Buy, sell or hold: NSE FOSECOIND Stock Forecast (Forecast)

The presented paper modeled and predicted stock returns using LSTM. The historical data of stock market were transformed into 30-days-long sequences with 10 learning features and 7-day earning rate labeling. The model was fitted by training on 1200000 sequences and tested using the other 350000 sequences. We evaluate Foseco India Limited prediction models with Statistical Inference (ML) and Paired T-Test^1,2,3,4 and conclude that the NSE FOSECOIND 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 FOSECOIND stock.

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

Key Points

1. What is the best way to predict stock prices?
2. Stock Forecast Based On a Predictive Algorithm
3. Can stock prices be predicted?

NSE FOSECOIND Target Price Prediction Modeling Methodology

The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. We consider Foseco India Limited Stock Decision Process with Paired T-Test where A is the set of discrete actions of NSE FOSECOIND 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(Paired T-Test)^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(Statistical Inference (ML)) X S(n):→ (n+4 weeks) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of NSE FOSECOIND 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 FOSECOIND Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: NSE FOSECOIND Foseco India Limited
Time series to forecast n: 29 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 FOSECOIND 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

Foseco India Limited assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Statistical Inference (ML) with Paired T-Test^1,2,3,4 and conclude that the NSE FOSECOIND 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 FOSECOIND stock.

Financial State Forecast for NSE FOSECOIND Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B1
Operational Risk	81	37
Market Risk	61	76
Technical Analysis	45	55
Fundamental Analysis	35	33
Risk Unsystematic	76	79

Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 690 signals.

References

1. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
2. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
3. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
4. Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
5. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
6. A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
7. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11

Frequently Asked Questions

Q: What is the prediction methodology for NSE FOSECOIND stock?
A: NSE FOSECOIND stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Paired T-Test
Q: Is NSE FOSECOIND stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE FOSECOIND Stock.
Q: Is Foseco India Limited stock a good investment?
A: The consensus rating for Foseco India Limited is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NSE FOSECOIND stock?
A: The consensus rating for NSE FOSECOIND is Hold.
Q: What is the prediction period for NSE FOSECOIND stock?
A: The prediction period for NSE FOSECOIND is (n+4 weeks)

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