NSE HINDUNILVR Stock Price Prediction

In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. We evaluate Hindustan Unilever Limited prediction models with Modular Neural Network (CNN Layer) and Multiple Regression^1,2,3,4 and conclude that the NSE HINDUNILVR stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE HINDUNILVR stock.

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

Key Points

1. Reaction Function
2. What are buy sell or hold recommendations?
3. What is a prediction confidence?

NSE HINDUNILVR Target Price Prediction Modeling Methodology

Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users' moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach. We consider Hindustan Unilever Limited Stock Decision Process with Multiple Regression where A is the set of discrete actions of NSE HINDUNILVR 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+16 weeks) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of NSE HINDUNILVR 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 HINDUNILVR Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: NSE HINDUNILVR Hindustan Unilever Limited
Time series to forecast n: 26 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE HINDUNILVR 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

Hindustan Unilever Limited assigned short-term B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Multiple Regression^1,2,3,4 and conclude that the NSE HINDUNILVR stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE HINDUNILVR stock.

Financial State Forecast for NSE HINDUNILVR Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	Ba3
Operational Risk	70	78
Market Risk	36	68
Technical Analysis	84	82
Fundamental Analysis	52	56
Risk Unsystematic	58	45

Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 626 signals.

References

1. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
2. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
3. Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
4. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
5. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
6. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
7. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.

Frequently Asked Questions

Q: What is the prediction methodology for NSE HINDUNILVR stock?
A: NSE HINDUNILVR stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Multiple Regression
Q: Is NSE HINDUNILVR stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE HINDUNILVR Stock.
Q: Is Hindustan Unilever Limited stock a good investment?
A: The consensus rating for Hindustan Unilever Limited is Hold and assigned short-term B1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE HINDUNILVR stock?
A: The consensus rating for NSE HINDUNILVR is Hold.
Q: What is the prediction period for NSE HINDUNILVR stock?
A: The prediction period for NSE HINDUNILVR is (n+16 weeks)

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