Modelling A.I. in Economics

What are buy sell or hold recommendations? (UGI Stock Forecast)

The presented paper modeled and predicted stock returns using LSTM. The historical data of stock market were transformed into 30-days-long sequences with 10 learning features and 7-day earning rate labeling. The model was fitted by training on 1200000 sequences and tested using the other 350000 sequences. We evaluate UGI Corp prediction models with Transfer Learning (ML) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the UGI stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell 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. Trading Signals
  2. What is prediction in deep learning?
  3. How do you pick a stock?

UGI Target Price Prediction Modeling Methodology

This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. We consider UGI Corp Stock Decision Process with Wilcoxon Rank-Sum Test 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(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(Transfer Learning (ML)) X S(n):→ (n+3 month) i = 1 n r i

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+3 month)


Sample Set: Neural Network
Stock/Index: UGI UGI Corp
Time series to forecast n: 02 Nov 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell 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%

Adjusted IFRS* Prediction Methods for UGI Corp

  1. An entity shall apply the impairment requirements in Section 5.5 retrospectively in accordance with IAS 8 subject to paragraphs 7.2.15 and 7.2.18–7.2.20.
  2. An alternative benchmark rate designated as a non-contractually specified risk component that is not separately identifiable (see paragraphs 6.3.7(a) and B6.3.8) at the date it is designated shall be deemed to have met that requirement at that date, if, and only if, the entity reasonably expects the alternative benchmark rate will be separately identifiable within 24 months. The 24-month period applies to each alternative benchmark rate separately and starts from the date the entity designates the alternative benchmark rate as a non-contractually specified risk component for the first time (ie the 24- month period applies on a rate-by-rate basis).
  3. Accordingly the date of the modification shall be treated as the date of initial recognition of that financial asset when applying the impairment requirements to the modified financial asset. This typically means measuring the loss allowance at an amount equal to 12-month expected credit losses until the requirements for the recognition of lifetime expected credit losses in paragraph 5.5.3 are met. However, in some unusual circumstances following a modification that results in derecognition of the original financial asset, there may be evidence that the modified financial asset is credit-impaired at initial recognition, and thus, the financial asset should be recognised as an originated credit-impaired financial asset. This might occur, for example, in a situation in which there was a substantial modification of a distressed asset that resulted in the derecognition of the original financial asset. In such a case, it may be possible for the modification to result in a new financial asset which is credit-impaired at initial recognition.
  4. At the date of initial application, an entity shall assess whether a financial asset meets the condition in paragraphs 4.1.2(a) or 4.1.2A(a) on the basis of the facts and circumstances that exist at that date. The resulting classification shall be applied retrospectively irrespective of the entity's business model in prior reporting periods.

*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

UGI Corp assigned short-term B3 & long-term B2 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the UGI stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell UGI stock.

Financial State Forecast for UGI UGI Corp Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3B2
Operational Risk 3736
Market Risk8586
Technical Analysis3852
Fundamental Analysis3333
Risk Unsystematic5063

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 841 signals.

References

  1. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
  2. Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
  3. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
  4. Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
  5. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
  6. S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.
  7. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
Frequently Asked QuestionsQ: What is the prediction methodology for UGI stock?
A: UGI stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is UGI stock a buy or sell?
A: The dominant strategy among neural network is to Sell UGI Stock.
Q: Is UGI Corp stock a good investment?
A: The consensus rating for UGI Corp is Sell and assigned short-term B3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of UGI stock?
A: The consensus rating for UGI is Sell.
Q: What is the prediction period for UGI stock?
A: The prediction period for UGI is (n+3 month)

Premium

  • Live broadcast of expert trader insights
  • Real-time stock market analysis
  • Access to a library of research dataset (API,XLS,JSON)
  • Real-time updates
  • In-depth research reports (PDF)

Login
This project is licensed under the license; additional terms may apply.