Complex networks in stock market and stock price volatility pattern prediction are the important issues in stock price research. Previous studies have used historical information regarding a single stock to predict the future trend of the stock's price, seldom considering comovement among stocks in the same market. In this study, in order to extract the information about relation stocks for prediction, we try to combine the complex network method with machine learning to predict stock price patterns. We evaluate Apollo Hospitals Enterprise Limited prediction models with Multi-Task Learning (ML) and Pearson Correlation1,2,3,4 and conclude that the NSE APOLLOHOSP 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 APOLLOHOSP stock.
Keywords: NSE APOLLOHOSP, Apollo Hospitals Enterprise Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- What are the most successful trading algorithms?
- What are the most successful trading algorithms?
- What is the best way to predict stock prices?
NSE APOLLOHOSP Target Price Prediction Modeling Methodology
Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators. We consider Apollo Hospitals Enterprise Limited Stock Decision Process with Pearson Correlation where A is the set of discrete actions of NSE APOLLOHOSP 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(Pearson Correlation)5,6,7= X R(Multi-Task Learning (ML)) X S(n):→ (n+4 weeks)
n:Time series to forecast
p:Price signals of NSE APOLLOHOSP 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 APOLLOHOSP Stock Forecast (Buy or Sell) for (n+4 weeks)
Sample Set: Neural NetworkStock/Index: NSE APOLLOHOSP Apollo Hospitals Enterprise Limited
Time series to forecast n: 26 Sep 2022 for (n+4 weeks)
According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NSE APOLLOHOSP 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
Apollo Hospitals Enterprise Limited assigned short-term B1 & long-term B3 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Pearson Correlation1,2,3,4 and conclude that the NSE APOLLOHOSP 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 APOLLOHOSP stock.
Financial State Forecast for NSE APOLLOHOSP Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B1 | B3 |
| Operational Risk | 76 | 33 |
| Market Risk | 56 | 37 |
| Technical Analysis | 33 | 62 |
| Fundamental Analysis | 90 | 30 |
| Risk Unsystematic | 40 | 75 |
Prediction Confidence Score
References
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- Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
- Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
- T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
Frequently Asked Questions
Q: What is the prediction methodology for NSE APOLLOHOSP stock?A: NSE APOLLOHOSP stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Pearson Correlation
Q: Is NSE APOLLOHOSP stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE APOLLOHOSP Stock.
Q: Is Apollo Hospitals Enterprise Limited stock a good investment?
A: The consensus rating for Apollo Hospitals Enterprise Limited is Hold and assigned short-term B1 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of NSE APOLLOHOSP stock?
A: The consensus rating for NSE APOLLOHOSP is Hold.
Q: What is the prediction period for NSE APOLLOHOSP stock?
A: The prediction period for NSE APOLLOHOSP is (n+4 weeks)
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