In recent years there has been a significant growth of interest in the incorporation of historical series of variables related to stock prediction into mathematical models or computational algorithms in order to generate predictions or indications about expected price movements. We evaluate National Fuel Gas prediction models with Modular Neural Network (DNN Layer) and ElasticNet Regression1,2,3,4 and conclude that the NFG stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NFG stock.
Keywords: NFG, National Fuel Gas, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Stock Rating
- Reaction Function
- Trust metric by Neural Network
NFG Target Price Prediction Modeling Methodology
Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each stock price and thus limited to certain experimental setups to obtain good prediction results. On the other hand, professional investors additionally use their rich knowledge of inter-market and inter-company relations to map the connectivity of companies and events, and use this map to make better market predictions. For instance, they would predict the movement of a certain company's stock price based not only on its former stock price trends but also on the performance of its suppliers or customers, the overall industry, macroeconomic factors and trade policies. This paper investigates the effectiveness of work at the intersection of market predictions and graph neural networks, which hold the potential to mimic the ways in which investors make decisions by incorporating company knowledge graphs directly into the predictive model. We consider National Fuel Gas Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of NFG 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(ElasticNet Regression)5,6,7= X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of NFG stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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How do AC Investment Research machine learning (predictive) algorithms actually work?
NFG Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: NFG National Fuel Gas
Time series to forecast n: 13 Sep 2022 for (n+16 weeks)
According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NFG 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
National Fuel Gas assigned short-term B1 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with ElasticNet Regression1,2,3,4 and conclude that the NFG stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NFG stock.
Financial State Forecast for NFG Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B1 | Ba1 |
| Operational Risk | 48 | 72 |
| Market Risk | 74 | 90 |
| Technical Analysis | 72 | 44 |
| Fundamental Analysis | 35 | 89 |
| Risk Unsystematic | 76 | 62 |
Prediction Confidence Score
References
- Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
- Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
- 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.
- Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
- 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
- V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
- Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
Frequently Asked Questions
Q: What is the prediction methodology for NFG stock?A: NFG stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and ElasticNet Regression
Q: Is NFG stock a buy or sell?
A: The dominant strategy among neural network is to Hold NFG Stock.
Q: Is National Fuel Gas stock a good investment?
A: The consensus rating for National Fuel Gas is Hold and assigned short-term B1 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of NFG stock?
A: The consensus rating for NFG is Hold.
Q: What is the prediction period for NFG stock?
A: The prediction period for NFG is (n+16 weeks)
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