Can stock prices be predicted? (LON:VRS Stock Forecast)

The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. We evaluate VERSARIEN PLC prediction models with Modular Neural Network (CNN Layer) and Beta^1,2,3,4 and conclude that the LON:VRS 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 LON:VRS stock.

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

Key Points

1. How do you pick a stock?
2. Stock Rating
3. Trading Signals

LON:VRS Target Price Prediction Modeling Methodology

Prediction of stock market movement is extremely difficult due to its high mutable nature. The rapid ups and downs occur in stock market because of impact from foreign commodities like emotional behavior of investors, political, psychological and economical factors. Continuous unsettlement in the stock market is major reason why investors sell out at the wrong time and often fail to gain the benefit. While investing in stock market investors must not forget the risk of reward rule and expose their holdings to greater risks. Although it is not possible predict stock market movement with full accuracy, losses from selling stocks at wrong time and its impacts can be reduce to greater extent using prediction of stock market movement based on analysis of historical data. We consider VERSARIEN PLC Stock Decision Process with Beta where A is the set of discrete actions of LON:VRS 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(Beta)^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 (CNN Layer)) X S(n):→ (n+16 weeks) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of LON:VRS 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:VRS Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:VRS VERSARIEN PLC
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 LON:VRS 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

VERSARIEN PLC assigned short-term B3 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Beta^1,2,3,4 and conclude that the LON:VRS 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 LON:VRS stock.

Financial State Forecast for LON:VRS Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	B3
Operational Risk	56	52
Market Risk	32	59
Technical Analysis	38	39
Fundamental Analysis	65	41
Risk Unsystematic	50	50

Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 590 signals.

References

1. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
2. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
3. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
4. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
5. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
6. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
7. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015

Frequently Asked Questions

Q: What is the prediction methodology for LON:VRS stock?
A: LON:VRS stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Beta
Q: Is LON:VRS stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:VRS Stock.
Q: Is VERSARIEN PLC stock a good investment?
A: The consensus rating for VERSARIEN PLC is Hold and assigned short-term B3 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LON:VRS stock?
A: The consensus rating for LON:VRS is Hold.
Q: What is the prediction period for LON:VRS stock?
A: The prediction period for LON:VRS is (n+16 weeks)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login