Neural networks (NNs), as artificial intelligence (AI) methods, have become very important in making stock market predictions. Much research on the applications of NNs for solving business problems have proven their advantages over statistical and other methods that do not include AI, although there is no optimal methodology for a certain problem. We evaluate Shanghai Composite Index prediction models with Modular Neural Network (Market Direction Analysis) and Chi-Square1,2,3,4 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 Sell 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
- Trading Signals
- Is it better to buy and sell or hold?
- What are the most successful trading algorithms?
Shanghai Composite Index Target Price Prediction Modeling Methodology
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 consider Shanghai Composite Index Stock Decision Process with Chi-Square 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(Chi-Square)5,6,7= X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+3 month)
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 NetworkStock/Index: Shanghai Composite Index Shanghai Composite Index
Time series to forecast n: 12 Oct 2022 for (n+3 month)
According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell 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 B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Chi-Square1,2,3,4 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 Sell Shanghai Composite Index stock.
Financial State Forecast for Shanghai Composite Index Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | B1 |
| Operational Risk | 66 | 68 |
| Market Risk | 30 | 33 |
| Technical Analysis | 63 | 76 |
| Fundamental Analysis | 37 | 58 |
| Risk Unsystematic | 80 | 67 |
Prediction Confidence Score
References
- C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
- M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
- A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
- Miller A. 2002. Subset Selection in Regression. New York: CRC Press
- J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
- S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
- Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
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 (Market Direction Analysis) and Chi-Square
Q: Is Shanghai Composite Index stock a buy or sell?
A: The dominant strategy among neural network is to Sell Shanghai Composite Index Stock.
Q: Is Shanghai Composite Index stock a good investment?
A: The consensus rating for Shanghai Composite Index is Sell and assigned short-term B2 & 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 Sell.
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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