This paper addresses problem of predicting direction of movement of stock and stock price index. The study compares four prediction models, Artificial Neural Network (ANN), Support Vector Machine (SVM), random forest and naive-Bayes with two approaches for input to these models.** We evaluate HFCL Limited prediction models with Modular Neural Network (Financial Sentiment Analysis) and Lasso Regression ^{1,2,3,4} and conclude that the NSE HFCL 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 Hold NSE HFCL stock.**

**NSE HFCL, HFCL 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
- What are main components of Markov decision process?
- What is the best way to predict stock prices?

## NSE HFCL Target Price Prediction Modeling Methodology

The categorization of high dimensional data present a fascinating challenge to machine learning models as frequent number of highly correlated dimensions or attributes can affect the accuracy of classification model. In this paper, the problem of high dimensionality of stock exchange is investigated to predict the market trends by applying the principal component analysis (PCA) with linear regression. PCA can help to improve the predictive performance of machine learning methods while reducing the redundancy among the data. We consider HFCL Limited Stock Decision Process with Lasso Regression where A is the set of discrete actions of NSE HFCL 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(Lasso 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 (Financial 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 NSE HFCL 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?

## NSE HFCL Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**NSE HFCL HFCL Limited

**Time series to forecast n: 28 Sep 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold NSE HFCL 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

HFCL Limited assigned short-term B1 & long-term B3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Lasso Regression ^{1,2,3,4} and conclude that the NSE HFCL 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 Hold NSE HFCL stock.**

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

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

Outlook* | B1 | B3 |

Operational Risk | 47 | 31 |

Market Risk | 68 | 33 |

Technical Analysis | 54 | 74 |

Fundamental Analysis | 85 | 50 |

Risk Unsystematic | 39 | 32 |

### Prediction Confidence Score

## References

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- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
- Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
- 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
- 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
- Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
- Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58

## Frequently Asked Questions

Q: What is the prediction methodology for NSE HFCL stock?A: NSE HFCL stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Lasso Regression

Q: Is NSE HFCL stock a buy or sell?

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

Q: Is HFCL Limited stock a good investment?

A: The consensus rating for HFCL Limited is Hold and assigned short-term B1 & long-term B3 forecasted stock rating.

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

A: The consensus rating for NSE HFCL is Hold.

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

A: The prediction period for NSE HFCL is (n+8 weeks)

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