Modelling A.I. in Economics

WATT Energous Corporation Common Stock

Outlook: Energous Corporation Common Stock assigned short-term Baa2 & long-term B3 forecasted stock rating.
Dominant Strategy : Wait until speculative trend diminishes
Time series to forecast n: 18 Dec 2022 for (n+6 month)
Methodology : Modular Neural Network (Market News Sentiment Analysis)

Abstract

This paper examines the theory and practice of regression techniques for prediction of stock price trend by using a transformed data set in ordinal data format. The original pretransformed data source contains data of heterogeneous data types used for handling of currency values and financial ratios. The data formats in currency values and financial ratios provide a process for computation of stock prices. The transformed data set contains only a standardized ordinal data type which provides a process to measure rankings of stock price trends.(Kohara, K., Ishikawa, T., Fukuhara, Y. and Nakamura, Y., 1997. Stock price prediction using prior knowledge and neural networks. Intelligent Systems in Accounting, Finance & Management, 6(1), pp.11-22.) We evaluate Energous Corporation Common Stock prediction models with Modular Neural Network (Market News Sentiment Analysis) and Multiple Regression1,2,3,4 and conclude that the WATT stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

Key Points

  1. What is statistical models in machine learning?
  2. What is the best way to predict stock prices?
  3. Is it better to buy and sell or hold?

WATT Target Price Prediction Modeling Methodology

We consider Energous Corporation Common Stock Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of WATT 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(Multiple 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(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+6 month) R = 1 0 0 0 1 0 0 0 1

n:Time series to forecast

p:Price signals of WATT 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?

WATT Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: WATT Energous Corporation Common Stock
Time series to forecast n: 18 Dec 2022 for (n+6 month)

According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Wait 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 Energous Corporation Common Stock

  1. An entity that first applies IFRS 17 as amended in June 2020 at the same time it first applies this Standard shall apply paragraphs 7.2.1–7.2.28 instead of paragraphs 7.2.38–7.2.42.
  2. 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).
  3. When determining whether the recognition of lifetime expected credit losses is required, an entity shall consider reasonable and supportable information that is available without undue cost or effort and that may affect the credit risk on a financial instrument in accordance with paragraph 5.5.17(c). An entity need not undertake an exhaustive search for information when determining whether credit risk has increased significantly since initial recognition.
  4. When an entity discontinues measuring the financial instrument that gives rise to the credit risk, or a proportion of that financial instrument, at fair value through profit or loss, that financial instrument's fair value at the date of discontinuation becomes its new carrying amount. Subsequently, the same measurement that was used before designating the financial instrument at fair value through profit or loss shall be applied (including amortisation that results from the new carrying amount). For example, a financial asset that had originally been classified as measured at amortised cost would revert to that measurement and its effective interest rate would be recalculated based on its new gross carrying amount on the date of discontinuing measurement at fair value through profit or loss.

*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

Energous Corporation Common Stock assigned short-term Baa2 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Multiple Regression1,2,3,4 and conclude that the WATT stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

Financial State Forecast for WATT Energous Corporation Common Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Baa2B3
Operational Risk 8674
Market Risk6530
Technical Analysis4844
Fundamental Analysis7646
Risk Unsystematic8747

Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 536 signals.

References

  1. Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
  2. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
  3. M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
  4. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
  5. Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
  6. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
  7. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
Frequently Asked QuestionsQ: What is the prediction methodology for WATT stock?
A: WATT stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Multiple Regression
Q: Is WATT stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes WATT Stock.
Q: Is Energous Corporation Common Stock stock a good investment?
A: The consensus rating for Energous Corporation Common Stock is Wait until speculative trend diminishes and assigned short-term Baa2 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of WATT stock?
A: The consensus rating for WATT is Wait until speculative trend diminishes.
Q: What is the prediction period for WATT stock?
A: The prediction period for WATT is (n+6 month)

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