Is LON:PU13 Stock Buy or Sell? (Stock Forecast) (Forecast)

This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. We evaluate PUMA VCT 13 PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Chi-Square^1,2,3,4 and conclude that the LON:PU13 stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy LON:PU13 stock.

Keywords: LON:PU13, PUMA VCT 13 PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Operational Risk
2. Market Risk
3. Is it better to buy and sell or hold?

LON:PU13 Target Price Prediction Modeling Methodology

The presented paper modeled and predicted stock returns using LSTM. The historical data of stock market were transformed into 30-days-long sequences with 10 learning features and 7-day earning rate labeling. The model was fitted by training on 1200000 sequences and tested using the other 350000 sequences. We consider PUMA VCT 13 PLC Stock Decision Process with Chi-Square where A is the set of discrete actions of LON:PU13 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(Chi-Square)^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 Volatility Analysis)) X S(n):→ (n+4 weeks) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of LON:PU13 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:PU13 Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:PU13 PUMA VCT 13 PLC
Time series to forecast n: 03 Oct 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy LON:PU13 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

PUMA VCT 13 PLC assigned short-term Ba3 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Chi-Square^1,2,3,4 and conclude that the LON:PU13 stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy LON:PU13 stock.

Financial State Forecast for LON:PU13 Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B2
Operational Risk	77	34
Market Risk	78	33
Technical Analysis	33	63
Fundamental Analysis	69	53
Risk Unsystematic	64	81

Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 468 signals.

References

1. S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
2. A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
3. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
4. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
5. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
6. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
7. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press

Frequently Asked Questions

Q: What is the prediction methodology for LON:PU13 stock?
A: LON:PU13 stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Chi-Square
Q: Is LON:PU13 stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:PU13 Stock.
Q: Is PUMA VCT 13 PLC stock a good investment?
A: The consensus rating for PUMA VCT 13 PLC is Buy and assigned short-term Ba3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:PU13 stock?
A: The consensus rating for LON:PU13 is Buy.
Q: What is the prediction period for LON:PU13 stock?
A: The prediction period for LON:PU13 is (n+4 weeks)

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