It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values. We evaluate Tadawul All Share Index prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Logistic Regression1,2,3,4 and conclude that the Tadawul All Share 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 Buy Tadawul All Share Index stock.
Keywords: Tadawul All Share Index, Tadawul All Share Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Which neural network is best for prediction?
- Can stock prices be predicted?
- Is Target price a good indicator?
Tadawul All Share Index Target Price Prediction Modeling Methodology
This study presents financial network indicators that can be applied to global stock market investment strategies. We propose to design both undirected and directed volatility networks of global stock market based on simple pair-wise correlation and system-wide connectedness of stock date using a vector auto-regressive model. We consider Tadawul All Share Index Stock Decision Process with Logistic Regression where A is the set of discrete actions of Tadawul All Share 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(Logistic Regression)5,6,7= X R(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+8 weeks)
n:Time series to forecast
p:Price signals of Tadawul All Share 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?
Tadawul All Share Index Stock Forecast (Buy or Sell) for (n+8 weeks)
Sample Set: Neural NetworkStock/Index: Tadawul All Share Index Tadawul All Share Index
Time series to forecast n: 03 Oct 2022 for (n+8 weeks)
According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy Tadawul All Share 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
Tadawul All Share Index assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Logistic Regression1,2,3,4 and conclude that the Tadawul All Share 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 Buy Tadawul All Share Index stock.
Financial State Forecast for Tadawul All Share Index Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B1 | B2 |
| Operational Risk | 57 | 69 |
| Market Risk | 43 | 36 |
| Technical Analysis | 59 | 67 |
| Fundamental Analysis | 74 | 36 |
| Risk Unsystematic | 79 | 40 |
Prediction Confidence Score
References
- J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
- E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
- Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
- J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
- Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
- Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
Frequently Asked Questions
Q: What is the prediction methodology for Tadawul All Share Index stock?A: Tadawul All Share Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Logistic Regression
Q: Is Tadawul All Share Index stock a buy or sell?
A: The dominant strategy among neural network is to Buy Tadawul All Share Index Stock.
Q: Is Tadawul All Share Index stock a good investment?
A: The consensus rating for Tadawul All Share Index is Buy and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of Tadawul All Share Index stock?
A: The consensus rating for Tadawul All Share Index is Buy.
Q: What is the prediction period for Tadawul All Share Index stock?
A: The prediction period for Tadawul All Share Index is (n+8 weeks)
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