Outlook: ATRenew Inc. American Depositary Shares (every three of which representing two Class A ordinary shares) assigned short-term B2 & long-term B1 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 17 Dec 2022 for (n+6 month)
Methodology : Statistical Inference (ML)

## Abstract

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making.(Sen, J. and Chaudhuri, T.D., 2018, December. Stock price prediction using machine learning and deep learning frameworks. In Proceedings of the 6th International Conference on Business Analytics and Intelligence, Bangalore, India (pp. 20-22).) We evaluate ATRenew Inc. American Depositary Shares (every three of which representing two Class A ordinary shares) prediction models with Statistical Inference (ML) and Lasso Regression1,2,3,4 and conclude that the RERE 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 is a prediction confidence?
2. Game Theory
3. Can neural networks predict stock market?

## RERE Target Price Prediction Modeling Methodology

We consider ATRenew Inc. American Depositary Shares (every three of which representing two Class A ordinary shares) Decision Process with Statistical Inference (ML) where A is the set of discrete actions of RERE 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Statistical Inference (ML)) X S(n):→ (n+6 month) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of RERE 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?

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

Sample Set: Neural Network
Stock/Index: RERE ATRenew Inc. American Depositary Shares (every three of which representing two Class A ordinary shares)
Time series to forecast n: 17 Dec 2022 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%

## Adjusted IFRS* Prediction Methods for ATRenew Inc. American Depositary Shares (every three of which representing two Class A ordinary shares)

1. Hedge effectiveness is the extent to which changes in the fair value or the cash flows of the hedging instrument offset changes in the fair value or the cash flows of the hedged item (for example, when the hedged item is a risk component, the relevant change in fair value or cash flows of an item is the one that is attributable to the hedged risk). Hedge ineffectiveness is the extent to which the changes in the fair value or the cash flows of the hedging instrument are greater or less than those on the hedged item.
3. An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight and the restated financial statements reflect all the requirements in this Standard. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
4. When designating a risk component as a hedged item, the hedge accounting requirements apply to that risk component in the same way as they apply to other hedged items that are not risk components. For example, the qualifying criteria apply, including that the hedging relationship must meet the hedge effectiveness requirements, and any hedge ineffectiveness must be measured and recognised.

*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

ATRenew Inc. American Depositary Shares (every three of which representing two Class A ordinary shares) assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Statistical Inference (ML) with Lasso Regression1,2,3,4 and conclude that the RERE 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

### Financial State Forecast for RERE ATRenew Inc. American Depositary Shares (every three of which representing two Class A ordinary shares) Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 7546
Market Risk3258
Technical Analysis4056
Fundamental Analysis4650
Risk Unsystematic7571

### Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 486 signals.

## References

1. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
2. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
3. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
4. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
5. B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765
6. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
7. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
Frequently Asked QuestionsQ: What is the prediction methodology for RERE stock?
A: RERE stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Lasso Regression
Q: Is RERE stock a buy or sell?
A: The dominant strategy among neural network is to Hold RERE Stock.
Q: Is ATRenew Inc. American Depositary Shares (every three of which representing two Class A ordinary shares) stock a good investment?
A: The consensus rating for ATRenew Inc. American Depositary Shares (every three of which representing two Class A ordinary shares) is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of RERE stock?
A: The consensus rating for RERE is Hold.
Q: What is the prediction period for RERE stock?
A: The prediction period for RERE is (n+6 month)