CLNE Clean Energy Fuels Corp. Common Stock

Outlook: Clean Energy Fuels Corp. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 06 Jan 2023 for (n+3 month)
Methodology : Inductive Learning (ML)

Abstract

Clean Energy Fuels Corp. Common Stock prediction model is evaluated with Inductive Learning (ML) and Lasso Regression^1,2,3,4 and it is concluded that the CLNE stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

Key Points

1. What is a prediction confidence?
2. Investment Risk
3. Can we predict stock market using machine learning?

CLNE Target Price Prediction Modeling Methodology

We consider Clean Energy Fuels Corp. Common Stock Decision Process with Inductive Learning (ML) where A is the set of discrete actions of CLNE 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(Lasso Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Inductive Learning (ML)) X S(n):→ (n+3 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of CLNE 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?

CLNE Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: CLNE Clean Energy Fuels Corp. Common Stock
Time series to forecast n: 06 Jan 2023 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

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%

IFRS Reconciliation Adjustments for Clean Energy Fuels Corp. Common Stock

1. An entity need not undertake an exhaustive search for information but shall consider all reasonable and supportable information that is available without undue cost or effort and that is relevant to the estimate of expected credit losses, including the effect of expected prepayments. The information used shall include factors that are specific to the borrower, general economic conditions and an assessment of both the current as well as the forecast direction of conditions at the reporting date. An entity may use various sources of data, that may be both internal (entity-specific) and external. Possible data sources include internal historical credit loss experience, internal ratings, credit loss experience of other entities and external ratings, reports and statistics. Entities that have no, or insufficient, sources of entityspecific data may use peer group experience for the comparable financial instrument (or groups of financial instruments).
2. In some circumstances, the renegotiation or modification of the contractual cash flows of a financial asset can lead to the derecognition of the existing financial asset in accordance with this Standard. When the modification of a financial asset results in the derecognition of the existing financial asset and the subsequent recognition of the modified financial asset, the modified asset is considered a 'new' financial asset for the purposes of this Standard.
3. Adjusting the hedge ratio by decreasing the volume of the hedging instrument does not affect how the changes in the value of the hedged item are measured. The measurement of the changes in the fair value of the hedging instrument related to the volume that continues to be designated also remains unaffected. However, from the date of rebalancing, the volume by which the hedging instrument was decreased is no longer part of the hedging relationship. For example, if an entity originally hedged the price risk of a commodity using a derivative volume of 100 tonnes as the hedging instrument and reduces that volume by 10 tonnes on rebalancing, a nominal amount of 90 tonnes of the hedging instrument volume would remain (see paragraph B6.5.16 for the consequences for the derivative volume (ie the 10 tonnes) that is no longer a part of the hedging relationship).
4. If an entity originates a loan that bears an off-market interest rate (eg 5 per cent when the market rate for similar loans is 8 per cent), and receives an upfront fee as compensation, the entity recognises the loan at its fair value, ie net of the fee it receives.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

Conclusions

Clean Energy Fuels Corp. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Clean Energy Fuels Corp. Common Stock prediction model is evaluated with Inductive Learning (ML) and Lasso Regression^1,2,3,4 and it is concluded that the CLNE stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

CLNE Clean Energy Fuels Corp. Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	Baa2
Balance Sheet	Baa2	Caa2
Leverage Ratios	Baa2	Baa2
Cash Flow	Ba3	B3
Rates of Return and Profitability	C	B2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 821 signals.

References

1. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
2. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
3. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
4. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
5. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
6. Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
7. J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.

Frequently Asked Questions

Q: What is the prediction methodology for CLNE stock?
A: CLNE stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Lasso Regression
Q: Is CLNE stock a buy or sell?
A: The dominant strategy among neural network is to Hold CLNE Stock.
Q: Is Clean Energy Fuels Corp. Common Stock stock a good investment?
A: The consensus rating for Clean Energy Fuels Corp. Common Stock is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of CLNE stock?
A: The consensus rating for CLNE is Hold.
Q: What is the prediction period for CLNE stock?
A: The prediction period for CLNE is (n+3 month)

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