Social media comments have in the past had an instantaneous effect on stock markets. This paper investigates the sentiments expressed on the social media platform Twitter and their pr edictive impact on the Stock Market. We evaluate IGAS ENERGY PLC prediction models with Transductive Learning (ML) and Multiple Regression1,2,3,4 and conclude that the LON:IGAS 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:IGAS stock.

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

## Key Points

1. Short/Long Term Stocks
2. Can we predict stock market using machine learning?
3. Can machine learning predict? ## LON:IGAS Target Price Prediction Modeling Methodology

Stock price forecasting is a popular and important topic in financial and academic studies. Share market is an volatile place for predicting since there are no significant rules to estimate or predict the price of a share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc. are used to predict the price in tie share market but none of these methods are proved as a consistently acceptable prediction tool. In this paper, we implemented a Random Forest approach to predict stock market prices. We consider IGAS ENERGY PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:IGAS 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(Multiple 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(Transductive Learning (ML)) X S(n):→ (n+1 year) $∑ i = 1 n a i$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:IGAS IGAS ENERGY PLC
Time series to forecast n: 22 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:IGAS 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

IGAS ENERGY PLC assigned short-term B3 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Multiple Regression1,2,3,4 and conclude that the LON:IGAS 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:IGAS stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3Ba1
Operational Risk 3675
Market Risk4390
Technical Analysis6563
Fundamental Analysis4057
Risk Unsystematic6370

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 518 signals.

## References

1. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
2. A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
3. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
4. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
5. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
6. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
7. 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.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:IGAS stock?
A: LON:IGAS stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Multiple Regression
Q: Is LON:IGAS stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:IGAS Stock.
Q: Is IGAS ENERGY PLC stock a good investment?
A: The consensus rating for IGAS ENERGY PLC is Hold and assigned short-term B3 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of LON:IGAS stock?
A: The consensus rating for LON:IGAS is Hold.
Q: What is the prediction period for LON:IGAS stock?
A: The prediction period for LON:IGAS is (n+1 year)