CBOE Volatility Index Target Price Prediction

CBOE Volatility Index Research Report

Summary

The prediction of stock price performance is a difficult and complex problem. Multivariate analytical techniques using both quantitative and qualitative variables have repeatedly been used to help form the basis of investor stock price expectations and, hence, influence investment decision making. However, the performance of multivariate analytical techniques is often less than conclusive and needs to be improved to more accurately forecast stock price performance. A neural network method has demonstrated its capability of addressing complex problems. We evaluate CBOE Volatility Index prediction models with Statistical Inference (ML) and Stepwise Regression1,2,3,4 and conclude that the CBOE Volatility Index stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold CBOE Volatility Index stock.

Key Points

  1. How useful are statistical predictions?
  2. Nash Equilibria
  3. What is Markov decision process in reinforcement learning?

CBOE Volatility Index Target Price Prediction Modeling Methodology

We consider CBOE Volatility Index Stock Decision Process with Statistical Inference (ML) where A is the set of discrete actions of CBOE Volatility 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(Stepwise Regression)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Statistical Inference (ML)) X S(n):→ (n+6 month) e x rx

n:Time series to forecast

p:Price signals of CBOE Volatility Index stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

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How do AC Investment Research machine learning (predictive) algorithms actually work?

CBOE Volatility Index Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: CBOE Volatility Index CBOE Volatility Index
Time series to forecast n: 21 Nov 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold CBOE Volatility 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%

Adjusted IFRS* Prediction Methods for CBOE Volatility Index

  1. IFRS 15, issued in May 2014, amended paragraphs 3.1.1, 4.2.1, 5.1.1, 5.2.1, 5.7.6, B3.2.13, B5.7.1, C5 and C42 and deleted paragraph C16 and its related heading. Paragraphs 5.1.3 and 5.7.1A, and a definition to Appendix A, were added. An entity shall apply those amendments when it applies IFRS 15.
  2. The characteristics of the hedged item, including how and when the hedged item affects profit or loss, also affect the period over which the forward element of a forward contract that hedges a time-period related hedged item is amortised, which is over the period to which the forward element relates. For example, if a forward contract hedges the exposure to variability in threemonth interest rates for a three-month period that starts in six months' time, the forward element is amortised during the period that spans months seven to nine.
  3. An entity's business model refers to how an entity manages its financial assets in order to generate cash flows. That is, the entity's business model determines whether cash flows will result from collecting contractual cash flows, selling financial assets or both. Consequently, this assessment is not performed on the basis of scenarios that the entity does not reasonably expect to occur, such as so-called 'worst case' or 'stress case' scenarios. For example, if an entity expects that it will sell a particular portfolio of financial assets only in a stress case scenario, that scenario would not affect the entity's assessment of the business model for those assets if the entity reasonably expects that such a scenario will not occur. If cash flows are realised in a way that is different from the entity's expectations at the date that the entity assessed the business model (for example, if the entity sells more or fewer financial assets than it expected when it classified the assets), that does not give rise to a prior period error in the entity's financial statements (see IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors) nor does it change the classification of the remaining financial assets held in that business model (ie those assets that the entity recognised in prior periods and still holds) as long as the entity considered all relevant information that was available at the time that it made the business model assessment.
  4. If there is a hedging relationship between a non-derivative monetary asset and a non-derivative monetary liability, changes in the foreign currency component of those financial instruments are presented 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

CBOE Volatility Index assigned short-term B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Statistical Inference (ML) with Stepwise Regression1,2,3,4 and conclude that the CBOE Volatility Index stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold CBOE Volatility Index stock.

Financial State Forecast for CBOE Volatility Index CBOE Volatility Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1Ba3
Operational Risk 5654
Market Risk9073
Technical Analysis5749
Fundamental Analysis5688
Risk Unsystematic5368

Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 545 signals.

References

  1. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
  2. A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
  3. E. Collins. Using Markov decision processes to optimize a nonlinear functional of the final distribution, with manufacturing applications. In Stochastic Modelling in Innovative Manufacturing, pages 30–45. Springer, 1997
  4. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
  5. Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
  6. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
  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 CBOE Volatility Index stock?
A: CBOE Volatility Index stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Stepwise Regression
Q: Is CBOE Volatility Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold CBOE Volatility Index Stock.
Q: Is CBOE Volatility Index stock a good investment?
A: The consensus rating for CBOE Volatility Index is Hold and assigned short-term B1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of CBOE Volatility Index stock?
A: The consensus rating for CBOE Volatility Index is Hold.
Q: What is the prediction period for CBOE Volatility Index stock?
A: The prediction period for CBOE Volatility Index is (n+6 month)

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