Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems.** We evaluate Shanghai Composite Index prediction models with Transfer Learning (ML) and Logistic Regression ^{1,2,3,4} and conclude that the Shanghai Composite Index stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold Shanghai Composite Index stock.**

**Shanghai Composite Index, Shanghai Composite Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Can stock prices be predicted?
- What are main components of Markov decision process?
- Market Risk

## Shanghai Composite Index Target Price Prediction Modeling Methodology

Machine Learning refers to a concept in which a machine has been programmed to learn specific patterns from historical data using powerful algorithms and make predictions in future based on the patterns it learnt. Machine learning is a branch of Artificial Intelligence (AI), the term proposed in 1959 by Arthur Samuel who defined it as the ability of computers or machines to learn new rules and concepts from data without being explicitly programmed. We consider Shanghai Composite Index Stock Decision Process with Logistic Regression where A is the set of discrete actions of Shanghai Composite 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(Logistic Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Transfer Learning (ML)) X S(n):→ (n+1 year) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of Shanghai Composite 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?

## Shanghai Composite Index Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**Shanghai Composite Index Shanghai Composite Index

**Time series to forecast n: 11 Oct 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold Shanghai Composite 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

Shanghai Composite Index assigned short-term Ba2 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Transfer Learning (ML) with Logistic Regression ^{1,2,3,4} and conclude that the Shanghai Composite Index stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold Shanghai Composite Index stock.**

### Financial State Forecast for Shanghai Composite Index Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba2 | Ba1 |

Operational Risk | 63 | 90 |

Market Risk | 78 | 79 |

Technical Analysis | 48 | 49 |

Fundamental Analysis | 63 | 69 |

Risk Unsystematic | 89 | 60 |

### Prediction Confidence Score

## References

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- Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
- Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
- Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
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## Frequently Asked Questions

Q: What is the prediction methodology for Shanghai Composite Index stock?A: Shanghai Composite Index stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Logistic Regression

Q: Is Shanghai Composite Index stock a buy or sell?

A: The dominant strategy among neural network is to Hold Shanghai Composite Index Stock.

Q: Is Shanghai Composite Index stock a good investment?

A: The consensus rating for Shanghai Composite Index is Hold and assigned short-term Ba2 & long-term Ba1 forecasted stock rating.

Q: What is the consensus rating of Shanghai Composite Index stock?

A: The consensus rating for Shanghai Composite Index is Hold.

Q: What is the prediction period for Shanghai Composite Index stock?

A: The prediction period for Shanghai Composite Index is (n+1 year)