Stock price prediction has always been a challenging task for the researchers in financial domain. While the Efficient Market Hypothesis claims that it is impossible to predict stock prices accurately, there are work in the literature that have demonstrated that stock price movements can be forecasted with a reasonable degree of accuracy, if appropriate variables are chosen and suitable predictive models are built using those variables. In this work, we present a robust and accurate framework of stock price prediction using statistical, machine learning and deep learning methods We evaluate ORIENT TELECOMS PLC prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Stepwise Regression1,2,3,4 and conclude that the LON:ORNT stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to SellHold LON:ORNT stock.

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

## Key Points

1. Probability Distribution
2. Buy, Sell and Hold Signals
3. Understanding Buy, Sell, and Hold Ratings ## LON:ORNT Target Price Prediction Modeling Methodology

Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction. We consider ORIENT TELECOMS PLC Stock Decision Process with Stepwise Regression where A is the set of discrete actions of LON:ORNT 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 (News Feed Sentiment Analysis)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:ORNT 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:ORNT Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: LON:ORNT ORIENT TELECOMS PLC
Time series to forecast n: 20 Oct 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to SellHold LON:ORNT 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

ORIENT TELECOMS PLC assigned short-term B3 & long-term Caa1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Stepwise Regression1,2,3,4 and conclude that the LON:ORNT stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to SellHold LON:ORNT stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3Caa1
Operational Risk 3733
Market Risk4649
Technical Analysis7335
Fundamental Analysis4336
Risk Unsystematic3141

### Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 831 signals.

## References

1. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
2. H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
3. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
4. Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
5. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
6. Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
7. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
Frequently Asked QuestionsQ: What is the prediction methodology for LON:ORNT stock?
A: LON:ORNT stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Stepwise Regression
Q: Is LON:ORNT stock a buy or sell?
A: The dominant strategy among neural network is to SellHold LON:ORNT Stock.
Q: Is ORIENT TELECOMS PLC stock a good investment?
A: The consensus rating for ORIENT TELECOMS PLC is SellHold and assigned short-term B3 & long-term Caa1 forecasted stock rating.
Q: What is the consensus rating of LON:ORNT stock?
A: The consensus rating for LON:ORNT is SellHold.
Q: What is the prediction period for LON:ORNT stock?
A: The prediction period for LON:ORNT is (n+8 weeks)