Modelling A.I. in Economics

When should you buy or sell a stock? (S&P/ASX 200 Index Stock Forecast)

Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We evaluate S&P/ASX 200 Index prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the S&P/ASX 200 Index stock is predictable in the short/long term. According to price forecasts for (n+3 month) 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 best way to predict stock prices?
  2. Can statistics predict the future?
  3. Can stock prices be predicted?

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

As stock data is characterized by highly noisy and non-stationary, stock price prediction is regarded as a knotty problem. In this paper, we propose new two-stage ensemble models by combining empirical mode decomposition (EMD) (or variational mode decomposition (VMD)), extreme learning machine (ELM) and improved harmony search (IHS) algorithm for stock price prediction, which are respectively named EMD–ELM–IHS and VMD–ELM–IHS. We consider S&P/ASX 200 Index Stock Decision Process with Wilcoxon Sign-Rank 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(Wilcoxon Sign-Rank Test)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+3 month) i = 1 n a i

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+3 month)

Sample Set: Neural Network
Stock/Index: S&P/ASX 200 Index S&P/ASX 200 Index
Time series to forecast n: 08 Oct 2022 for (n+3 month)

According to price forecasts for (n+3 month) 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 B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the S&P/ASX 200 Index stock is predictable in the short/long term. According to price forecasts for (n+3 month) 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*B2B1
Operational Risk 3844
Market Risk3065
Technical Analysis7742
Fundamental Analysis6470
Risk Unsystematic6964

Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 792 signals.

References

  1. Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
  2. Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
  3. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
  4. Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
  5. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
  6. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
  7. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
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 (Speculative Sentiment Analysis) and Wilcoxon Sign-Rank 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 B2 & 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+3 month)

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