The search for models to predict the prices of financial markets is still a highly researched topic, despite major related challenges. The prices of financial assets are non-linear, dynamic, and chaotic; thus, they are financial time series that are difficult to predict. Among the latest techniques, machine learning models are some of the most researched, given their capabilities for recognizing complex patterns in various applications. We evaluate NETWORK INTERNATIONAL HOLDINGS PLC prediction models with Modular Neural Network (Market Direction Analysis) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:NETW stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:NETW stock.
Keywords: LON:NETW, NETWORK INTERNATIONAL HOLDINGS PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- What is Markov decision process in reinforcement learning?
- What are the most successful trading algorithms?
- Is it better to buy and sell or hold?
LON:NETW Target Price Prediction Modeling Methodology
In this paper we investigate ways to use prior knowledge and neural networks to improve multivariate prediction ability. Daily stock prices are predicted as a complicated real-world problem, taking non-numerical factors such as political and international events are into account. We have studied types of prior knowledge which are difficult to insert into initial network structures or to represent in the form of error measurements. We consider NETWORK INTERNATIONAL HOLDINGS PLC Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of LON:NETW 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 Rank-Sum Test)5,6,7= X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+6 month)
n:Time series to forecast
p:Price signals of LON:NETW 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?
LON:NETW Stock Forecast (Buy or Sell) for (n+6 month)
Sample Set: Neural NetworkStock/Index: LON:NETW NETWORK INTERNATIONAL HOLDINGS PLC
Time series to forecast n: 15 Oct 2022 for (n+6 month)
According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:NETW 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
NETWORK INTERNATIONAL HOLDINGS PLC assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:NETW stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:NETW stock.
Financial State Forecast for LON:NETW Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B1 | B2 |
| Operational Risk | 80 | 77 |
| Market Risk | 46 | 39 |
| Technical Analysis | 80 | 50 |
| Fundamental Analysis | 72 | 73 |
| Risk Unsystematic | 33 | 33 |
Prediction Confidence Score
References
- Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
- V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
- Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
- Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
- uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
- 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
- M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
Frequently Asked Questions
Q: What is the prediction methodology for LON:NETW stock?A: LON:NETW stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Wilcoxon Rank-Sum Test
Q: Is LON:NETW stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:NETW Stock.
Q: Is NETWORK INTERNATIONAL HOLDINGS PLC stock a good investment?
A: The consensus rating for NETWORK INTERNATIONAL HOLDINGS PLC is Hold and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:NETW stock?
A: The consensus rating for LON:NETW is Hold.
Q: What is the prediction period for LON:NETW stock?
A: The prediction period for LON:NETW is (n+6 month)
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