Modelling A.I. in Economics

NREF^A NexPoint Real Estate Finance Inc. 8.50% Series A Cumulative Redeemable Preferred Stock

Outlook: NexPoint Real Estate Finance Inc. 8.50% Series A Cumulative Redeemable Preferred Stock assigned short-term B3 & long-term Ba3 forecasted stock rating.
Dominant Strategy : HoldWait until speculative trend diminishes
Time series to forecast n: 18 Dec 2022 for (n+1 year)
Methodology : Multi-Task Learning (ML)

Abstract

Stock market is a promising financial investment that can generate great wealth. However, the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment. This paper investigates various techniques for the stock market prediction using artificial neural network (ANN).(Morris, K.J., Egan, S.D., Linsangan, J.L., Leung, C.K., Cuzzocrea, A. and Hoi, C.S., 2018, December. Token-based adaptive time-series prediction by ensembling linear and non-linear estimators: a machine learning approach for predictive analytics on big stock data. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1486-1491). IEEE.) We evaluate NexPoint Real Estate Finance Inc. 8.50% Series A Cumulative Redeemable Preferred Stock prediction models with Multi-Task Learning (ML) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the NREF^A stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: HoldWait until speculative trend diminishes

Key Points

  1. What is the use of Markov decision process?
  2. What is statistical models in machine learning?
  3. How do you know when a stock will go up or down?

NREF^A Target Price Prediction Modeling Methodology

We consider NexPoint Real Estate Finance Inc. 8.50% Series A Cumulative Redeemable Preferred Stock Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of NREF^A 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(Wilcoxon Rank-Sum Test)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) i = 1 n a i

n:Time series to forecast

p:Price signals of NREF^A 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?

NREF^A Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: NREF^A NexPoint Real Estate Finance Inc. 8.50% Series A Cumulative Redeemable Preferred Stock
Time series to forecast n: 18 Dec 2022 for (n+1 year)

According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: HoldWait until speculative trend diminishes

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 NexPoint Real Estate Finance Inc. 8.50% Series A Cumulative Redeemable Preferred Stock

  1. 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).
  2. Adjusting the hedge ratio by increasing the volume of the hedging instrument does not affect how the changes in the value of the hedged item are measured. The measurement of the changes in the fair value of the hedging instrument related to the previously designated volume also remains unaffected. However, from the date of rebalancing, the changes in the fair value of the hedging instrument also include the changes in the value of the additional volume of the hedging instrument. The changes are measured starting from, and by reference to, the date of rebalancing instead of the date on which the hedging relationship was designated. For example, if an entity originally hedged the price risk of a commodity using a derivative volume of 100 tonnes as the hedging instrument and added a volume of 10 tonnes on rebalancing, the hedging instrument after rebalancing would comprise a total derivative volume of 110 tonnes. The change in the fair value of the hedging instrument is the total change in the fair value of the derivatives that make up the total volume of 110 tonnes. These derivatives could (and probably would) have different critical terms, such as their forward rates, because they were entered into at different points in time (including the possibility of designating derivatives into hedging relationships after their initial recognition).
  3. The assessment of whether an economic relationship exists includes an analysis of the possible behaviour of the hedging relationship during its term to ascertain whether it can be expected to meet the risk management objective. The mere existence of a statistical correlation between two variables does not, by itself, support a valid conclusion that an economic relationship exists.
  4. Because the hedge accounting model is based on a general notion of offset between gains and losses on the hedging instrument and the hedged item, hedge effectiveness is determined not only by the economic relationship between those items (ie the changes in their underlyings) but also by the effect of credit risk on the value of both the hedging instrument and the hedged item. The effect of credit risk means that even if there is an economic relationship between the hedging instrument and the hedged item, the level of offset might become erratic. This can result from a change in the credit risk of either the hedging instrument or the hedged item that is of such a magnitude that the credit risk dominates the value changes that result from the economic relationship (ie the effect of the changes in the underlyings). A level of magnitude that gives rise to dominance is one that would result in the loss (or gain) from credit risk frustrating the effect of changes in the underlyings on the value of the hedging instrument or the hedged item, even if those changes were significant.

*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

NexPoint Real Estate Finance Inc. 8.50% Series A Cumulative Redeemable Preferred Stock assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the NREF^A stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: HoldWait until speculative trend diminishes

Financial State Forecast for NREF^A NexPoint Real Estate Finance Inc. 8.50% Series A Cumulative Redeemable Preferred Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3Ba3
Operational Risk 3673
Market Risk3390
Technical Analysis6351
Fundamental Analysis4445
Risk Unsystematic6751

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 712 signals.

References

  1. P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
  2. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
  3. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
  4. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Trading Signals (WTS Stock Forecast). AC Investment Research Journal, 101(3).
  5. L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
  6. 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.
  7. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
Frequently Asked QuestionsQ: What is the prediction methodology for NREF^A stock?
A: NREF^A stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is NREF^A stock a buy or sell?
A: The dominant strategy among neural network is to HoldWait until speculative trend diminishes NREF^A Stock.
Q: Is NexPoint Real Estate Finance Inc. 8.50% Series A Cumulative Redeemable Preferred Stock stock a good investment?
A: The consensus rating for NexPoint Real Estate Finance Inc. 8.50% Series A Cumulative Redeemable Preferred Stock is HoldWait until speculative trend diminishes and assigned short-term B3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NREF^A stock?
A: The consensus rating for NREF^A is HoldWait until speculative trend diminishes.
Q: What is the prediction period for NREF^A stock?
A: The prediction period for NREF^A is (n+1 year)

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