Modelling A.I. in Economics

LON:QBT QUANTUM BLOCKCHAIN TECHNOLOGIES PLC (Forecast)

Outlook: QUANTUM BLOCKCHAIN TECHNOLOGIES PLC assigned short-term B1 & long-term B2 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 08 Dec 2022 for (n+6 month)
Methodology : Modular Neural Network (Market Volatility Analysis)

Abstract

This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media.(Xia, Y., Liu, Y. and Chen, Z., 2013, November. Support Vector Regression for prediction of stock trend. In 2013 6th international conference on information management, innovation management and industrial engineering (Vol. 2, pp. 123-126). IEEE.) We evaluate QUANTUM BLOCKCHAIN TECHNOLOGIES PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Beta1,2,3,4 and conclude that the LON:QBT stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

Key Points

  1. Is Target price a good indicator?
  2. What are buy sell or hold recommendations?
  3. Technical Analysis with Algorithmic Trading

LON:QBT Target Price Prediction Modeling Methodology

We consider QUANTUM BLOCKCHAIN TECHNOLOGIES PLC Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of LON:QBT 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(Beta)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(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+6 month) R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of LON:QBT stock

j:Nash equilibria (Neural Network)

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?

LON:QBT Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:QBT QUANTUM BLOCKCHAIN TECHNOLOGIES PLC
Time series to forecast n: 08 Dec 2022 for (n+6 month)

According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

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 QUANTUM BLOCKCHAIN TECHNOLOGIES PLC

  1. All investments in equity instruments and contracts on those instruments must be measured at fair value. However, in limited circumstances, cost may be an appropriate estimate of fair value. That may be the case if insufficient more recent information is available to measure fair value, or if there is a wide range of possible fair value measurements and cost represents the best estimate of fair value within that range.
  2. One of the defining characteristics of a derivative is that it has an initial net investment that is smaller than would be required for other types of contracts that would be expected to have a similar response to changes in market factors. An option contract meets that definition because the premium is less than the investment that would be required to obtain the underlying financial instrument to which the option is linked. A currency swap that requires an initial exchange of different currencies of equal fair values meets the definition because it has a zero initial net investment.
  3. Hedge effectiveness is the extent to which changes in the fair value or the cash flows of the hedging instrument offset changes in the fair value or the cash flows of the hedged item (for example, when the hedged item is a risk component, the relevant change in fair value or cash flows of an item is the one that is attributable to the hedged risk). Hedge ineffectiveness is the extent to which the changes in the fair value or the cash flows of the hedging instrument are greater or less than those on the hedged item.
  4. Although the objective of an entity's business model may be to hold financial assets in order to collect contractual cash flows, the entity need not hold all of those instruments until maturity. Thus an entity's business model can be to hold financial assets to collect contractual cash flows even when sales of financial assets occur or are expected to occur in the future.

*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

QUANTUM BLOCKCHAIN TECHNOLOGIES PLC assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Beta1,2,3,4 and conclude that the LON:QBT stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

Financial State Forecast for LON:QBT QUANTUM BLOCKCHAIN TECHNOLOGIES PLC Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B2
Operational Risk 4550
Market Risk5737
Technical Analysis8964
Fundamental Analysis5862
Risk Unsystematic4443

Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 611 signals.

References

  1. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
  2. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
  3. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
  4. Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
  5. Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
  6. Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
  7. Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:QBT stock?
A: LON:QBT stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Beta
Q: Is LON:QBT stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:QBT Stock.
Q: Is QUANTUM BLOCKCHAIN TECHNOLOGIES PLC stock a good investment?
A: The consensus rating for QUANTUM BLOCKCHAIN TECHNOLOGIES PLC is Sell and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:QBT stock?
A: The consensus rating for LON:QBT is Sell.
Q: What is the prediction period for LON:QBT stock?
A: The prediction period for LON:QBT is (n+6 month)

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