This paper proposes genetic algorithms (GAs) approach to feature discretization and the determination of connection weights for artificial neural networks (ANNs) to predict the stock price index. Previous research proposed many hybrid models of ANN and GA for the method of training the network, feature subset selection, and topology optimization. We evaluate TV18 Broadcast Limited prediction models with Active Learning (ML) and Paired T-Test1,2,3,4 and conclude that the NSE TV18BRDCST 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 TV18BRDCST stock.

Keywords: NSE TV18BRDCST, TV18 Broadcast Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Market Signals
2. Can stock prices be predicted?
3. What is statistical models in machine learning?

## NSE TV18BRDCST Target Price Prediction Modeling Methodology

Stock market prediction is a crucial and challenging task due to its nonlinear, evolutionary, complex, and dynamic nature. Research on the stock market has been an important issue for researchers in recent years. Companies invest in trading the stock market. Predicting the stock market trend accurately will minimize the risk and bring a maximum amount of profit for all the stakeholders. During the last several years, a lot of studies have been done to predict stock market trends using Traditional, Machine learning and deep learning techniques. We consider TV18 Broadcast Limited Stock Decision Process with Paired T-Test where A is the set of discrete actions of NSE TV18BRDCST 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(Paired T-Test)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(Active Learning (ML)) X S(n):→ (n+3 month) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of NSE TV18BRDCST 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 TV18BRDCST Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: NSE TV18BRDCST TV18 Broadcast Limited
Time series to forecast n: 01 Oct 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold NSE TV18BRDCST 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

TV18 Broadcast Limited assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Paired T-Test1,2,3,4 and conclude that the NSE TV18BRDCST 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 TV18BRDCST stock.

### Financial State Forecast for NSE TV18BRDCST Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B2
Operational Risk 4864
Market Risk6430
Technical Analysis4350
Fundamental Analysis8569
Risk Unsystematic3962

### Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 872 signals.

## References

1. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
2. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
3. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
4. Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
5. Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
6. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
7. Bessler, D. A. S. W. Fuller (1993), "Cointegration between U.S. wheat markets," Journal of Regional Science, 33, 481–501.
Frequently Asked QuestionsQ: What is the prediction methodology for NSE TV18BRDCST stock?
A: NSE TV18BRDCST stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Paired T-Test
Q: Is NSE TV18BRDCST stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE TV18BRDCST Stock.
Q: Is TV18 Broadcast Limited stock a good investment?
A: The consensus rating for TV18 Broadcast Limited is Hold and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of NSE TV18BRDCST stock?
A: The consensus rating for NSE TV18BRDCST is Hold.
Q: What is the prediction period for NSE TV18BRDCST stock?
A: The prediction period for NSE TV18BRDCST is (n+3 month)