Stocks are possibly the most popular financial instrument invented for building wealth and are the centerpiece of any investment portfolio. The advances in trading technology has opened up the markets so that nowadays nearly anybody can own stocks. From last few decades, there seen explosive increase in the average person's interest for stock market. In a financially explosive market, as the stock market, it is important to have a very accurate prediction of a future trend. Because of the financial crisis and recording profits, it is compulsory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires progressive algorithms of machine learning with help of Artificial Intelligence (AI). We evaluate FONIX MOBILE PLC prediction models with Modular Neural Network (Market News Sentiment Analysis) and Spearman Correlation1,2,3,4 and conclude that the LON:FNX 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 Buy LON:FNX stock.
Keywords: LON:FNX, FONIX MOBILE PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Reaction Function
- Trading Interaction
- What are main components of Markov decision process?
LON:FNX Target Price Prediction Modeling Methodology
Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend. We consider FONIX MOBILE PLC Stock Decision Process with Spearman Correlation where A is the set of discrete actions of LON:FNX 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(Spearman Correlation)5,6,7= X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+3 month)
n:Time series to forecast
p:Price signals of LON:FNX stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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LON:FNX Stock Forecast (Buy or Sell) for (n+3 month)
Sample Set: Neural NetworkStock/Index: LON:FNX FONIX MOBILE PLC
Time series to forecast n: 24 Sep 2022 for (n+3 month)
According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy LON:FNX 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
FONIX MOBILE PLC assigned short-term Baa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Spearman Correlation1,2,3,4 and conclude that the LON:FNX 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 Buy LON:FNX stock.
Financial State Forecast for LON:FNX Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Baa2 | B1 |
| Operational Risk | 41 | 38 |
| Market Risk | 87 | 84 |
| Technical Analysis | 83 | 30 |
| Fundamental Analysis | 69 | 86 |
| Risk Unsystematic | 86 | 58 |
Prediction Confidence Score
References
- S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
- Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
- Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
- F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
- 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.
- Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
Frequently Asked Questions
Q: What is the prediction methodology for LON:FNX stock?A: LON:FNX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Spearman Correlation
Q: Is LON:FNX stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:FNX Stock.
Q: Is FONIX MOBILE PLC stock a good investment?
A: The consensus rating for FONIX MOBILE PLC is Buy and assigned short-term Baa2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:FNX stock?
A: The consensus rating for LON:FNX is Buy.
Q: What is the prediction period for LON:FNX stock?
A: The prediction period for LON:FNX is (n+3 month)
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