Financial markets are fascinating if you can predict them. Also, the traders acting on financial markets produce a vast amount of information to analyse the consequences of investing according to the current market trends. Stock Market prediction is the technique to determine whether stock value will go up or down as it plays an active role in the financial gain of nation's economic status. We evaluate VN 30 Index prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Multiple Regression1,2,3,4 and conclude that the VN 30 Index stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold VN 30 Index stock.

Keywords: VN 30 Index, VN 30 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Is it better to buy and sell or hold?
2. Decision Making

## VN 30 Index 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 VN 30 Index Stock Decision Process with Multiple Regression where A is the set of discrete actions of VN 30 Index 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(Multiple 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(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+1 year) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of VN 30 Index 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?

## VN 30 Index Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: VN 30 Index VN 30 Index
Time series to forecast n: 12 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold VN 30 Index 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

VN 30 Index assigned short-term Caa2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Multiple Regression1,2,3,4 and conclude that the VN 30 Index stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold VN 30 Index stock.

### Financial State Forecast for VN 30 Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Caa2Ba3
Operational Risk 3368
Market Risk3545
Technical Analysis3085
Fundamental Analysis6274
Risk Unsystematic6636

### Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 527 signals.

## References

1. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
2. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
3. Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
4. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
5. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
6. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
7. Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
Frequently Asked QuestionsQ: What is the prediction methodology for VN 30 Index stock?
A: VN 30 Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Multiple Regression
Q: Is VN 30 Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold VN 30 Index Stock.
Q: Is VN 30 Index stock a good investment?
A: The consensus rating for VN 30 Index is Hold and assigned short-term Caa2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of VN 30 Index stock?
A: The consensus rating for VN 30 Index is Hold.
Q: What is the prediction period for VN 30 Index stock?
A: The prediction period for VN 30 Index is (n+1 year)

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