NSE IDFC Target Price Prediction

Prediction of stock market movement is extremely difficult due to its high mutable nature. The rapid ups and downs occur in stock market because of impact from foreign commodities like emotional behavior of investors, political, psychological and economical factors. Continuous unsettlement in the stock market is major reason why investors sell out at the wrong time and often fail to gain the benefit. While investing in stock market investors must not forget the risk of reward rule and expose their holdings to greater risks. Although it is not possible predict stock market movement with full accuracy, losses from selling stocks at wrong time and its impacts can be reduce to greater extent using prediction of stock market movement based on analysis of historical data. We evaluate IDFC Limited prediction models with Inductive Learning (ML) and Spearman Correlation^1,2,3,4 and conclude that the NSE IDFC stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold NSE IDFC stock.

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

Key Points

1. Market Outlook
2. What is the use of Markov decision process?
3. Is it better to buy and sell or hold?

NSE IDFC 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 IDFC Limited Stock Decision Process with Spearman Correlation where A is the set of discrete actions of NSE IDFC 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(Spearman Correlation)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Inductive Learning (ML)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of NSE IDFC 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 IDFC Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: NSE IDFC IDFC Limited
Time series to forecast n: 29 Sep 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold NSE IDFC 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

IDFC Limited assigned short-term B1 & long-term B3 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Spearman Correlation^1,2,3,4 and conclude that the NSE IDFC stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold NSE IDFC stock.

Financial State Forecast for NSE IDFC Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B3
Operational Risk	72	43
Market Risk	70	64
Technical Analysis	51	31
Fundamental Analysis	66	54
Risk Unsystematic	41	42

Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 715 signals.

References

1. Artis, M. J. W. Zhang (1990), "BVAR forecasts for the G-7," International Journal of Forecasting, 6, 349–362.
2. N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
3. Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
4. S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
5. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
6. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
7. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99

Frequently Asked Questions

Q: What is the prediction methodology for NSE IDFC stock?
A: NSE IDFC stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Spearman Correlation
Q: Is NSE IDFC stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE IDFC Stock.
Q: Is IDFC Limited stock a good investment?
A: The consensus rating for IDFC Limited is Hold and assigned short-term B1 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of NSE IDFC stock?
A: The consensus rating for NSE IDFC is Hold.
Q: What is the prediction period for NSE IDFC stock?
A: The prediction period for NSE IDFC is (n+8 weeks)

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