock?
A: The consensus rating for DS is Sell.
Q: What is the prediction period for DS stock?
A: The prediction period for DS is (n+1 year)