The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. We evaluate SZSE Component Index prediction models with Reinforcement Machine Learning (ML) and Ridge Regression1,2,3,4 and conclude that the SZSE Component Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy SZSE Component Index stock.

Keywords: SZSE Component Index, SZSE Component Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. How useful are statistical predictions?
2. Reaction Function
3. Market Outlook

## SZSE Component Index Target Price Prediction Modeling Methodology

Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models which are known to be dynamic and effective in stock-market predictions. We consider SZSE Component Index Stock Decision Process with Ridge Regression 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(Ridge 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(Reinforcement Machine Learning (ML)) X S(n):→ (n+16 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of SZSE Component Index stock

j:Nash equilibria

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+16 weeks)

Sample Set: Neural Network
Stock/Index: SZSE Component Index SZSE Component Index
Time series to forecast n: 05 Nov 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy 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. At the date of initial application, an entity shall assess whether a financial asset meets the condition in paragraphs 4.1.2(a) or 4.1.2A(a) on the basis of the facts and circumstances that exist at that date. The resulting classification shall be applied retrospectively irrespective of the entity's business model in prior reporting periods.
2. Lifetime expected credit losses are generally expected to be recognised before a financial instrument becomes past due. Typically, credit risk increases significantly before a financial instrument becomes past due or other lagging borrower-specific factors (for example, a modification or restructuring) are observed. Consequently when reasonable and supportable information that is more forward-looking than past due information is available without undue cost or effort, it must be used to assess changes in credit risk.
3. Annual Improvements to IFRS Standards 2018–2020, issued in May 2020, added paragraphs 7.2.35 and B3.3.6A and amended paragraph B3.3.6. An entity shall apply that amendment for annual reporting periods beginning on or after 1 January 2022. Earlier application is permitted. If an entity applies the amendment for an earlier period, it shall disclose that fact.
4. The accounting for the forward element of forward contracts in accordance with paragraph 6.5.16 applies only to the extent that the forward element relates to the hedged item (aligned forward element). The forward element of a forward contract relates to the hedged item if the critical terms of the forward contract (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the forward contract and the hedged item are not fully aligned, an entity shall determine the aligned forward element, ie how much of the forward element included in the forward contract (actual forward element) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.16). An entity determines the aligned forward element using the valuation of the forward contract that would have critical terms that perfectly match the hedged item.

*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 B1 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Ridge Regression1,2,3,4 and conclude that the SZSE Component Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy SZSE Component Index stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 8132
Market Risk8360
Technical Analysis3343
Fundamental Analysis3182
Risk Unsystematic5773

### Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 781 signals.

## References

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3. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
4. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
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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 Reinforcement Machine Learning (ML) and Ridge Regression
Q: Is SZSE Component Index stock a buy or sell?
A: The dominant strategy among neural network is to Buy SZSE Component Index Stock.
Q: Is SZSE Component Index stock a good investment?
A: The consensus rating for SZSE Component Index is Buy and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of SZSE Component Index stock?
A: The consensus rating for SZSE Component Index is Buy.
Q: What is the prediction period for SZSE Component Index stock?
A: The prediction period for SZSE Component Index is (n+16 weeks)