Shanghai Composite Index Stock Forecast, Price & Rating (Shanghai Composite Index)

Abstract

We evaluate Shanghai Composite Index prediction models with Modular Neural Network (DNN Layer) and ElasticNet 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.

Keywords: 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.

Key Points

1. What is Markov decision process in reinforcement learning?
2. Is Target price a good indicator?
3. What are main components of Markov decision process?

Shanghai Composite Index Target Price Prediction Modeling Methodology

We consider Shanghai Composite Index Stock Decision Process with ElasticNet 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(ElasticNet Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (DNN Layer)) 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

 

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+3 month)

Sample Set: Neural Network
Stock/Index: Shanghai Composite Index Shanghai Composite Index
Time series to forecast n: 03 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 B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with ElasticNet 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	B1
Operational Risk	82	42
Market Risk	61	75
Technical Analysis	45	81
Fundamental Analysis	43	51
Risk Unsystematic	84	43

Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 654 signals.

References

1. Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
2. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
3. Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
4. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
5. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
6. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
7. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.

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 Modular Neural Network (DNN Layer) and ElasticNet 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 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+3 month)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login