LON:KNM KONAMI GROUP CORPORATION Stock Forecast

KONAMI GROUP CORPORATION Research Report

Summary

Impact of many factors on the stock prices makes the stock prediction a difficult and highly complicated task. In this paper, machine learning techniques have been applied for the stock price prediction in order to overcome such difficulties. In the implemented work, five models have been developed and their performances are compared in predicting the stock market trends. We evaluate KONAMI GROUP CORPORATION prediction models with Transfer Learning (ML) and Factor^1,2,3,4 and conclude that the LON:KNM stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:KNM stock.

Key Points

1. Stock Forecast Based On a Predictive Algorithm
2. How accurate is machine learning in stock market?
3. What are buy sell or hold recommendations?

LON:KNM Target Price Prediction Modeling Methodology

We consider KONAMI GROUP CORPORATION Stock Decision Process with Transfer Learning (ML) where A is the set of discrete actions of LON:KNM 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(Factor)^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(Transfer Learning (ML)) X S(n):→ (n+4 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of LON:KNM 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:KNM Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:KNM KONAMI GROUP CORPORATION
Time series to forecast n: 25 Nov 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:KNM 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 KONAMI GROUP CORPORATION

1. The significance of a change in the credit risk since initial recognition depends on the risk of a default occurring as at initial recognition. Thus, a given change, in absolute terms, in the risk of a default occurring will be more significant for a financial instrument with a lower initial risk of a default occurring compared to a financial instrument with a higher initial risk of a default occurring.
2. An entity can also designate only changes in the cash flows or fair value of a hedged item above or below a specified price or other variable (a 'one-sided risk'). The intrinsic value of a purchased option hedging instrument (assuming that it has the same principal terms as the designated risk), but not its time value, reflects a one-sided risk in a hedged item. For example, an entity can designate the variability of future cash flow outcomes resulting from a price increase of a forecast commodity purchase. In such a situation, the entity designates only cash flow losses that result from an increase in the price above the specified level. The hedged risk does not include the time value of a purchased option, because the time value is not a component of the forecast transaction that affects profit or loss.
3. At the date of initial application, an entity shall use reasonable and supportable information that is available without undue cost or effort to determine the credit risk at the date that a financial instrument was initially recognised (or for loan commitments and financial guarantee contracts at the date that the entity became a party to the irrevocable commitment in accordance with paragraph 5.5.6) and compare that to the credit risk at the date of initial application of this Standard.
4. A contractually specified inflation risk component of the cash flows of a recognised inflation-linked bond (assuming that there is no requirement to account for an embedded derivative separately) is separately identifiable and reliably measurable, as long as other cash flows of the instrument are not affected by the inflation risk component.

*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

KONAMI GROUP CORPORATION assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Factor^1,2,3,4 and conclude that the LON:KNM stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:KNM stock.

Financial State Forecast for LON:KNM KONAMI GROUP CORPORATION Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B2
Operational Risk	57	61
Market Risk	73	45
Technical Analysis	77	66
Fundamental Analysis	47	49
Risk Unsystematic	42	34

Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 872 signals.

References

1. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
2. Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
3. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
4. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
5. Artis, M. J. W. Zhang (1990), "BVAR forecasts for the G-7," International Journal of Forecasting, 6, 349–362.
6. Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.
7. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37

Frequently Asked Questions

Q: What is the prediction methodology for LON:KNM stock?
A: LON:KNM stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Factor
Q: Is LON:KNM stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:KNM Stock.
Q: Is KONAMI GROUP CORPORATION stock a good investment?
A: The consensus rating for KONAMI GROUP CORPORATION is Hold and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:KNM stock?
A: The consensus rating for LON:KNM is Hold.
Q: What is the prediction period for LON:KNM stock?
A: The prediction period for LON:KNM is (n+4 weeks)

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