SZSE Component Index Research Report

## Summary

Stock market predictions are one of the challenging tasks for financial investors across the globe. This challenge is due to the uncertainty and volatility of the stock prices in the market. Due to technology and globalization of business and financial markets it is important to predict the stock prices more quickly and accurately. Last few years there has been much improvement in the field of Neural Network (NN) applications in business and financial markets. Artificial Neural Network (ANN) methods are mostly implemented and play a vital role in decision making for stock market predictions. We evaluate SZSE Component Index prediction models with Ensemble Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the SZSE Component Index stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold SZSE Component Index stock.

## Key Points

2. Can neural networks predict stock market?
3. Probability Distribution

## SZSE Component Index Target Price Prediction Modeling Methodology

We consider SZSE Component Index Stock Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of SZSE Component Index 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(Ensemble Learning (ML)) X S(n):→ (n+3 month) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of SZSE Component Index 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?

## SZSE Component Index Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: SZSE Component Index SZSE Component Index
Time series to forecast n: 19 Nov 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold SZSE Component Index stock.

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 (Yellow to Green): *Technical Analysis%

## Adjusted IFRS* Prediction Methods for SZSE Component Index

1. Paragraph 4.1.1(a) requires an entity to classify financial assets on the basis of the entity's business model for managing the financial assets, unless paragraph 4.1.5 applies. An entity assesses whether its financial assets meet the condition in paragraph 4.1.2(a) or the condition in paragraph 4.1.2A(a) on the basis of the business model as determined by the entity's key management personnel (as defined in IAS 24 Related Party Disclosures).
2. 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).
3. IFRS 7 defines credit risk as 'the risk that one party to a financial instrument will cause a financial loss for the other party by failing to discharge an obligation'. The requirement in paragraph 5.7.7(a) relates to the risk that the issuer will fail to perform on that particular liability. It does not necessarily relate to the creditworthiness of the issuer. For example, if an entity issues a collateralised liability and a non-collateralised liability that are otherwise identical, the credit risk of those two liabilities will be different, even though they are issued by the same entity. The credit risk on the collateralised liability will be less than the credit risk of the non-collateralised liability. The credit risk for a collateralised liability may be close to zero.
4. An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34

*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

SZSE Component Index assigned short-term B2 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the SZSE Component Index stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold SZSE Component Index stock.

### Financial State Forecast for SZSE Component Index SZSE Component Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2Ba1
Operational Risk 5484
Market Risk5348
Technical Analysis4888
Fundamental Analysis7652
Risk Unsystematic3282

### Prediction Confidence Score

Trust metric by Neural Network: 75 out of 100 with 558 signals.

## References

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3. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
4. J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
5. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
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Frequently Asked QuestionsQ: What is the prediction methodology for SZSE Component Index stock?
A: SZSE Component Index stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is SZSE Component Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold SZSE Component Index Stock.
Q: Is SZSE Component Index stock a good investment?
A: The consensus rating for SZSE Component Index is Hold and assigned short-term B2 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of SZSE Component Index stock?
A: The consensus rating for SZSE Component Index is Hold.
Q: What is the prediction period for SZSE Component Index stock?
A: The prediction period for SZSE Component Index is (n+3 month)