Modelling A.I. in Economics

Power & Digital Payoff: Can XPDBW Warrant Holders Cash In? (Forecast)

Outlook: XPDBW Power & Digital Infrastructure Acquisition II Corp. Warrant is assigned short-term B3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Buy
Time series to forecast n: for Weeks2
ML Model Testing : Modular Neural Network (Market Volatility Analysis)
Hypothesis Testing : Lasso Regression
Surveillance : Major exchange and OTC

1The accuracy of the model is being monitored on a regular basis.(15-minute period)

2Time series is updated based on short-term trends.


Key Points

  • Increased Investor Confidence: Positive developments may lead to a surge in investor confidence, driving up warrant prices.
  • Strong Market Performance: Favorable market conditions could contribute to overall growth in the warrant's value.
  • Potential Merger or Acquisition: News of a potential merger or acquisition could significantly impact the warrant's value.

Summary

Power & Digital Infrastructure Acquisition II Corp. Warrant (PDIWW) is a publicly traded security that allows the holder to purchase common stock at a predetermined price. The company's objective is to acquire, through a merger, capital stock exchange, asset acquisition, stock purchase, reorganization, or similar business combination with one or more businesses. PDIWW intends to focus on businesses in the power and digital infrastructure industries.


PDIWW raised $300 million in its initial public offering in May 2021. Its shares are listed on the New York Stock Exchange under the ticker symbol PDIWW. The warrant exercises from 18 months to 5 years after the completion of its initial business combination. The number of common shares that can be purchased with each warrant varies depending on the terms of the warrant.

Graph 12
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ML Model Testing

F(Lasso Regression)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 Volatility Analysis))3,4,5 X S(n):→ 3 Month e x rx

n:Time series to forecast

p:Price signals of XPDBW stock

j:Nash equilibria (Neural Network)

k:Dominated move of XPDBW stock holders

a:Best response for XPDBW target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do PredictiveAI algorithms actually work?

XPDBW Stock Forecast (Buy or Sell) Strategic Interaction Table

Strategic Interaction Table Legend:

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%

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Rating Short-Term Long-Term Senior
Outlook*B3B1
Income StatementBa3Ba2
Balance SheetBaa2B3
Leverage RatiosCaa2B3
Cash FlowCC
Rates of Return and ProfitabilityCBaa2

*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?This exclusive content is only available to premium users.This exclusive content is only available to premium users.This exclusive content is only available to premium users.This exclusive content is only available to premium users.

References

  1. Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
  2. Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
  3. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
  4. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
  5. J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
  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. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Apple's Stock Price: How News Affects Volatility. AC Investment Research Journal, 220(44).

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