Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history. We evaluate Techno Electric & Engineering Company Limited prediction models with Supervised Machine Learning (ML) and Independent T-Test1,2,3,4 and conclude that the NSE TECHNOE 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 Sell NSE TECHNOE stock.
Keywords: NSE TECHNOE, Techno Electric & Engineering Company Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Can statistics predict the future?
- What are the most successful trading algorithms?
- Can we predict stock market using machine learning?
NSE TECHNOE Target Price Prediction Modeling Methodology
How to predict stock price movements based on quantitative market data modeling is an attractive topic. In front of the market news and stock prices that are commonly believed as two important market data sources, how to extract and exploit the hidden information within the raw data and make both accurate and fast predictions simultaneously becomes a challenging problem. In this paper, we present the design and architecture of our trading signal mining platform that employs extreme learning machine (ELM) to make stock price prediction based on those two data sources concurrently. We consider Techno Electric & Engineering Company Limited Stock Decision Process with Independent T-Test where A is the set of discrete actions of NSE TECHNOE 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(Independent T-Test)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 TECHNOE 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 TECHNOE Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: NSE TECHNOE Techno Electric & Engineering Company Limited
Time series to forecast n: 28 Sep 2022 for (n+16 weeks)
According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell NSE TECHNOE 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
Techno Electric & Engineering Company Limited assigned short-term Ba2 & long-term B2 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Independent T-Test1,2,3,4 and conclude that the NSE TECHNOE 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 Sell NSE TECHNOE stock.
Financial State Forecast for NSE TECHNOE Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba2 | B2 |
| Operational Risk | 59 | 32 |
| Market Risk | 83 | 46 |
| Technical Analysis | 61 | 86 |
| Fundamental Analysis | 56 | 66 |
| Risk Unsystematic | 88 | 43 |
Prediction Confidence Score
References
- Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
- S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
- B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
- K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012
- Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
Frequently Asked Questions
Q: What is the prediction methodology for NSE TECHNOE stock?A: NSE TECHNOE stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Independent T-Test
Q: Is NSE TECHNOE stock a buy or sell?
A: The dominant strategy among neural network is to Sell NSE TECHNOE Stock.
Q: Is Techno Electric & Engineering Company Limited stock a good investment?
A: The consensus rating for Techno Electric & Engineering Company Limited is Sell and assigned short-term Ba2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of NSE TECHNOE stock?
A: The consensus rating for NSE TECHNOE is Sell.
Q: What is the prediction period for NSE TECHNOE stock?
A: The prediction period for NSE TECHNOE is (n+16 weeks)
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