Modelling A.I. in Economics

Should You Buy NSE CHEMBOND Right Now? (Stock Forecast) (Forecast)

Investors raise profit from stock market by maximising gains and minimising loses. The profit is difficult to raise because of the volatile nature of stock market prices. Predictive modelling allows investors to make informed decisions. We evaluate Chembond Chemicals Ltd prediction models with Modular Neural Network (Market Direction Analysis) and Lasso Regression1,2,3,4 and conclude that the NSE CHEMBOND stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NSE CHEMBOND stock.


Keywords: NSE CHEMBOND, Chembond Chemicals Ltd, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

  1. Decision Making
  2. What is prediction in deep learning?
  3. Market Risk

NSE CHEMBOND 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 Chembond Chemicals Ltd Stock Decision Process with Lasso Regression where A is the set of discrete actions of NSE CHEMBOND 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= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+4 weeks) i = 1 n a i

n:Time series to forecast

p:Price signals of NSE CHEMBOND 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 CHEMBOND Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: NSE CHEMBOND Chembond Chemicals Ltd
Time series to forecast n: 30 Sep 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NSE CHEMBOND 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

Chembond Chemicals Ltd assigned short-term Ba3 & long-term Caa1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Lasso Regression1,2,3,4 and conclude that the NSE CHEMBOND stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NSE CHEMBOND stock.

Financial State Forecast for NSE CHEMBOND Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3Caa1
Operational Risk 5044
Market Risk6752
Technical Analysis4235
Fundamental Analysis8330
Risk Unsystematic7338

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 773 signals.

References

  1. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
  2. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
  3. Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
  4. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
  5. 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
  6. 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
  7. Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
Frequently Asked QuestionsQ: What is the prediction methodology for NSE CHEMBOND stock?
A: NSE CHEMBOND stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Lasso Regression
Q: Is NSE CHEMBOND stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE CHEMBOND Stock.
Q: Is Chembond Chemicals Ltd stock a good investment?
A: The consensus rating for Chembond Chemicals Ltd is Hold and assigned short-term Ba3 & long-term Caa1 forecasted stock rating.
Q: What is the consensus rating of NSE CHEMBOND stock?
A: The consensus rating for NSE CHEMBOND is Hold.
Q: What is the prediction period for NSE CHEMBOND stock?
A: The prediction period for NSE CHEMBOND is (n+4 weeks)

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