This paper surveys machine learning techniques for stock market prediction. The prediction of stock markets is regarded as a challenging task of financial time series prediction. We evaluate HT Media Limited prediction models with Modular Neural Network (CNN Layer) and ElasticNet Regression1,2,3,4 and conclude that the NSE HTMEDIA stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE HTMEDIA stock.

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

## Key Points

1. What statistical methods are used to analyze data?
2. What is statistical models in machine learning?
3. Understanding Buy, Sell, and Hold Ratings

## NSE HTMEDIA Target Price Prediction Modeling Methodology

Stocks are possibly the most popular financial instrument invented for building wealth and are the centerpiece of any investment portfolio. The advances in trading technology has opened up the markets so that nowadays nearly anybody can own stocks. From last few decades, there seen explosive increase in the average person's interest for stock market. In a financially explosive market, as the stock market, it is important to have a very accurate prediction of a future trend. Because of the financial crisis and recording profits, it is compulsory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires progressive algorithms of machine learning with help of Artificial Intelligence (AI). We consider HT Media Limited Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of NSE HTMEDIA 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(ElasticNet 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 (CNN Layer)) X S(n):→ (n+16 weeks) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of NSE HTMEDIA 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 HTMEDIA Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: NSE HTMEDIA HT Media Limited
Time series to forecast n: 16 Nov 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE HTMEDIA 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%

## Adjusted IFRS* Prediction Methods for HT Media Limited

1. Amounts presented in other comprehensive income shall not be subsequently transferred to profit or loss. However, the entity may transfer the cumulative gain or loss within equity.
2. In applying the effective interest method, an entity identifies fees that are an integral part of the effective interest rate of a financial instrument. The description of fees for financial services may not be indicative of the nature and substance of the services provided. Fees that are an integral part of the effective interest rate of a financial instrument are treated as an adjustment to the effective interest rate, unless the financial instrument is measured at fair value, with the change in fair value being recognised in profit or loss. In those cases, the fees are recognised as revenue or expense when the instrument is initially recognised.
3. When measuring the fair values of the part that continues to be recognised and the part that is derecognised for the purposes of applying paragraph 3.2.13, an entity applies the fair value measurement requirements in IFRS 13 Fair Value Measurement in addition to paragraph 3.2.14.
4. An equity method investment cannot be a hedged item in a fair value hedge. This is because the equity method recognises in profit or loss the investor's share of the investee's profit or loss, instead of changes in the investment's fair value. For a similar reason, an investment in a consolidated subsidiary cannot be a hedged item in a fair value hedge. This is because consolidation recognises in profit or loss the subsidiary's profit or loss, instead of changes in the investment's fair value. A hedge of a net investment in a foreign operation is different because it is a hedge of the foreign currency exposure, not a fair value hedge of the change in the value of the investment.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

HT Media Limited assigned short-term Ba2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with ElasticNet Regression1,2,3,4 and conclude that the NSE HTMEDIA stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE HTMEDIA stock.

### Financial State Forecast for NSE HTMEDIA HT Media Limited Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba2B2
Operational Risk 7363
Market Risk8532
Technical Analysis4347
Fundamental Analysis8359
Risk Unsystematic5952

### Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 835 signals.

## References

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3. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
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. R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000
6. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
7. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
Frequently Asked QuestionsQ: What is the prediction methodology for NSE HTMEDIA stock?
A: NSE HTMEDIA stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and ElasticNet Regression
Q: Is NSE HTMEDIA stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE HTMEDIA Stock.
Q: Is HT Media Limited stock a good investment?
A: The consensus rating for HT Media Limited is Hold and assigned short-term Ba2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of NSE HTMEDIA stock?
A: The consensus rating for NSE HTMEDIA is Hold.
Q: What is the prediction period for NSE HTMEDIA stock?
A: The prediction period for NSE HTMEDIA is (n+16 weeks)
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