Stock market prediction is a major exertion in the field of finance and establishing businesses. Stock market is totally uncertain as the prices of stocks keep fluctuating on a daily basis because of numerous factors that influence it. One of the traditional ways of predicting stock prices was by using only historical data. But with time it was observed that other factors such as peoples' sentiments and other news events occurring in and around the country affect the stock market, for e.g. national elections, natural calamity etc. We evaluate HCL Technologies Limited prediction models with Supervised Machine Learning (ML) and Ridge Regression1,2,3,4 and conclude that the NSE HCLTECH 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 HCLTECH stock.

Keywords: NSE HCLTECH, HCL Technologies Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Operational Risk
2. How do you pick a stock?
3. Is Target price a good indicator?

## NSE HCLTECH Target Price Prediction Modeling Methodology

Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We consider HCL Technologies Limited Stock Decision Process with Ridge Regression where A is the set of discrete actions of NSE HCLTECH 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(Ridge 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(Supervised Machine Learning (ML)) X S(n):→ (n+6 month) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of NSE HCLTECH 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 HCLTECH Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: NSE HCLTECH HCL Technologies Limited
Time series to forecast n: 27 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 HCLTECH 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

HCL Technologies Limited assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Ridge Regression1,2,3,4 and conclude that the NSE HCLTECH 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 HCLTECH stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 3941
Market Risk3982
Technical Analysis4684
Fundamental Analysis7039
Risk Unsystematic8751

### Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 780 signals.

## References

1. Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
2. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
3. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
4. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
5. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
6. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
7. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
Frequently Asked QuestionsQ: What is the prediction methodology for NSE HCLTECH stock?
A: NSE HCLTECH stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Ridge Regression
Q: Is NSE HCLTECH stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE HCLTECH Stock.
Q: Is HCL Technologies Limited stock a good investment?
A: The consensus rating for HCL Technologies Limited is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NSE HCLTECH stock?
A: The consensus rating for NSE HCLTECH is Hold.
Q: What is the prediction period for NSE HCLTECH stock?
A: The prediction period for NSE HCLTECH is (n+6 month)