Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history.** We evaluate Shanghai Composite Index prediction models with Reinforcement Machine 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+3 month) 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

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- Trading Interaction
- How accurate is machine learning in stock market?

## Shanghai Composite Index Target Price Prediction Modeling Methodology

Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each stock price and thus limited to certain experimental setups to obtain good prediction results. On the other hand, professional investors additionally use their rich knowledge of inter-market and inter-company relations to map the connectivity of companies and events, and use this map to make better market predictions. For instance, they would predict the movement of a certain company's stock price based not only on its former stock price trends but also on the performance of its suppliers or customers, the overall industry, macroeconomic factors and trade policies. This paper investigates the effectiveness of work at the intersection of market predictions and graph neural networks, which hold the potential to mimic the ways in which investors make decisions by incorporating company knowledge graphs directly into the predictive model. 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(Reinforcement Machine Learning (ML)) X S(n):→ (n+3 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

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

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## Shanghai Composite Index Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

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

**Time series to forecast n: 09 Sep 2022**for (n+3 month)

**According to price forecasts for (n+3 month) 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 Ba3 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Reinforcement Machine 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+3 month) 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* | Ba3 | Ba1 |

Operational Risk | 62 | 72 |

Market Risk | 71 | 70 |

Technical Analysis | 78 | 39 |

Fundamental Analysis | 88 | 87 |

Risk Unsystematic | 35 | 83 |

### Prediction Confidence Score

## References

- 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.
- E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
- Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
- G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
- Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
- E. Collins. Using Markov decision processes to optimize a nonlinear functional of the final distribution, with manufacturing applications. In Stochastic Modelling in Innovative Manufacturing, pages 30–45. Springer, 1997
- Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94

## 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 Reinforcement Machine 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 Ba3 & 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+3 month)

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