LON:IPF Target Price Prediction

Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 82 different combinations of technical indicators to predict the stock prices. We evaluate INTERNATIONAL PERSONAL FINANCE PLC prediction models with Modular Neural Network (Market Direction Analysis) and Factor^1,2,3,4 and conclude that the LON:IPF 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:IPF stock.

Keywords: LON:IPF, INTERNATIONAL PERSONAL FINANCE PLC, 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. Fundemental Analysis with Algorithmic Trading
3. Short/Long Term Stocks

LON:IPF Target Price Prediction Modeling Methodology

One decision in Stock Market can make huge impact on an investor's life. The stock market is a complex system and often covered in mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is based on different factors. We consider INTERNATIONAL PERSONAL FINANCE PLC Stock Decision Process with Factor where A is the set of discrete actions of LON:IPF 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(Factor)^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 Direction Analysis)) X S(n):→ (n+1 year) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:IPF INTERNATIONAL PERSONAL FINANCE PLC
Time series to forecast n: 10 Oct 2022 for (n+1 year)

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

INTERNATIONAL PERSONAL FINANCE PLC assigned short-term Ba3 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Factor^1,2,3,4 and conclude that the LON:IPF 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:IPF stock.

Financial State Forecast for LON:IPF Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B2
Operational Risk	38	40
Market Risk	77	30
Technical Analysis	74	50
Fundamental Analysis	73	57
Risk Unsystematic	59	82

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 540 signals.

References

1. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
2. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
3. Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
4. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
5. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
6. Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.
7. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press

Frequently Asked Questions

Q: What is the prediction methodology for LON:IPF stock?
A: LON:IPF stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Factor
Q: Is LON:IPF stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:IPF Stock.
Q: Is INTERNATIONAL PERSONAL FINANCE PLC stock a good investment?
A: The consensus rating for INTERNATIONAL PERSONAL FINANCE PLC is Hold and assigned short-term Ba3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:IPF stock?
A: The consensus rating for LON:IPF is Hold.
Q: What is the prediction period for LON:IPF stock?
A: The prediction period for LON:IPF is (n+1 year)

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