Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors' behavior. We evaluate Trident Limited prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Polynomial Regression1,2,3,4 and conclude that the NSE TRIDENT 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 TRIDENT stock.

Keywords: NSE TRIDENT, Trident 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 the use of Markov decision process?
2. How do predictive algorithms actually work?
3. How useful are statistical predictions?

## NSE TRIDENT Target Price Prediction Modeling Methodology

Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised learning which is not so adequate for learning problems with long-term goals and delayed rewards. This paper proposes a method of applying reinforcement learning, suitable for modeling and learning various kinds of interactions in real situations, to the problem of stock price prediction. We consider Trident Limited Stock Decision Process with Polynomial Regression where A is the set of discrete actions of NSE TRIDENT 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(Polynomial 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(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+8 weeks) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of NSE TRIDENT 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 TRIDENT Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: NSE TRIDENT Trident Limited
Time series to forecast n: 26 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 TRIDENT 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

Trident Limited assigned short-term B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Polynomial Regression1,2,3,4 and conclude that the NSE TRIDENT 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 TRIDENT stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1Ba3
Operational Risk 5678
Market Risk7641
Technical Analysis4939
Fundamental Analysis7758
Risk Unsystematic5089

### Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 842 signals.

## References

1. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
2. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
3. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
4. O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.
5. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
6. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
7. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
Frequently Asked QuestionsQ: What is the prediction methodology for NSE TRIDENT stock?
A: NSE TRIDENT stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Polynomial Regression
Q: Is NSE TRIDENT stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE TRIDENT Stock.
Q: Is Trident Limited stock a good investment?
A: The consensus rating for Trident Limited is Hold and assigned short-term B1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE TRIDENT stock?
A: The consensus rating for NSE TRIDENT is Hold.
Q: What is the prediction period for NSE TRIDENT stock?
A: The prediction period for NSE TRIDENT is (n+8 weeks)