Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor's gains. This paper proposes a machine learning model to predict stock market price. We evaluate Shanghai Composite Index prediction models with Modular Neural Network (Financial Sentiment Analysis) and Pearson Correlation1,2,3,4 and conclude that the Shanghai Composite Index stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) 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. How do you know when a stock will go up or down?
3. Which neural network is best for prediction?

## Shanghai Composite Index Target Price Prediction Modeling Methodology

Recurrent Neural Networks (RNNs) is a sub type of neural networks that use feedback connections. Several types of RNN models are used in predicting financial time series. This study was conducted to develop models to predict daily stock prices based on Recurrent Neural Network (RNN) Approach and to measure the accuracy of the models developed and identify the shortcomings of the models if present. We consider Shanghai Composite Index Stock Decision Process with Pearson Correlation 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(Pearson Correlation)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

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+4 weeks)

Sample Set: Neural Network
Stock/Index: Shanghai Composite Index Shanghai Composite Index
Time series to forecast n: 06 Oct 2022 for (n+4 weeks)

According to price forecasts for (n+4 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 Ba3 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Pearson Correlation1,2,3,4 and conclude that the Shanghai Composite Index stock is predictable in the short/long term. According to price forecasts for (n+4 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*Ba3Baa2
Operational Risk 8590
Market Risk5659
Technical Analysis3256
Fundamental Analysis8487
Risk Unsystematic7470

### Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 783 signals.

## References

1. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
2. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
3. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
4. Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
5. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
6. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
7. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
Frequently Asked QuestionsQ: 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 (Financial Sentiment Analysis) and Pearson Correlation
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 Baa2 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+4 weeks)