This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. We evaluate VN 30 Index prediction models with Transfer Learning (ML) and Sign Test1,2,3,4 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 Buy 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. What is a prediction confidence?
2. Nash Equilibria
3. What are main components of Markov decision process?

## VN 30 Index Target Price Prediction Modeling Methodology

Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. We consider VN 30 Index Stock Decision Process with Sign Test 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(Sign Test)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(Transfer Learning (ML)) 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: 16 Sep 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy 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 B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Sign Test1,2,3,4 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 Buy VN 30 Index stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 3548
Market Risk6157
Technical Analysis7582
Fundamental Analysis8651
Risk Unsystematic4749

### Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 503 signals.

## References

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2. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
3. 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.
4. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
5. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
6. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
7. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
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 Transfer Learning (ML) and Sign Test
Q: Is VN 30 Index stock a buy or sell?
A: The dominant strategy among neural network is to Buy VN 30 Index Stock.
Q: Is VN 30 Index stock a good investment?
A: The consensus rating for VN 30 Index is Buy and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of VN 30 Index stock?
A: The consensus rating for VN 30 Index is Buy.
Q: What is the prediction period for VN 30 Index stock?
A: The prediction period for VN 30 Index is (n+1 year)