Machine Learning stock prediction: LON:FPP Stock Prediction

Investors raise profit from stock market by maximising gains and minimising loses. The profit is difficult to raise because of the volatile nature of stock market prices. Predictive modelling allows investors to make informed decisions. We evaluate FRAGRANT PROSPERITY HOLDINGS LIMITED prediction models with Modular Neural Network (Market News Sentiment Analysis) and ElasticNet Regression^1,2,3,4 and conclude that the LON:FPP stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:FPP stock.

Keywords: LON:FPP, FRAGRANT PROSPERITY HOLDINGS LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Stock Rating
2. Probability Distribution
3. Decision Making

LON:FPP Target Price Prediction Modeling Methodology

Financial markets are fascinating if you can predict them. Also, the traders acting on financial markets produce a vast amount of information to analyse the consequences of investing according to the current market trends. Stock Market prediction is the technique to determine whether stock value will go up or down as it plays an active role in the financial gain of nation's economic status. We consider FRAGRANT PROSPERITY HOLDINGS LIMITED Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of LON:FPP 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(ElasticNet Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:FPP 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:FPP Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:FPP FRAGRANT PROSPERITY HOLDINGS LIMITED
Time series to forecast n: 22 Sep 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:FPP 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

FRAGRANT PROSPERITY HOLDINGS LIMITED assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with ElasticNet Regression^1,2,3,4 and conclude that the LON:FPP stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:FPP stock.

Financial State Forecast for LON:FPP Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B1
Operational Risk	33	30
Market Risk	55	56
Technical Analysis	50	62
Fundamental Analysis	74	87
Risk Unsystematic	55	50

Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 857 signals.

References

1. J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
2. Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
3. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
4. M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
5. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
6. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
7. E. Collins. Using Markov decision processes to optimize a nonlinear functional of the final distribution, with manufacturing applications. In Stochastic Modelling in Innovative Manufacturing, pages 30–45. Springer, 1997

Frequently Asked Questions

Q: What is the prediction methodology for LON:FPP stock?
A: LON:FPP stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and ElasticNet Regression
Q: Is LON:FPP stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:FPP Stock.
Q: Is FRAGRANT PROSPERITY HOLDINGS LIMITED stock a good investment?
A: The consensus rating for FRAGRANT PROSPERITY HOLDINGS LIMITED is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:FPP stock?
A: The consensus rating for LON:FPP is Hold.
Q: What is the prediction period for LON:FPP stock?
A: The prediction period for LON:FPP is (n+1 year)

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