Outlook: FuelCell Energy Inc. Common Stock assigned short-term Ba3 & long-term Ba3 forecasted stock rating.
Time series to forecast n: 09 Dec 2022 for (n+6 month)
Methodology : Reinforcement Machine Learning (ML)

## Abstract

A speculator on a Stock Market, aside from having money to spare, needs at least one other thing — a means of producing accurate and understandable predictions ahead of others in the Market, so that a tactical and price advantage can be gained. This work demonstrates that it is possible to predict one such Market to a high degree of accuracy. (Chen, S. and He, H., 2018, October. Stock prediction using convolutional neural network. In IOP Conference series: materials science and engineering (Vol. 435, No. 1, p. 012026). IOP Publishing.) We evaluate FuelCell Energy Inc. Common Stock prediction models with Reinforcement Machine Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the FCEL stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

## Key Points

1. Technical Analysis with Algorithmic Trading
2. Investment Risk
3. What is the use of Markov decision process?

## FCEL Target Price Prediction Modeling Methodology

We consider FuelCell Energy Inc. Common Stock Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of FCEL 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 Sign-Rank Test)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Reinforcement Machine Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of FCEL stock

j:Nash equilibria (Neural Network)

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?

## FCEL Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: FCEL FuelCell Energy Inc. Common Stock
Time series to forecast n: 09 Dec 2022 for (n+6 month)

According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

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%

## Adjusted IFRS* Prediction Methods for FuelCell Energy Inc. Common Stock

1. Adjusting the hedge ratio allows an entity to respond to changes in the relationship between the hedging instrument and the hedged item that arise from their underlyings or risk variables. For example, a hedging relationship in which the hedging instrument and the hedged item have different but related underlyings changes in response to a change in the relationship between those two underlyings (for example, different but related reference indices, rates or prices). Hence, rebalancing allows the continuation of a hedging relationship in situations in which the relationship between the hedging instrument and the hedged item chang
2. An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.
3. Alternatively, the entity may base the assessment on both types of information, ie qualitative factors that are not captured through the internal ratings process and a specific internal rating category at the reporting date, taking into consideration the credit risk characteristics at initial recognition, if both types of information are relevant.
4. Paragraph 5.7.5 permits an entity to make an irrevocable election to present in other comprehensive income subsequent changes in the fair value of particular investments in equity instruments. Such an investment is not a monetary item. Accordingly, the gain or loss that is presented in other comprehensive income in accordance with paragraph 5.7.5 includes any related foreign exchange component.

*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

FuelCell Energy Inc. Common Stock assigned short-term Ba3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the FCEL stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

### Financial State Forecast for FCEL FuelCell Energy Inc. Common Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba3
Operational Risk 5374
Market Risk6971
Technical Analysis7782
Fundamental Analysis4543
Risk Unsystematic7040

### Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 855 signals.

## References

1. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
2. J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
3. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
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. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
6. Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
7. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
Frequently Asked QuestionsQ: What is the prediction methodology for FCEL stock?
A: FCEL stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is FCEL stock a buy or sell?
A: The dominant strategy among neural network is to Buy FCEL Stock.
Q: Is FuelCell Energy Inc. Common Stock stock a good investment?
A: The consensus rating for FuelCell Energy Inc. Common Stock is Buy and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of FCEL stock?
A: The consensus rating for FCEL is Buy.
Q: What is the prediction period for FCEL stock?
A: The prediction period for FCEL is (n+6 month)