ac investment research

Should You Buy Now or Wait? VN 30 Index Stock Forecast


Abstract

For example, if a company incurred a large entry of working capital in the fourth quarter, which rather than compensating working capital outputs during the first three quarters, we would use the maximum exits of working capital within our calculation A/ B and A-B. However, we avoid double counting when the working capital flow flow is already captured through our assumption of the maximum amount of CP. We evaluate the prediction models (Anomaly with ANOVA)1,2,3 and conclude that the VN 30 Index stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold VN 30 Index stock.


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

Introduction

We consider the full spectrum of human trading interaction (varying from data based analysis to market signals, from trend actions to speculative ones and many more) and adapt them to the machine learning model with support of engineers to mimic and future-reflect everyday trading experiences. To do that we focus on an approach known as Decision making using Game Theory. We apply principles from Game Theory to model the relationships between rating actions, news, market signals and decision making. 

 

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+3 month)

Stock/Index: VN 30 Index VN 30 Index
Time series to forecast n: 06 Aug 2022 for (n+3 month)

According to price forecasts for (n+3 month) 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%


*As part of stock rating surveillance, Neural network continuously analyze real-time and historical data. If network see events taking place that impact our view on an issuer's relative performance, we adjust our ratings accordingly to communicate our views so the market has the correct perception of how we view relative stock performance.

What Are the Top Stocks to Invest in Right Now?

Forecast Model for VN 30 Index

  • We cannot assign equity content to hybrid instruments that do not meet the requirements of high or intermediate equity content, including the times when the exporting intent is missing, and therefore we do not consider these tools similarly in our analysis when they are applicable.
  • Criteria at the relevant sector level may show more detailed assumptions to be used for these stress tests. The main effects of the stress scenario on liquidity will be a haircut of serious macroeconomic stress, a haircut of assets of domestic marketable securities, a sharp increase in financing costs for a floating ratio or short -term debt. Lack of capital market access for interest rate shock and re -financing. In addition, in the countries we assume a money shock , the potential effect will be increased.
  • Based on our observations of loan losses during past economic decline, we believe that credit losses can last for three years to flow from the financial statements of a bank, except for the credit cards we look at the highest damage for a year. The three -year normalized loss ratio and RACF capital charging combine to comply with the idealized loss rate for each asset class. According to our opinion, the average or "normal", which we call "normalized losses", can absorb annual credit losses and banks, which we call "normalized losses", and banks have capital to absorb larger losses than normal. "
  • The analysis also deals with some factors specific to the beings we believe that they can distinguish an individual obligation from their peers. These may include diversification of the products and services of the obligation for a financial institution and risk concentrations. Obliging factors may also include operational activity, general competitive location, strategy, governance, financial policies, risk management practices and risk tolerance.
  • Income -based risk weights: Our risk weights to take into account the operational risk for different business lines are based on the income generated by these enterprises. We apply risk weights according to the highest annual income of the last three years. This aims to meet the latest activities and growth momentum and to avoid providing capital relief to organizations that experience a final decline in income as a result of operational or trade losses.
  • Potential rating includes our opinion on exposing the business to extensively relevant country risks. The dominant degree does not act as a "ceiling" for non -izer ratings.
  • If a M&A transaction creates a negative honor, we do not set the capital reported, but when we evaluate the job position and earning capacity of an enterprise, we discuss the effects of such a transaction.

Conclusions

VN 30 Index assigned short-term B1 & long-term B3 forecasted stock rating. We evaluate the prediction models (Anomaly with ANOVA)1,2,3 and conclude that the VN 30 Index stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold VN 30 Index stock.

Financial State Forecast for VN 30 Index

Rating Short-Term Long-Term Senior
Outlook*B1B3
Operational Risk 6446
Market Risk7987
Technical Analysis4734
Fundamental Analysis3933
Risk Unsystematic7441

Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 509 signals.

References

  1. W. Bruce Croft, Donald Metzler, and Trevor Strohman. Search engines: information retrieval in practice. Addison-Wesley, Boston, 1st edition, February 2010.
  2. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998.
  3. David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin A. Riedmiller. Deterministic policy gradient algorithms. In Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014, volume 32 of JMLR Pro- ceedings, pages 387–395. JMLR.org, 2014.
AC Investment Research

In our experiment, we focus on an approach known as Decision making using game theory. We apply principles from game theory to model the relationships between rating actions, news, market signals and decision making.

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