Outlook: Recon Technology Ltd. Class A Ordinary Shares is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 24 Apr 2023 for (n+3 month)
Methodology : Modular Neural Network (Emotional Trigger/Responses Analysis)

## Abstract

Recon Technology Ltd. Class A Ordinary Shares prediction model is evaluated with Modular Neural Network (Emotional Trigger/Responses Analysis) and Stepwise Regression1,2,3,4 and it is concluded that the RCON 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. Understanding Buy, Sell, and Hold Ratings
2. How do you pick a stock?
3. Technical Analysis with Algorithmic Trading

## RCON Target Price Prediction Modeling Methodology

We consider Recon Technology Ltd. Class A Ordinary Shares Decision Process with Modular Neural Network (Emotional Trigger/Responses Analysis) where A is the set of discrete actions of RCON 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(Stepwise Regression)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(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+3 month) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of RCON 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?

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

Sample Set: Neural Network
Stock/Index: RCON Recon Technology Ltd. Class A Ordinary Shares
Time series to forecast n: 24 Apr 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 Recon Technology Ltd. Class A 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. Paragraph 6.3.6 states that in consolidated financial statements the foreign currency risk of a highly probable forecast intragroup transaction may qualify as a hedged item in a cash flow hedge, provided that the transaction is denominated in a currency other than the functional currency of the entity entering into that transaction and that the foreign currency risk will affect consolidated profit or loss. For this purpose an entity can be a parent, subsidiary, associate, joint arrangement or branch. If the foreign currency risk of a forecast intragroup transaction does not affect consolidated profit or loss, the intragroup transaction cannot qualify as a hedged item. This is usually the case for royalty payments, interest payments or management charges between members of the same group, unless there is a related external transaction. However, when the foreign currency risk of a forecast intragroup transaction will affect consolidated profit or loss, the intragroup transaction can qualify as a hedged item. An example is forecast sales or purchases of inventories between members of the same group if there is an onward sale of the inventory to a party external to the group. Similarly, a forecast intragroup sale of plant and equipment from the group entity that manufactured it to a group entity that will use the plant and equipment in its operations may affect consolidated profit or loss. This could occur, for example, because the plant and equipment will be depreciated by the purchasing entity and the amount initially recognised for the plant and equipment may change if the forecast intragroup transaction is denominated in a currency other than the functional currency of the purchasing entity.
3. The fair value of a financial instrument at initial recognition is normally the transaction price (ie the fair value of the consideration given or received, see also paragraph B5.1.2A and IFRS 13). However, if part of the consideration given or received is for something other than the financial instrument, an entity shall measure the fair value of the financial instrument. For example, the fair value of a long-term loan or receivable that carries no interest can be measured as the present value of all future cash receipts discounted using the prevailing market rate(s) of interest for a similar instrument (similar as to currency, term, type of interest rate and other factors) with a similar credit rating. Any additional amount lent is an expense or a reduction of income unless it qualifies for recognition as some other type of asset.
4. If, at the date of initial application, it is impracticable (as defined in IAS 8) for an entity to assess whether the fair value of a prepayment feature was insignificant in accordance with paragraph B4.1.12(c) on the basis of the facts and circumstances that existed at the initial recognition of the financial asset, an entity shall assess the contractual cash flow characteristics of that financial asset on the basis of the facts and circumstances that existed at the initial recognition of the financial asset without taking into account the exception for prepayment features in paragraph B4.1.12. (See also paragraph 42S of IFRS 7.)

*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

Recon Technology Ltd. Class A Ordinary Shares is assigned short-term Ba1 & long-term Ba1 estimated rating. Recon Technology Ltd. Class A Ordinary Shares prediction model is evaluated with Modular Neural Network (Emotional Trigger/Responses Analysis) and Stepwise Regression1,2,3,4 and it is concluded that the RCON 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

### RCON Recon Technology Ltd. Class A Ordinary Shares Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementB1Baa2
Balance SheetBaa2Caa2
Leverage RatiosCaa2B3
Cash FlowB3B2
Rates of Return and ProfitabilityB2Baa2

*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: 88 out of 100 with 759 signals. ## References

1. Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
2. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
3. Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
4. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
5. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
6. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
7. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
Frequently Asked QuestionsQ: What is the prediction methodology for RCON stock?
A: RCON stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Stepwise Regression
Q: Is RCON stock a buy or sell?
A: The dominant strategy among neural network is to Hold RCON Stock.
Q: Is Recon Technology Ltd. Class A Ordinary Shares stock a good investment?
A: The consensus rating for Recon Technology Ltd. Class A Ordinary Shares is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of RCON stock?
A: The consensus rating for RCON is Hold.
Q: What is the prediction period for RCON stock?
A: The prediction period for RCON is (n+3 month)