Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. We evaluate DAX Index prediction models with Inductive Learning (ML) and Polynomial Regression1,2,3,4 and conclude that the DAX 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 Hold DAX Index stock.

Keywords: DAX Index, DAX 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 prediction in deep learning?
2. Dominated Move
3. Can machine learning predict?

## DAX Index Target Price Prediction Modeling Methodology

Sentiment Analysis is new way of machine learning to extract opinion orientation (positive, negative, neutral) from a text segment written for any product, organization, person or any other entity. Sentiment Analysis can be used to predict the mood of people that have impact on stock prices, therefore it can help in prediction of actual stock movement. We consider DAX Index Stock Decision Process with Polynomial Regression where A is the set of discrete actions of DAX 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(Polynomial 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(Inductive Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of DAX 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?

## DAX Index Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: DAX Index DAX Index
Time series to forecast n: 17 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold DAX 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

DAX Index assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Polynomial Regression1,2,3,4 and conclude that the DAX 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 Hold DAX Index stock.

### Financial State Forecast for DAX Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B2
Operational Risk 3571
Market Risk3762
Technical Analysis5037
Fundamental Analysis8730
Risk Unsystematic5871

### Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 561 signals.

## References

1. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
2. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
3. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
4. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
5. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
6. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
7. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
Frequently Asked QuestionsQ: What is the prediction methodology for DAX Index stock?
A: DAX Index stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Polynomial Regression
Q: Is DAX Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold DAX Index Stock.
Q: Is DAX Index stock a good investment?
A: The consensus rating for DAX Index is Hold and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of DAX Index stock?
A: The consensus rating for DAX Index is Hold.
Q: What is the prediction period for DAX Index stock?
A: The prediction period for DAX Index is (n+6 month)