Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. We evaluate Hewlett Packard Enterprise prediction models with Multi-Instance Learning (ML) and Multiple Regression1,2,3,4 and conclude that the HPE stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold HPE stock.

Keywords: HPE, Hewlett Packard Enterprise, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Short/Long Term Stocks
2. Understanding Buy, Sell, and Hold Ratings
3. How useful are statistical predictions? ## HPE Target Price Prediction Modeling Methodology

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. We consider Hewlett Packard Enterprise Stock Decision Process with Multiple Regression where A is the set of discrete actions of HPE 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(Multi-Instance Learning (ML)) X S(n):→ (n+8 weeks) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of HPE 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?

## HPE Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: HPE Hewlett Packard Enterprise
Time series to forecast n: 18 Oct 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold HPE 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

Hewlett Packard Enterprise assigned short-term B1 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Multi-Instance Learning (ML) with Multiple Regression1,2,3,4 and conclude that the HPE stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold HPE stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1Ba1
Operational Risk 5167
Market Risk6043
Technical Analysis6182
Fundamental Analysis8484
Risk Unsystematic3483

### Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 571 signals.

## References

1. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
2. J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.
3. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
4. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
5. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
6. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
7. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
Frequently Asked QuestionsQ: What is the prediction methodology for HPE stock?
A: HPE stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Multiple Regression
Q: Is HPE stock a buy or sell?
A: The dominant strategy among neural network is to Hold HPE Stock.
Q: Is Hewlett Packard Enterprise stock a good investment?
A: The consensus rating for Hewlett Packard Enterprise is Hold and assigned short-term B1 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of HPE stock?
A: The consensus rating for HPE is Hold.
Q: What is the prediction period for HPE stock?
A: The prediction period for HPE is (n+8 weeks)