Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend. We evaluate ICICI Prudential Life Insurance Company Limited prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the NSE ICICIPRULI stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell NSE ICICIPRULI stock.
Keywords: NSE ICICIPRULI, ICICI Prudential Life Insurance Company Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Buy, Sell and Hold Signals
- How do predictive algorithms actually work?
- How do predictive algorithms actually work?
NSE ICICIPRULI Target Price Prediction Modeling Methodology
Social media comments have in the past had an instantaneous effect on stock markets. This paper investigates the sentiments expressed on the social media platform Twitter and their pr edictive impact on the Stock Market. We consider ICICI Prudential Life Insurance Company Limited Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of NSE ICICIPRULI 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(Wilcoxon Sign-Rank Test)5,6,7= X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+1 year)
n:Time series to forecast
p:Price signals of NSE ICICIPRULI 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 ICICIPRULI Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: NSE ICICIPRULI ICICI Prudential Life Insurance Company Limited
Time series to forecast n: 29 Sep 2022 for (n+1 year)
According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell NSE ICICIPRULI 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
ICICI Prudential Life Insurance Company Limited assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the NSE ICICIPRULI stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell NSE ICICIPRULI stock.
Financial State Forecast for NSE ICICIPRULI Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B3 | Ba3 |
| Operational Risk | 81 | 60 |
| Market Risk | 45 | 66 |
| Technical Analysis | 35 | 67 |
| Fundamental Analysis | 32 | 89 |
| Risk Unsystematic | 44 | 44 |
Prediction Confidence Score
References
- Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
- Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
- Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
- LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
- 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.
- Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
- Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
Frequently Asked Questions
Q: What is the prediction methodology for NSE ICICIPRULI stock?A: NSE ICICIPRULI stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Wilcoxon Sign-Rank Test
Q: Is NSE ICICIPRULI stock a buy or sell?
A: The dominant strategy among neural network is to Sell NSE ICICIPRULI Stock.
Q: Is ICICI Prudential Life Insurance Company Limited stock a good investment?
A: The consensus rating for ICICI Prudential Life Insurance Company Limited is Sell and assigned short-term B3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE ICICIPRULI stock?
A: The consensus rating for NSE ICICIPRULI is Sell.
Q: What is the prediction period for NSE ICICIPRULI stock?
A: The prediction period for NSE ICICIPRULI is (n+1 year)
- Live broadcast of expert trader insights
- Real-time stock market analysis
- Access to a library of research dataset (API,XLS,JSON)
- Real-time updates
- In-depth research reports (PDF)