LON:TBLD TINYBUILD INC. (Forecast)

Buy

Hold

Sell

Speculative

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Outlook: TINYBUILD INC. assigned short-term B2 & long-term Ba2 forecasted stock rating.
Dominant Strategy : Wait until speculative trend diminishes
Time series to forecast n: 06 Dec 2022 for (n+16 weeks)

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Abstract

Neural networks (NNs), as artificial intelligence (AI) methods, have become very important in making stock market predictions. Much research on the applications of NNs for solving business problems have proven their advantages over statistical and other methods that do not include AI, although there is no optimal methodology for a certain problem. (Naik, N. and Mohan, B.R., 2019, February. Optimal feature selection of technical indicator and stock prediction using machine learning technique. In International Conference on Emerging Technologies in Computer Engineering (pp. 261-268). Springer, Singapore.) We evaluate TINYBUILD INC. prediction models with Supervised Machine Learning (ML) and Pearson Correlation^1,2,3,4 and conclude that the LON:TBLD stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:TBLD stock.

Key Points

1. Is Target price a good indicator?
2. Trust metric by Neural Network
3. Should I buy stocks now or wait amid such uncertainty?

LON:TBLD Target Price Prediction Modeling Methodology

We consider TINYBUILD INC. Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of LON:TBLD 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(Pearson Correlation)^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(Supervised Machine Learning (ML)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of LON:TBLD 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:TBLD Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:TBLD TINYBUILD INC.
Time series to forecast n: 06 Dec 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:TBLD 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 TINYBUILD INC.

1. IFRS 17, issued in May 2017, amended paragraphs 2.1, B2.1, B2.4, B2.5 and B4.1.30, and added paragraph 3.3.5. Amendments to IFRS 17, issued in June 2020, further amended paragraph 2.1 and added paragraphs 7.2.36‒7.2.42. An entity shall apply those amendments when it applies IFRS 17.
2. 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.
3. If, in applying paragraph 7.2.44, an entity reinstates a discontinued hedging relationship, the entity shall read references in paragraphs 6.9.11 and 6.9.12 to the date the alternative benchmark rate is designated as a noncontractually specified risk component for the first time as referring to the date of initial application of these amendments (ie the 24-month period for that alternative benchmark rate designated as a non-contractually specified risk component begins from the date of initial application of these amendments).
4. If changes are made in addition to those changes required by interest rate benchmark reform to the financial asset or financial liability designated in a hedging relationship (as described in paragraphs 5.4.6–5.4.8) or to the designation of the hedging relationship (as required by paragraph 6.9.1), an entity shall first apply the applicable requirements in this Standard to determine if those additional changes result in the discontinuation of hedge accounting. If the additional changes do not result in the discontinuation of hedge accounting, an entity shall amend the formal designation of the hedging relationship as specified in paragraph 6.9.1.

*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

TINYBUILD INC. assigned short-term B2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Pearson Correlation^1,2,3,4 and conclude that the LON:TBLD stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:TBLD stock.

Financial State Forecast for LON:TBLD TINYBUILD INC. Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba2
Operational Risk	51	81
Market Risk	40	72
Technical Analysis	41	70
Fundamental Analysis	54	52
Risk Unsystematic	90	58

Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 853 signals.

References

1. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
2. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
3. J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
4. Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
5. Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
6. Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
7. Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.

Frequently Asked Questions

Q: What is the prediction methodology for LON:TBLD stock?
A: LON:TBLD stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Pearson Correlation
Q: Is LON:TBLD stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:TBLD Stock.
Q: Is TINYBUILD INC. stock a good investment?
A: The consensus rating for TINYBUILD INC. is Wait until speculative trend diminishes and assigned short-term B2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of LON:TBLD stock?
A: The consensus rating for LON:TBLD is Wait until speculative trend diminishes.
Q: What is the prediction period for LON:TBLD stock?
A: The prediction period for LON:TBLD is (n+16 weeks)

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