This study presents financial network indicators that can be applied to global stock market investment strategies. We propose to design both undirected and directed volatility networks of global stock market based on simple pair-wise correlation and system-wide connectedness of stock date using a vector auto-regressive model. We evaluate I-NEXUS GLOBAL PLC prediction models with Modular Neural Network (Market Direction Analysis) and Stepwise Regression1,2,3,4 and conclude that the LON:INX 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 Hold LON:INX stock.

Keywords: LON:INX, I-NEXUS GLOBAL PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. What is the use of Markov decision process?
2. Should I buy stocks now or wait amid such uncertainty?
3. Investment Risk

## LON:INX Target Price Prediction Modeling Methodology

Stock market also called as equity market is the aggregation of the sellers and buyers. It is concerned with the domain where the shares of various public listed companies are traded. For predicting the growth of economy, stock market acts as an index. Due to the nonlinear nature, the prediction of the stock market becomes a difficult task. But the application of various machine learning techniques has been becoming a powerful source for the prediction. We consider I-NEXUS GLOBAL PLC Stock Decision Process with Stepwise Regression where A is the set of discrete actions of LON:INX 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(Stepwise Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+3 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:INX 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:INX Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: LON:INX I-NEXUS GLOBAL PLC
Time series to forecast n: 17 Sep 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:INX 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

I-NEXUS GLOBAL PLC assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Stepwise Regression1,2,3,4 and conclude that the LON:INX 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 Hold LON:INX stock.

### Financial State Forecast for LON:INX Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B2
Operational Risk 6673
Market Risk4672
Technical Analysis3530
Fundamental Analysis6431
Risk Unsystematic6943

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 769 signals.

## References

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2. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
3. T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
4. R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000
5. 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
6. Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
7. M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
Frequently Asked QuestionsQ: What is the prediction methodology for LON:INX stock?
A: LON:INX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Stepwise Regression
Q: Is LON:INX stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:INX Stock.
Q: Is I-NEXUS GLOBAL PLC stock a good investment?
A: The consensus rating for I-NEXUS GLOBAL PLC is Hold and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:INX stock?
A: The consensus rating for LON:INX is Hold.
Q: What is the prediction period for LON:INX stock?
A: The prediction period for LON:INX is (n+3 month)