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

The research reported in the paper focuses on the stock market prediction problem, the main aim being the development of a methodology to forecast the stock closing price. The methodology is based on some novel variable selection methods and an analysis of neural network and support vector machines based prediction models. Also, a hybrid approach which combines the use of the variables derived from technical and fundamental analysis of stock market indicators in order to improve prediction results of the proposed approaches is reported in this paper. We evaluate VINACAPITAL VIETNAM OPPORTUNITY FUND LD prediction models with Modular Neural Network (Market News Sentiment Analysis) and Pearson Correlation^1,2,3,4 and conclude that the LON:VOF 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 LON:VOF stock.

Keywords: LON:VOF, VINACAPITAL VIETNAM OPPORTUNITY FUND LD, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Market Risk
2. Can statistics predict the future?
3. Investment Risk

LON:VOF Target Price Prediction Modeling Methodology

The study of financial markets has been addressed in many works during the last years. Different methods have been used in order to capture the non-linear behavior which is characteristic of these complex systems. The development of profitable strategies has been associated with the predictive character of the market movement, and special attention has been devoted to forecast the trends of financial markets. We consider VINACAPITAL VIETNAM OPPORTUNITY FUND LD Stock Decision Process with Pearson Correlation where A is the set of discrete actions of LON:VOF 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(Pearson Correlation)^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 (Market News Sentiment Analysis)) X S(n):→ (n+4 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:VOF VINACAPITAL VIETNAM OPPORTUNITY FUND LD
Time series to forecast n: 15 Oct 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:VOF 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

VINACAPITAL VIETNAM OPPORTUNITY FUND LD assigned short-term B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Pearson Correlation^1,2,3,4 and conclude that the LON:VOF 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 LON:VOF stock.

Financial State Forecast for LON:VOF Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	Ba3
Operational Risk	82	81
Market Risk	37	66
Technical Analysis	65	42
Fundamental Analysis	72	77
Risk Unsystematic	38	50

Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 632 signals.

References

1. Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
2. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
3. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
4. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
5. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
6. Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
7. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67

Frequently Asked Questions

Q: What is the prediction methodology for LON:VOF stock?
A: LON:VOF stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Pearson Correlation
Q: Is LON:VOF stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:VOF Stock.
Q: Is VINACAPITAL VIETNAM OPPORTUNITY FUND LD stock a good investment?
A: The consensus rating for VINACAPITAL VIETNAM OPPORTUNITY FUND LD is Hold and assigned short-term B1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:VOF stock?
A: The consensus rating for LON:VOF is Hold.
Q: What is the prediction period for LON:VOF stock?
A: The prediction period for LON:VOF is (n+4 weeks)

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