Prediction of the Stock Market is a challenging task in predicting the stock prices in the future. Due to the fluctuating nature of the stock, the stock market is too difficult to predict. Stock prices are constantly changing every day. Estimating of the stock market has a high demand for stock customers. Applying all extracted rules at any time is a major challenge to estimate the future stock price with high accuracy. The latest prediction techniques adopted for the stock market such as Artificial Neural Network, Neuro-Fuzzy System, Time Series Linear Models (TSLM), Recurrent Neural Network (RNN). We evaluate AEX Index prediction models with Modular Neural Network (DNN Layer) and Pearson Correlation1,2,3,4 and conclude that the AEX 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 AEX Index stock.
Keywords: AEX Index, AEX Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Trading Interaction
- Reaction Function
- Reaction Function
AEX Index Target Price Prediction Modeling Methodology
Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We consider AEX Index Stock Decision Process with Pearson Correlation where A is the set of discrete actions of AEX 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= X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+3 month)
n:Time series to forecast
p:Price signals of AEX Index stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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AEX Index Stock Forecast (Buy or Sell) for (n+3 month)
Sample Set: Neural NetworkStock/Index: AEX Index AEX Index
Time series to forecast n: 12 Sep 2022 for (n+3 month)
According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold AEX 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
AEX Index assigned short-term B2 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Pearson Correlation1,2,3,4 and conclude that the AEX 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 AEX Index stock.
Financial State Forecast for AEX Index Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | Baa2 |
| Operational Risk | 42 | 60 |
| Market Risk | 72 | 80 |
| Technical Analysis | 36 | 85 |
| Fundamental Analysis | 67 | 61 |
| Risk Unsystematic | 65 | 75 |
Prediction Confidence Score
References
- White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
- G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
- D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
- V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
- C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
- 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
- Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
Frequently Asked Questions
Q: What is the prediction methodology for AEX Index stock?A: AEX Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Pearson Correlation
Q: Is AEX Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold AEX Index Stock.
Q: Is AEX Index stock a good investment?
A: The consensus rating for AEX Index is Hold and assigned short-term B2 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of AEX Index stock?
A: The consensus rating for AEX Index is Hold.
Q: What is the prediction period for AEX Index stock?
A: The prediction period for AEX Index is (n+3 month)
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