In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. We evaluate Lemon Tree Hotels Limited prediction models with Supervised Machine Learning (ML) and ElasticNet Regression1,2,3,4 and conclude that the NSE LEMONTREE 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 LEMONTREE stock.
Keywords: NSE LEMONTREE, Lemon Tree Hotels Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Should I buy stocks now or wait amid such uncertainty?
- Can stock prices be predicted?
- What is neural prediction?
NSE LEMONTREE Target Price Prediction Modeling Methodology
Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto- Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. We consider Lemon Tree Hotels Limited Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of NSE LEMONTREE 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= X R(Supervised Machine Learning (ML)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of NSE LEMONTREE stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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NSE LEMONTREE Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: NSE LEMONTREE Lemon Tree Hotels Limited
Time series to forecast n: 27 Sep 2022 for (n+16 weeks)
According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE LEMONTREE 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
Lemon Tree Hotels Limited assigned short-term Ba3 & long-term B1 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with ElasticNet Regression1,2,3,4 and conclude that the NSE LEMONTREE 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 LEMONTREE stock.
Financial State Forecast for NSE LEMONTREE Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba3 | B1 |
| Operational Risk | 70 | 64 |
| Market Risk | 76 | 34 |
| Technical Analysis | 85 | 50 |
| Fundamental Analysis | 62 | 80 |
| Risk Unsystematic | 32 | 75 |
Prediction Confidence Score
References
- M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
- Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
- Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
- Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
- J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
Frequently Asked Questions
Q: What is the prediction methodology for NSE LEMONTREE stock?A: NSE LEMONTREE stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and ElasticNet Regression
Q: Is NSE LEMONTREE stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE LEMONTREE Stock.
Q: Is Lemon Tree Hotels Limited stock a good investment?
A: The consensus rating for Lemon Tree Hotels Limited is Hold and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NSE LEMONTREE stock?
A: The consensus rating for NSE LEMONTREE is Hold.
Q: What is the prediction period for NSE LEMONTREE stock?
A: The prediction period for NSE LEMONTREE is (n+16 weeks)
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