Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend.** We evaluate Shanghai Composite Index prediction models with Transductive Learning (ML) and Statistical Hypothesis Testing ^{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+8 weeks) 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

- How do you decide buy or sell a stock?
- Can stock prices be predicted?
- Can we predict stock market using machine learning?

## Shanghai Composite Index Target Price Prediction Modeling Methodology

The stock market has been an attractive field for a large number of organizers and investors to derive useful predictions. Fundamental knowledge of stock market can be utilised with technical indicators to investigate different perspectives of the financial market; also, the influence of various events, financial news, and/or opinions on investors' decisions and hence, market trends have been observed. Such information can be exploited to make reliable predictions and achieve higher profitability. Computational intelligence has emerged with various deep neural network (DNN) techniques to address complex stock market problems. We consider Shanghai Composite Index Stock Decision Process with Statistical Hypothesis Testing 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(Statistical Hypothesis Testing)

^{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(Transductive Learning (ML)) X S(n):→ (n+8 weeks) $\sum _{i=1}^{n}\left({a}_{i}\right)$

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+8 weeks)

**Sample Set:**Neural Network

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

**Time series to forecast n: 22 Oct 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) 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 Baa2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Transductive Learning (ML) with Statistical Hypothesis Testing ^{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+8 weeks) 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* | Baa2 | B1 |

Operational Risk | 86 | 34 |

Market Risk | 61 | 61 |

Technical Analysis | 69 | 69 |

Fundamental Analysis | 87 | 65 |

Risk Unsystematic | 78 | 51 |

### Prediction Confidence Score

## References

- V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
- M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
- V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
- V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
- D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
- Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
- Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London

## 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 Transductive Learning (ML) and Statistical Hypothesis Testing

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 Baa2 & long-term B1 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+8 weeks)

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