Machine Learning stock prediction: LIN Stock Prediction

This paper surveys machine learning techniques for stock market prediction. The prediction of stock markets is regarded as a challenging task of financial time series prediction. We evaluate Linde prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Wilcoxon Sign-Rank Test^1,2,3,4 and conclude that the LIN stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LIN stock.

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

Key Points

1. Short/Long Term Stocks
2. How can neural networks improve predictions?
3. Which neural network is best for prediction?

LIN Target Price Prediction Modeling Methodology

Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. We consider Linde Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of LIN 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of LIN 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?

LIN Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: LIN Linde
Time series to forecast n: 13 Oct 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LIN 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

Linde assigned short-term B2 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Wilcoxon Sign-Rank Test^1,2,3,4 and conclude that the LIN stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LIN stock.

Financial State Forecast for LIN Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba1
Operational Risk	37	62
Market Risk	59	79
Technical Analysis	60	74
Fundamental Analysis	54	53
Risk Unsystematic	56	83

Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 643 signals.

References

1. V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
2. Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
3. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
4. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
5. 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
6. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
7. S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013

Frequently Asked Questions

Q: What is the prediction methodology for LIN stock?
A: LIN stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Wilcoxon Sign-Rank Test
Q: Is LIN stock a buy or sell?
A: The dominant strategy among neural network is to Hold LIN Stock.
Q: Is Linde stock a good investment?
A: The consensus rating for Linde is Hold and assigned short-term B2 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of LIN stock?
A: The consensus rating for LIN is Hold.
Q: What is the prediction period for LIN stock?
A: The prediction period for LIN is (n+3 month)

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