UGI Target Price Prediction


Prediction of stock market movement is extremely difficult due to its high mutable nature. The rapid ups and downs occur in stock market because of impact from foreign commodities like emotional behavior of investors, political, psychological and economical factors. Continuous unsettlement in the stock market is major reason why investors sell out at the wrong time and often fail to gain the benefit. While investing in stock market investors must not forget the risk of reward rule and expose their holdings to greater risks. Although it is not possible predict stock market movement with full accuracy, losses from selling stocks at wrong time and its impacts can be reduce to greater extent using prediction of stock market movement based on analysis of historical data. We evaluate UGI Corp prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Pearson Correlation1,2,3,4 and conclude that the UGI 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 Hold UGI stock.


Keywords: UGI, UGI Corp, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

  1. What are main components of Markov decision process?
  2. What are buy sell or hold recommendations?
  3. Trust metric by Neural Network

UGI Target Price Prediction Modeling Methodology

The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. We consider UGI Corp Stock Decision Process with Pearson Correlation where A is the set of discrete actions of UGI 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(Pearson Correlation)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 (Social Media Sentiment Analysis)) X S(n):→ (n+4 weeks) R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of UGI 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?

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

Sample Set: Neural Network
Stock/Index: UGI UGI Corp
Time series to forecast n: 15 Sep 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold UGI 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

UGI Corp assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Pearson Correlation1,2,3,4 and conclude that the UGI 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 Hold UGI stock.

Financial State Forecast for UGI Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 4064
Market Risk6334
Technical Analysis7632
Fundamental Analysis7286
Risk Unsystematic5472

Prediction Confidence Score

Trust metric by Neural Network: 75 out of 100 with 666 signals.

References

  1. Harris ZS. 1954. Distributional structure. Word 10:146–62
  2. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
  3. Lai TL, Robbins H. 1985. Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6:4–22
  4. J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
  5. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
  6. Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
  7. Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
Frequently Asked QuestionsQ: What is the prediction methodology for UGI stock?
A: UGI stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Pearson Correlation
Q: Is UGI stock a buy or sell?
A: The dominant strategy among neural network is to Hold UGI Stock.
Q: Is UGI Corp stock a good investment?
A: The consensus rating for UGI Corp is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of UGI stock?
A: The consensus rating for UGI is Hold.
Q: What is the prediction period for UGI stock?
A: The prediction period for UGI is (n+4 weeks)

People also ask

What are the top stocks to invest in right now?
Our Mission

As AC Investment Research, our goal is to do fundamental research, bring forward a totally new, scientific technology and create frameworks for objective forecasting using machine learning and fundamentals of Game Theory.

301 Massachusetts Avenue Cambridge, MA 02139 667-253-1000 pr@ademcetinkaya.com

Follow Us | Send Feedback