Machine Learning stock prediction: LON:CTA Stock Prediction

Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users' moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach. We evaluate CT AUTOMOTIVE GROUP PLC prediction models with Supervised Machine Learning (ML) and Sign Test^1,2,3,4 and conclude that the LON:CTA stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:CTA stock.

Keywords: LON:CTA, CT AUTOMOTIVE GROUP PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Stock Forecast Based On a Predictive Algorithm
2. Operational Risk
3. Game Theory

LON:CTA Target Price Prediction Modeling Methodology

Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend. We consider CT AUTOMOTIVE GROUP PLC Stock Decision Process with Sign Test where A is the set of discrete actions of LON:CTA 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(Sign 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(Supervised Machine Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of LON:CTA 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?

LON:CTA Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:CTA CT AUTOMOTIVE GROUP PLC
Time series to forecast n: 14 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:CTA 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

CT AUTOMOTIVE GROUP PLC assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Sign Test^1,2,3,4 and conclude that the LON:CTA stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:CTA stock.

Financial State Forecast for LON:CTA Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B1
Operational Risk	49	36
Market Risk	46	52
Technical Analysis	65	86
Fundamental Analysis	74	44
Risk Unsystematic	75	69

Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 650 signals.

References

1. Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
2. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
3. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
4. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
5. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
6. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
7. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]

Frequently Asked Questions

Q: What is the prediction methodology for LON:CTA stock?
A: LON:CTA stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Sign Test
Q: Is LON:CTA stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:CTA Stock.
Q: Is CT AUTOMOTIVE GROUP PLC stock a good investment?
A: The consensus rating for CT AUTOMOTIVE GROUP PLC is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:CTA stock?
A: The consensus rating for LON:CTA is Hold.
Q: What is the prediction period for LON:CTA stock?
A: The prediction period for LON:CTA is (n+6 month)

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