Can neural networks predict stock market? (NSE COROMANDEL Stock Forecast)

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor's gains. This paper proposes a machine learning model to predict stock market price. We evaluate Coromandel International Limited prediction models with Modular Neural Network (DNN Layer) and Statistical Hypothesis Testing^1,2,3,4 and conclude that the NSE COROMANDEL stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold NSE COROMANDEL stock.

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

Key Points

1. What is Markov decision process in reinforcement learning?
2. How can neural networks improve predictions?
3. Trust metric by Neural Network

NSE COROMANDEL Target Price Prediction Modeling Methodology

In recent years there has been a significant growth of interest in the incorporation of historical series of variables related to stock prediction into mathematical models or computational algorithms in order to generate predictions or indications about expected price movements. We consider Coromandel International Limited Stock Decision Process with Statistical Hypothesis Testing where A is the set of discrete actions of NSE COROMANDEL 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(Statistical Hypothesis Testing)^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 (DNN Layer)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of NSE COROMANDEL 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 COROMANDEL Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: NSE COROMANDEL Coromandel International Limited
Time series to forecast n: 30 Sep 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold NSE COROMANDEL 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

Coromandel International Limited assigned short-term Ba2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Statistical Hypothesis Testing^1,2,3,4 and conclude that the NSE COROMANDEL stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold NSE COROMANDEL stock.

Financial State Forecast for NSE COROMANDEL Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba2	Ba2
Operational Risk	88	82
Market Risk	76	62
Technical Analysis	87	63
Fundamental Analysis	36	64
Risk Unsystematic	61	71

Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 877 signals.

References

1. Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
2. Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791
3. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
4. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
5. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
6. Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
7. Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM

Frequently Asked Questions

Q: What is the prediction methodology for NSE COROMANDEL stock?
A: NSE COROMANDEL stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Statistical Hypothesis Testing
Q: Is NSE COROMANDEL stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE COROMANDEL Stock.
Q: Is Coromandel International Limited stock a good investment?
A: The consensus rating for Coromandel International Limited is Hold and assigned short-term Ba2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of NSE COROMANDEL stock?
A: The consensus rating for NSE COROMANDEL is Hold.
Q: What is the prediction period for NSE COROMANDEL stock?
A: The prediction period for NSE COROMANDEL is (n+3 month)

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