The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. We evaluate VINACAPITAL VIETNAM OPPORTUNITY FUND LD prediction models with Active Learning (ML) and Lasso Regression1,2,3,4 and conclude that the LON:VOF stock is predictable in the short/long term. According to price forecasts for (n+8 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. Can we predict stock market using machine learning?
2. Stock Forecast Based On a Predictive Algorithm
3. How useful are statistical predictions? ## LON:VOF Target Price Prediction Modeling Methodology

In this paper, we introduce a new prediction model depend on Bidirectional Gated Recurrent Unit (BGRU). Our predictive model relies on both online financial news and historical stock prices data to predict the stock movements in the future. We consider VINACAPITAL VIETNAM OPPORTUNITY FUND LD Stock Decision Process with Lasso Regression 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(Lasso Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Active Learning (ML)) X S(n):→ (n+8 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

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+8 weeks)

Sample Set: Neural Network
Stock/Index: LON:VOF VINACAPITAL VIETNAM OPPORTUNITY FUND LD
Time series to forecast n: 27 Sep 2022 for (n+8 weeks)

According to price forecasts for (n+8 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 B2 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Lasso Regression1,2,3,4 and conclude that the LON:VOF stock is predictable in the short/long term. According to price forecasts for (n+8 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*B1B2
Operational Risk 4653
Market Risk8756
Technical Analysis4169
Fundamental Analysis8832
Risk Unsystematic3264

### Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 486 signals.

## References

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2. D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
3. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
4. V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
5. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
6. 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
7. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
Frequently Asked QuestionsQ: What is the prediction methodology for LON:VOF stock?
A: LON:VOF stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Lasso Regression
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 B2 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+8 weeks)