Finance is one of the pioneering industries that started using Machine Learning (ML), a subset of Artificial Intelligence (AI) in the early 80s for market prediction. Since then, major firms and hedge funds have adopted machine learning for stock prediction, portfolio optimization, credit lending, stock betting, etc. In this paper, we survey all the different approaches of machine learning that can be incorporated in applied finance.** We evaluate Sterlite Technologies Limited prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Multiple Regression ^{1,2,3,4} and conclude that the NSE STRTECH stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold NSE STRTECH stock.**

**NSE STRTECH, Sterlite Technologies Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Market Risk
- Is Target price a good indicator?
- Nash Equilibria

## NSE STRTECH Target Price Prediction Modeling Methodology

Prediction of the Stock Market is a challenging task in predicting the stock prices in the future. Due to the fluctuating nature of the stock, the stock market is too difficult to predict. Stock prices are constantly changing every day. Estimating of the stock market has a high demand for stock customers. Applying all extracted rules at any time is a major challenge to estimate the future stock price with high accuracy. The latest prediction techniques adopted for the stock market such as Artificial Neural Network, Neuro-Fuzzy System, Time Series Linear Models (TSLM), Recurrent Neural Network (RNN). We consider Sterlite Technologies Limited Stock Decision Process with Multiple Regression where A is the set of discrete actions of NSE STRTECH 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+6 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of NSE STRTECH stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

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## NSE STRTECH Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**NSE STRTECH Sterlite Technologies Limited

**Time series to forecast n: 30 Sep 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold NSE STRTECH 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

Sterlite Technologies Limited assigned short-term Ba2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Multiple Regression ^{1,2,3,4} and conclude that the NSE STRTECH stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold NSE STRTECH stock.**

### Financial State Forecast for NSE STRTECH Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba2 | B2 |

Operational Risk | 71 | 42 |

Market Risk | 84 | 36 |

Technical Analysis | 76 | 50 |

Fundamental Analysis | 30 | 54 |

Risk Unsystematic | 83 | 81 |

### Prediction Confidence Score

## References

- P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
- Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
- P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
- R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
- Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
- Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
- Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.

## Frequently Asked Questions

Q: What is the prediction methodology for NSE STRTECH stock?A: NSE STRTECH stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Multiple Regression

Q: Is NSE STRTECH stock a buy or sell?

A: The dominant strategy among neural network is to Hold NSE STRTECH Stock.

Q: Is Sterlite Technologies Limited stock a good investment?

A: The consensus rating for Sterlite Technologies Limited is Hold and assigned short-term Ba2 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of NSE STRTECH stock?

A: The consensus rating for NSE STRTECH is Hold.

Q: What is the prediction period for NSE STRTECH stock?

A: The prediction period for NSE STRTECH is (n+6 month)

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