ADSE ADS-TEC ENERGY PLC Ordinary Shares

Outlook: ADS-TEC ENERGY PLC Ordinary Shares is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 25 Mar 2023 for (n+6 month)
Methodology : Modular Neural Network (Social Media Sentiment Analysis)

Abstract

ADS-TEC ENERGY PLC Ordinary Shares prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Linear Regression^1,2,3,4 and it is concluded that the ADSE stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

Key Points

1. What are the most successful trading algorithms?
2. What is a prediction confidence?
3. Decision Making

ADSE Target Price Prediction Modeling Methodology

We consider ADS-TEC ENERGY PLC Ordinary Shares Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of ADSE 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(Linear 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(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+6 month) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of ADSE 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?

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

Sample Set: Neural Network
Stock/Index: ADSE ADS-TEC ENERGY PLC Ordinary Shares
Time series to forecast n: 25 Mar 2023 for (n+6 month)

According to price forecasts for (n+6 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 ADS-TEC ENERGY PLC Ordinary Shares

1. Expected credit losses are a probability-weighted estimate of credit losses (ie the present value of all cash shortfalls) over the expected life of the financial instrument. A cash shortfall is the difference between the cash flows that are due to an entity in accordance with the contract and the cash flows that the entity expects to receive. Because expected credit losses consider the amount and timing of payments, a credit loss arises even if the entity expects to be paid in full but later than when contractually due.
2. For the purpose of determining whether a forecast transaction (or a component thereof) is highly probable as required by paragraph 6.3.3, an entity shall assume that the interest rate benchmark on which the hedged cash flows (contractually or non-contractually specified) are based is not altered as a result of interest rate benchmark reform.
3. Contractual cash flows that are solely payments of principal and interest on the principal amount outstanding are consistent with a basic lending arrangement. In a basic lending arrangement, consideration for the time value of money (see paragraphs B4.1.9A–B4.1.9E) and credit risk are typically the most significant elements of interest. However, in such an arrangement, interest can also include consideration for other basic lending risks (for example, liquidity risk) and costs (for example, administrative costs) associated with holding the financial asset for a particular period of time. In addition, interest can include a profit margin that is consistent with a basic lending arrangement. In extreme economic circumstances, interest can be negative if, for example, the holder of a financial asset either explicitly or implicitly pays for the deposit of its money for a particular period of time (and that fee exceeds the consideration that the holder receives for the time value of money, credit risk and other basic lending risks and costs).
4. Adjusting the hedge ratio by increasing 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 previously designated volume also remains unaffected. However, from the date of rebalancing, the changes in the fair value of the hedging instrument also include the changes in the value of the additional volume of the hedging instrument. The changes are measured starting from, and by reference to, the date of rebalancing instead of the date on which the hedging relationship was designated. 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 added a volume of 10 tonnes on rebalancing, the hedging instrument after rebalancing would comprise a total derivative volume of 110 tonnes. The change in the fair value of the hedging instrument is the total change in the fair value of the derivatives that make up the total volume of 110 tonnes. These derivatives could (and probably would) have different critical terms, such as their forward rates, because they were entered into at different points in time (including the possibility of designating derivatives into hedging relationships after their initial recognition).

*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

ADS-TEC ENERGY PLC Ordinary Shares is assigned short-term Ba1 & long-term Ba1 estimated rating. ADS-TEC ENERGY PLC Ordinary Shares prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Linear Regression^1,2,3,4 and it is concluded that the ADSE stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

ADSE ADS-TEC ENERGY PLC Ordinary Shares Financial Analysis*

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

*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: 86 out of 100 with 760 signals.

References

1. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
2. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
3. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
4. Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
5. Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791
6. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
7. Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press

Frequently Asked Questions

Q: What is the prediction methodology for ADSE stock?
A: ADSE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Linear Regression
Q: Is ADSE stock a buy or sell?
A: The dominant strategy among neural network is to Hold ADSE Stock.
Q: Is ADS-TEC ENERGY PLC Ordinary Shares stock a good investment?
A: The consensus rating for ADS-TEC ENERGY PLC Ordinary Shares is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of ADSE stock?
A: The consensus rating for ADSE is Hold.
Q: What is the prediction period for ADSE stock?
A: The prediction period for ADSE is (n+6 month)

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