## Abstract

We evaluate S&P/BMV IPC Index prediction models with Active Learning (ML) and Multiple Regression1,2,3,4 and conclude that the S&P/BMV IPC 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 Buy S&P/BMV IPC Index stock.

Keywords: S&P/BMV IPC Index, S&P/BMV IPC Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Probability Distribution
2. Stock Rating
3. Fundemental Analysis with Algorithmic Trading ## S&P/BMV IPC Index Target Price Prediction Modeling Methodology

We consider S&P/BMV IPC Index Stock Decision Process with Multiple Regression where A is the set of discrete actions of S&P/BMV IPC 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(Active Learning (ML)) X S(n):→ (n+3 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of S&P/BMV IPC 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?

## S&P/BMV IPC Index Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: S&P/BMV IPC Index S&P/BMV IPC Index
Time series to forecast n: 07 Sep 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy S&P/BMV IPC 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

S&P/BMV IPC Index assigned short-term Baa2 & long-term B2 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Multiple Regression1,2,3,4 and conclude that the S&P/BMV IPC 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 Buy S&P/BMV IPC Index stock.

### Financial State Forecast for S&P/BMV IPC Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Baa2B2
Operational Risk 8835
Market Risk7153
Technical Analysis4939
Fundamental Analysis8537
Risk Unsystematic8379

### Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 753 signals.

## References

1. 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.
2. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
3. Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
4. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
5. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
6. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
7. Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.
Frequently Asked QuestionsQ: What is the prediction methodology for S&P/BMV IPC Index stock?
A: S&P/BMV IPC Index stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Multiple Regression
Q: Is S&P/BMV IPC Index stock a buy or sell?
A: The dominant strategy among neural network is to Buy S&P/BMV IPC Index Stock.
Q: Is S&P/BMV IPC Index stock a good investment?
A: The consensus rating for S&P/BMV IPC Index is Buy and assigned short-term Baa2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of S&P/BMV IPC Index stock?
A: The consensus rating for S&P/BMV IPC Index is Buy.
Q: What is the prediction period for S&P/BMV IPC Index stock?
A: The prediction period for S&P/BMV IPC Index is (n+3 month)