We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief r ́esum ́e of the existing literature on the subject, we consider the Multi-layer Perceptron (MLP), the Convolutional Neural Net- works (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks techniques. We evaluate SZSE Component Index prediction models with Modular Neural Network (CNN Layer) 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.

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. Game Theory
2. What is statistical models in machine learning?
3. What is the use of Markov decision process?

## SZSE Component Index Target Price Prediction Modeling Methodology

Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. We consider SZSE Component Index Stock Decision Process with Wilcoxon Sign-Rank Test 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(Modular Neural Network (CNN Layer)) X S(n):→ (n+3 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

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+3 month)

Sample Set: Neural Network
Stock/Index: SZSE Component Index SZSE Component Index
Time series to forecast n: 20 Sep 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%

## Conclusions

SZSE Component Index assigned short-term Caa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) 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 Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Caa2B1
Operational Risk 4275
Market Risk3637
Technical Analysis5470
Fundamental Analysis5657
Risk Unsystematic3765

### Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 452 signals.

## References

1. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
2. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
3. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
4. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
5. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
6. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
7. 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
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 (CNN Layer) 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 Caa2 & 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 Hold.
Q: What is the prediction period for SZSE Component Index stock?
A: The prediction period for SZSE Component Index is (n+3 month)