Modelling A.I. in Economics

DS Drive Shack Inc. (Forecast)

Outlook: Drive Shack Inc. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 31 Mar 2023 for (n+1 year)
Methodology : Multi-Task Learning (ML)

Abstract

Drive Shack Inc. prediction model is evaluated with Multi-Task Learning (ML) and Linear Regression1,2,3,4 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: Hold

Key Points

  1. Is now good time to invest?
  2. Operational Risk
  3. Reaction Function

DS Target Price Prediction Modeling Methodology

We consider Drive Shack Inc. Decision Process with Multi-Task Learning (ML) 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(Linear Regression)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Multi-Task Learning (ML)) X S(n):→ (n+1 year) e x rx

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: 31 Mar 2023 for (n+1 year)

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

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. To calculate the change in the value of the hedged item for the purpose of measuring hedge ineffectiveness, an entity may use a derivative that would have terms that match the critical terms of the hedged item (this is commonly referred to as a 'hypothetical derivative'), and, for example for a hedge of a forecast transaction, would be calibrated using the hedged price (or rate) level. For example, if the hedge was for a two-sided risk at the current market level, the hypothetical derivative would represent a hypothetical forward contract that is calibrated to a value of nil at the time of designation of the hedging relationship. If the hedge was for example for a one-sided risk, the hypothetical derivative would represent the intrinsic value of a hypothetical option that at the time of designation of the hedging relationship is at the money if the hedged price level is the current market level, or out of the money if the hedged price level is above (or, for a hedge of a long position, below) the current market level. Using a hypothetical derivative is one possible way of calculating the change in the value of the hedged item. The hypothetical derivative replicates the hedged item and hence results in the same outcome as if that change in value was determined by a different approach. Hence, using a 'hypothetical derivative' is not a method in its own right but a mathematical expedient that can only be used to calculate the value of the hedged item. Consequently, a 'hypothetical derivative' cannot be used to include features in the value of the hedged item that only exist in the hedging instrument (but not in the hedged item). An example is debt denominated in a foreign currency (irrespective of whether it is fixed-rate or variable-rate debt). When using a hypothetical derivative to calculate the change in the value of such debt or the present value of the cumulative change in its cash flows, the hypothetical derivative cannot simply impute a charge for exchanging different currencies even though actual derivatives under which different currencies are exchanged might include such a charge (for example, cross-currency interest rate swaps).
  2. Paragraph 5.5.4 requires that lifetime expected credit losses are recognised on all financial instruments for which there has been significant increases in credit risk since initial recognition. In order to meet this objective, if an entity is not able to group financial instruments for which the credit risk is considered to have increased significantly since initial recognition based on shared credit risk characteristics, the entity should recognise lifetime expected credit losses on a portion of the financial assets for which credit risk is deemed to have increased significantly. The aggregation of financial instruments to assess whether there are changes in credit risk on a collective basis may change over time as new information becomes available on groups of, or individual, financial instruments.
  3. Rebalancing refers to the adjustments made to the designated quantities of the hedged item or the hedging instrument of an already existing hedging relationship for the purpose of maintaining a hedge ratio that complies with the hedge effectiveness requirements. Changes to designated quantities of a hedged item or of a hedging instrument for a different purpose do not constitute rebalancing for the purpose of this Standard
  4. The credit risk on a financial instrument is considered low for the purposes of paragraph 5.5.10, if the financial instrument has a low risk of default, the borrower has a strong capacity to meet its contractual cash flow obligations in the near term and adverse changes in economic and business conditions in the longer term may, but will not necessarily, reduce the ability of the borrower to fulfil its contractual cash flow obligations. Financial instruments are not considered to have low credit risk when they are regarded as having a low risk of loss simply because of the value of collateral and the financial instrument without that collateral would not be considered low credit risk. Financial instruments are also not considered to have low credit risk simply because they have a lower risk of default than the entity's other financial instruments or relative to the credit risk of the jurisdiction within which an entity operates.

*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 Multi-Task Learning (ML) and Linear Regression1,2,3,4 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: Hold

DS Drive Shack Inc. Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa2Ba1
Balance SheetBaa2Caa2
Leverage RatiosBaa2Baa2
Cash FlowBa3B2
Rates of Return and ProfitabilityCaa2C

*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: 80 out of 100 with 720 signals.

References

  1. Bessler, D. A. R. A. Babula, (1987), "Forecasting wheat exports: Do exchange rates matter?" Journal of Business and Economic Statistics, 5, 397–406.
  2. Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
  3. Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
  4. Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
  5. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
  6. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
  7. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can stock prices be predicted?(SMI Index Stock Forecast). AC Investment Research Journal, 101(3).
Frequently Asked QuestionsQ: What is the prediction methodology for DS stock?
A: DS stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Linear Regression
Q: Is DS stock a buy or sell?
A: The dominant strategy among neural network is to Hold DS Stock.
Q: Is Drive Shack Inc. stock a good investment?
A: The consensus rating for Drive Shack Inc. is Hold 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 Hold.
Q: What is the prediction period for DS stock?
A: The prediction period for DS is (n+1 year)

Premium

  • Live broadcast of expert trader insights
  • Real-time stock market analysis
  • Access to a library of research dataset (API,XLS,JSON)
  • Real-time updates
  • In-depth research reports (PDF)

Login
This project is licensed under the license; additional terms may apply.