AQN Options & Futures Prediction

Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. We evaluate Algonquin Power & Utilities Corp. prediction models with Active Learning (ML) and Logistic Regression1,2,3,4 and conclude that the AQN stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to SellHold AQN stock.


Keywords: AQN, Algonquin Power & Utilities Corp., stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

  1. Can machine learning predict?
  2. Dominated Move
  3. Reaction Function

AQN Target Price Prediction Modeling Methodology

As part of this research, different techniques have been studied for data extraction and analysis. After having reviewed the work related to the initial idea of the research, it is shown the development carried out, together with the data extraction and the machine learning algorithms for prediction used. The calculation of technical analysis metrics is also included. The development of a visualization platform has been proposed for high-level interaction between the user and the recommendation system. We consider Algonquin Power & Utilities Corp. Stock Decision Process with Logistic Regression where A is the set of discrete actions of AQN 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(Logistic 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(Active Learning (ML)) X S(n):→ (n+4 weeks) r s rs

n:Time series to forecast

p:Price signals of AQN stock

j:Nash equilibria

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?

AQN Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: AQN Algonquin Power & Utilities Corp.
Time series to forecast n: 13 Oct 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to SellHold AQN stock.

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 (Yellow to Green): *Technical Analysis%


Conclusions

Algonquin Power & Utilities Corp. assigned short-term Ba3 & long-term B1 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Logistic Regression1,2,3,4 and conclude that the AQN stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to SellHold AQN stock.

Financial State Forecast for AQN Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3B1
Operational Risk 7136
Market Risk6161
Technical Analysis7649
Fundamental Analysis5683
Risk Unsystematic5668

Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 847 signals.

References

  1. Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
  2. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
  3. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
  4. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
  5. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
  6. Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
  7. 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.
Frequently Asked QuestionsQ: What is the prediction methodology for AQN stock?
A: AQN stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Logistic Regression
Q: Is AQN stock a buy or sell?
A: The dominant strategy among neural network is to SellHold AQN Stock.
Q: Is Algonquin Power & Utilities Corp. stock a good investment?
A: The consensus rating for Algonquin Power & Utilities Corp. is SellHold and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of AQN stock?
A: The consensus rating for AQN is SellHold.
Q: What is the prediction period for AQN stock?
A: The prediction period for AQN is (n+4 weeks)

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