SZSE Component Index Research Report

## Summary

This study presents financial network indicators that can be applied to global stock market investment strategies. We propose to design both undirected and directed volatility networks of global stock market based on simple pair-wise correlation and system-wide connectedness of stock date using a vector auto-regressive model. We evaluate SZSE Component Index prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Sign 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+16 weeks) period: The dominant strategy among neural network is to Hold SZSE Component Index stock.

## Key Points

1. Is now good time to invest?
2. What is neural prediction?
3. How do you know when a stock will go up or down?

## SZSE Component Index Target Price Prediction Modeling Methodology

We consider SZSE Component Index Stock Decision Process with Modular Neural Network (Speculative Sentiment Analysis) 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(Sign 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(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+16 weeks) $∑ i = 1 n a i$

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+16 weeks)

Sample Set: Neural Network
Stock/Index: SZSE Component Index SZSE Component Index
Time series to forecast n: 20 Nov 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) 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. If a guarantee provided by an entity to pay for default losses on a transferred asset prevents the transferred asset from being derecognised to the extent of the continuing involvement, the transferred asset at the date of the transfer is measured at the lower of (i) the carrying amount of the asset and (ii) the maximum amount of the consideration received in the transfer that the entity could be required to repay ('the guarantee amount'). The associated liability is initially measured at the guarantee amount plus the fair value of the guarantee (which is normally the consideration received for the guarantee). Subsequently, the initial fair value of the guarantee is recognised in profit or loss when (or as) the obligation is satisfied (in accordance with the principles of IFRS 15) and the carrying value of the asset is reduced by any loss allowance.
2. A similar example of a non-financial item is a specific type of crude oil from a particular oil field that is priced off the relevant benchmark crude oil. If an entity sells that crude oil under a contract using a contractual pricing formula that sets the price per barrel at the benchmark crude oil price minus CU10 with a floor of CU15, the entity can designate as the hedged item the entire cash flow variability under the sales contract that is attributable to the change in the benchmark crude oil price. However, the entity cannot designate a component that is equal to the full change in the benchmark crude oil price. Hence, as long as the forward price (for each delivery) does not fall below CU25, the hedged item has the same cash flow variability as a crude oil sale at the benchmark crude oil price (or with a positive spread). However, if the forward price for any delivery falls below CU25, the hedged item has a lower cash flow variability than a crude oil sale at the benchmark crude oil price (or with a positive spread).
3. IFRS 16, issued in January 2016, amended paragraphs 2.1, 5.5.15, B4.3.8, B5.5.34 and B5.5.46. An entity shall apply those amendments when it applies IFRS 16.
4. For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the contract.

*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 B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Sign 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+16 weeks) 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*B1Ba3
Operational Risk 4485
Market Risk6759
Technical Analysis4247
Fundamental Analysis8758
Risk Unsystematic7275

### Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 748 signals.

## References

1. Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
2. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
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. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
5. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
6. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
7. Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791
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 Modular Neural Network (Speculative Sentiment Analysis) and Sign 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 B1 & long-term Ba3 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+16 weeks)

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