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 evaluate S&P/ASX 200 Index prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Beta1,2,3,4 and conclude that the S&P/ASX 200 Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold S&P/ASX 200 Index stock.

Keywords: S&P/ASX 200 Index, S&P/ASX 200 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. How do you decide buy or sell a stock?
2. Should I buy stocks now or wait amid such uncertainty?
3. How do you know when a stock will go up or down?

## S&P/ASX 200 Index Target Price Prediction Modeling Methodology

This paper addresses problem of predicting direction of movement of stock and stock price index. The study compares four prediction models, Artificial Neural Network (ANN), Support Vector Machine (SVM), random forest and naive-Bayes with two approaches for input to these models. We consider S&P/ASX 200 Index Stock Decision Process with Beta where A is the set of discrete actions of S&P/ASX 200 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(Beta)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 (Social Media Sentiment Analysis)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of S&P/ASX 200 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?

## S&P/ASX 200 Index Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: S&P/ASX 200 Index S&P/ASX 200 Index
Time series to forecast n: 15 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold S&P/ASX 200 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

S&P/ASX 200 Index assigned short-term Ba3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Beta1,2,3,4 and conclude that the S&P/ASX 200 Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold S&P/ASX 200 Index stock.

### Financial State Forecast for S&P/ASX 200 Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba3
Operational Risk 7439
Market Risk7660
Technical Analysis6279
Fundamental Analysis5055
Risk Unsystematic6880

### Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 717 signals.

## References

1. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
2. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
3. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
4. 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
5. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
6. Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
7. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
Frequently Asked QuestionsQ: What is the prediction methodology for S&P/ASX 200 Index stock?
A: S&P/ASX 200 Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Beta
Q: Is S&P/ASX 200 Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold S&P/ASX 200 Index Stock.
Q: Is S&P/ASX 200 Index stock a good investment?
A: The consensus rating for S&P/ASX 200 Index is Hold and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of S&P/ASX 200 Index stock?
A: The consensus rating for S&P/ASX 200 Index is Hold.
Q: What is the prediction period for S&P/ASX 200 Index stock?
A: The prediction period for S&P/ASX 200 Index is (n+16 weeks)