VOYA Options & Futures Prediction

Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demonstrated that the seemingly random movement patterns in the time series of a stock price can be predicted with a high level of accuracy. We evaluate Voya Financial prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Chi-Square^1,2,3,4 and conclude that the VOYA 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 VOYA stock.

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

Key Points

1. Which neural network is best for prediction?
2. Game Theory
3. What is statistical models in machine learning?

VOYA Target Price Prediction Modeling Methodology

The stock market prediction has attracted much attention from academia as well as business. Due to the non-linear, volatile and complex nature of the market, it is quite difficult to predict. As the stock markets grow bigger, more investors pay attention to develop a systematic approach to predict the stock market. We consider Voya Financial Stock Decision Process with Chi-Square where A is the set of discrete actions of VOYA 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(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+8 weeks) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of VOYA 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?

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

Sample Set: Neural Network
Stock/Index: VOYA Voya Financial
Time series to forecast n: 17 Nov 2022 for (n+8 weeks)

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

1. Annual Improvements to IFRSs 2010–2012 Cycle, issued in December 2013, amended paragraphs 4.2.1 and 5.7.5 as a consequential amendment derived from the amendment to IFRS 3. An entity shall apply that amendment prospectively to business combinations to which the amendment to IFRS 3 applies.
2. Paragraph 5.7.5 permits an entity to make an irrevocable election to present in other comprehensive income changes in the fair value of an investment in an equity instrument that is not held for trading. This election is made on an instrument-by-instrument (ie share-by-share) basis. Amounts presented in other comprehensive income shall not be subsequently transferred to profit or loss. However, the entity may transfer the cumulative gain or loss within equity. Dividends on such investments are recognised in profit or loss in accordance with paragraph 5.7.6 unless the dividend clearly represents a recovery of part of the cost of the investment.
3. An entity's business model is determined at a level that reflects how groups of financial assets are managed together to achieve a particular business objective. The entity's business model does not depend on management's intentions for an individual instrument. Accordingly, this condition is not an instrument-by-instrument approach to classification and should be determined on a higher level of aggregation. However, a single entity may have more than one business model for managing its financial instruments. Consequently, classification need not be determined at the reporting entity level. For example, an entity may hold a portfolio of investments that it manages in order to collect contractual cash flows and another portfolio of investments that it manages in order to trade to realise fair value changes. Similarly, in some circumstances, it may be appropriate to separate a portfolio of financial assets into subportfolios in order to reflect the level at which an entity manages those financial assets. For example, that may be the case if an entity originates or purchases a portfolio of mortgage loans and manages some of the loans with an objective of collecting contractual cash flows and manages the other loans with an objective of selling them.
4. If a put option obligation written by an entity or call option right held by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at amortised cost, the associated liability is measured at its cost (ie the consideration received) adjusted for the amortisation of any difference between that cost and the gross carrying amount of the transferred asset at the expiration date of the option. For example, assume that the gross carrying amount of the asset on the date of the transfer is CU98 and that the consideration received is CU95. The gross carrying amount of the asset on the option exercise date will be CU100. The initial carrying amount of the associated liability is CU95 and the difference between CU95 and CU100 is recognised in profit or loss using the effective interest method. If the option is exercised, any difference between the carrying amount of the associated liability and the exercise price is recognised in profit or loss.

*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

Voya Financial assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Chi-Square^1,2,3,4 and conclude that the VOYA 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 VOYA stock.

Financial State Forecast for VOYA Voya Financial Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B1
Operational Risk	90	56
Market Risk	48	33
Technical Analysis	62	34
Fundamental Analysis	58	84
Risk Unsystematic	47	88

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 494 signals.

References

1. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
2. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
3. 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
4. Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
5. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
6. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
7. Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press

Frequently Asked Questions

Q: What is the prediction methodology for VOYA stock?
A: VOYA stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Chi-Square
Q: Is VOYA stock a buy or sell?
A: The dominant strategy among neural network is to Hold VOYA Stock.
Q: Is Voya Financial stock a good investment?
A: The consensus rating for Voya Financial is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of VOYA stock?
A: The consensus rating for VOYA is Hold.
Q: What is the prediction period for VOYA stock?
A: The prediction period for VOYA is (n+8 weeks)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login