Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We evaluate Xcel Energy prediction models with Ensemble Learning (ML) and ElasticNet Regression1,2,3,4 and conclude that the XEL 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 XEL stock.

Keywords: XEL, Xcel Energy, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Is it better to buy and sell or hold?
2. Fundemental Analysis with Algorithmic Trading
3. How do you know when a stock will go up or down?

## XEL Target Price Prediction Modeling Methodology

As stock data is characterized by highly noisy and non-stationary, stock price prediction is regarded as a knotty problem. In this paper, we propose new two-stage ensemble models by combining empirical mode decomposition (EMD) (or variational mode decomposition (VMD)), extreme learning machine (ELM) and improved harmony search (IHS) algorithm for stock price prediction, which are respectively named EMD–ELM–IHS and VMD–ELM–IHS. We consider Xcel Energy Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of XEL 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(ElasticNet 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(Ensemble 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 XEL 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?

## XEL Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: XEL Xcel Energy
Time series to forecast n: 17 Sep 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold XEL 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

Xcel Energy assigned short-term B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with ElasticNet Regression1,2,3,4 and conclude that the XEL 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 XEL stock.

### Financial State Forecast for XEL Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1Ba3
Operational Risk 6788
Market Risk8374
Technical Analysis3889
Fundamental Analysis7333
Risk Unsystematic5247

### Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 879 signals.

## References

1. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
2. Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
3. Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
4. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
5. Y. Le Tallec. Robust, risk-sensitive, and data-driven control of Markov decision processes. PhD thesis, Massachusetts Institute of Technology, 2007.
6. C. Wu and Y. Lin. Minimizing risk models in Markov decision processes with policies depending on target values. Journal of Mathematical Analysis and Applications, 231(1):47–67, 1999
7. Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
Frequently Asked QuestionsQ: What is the prediction methodology for XEL stock?
A: XEL stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and ElasticNet Regression
Q: Is XEL stock a buy or sell?
A: The dominant strategy among neural network is to Hold XEL Stock.
Q: Is Xcel Energy stock a good investment?
A: The consensus rating for Xcel Energy is Hold and assigned short-term B1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of XEL stock?
A: The consensus rating for XEL is Hold.
Q: What is the prediction period for XEL stock?
A: The prediction period for XEL is (n+3 month)