How do you determine buy or sell? (HUM Stock Forecast)

Understanding the pattern of financial activities and predicting their development and changes are research hotspots in academic and financial circles. Because financial data contain complex, incomplete and fuzzy information, predicting their development trends is an extremely difficult challenge. Fluctuations in financial data depend on a myriad of correlated constantly changing factors. Therefore, predicting and analysing financial data are a nonlinear, time-dependent problem. Deep neural networks (DNNs) combine the advantages of deep learning (DL) and neural networks and can be used to solve nonlinear problems more satisfactorily compared to conventional machine learning algorithms. We evaluate Humana prediction models with Multi-Instance Learning (ML) and Chi-Square^1,2,3,4 and conclude that the HUM 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 HUM stock.

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

Key Points

1. How do you pick a stock?
2. Why do we need predictive models?
3. What is prediction in deep learning?

HUM Target Price Prediction Modeling Methodology

Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We consider Humana Stock Decision Process with Chi-Square where A is the set of discrete actions of HUM 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(Chi-Square)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Multi-Instance Learning (ML)) X S(n):→ (n+3 month) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of HUM 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?

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

Sample Set: Neural Network
Stock/Index: HUM Humana
Time series to forecast n: 26 Oct 2022 for (n+3 month)

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

Adjusted IFRS* Prediction Methods for Humana

1. Alternatively, the entity may base the assessment on both types of information, ie qualitative factors that are not captured through the internal ratings process and a specific internal rating category at the reporting date, taking into consideration the credit risk characteristics at initial recognition, if both types of information are relevant.
2. For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the contract.
3. Sales that occur for other reasons, such as sales made to manage credit concentration risk (without an increase in the assets' credit risk), may also be consistent with a business model whose objective is to hold financial assets in order to collect contractual cash flows. In particular, such sales may be consistent with a business model whose objective is to hold financial assets in order to collect contractual cash flows if those sales are infrequent (even if significant in value) or insignificant in value both individually and in aggregate (even if frequent). If more than an infrequent number of such sales are made out of a portfolio and those sales are more than insignificant in value (either individually or in aggregate), the entity needs to assess whether and how such sales are consistent with an objective of collecting contractual cash flows. Whether a third party imposes the requirement to sell the financial assets, or that activity is at the entity's discretion, is not relevant to this assessment. An increase in the frequency or value of sales in a particular period is not necessarily inconsistent with an objective to hold financial assets in order to collect contractual cash flows, if an entity can explain the reasons for those sales and demonstrate why those sales do not reflect a change in the entity's business model. In addition, sales may be consistent with the objective of holding financial assets in order to collect contractual cash flows if the sales are made close to the maturity of the financial assets and the proceeds from the sales approximate the collection of the remaining contractual cash flows.
4. For the purpose of this Standard, reasonable and supportable information is that which is reasonably available at the reporting date without undue cost or effort, including information about past events, current conditions and forecasts of future economic conditions. Information that is available for financial reporting purposes is considered to be available without undue cost or effort.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

Humana assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Multi-Instance Learning (ML) with Chi-Square^1,2,3,4 and conclude that the HUM 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 HUM stock.

Financial State Forecast for HUM Humana Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B2
Operational Risk	69	37
Market Risk	46	44
Technical Analysis	84	51
Fundamental Analysis	33	74
Risk Unsystematic	45	48

Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 723 signals.

References

1. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
2. Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
3. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
4. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
5. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
6. Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
7. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]

Frequently Asked Questions

Q: What is the prediction methodology for HUM stock?
A: HUM stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Chi-Square
Q: Is HUM stock a buy or sell?
A: The dominant strategy among neural network is to Buy HUM Stock.
Q: Is Humana stock a good investment?
A: The consensus rating for Humana is Buy and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of HUM stock?
A: The consensus rating for HUM is Buy.
Q: What is the prediction period for HUM stock?
A: The prediction period for HUM is (n+3 month)

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