Can we predict stock market using machine learning? (LON:PNS Stock Forecast)

Complex networks in stock market and stock price volatility pattern prediction are the important issues in stock price research. Previous studies have used historical information regarding a single stock to predict the future trend of the stock's price, seldom considering comovement among stocks in the same market. In this study, in order to extract the information about relation stocks for prediction, we try to combine the complex network method with machine learning to predict stock price patterns. We evaluate PANTHER SECURITIES PLC prediction models with Supervised Machine Learning (ML) and Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the LON:PNS stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:PNS stock.

Keywords: LON:PNS, PANTHER SECURITIES PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. What are buy sell or hold recommendations?
2. What statistical methods are used to analyze data?
3. How can neural networks improve predictions?

LON:PNS Target Price Prediction Modeling Methodology

This paper proposes genetic algorithms (GAs) approach to feature discretization and the determination of connection weights for artificial neural networks (ANNs) to predict the stock price index. Previous research proposed many hybrid models of ANN and GA for the method of training the network, feature subset selection, and topology optimization. We consider PANTHER SECURITIES PLC Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of LON:PNS 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ (n+16 weeks) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of LON:PNS 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:PNS Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:PNS PANTHER SECURITIES PLC
Time series to forecast n: 05 Nov 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:PNS 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%

Adjusted IFRS* Prediction Methods for PANTHER SECURITIES PLC

1. Lifetime expected credit losses are not recognised on a financial instrument simply because it was considered to have low credit risk in the previous reporting period and is not considered to have low credit risk at the reporting date. In such a case, an entity shall determine whether there has been a significant increase in credit risk since initial recognition and thus whether lifetime expected credit losses are required to be recognised in accordance with paragraph 5.5.3.
2. For a financial guarantee contract, the entity is required to make payments only in the event of a default by the debtor in accordance with the terms of the instrument that is guaranteed. Accordingly, cash shortfalls are the expected payments to reimburse the holder for a credit loss that it incurs less any amounts that the entity expects to receive from the holder, the debtor or any other party. If the asset is fully guaranteed, the estimation of cash shortfalls for a financial guarantee contract would be consistent with the estimations of cash shortfalls for the asset subject to the guarantee
3. An entity's estimate of expected credit losses on loan commitments shall be consistent with its expectations of drawdowns on that loan commitment, ie it shall consider the expected portion of the loan commitment that will be drawn down within 12 months of the reporting date when estimating 12-month expected credit losses, and the expected portion of the loan commitment that will be drawn down over the expected life of the loan commitment when estimating lifetime expected credit losses.
4. An entity's business model refers to how an entity manages its financial assets in order to generate cash flows. That is, the entity's business model determines whether cash flows will result from collecting contractual cash flows, selling financial assets or both. Consequently, this assessment is not performed on the basis of scenarios that the entity does not reasonably expect to occur, such as so-called 'worst case' or 'stress case' scenarios. For example, if an entity expects that it will sell a particular portfolio of financial assets only in a stress case scenario, that scenario would not affect the entity's assessment of the business model for those assets if the entity reasonably expects that such a scenario will not occur. If cash flows are realised in a way that is different from the entity's expectations at the date that the entity assessed the business model (for example, if the entity sells more or fewer financial assets than it expected when it classified the assets), that does not give rise to a prior period error in the entity's financial statements (see IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors) nor does it change the classification of the remaining financial assets held in that business model (ie those assets that the entity recognised in prior periods and still holds) as long as the entity considered all relevant information that was available at the time that it made the business model assessment.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

PANTHER SECURITIES PLC assigned short-term B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the LON:PNS stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:PNS stock.

Financial State Forecast for LON:PNS PANTHER SECURITIES PLC Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	Ba3
Operational Risk	67	40
Market Risk	52	62
Technical Analysis	89	74
Fundamental Analysis	72	56
Risk Unsystematic	31	87

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 576 signals.

References

1. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
2. Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
3. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
4. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
5. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
6. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
7. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32

Frequently Asked Questions

Q: What is the prediction methodology for LON:PNS stock?
A: LON:PNS stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is LON:PNS stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:PNS Stock.
Q: Is PANTHER SECURITIES PLC stock a good investment?
A: The consensus rating for PANTHER SECURITIES PLC is Hold and assigned short-term B1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:PNS stock?
A: The consensus rating for LON:PNS is Hold.
Q: What is the prediction period for LON:PNS stock?
A: The prediction period for LON:PNS is (n+16 weeks)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login