Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend. We evaluate AstraZeneca Pharma India Limited prediction models with Reinforcement Machine Learning (ML) and Chi-Square1,2,3,4 and conclude that the NSE ASTRAZEN stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy NSE ASTRAZEN stock.

Keywords: NSE ASTRAZEN, AstraZeneca Pharma 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 is the best way to predict stock prices?
2. Can neural networks predict stock market?
3. Technical Analysis with Algorithmic Trading

## NSE ASTRAZEN Target Price Prediction Modeling Methodology

Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We consider AstraZeneca Pharma India Limited Stock Decision Process with Chi-Square where A is the set of discrete actions of NSE ASTRAZEN 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(Chi-Square)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(Reinforcement Machine Learning (ML)) X S(n):→ (n+6 month) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of NSE ASTRAZEN 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 ASTRAZEN Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: NSE ASTRAZEN AstraZeneca Pharma India Limited
Time series to forecast n: 26 Sep 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy NSE ASTRAZEN 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

AstraZeneca Pharma India Limited assigned short-term B1 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Chi-Square1,2,3,4 and conclude that the NSE ASTRAZEN stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy NSE ASTRAZEN stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1Ba2
Operational Risk 4446
Market Risk9077
Technical Analysis6871
Fundamental Analysis3560
Risk Unsystematic6589

### Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 610 signals.

## References

1. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
2. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
3. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
4. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
5. Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
6. L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
7. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
Frequently Asked QuestionsQ: What is the prediction methodology for NSE ASTRAZEN stock?
A: NSE ASTRAZEN stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Chi-Square
Q: Is NSE ASTRAZEN stock a buy or sell?
A: The dominant strategy among neural network is to Buy NSE ASTRAZEN Stock.
Q: Is AstraZeneca Pharma India Limited stock a good investment?
A: The consensus rating for AstraZeneca Pharma India Limited is Buy and assigned short-term B1 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of NSE ASTRAZEN stock?
A: The consensus rating for NSE ASTRAZEN is Buy.
Q: What is the prediction period for NSE ASTRAZEN stock?
A: The prediction period for NSE ASTRAZEN is (n+6 month)