Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. We evaluate Dish TV India Limited prediction models with Deductive Inference (ML) and Logistic Regression1,2,3,4 and conclude that the NSE DISHTV 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 DISHTV stock.

Keywords: NSE DISHTV, Dish TV India Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. What are the most successful trading algorithms?
2. Dominated Move
3. How do you pick a stock?

## NSE DISHTV Target Price Prediction Modeling Methodology

This paper examines the theory and practice of regression techniques for prediction of stock price trend by using a transformed data set in ordinal data format. The original pretransformed data source contains data of heterogeneous data types used for handling of currency values and financial ratios. The data formats in currency values and financial ratios provide a process for computation of stock prices. The transformed data set contains only a standardized ordinal data type which provides a process to measure rankings of stock price trends. We consider Dish TV India Limited Stock Decision Process with Logistic Regression where A is the set of discrete actions of NSE DISHTV 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(Logistic 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(Deductive Inference (ML)) X S(n):→ (n+4 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: NSE DISHTV Dish TV India Limited
Time series to forecast n: 01 Oct 2022 for (n+4 weeks)

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

Dish TV India Limited assigned short-term B3 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Logistic Regression1,2,3,4 and conclude that the NSE DISHTV 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 DISHTV stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3Ba2
Operational Risk 5078
Market Risk3157
Technical Analysis7270
Fundamental Analysis4861
Risk Unsystematic4969

### Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 513 signals.

## References

1. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
2. 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.
3. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
4. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
5. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
6. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
7. Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
Frequently Asked QuestionsQ: What is the prediction methodology for NSE DISHTV stock?
A: NSE DISHTV stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Logistic Regression
Q: Is NSE DISHTV stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE DISHTV Stock.
Q: Is Dish TV India Limited stock a good investment?
A: The consensus rating for Dish TV India Limited is Hold and assigned short-term B3 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of NSE DISHTV stock?
A: The consensus rating for NSE DISHTV is Hold.
Q: What is the prediction period for NSE DISHTV stock?
A: The prediction period for NSE DISHTV is (n+4 weeks)