ac investment research

Growth Investing Stocks: Budapest SE Index Stock Forecast


Abstract

While we only include contractual acquisitions when calculating A/B and A-B, when evaluating qualitative factors, we focus more on the history of a company and our financial management expectations. In this regard, quantitative and qualitative factors under the liquidity criteria are intended to complement each other and produce a more complete vision of the future liquidity position of a company. We evaluate the prediction models (Moving Average Convergence Divergence (MACD) with Spearman Correlation)1,2,3 and conclude that the Budapest SE Index stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy Budapest SE Index stock.


Keywords: Budapest SE 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?

Budapest SE Index Stock Forecast (Buy or Sell) for (n+6 month)

Stock/Index: Budapest SE Index Budapest SE Index
Time series to forecast n: 06 Aug 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy Budapest SE 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 Budapest SE Index

  • In our general classification of asset classes and corresponding risk weights, we usually aim to accurately distinguish the risks on the balance sheets of assets globally consistently. However, sometimes, a financial system or institution may have unique risks that we choose to capture by re -classifying exposure to alternative asset classes more than we have typically use it. This shall be valid for a system or asset for a system or lower loss of losses for this unique exposure set for this unique exposure set, typically for the relevant class of assets in the economic risk or rating category. .
  • Due to the inconsistencies in the data reported by institutions in different judicial regions, we apply a single risk weight for a wide variety of corporate risks. For corporate exposure, the broad category includes direct exposure to corporate organizations, commercial real estate, object financing, purchased receivables and project financing. RACF does not distinguish between large, blue chip companies and small and medium -sized enterprises (SMEs).
  • Probably a hybrid given by an unrelated entity, if it was not given to one or two investors at the beginning as mentioned above, but if it comes to our attention, the ownership of the Hybrid series is now in the secondary market because it belongs to one. Or we expect two investors and ownership structure to be preserved in the future, we can decide to remove the previously assigned equity content.
  • In addition to a compulsory coupon postponement, if a particular ongoing capital -based financial trigger is violated, it is an exporting concern and has no significant restriction on postponing losses or otherwise absorbing the losses.
  • For market risk and operational risk, risk weights are more absolute and aim to take into account stress to a consistent degree with other risk weights. We see all the losses related to market and operational risk unexpectedly, so we do not calculate normalized loss rates for these risk types.
  • We assume 10% haircut unless it is mitigated with deposit insurance or systemically important banks.
  • The change does not significantly weaken the exporter's credit, including our opinion that it will not cause a low -time credit rating or a downward revision at a long -term credit rating.

Conclusions

Budapest SE Index assigned short-term B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models (Moving Average Convergence Divergence (MACD) with Spearman Correlation)1,2,3 and conclude that the Budapest SE Index stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy Budapest SE Index stock.

Financial State Forecast for Budapest SE Index

Rating Short-Term Long-Term Senior
Outlook*B1Ba3
Operational Risk 7260
Market Risk6766
Technical Analysis3468
Fundamental Analysis3837
Risk Unsystematic8178

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 510 signals.

References

  1. Dulac-Arnold, Gabriel, Denoyer, Ludovic, Preux, Philippe, and Gallinari, Patrick. Fast reinforcement learning with large action sets using error-correcting output codes for mdp factorization. In Machine Learning and Knowledge Discovery in Databases, pp. 180–194. Springer, 2012.
  2. Dean, Jeffrey, Corrado, Greg, Monga, Rajat, Chen, Kai, Devin, Matthieu, Mao, Mark, Senior, Andrew, Tucker, Paul, Yang, Ke, Le, Quoc V, et al. Large scale distributed deep networks. In Advances in Neural Information Pro- cessing Systems, pp. 1223–1231, 2012.
  3. Pazis, Jason and Parr, Ron. Generalized value functions for large action sets. In Proceedings of the 28th Interna- tional Conference on Machine Learning (ICML-11), pp. 1185–1192, 2011.
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.

301 Massachusetts Avenue Cambridge, MA 02139 667-253-1000 pr@ademcetinkaya.com