S&P/ASX 200 Index Target Price Prediction

The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. We evaluate S&P/ASX 200 Index prediction models with Reinforcement Machine Learning (ML) and Sign Test^1,2,3,4 and conclude that the S&P/ASX 200 Index stock is predictable in the short/long term. According to price forecasts for (n+8 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. What is the use of Markov decision process?
2. Probability Distribution
3. Decision Making

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

Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. We consider S&P/ASX 200 Index Stock Decision Process with Sign Test 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(Sign Test)^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(Reinforcement Machine Learning (ML)) X S(n):→ (n+8 weeks) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

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+8 weeks)

Sample Set: Neural Network
Stock/Index: S&P/ASX 200 Index S&P/ASX 200 Index
Time series to forecast n: 06 Oct 2022 for (n+8 weeks)

According to price forecasts for (n+8 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 Ba1 & long-term B1 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Sign Test^1,2,3,4 and conclude that the S&P/ASX 200 Index stock is predictable in the short/long term. According to price forecasts for (n+8 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*	Ba1	B1
Operational Risk	76	90
Market Risk	41	55
Technical Analysis	85	46
Fundamental Analysis	70	36
Risk Unsystematic	82	55

Prediction Confidence Score

Trust metric by Neural Network: 75 out of 100 with 635 signals.

References

1. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
2. Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
3. 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
4. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
5. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
6. Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM
7. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.

Frequently Asked Questions

Q: 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 Reinforcement Machine Learning (ML) and Sign Test
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 Ba1 & long-term B1 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+8 weeks)

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