ABA AUSWIDE BANK LTD

AUSWIDE BANK LTD Research Report

Abstract

As stock data is characterized by highly noisy and non-stationary, stock price prediction is regarded as a knotty problem. In this paper, we propose new two-stage ensemble models by combining empirical mode decomposition (EMD) (or variational mode decomposition (VMD)), extreme learning machine (ELM) and improved harmony search (IHS) algorithm for stock price prediction, which are respectively named EMD–ELM–IHS and VMD–ELM–IHS.(Morris, K.J., Egan, S.D., Linsangan, J.L., Leung, C.K., Cuzzocrea, A. and Hoi, C.S., 2018, December. Token-based adaptive time-series prediction by ensembling linear and non-linear estimators: a machine learning approach for predictive analytics on big stock data. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1486-1491). IEEE.) We evaluate AUSWIDE BANK LTD prediction models with Supervised Machine Learning (ML) and Lasso Regression^1,2,3,4 and conclude that the ABA stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy ABA stock.

Key Points

1. How accurate is machine learning in stock market?
2. Is it better to buy and sell or hold?
3. Can neural networks predict stock market?

ABA Target Price Prediction Modeling Methodology

We consider AUSWIDE BANK LTD Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of ABA 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(Lasso Regression)^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+3 month) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of ABA 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?

ABA Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: ABA AUSWIDE BANK LTD
Time series to forecast n: 04 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy ABA 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 AUSWIDE BANK LTD

1. In cases such as those described in the preceding paragraph, to designate, at initial recognition, the financial assets and financial liabilities not otherwise so measured as at fair value through profit or loss may eliminate or significantly reduce the measurement or recognition inconsistency and produce more relevant information. For practical purposes, the entity need not enter into all of the assets and liabilities giving rise to the measurement or recognition inconsistency at exactly the same time. A reasonable delay is permitted provided that each transaction is designated as at fair value through profit or loss at its initial recognition and, at that time, any remaining transactions are expected to occur.
2. An entity's estimate of expected credit losses on loan commitments shall be consistent with its expectations of drawdowns on that loan commitment, ie it shall consider the expected portion of the loan commitment that will be drawn down within 12 months of the reporting date when estimating 12-month expected credit losses, and the expected portion of the loan commitment that will be drawn down over the expected life of the loan commitment when estimating lifetime expected credit losses.
3. Adjusting the hedge ratio allows an entity to respond to changes in the relationship between the hedging instrument and the hedged item that arise from their underlyings or risk variables. For example, a hedging relationship in which the hedging instrument and the hedged item have different but related underlyings changes in response to a change in the relationship between those two underlyings (for example, different but related reference indices, rates or prices). Hence, rebalancing allows the continuation of a hedging relationship in situations in which the relationship between the hedging instrument and the hedged item chang
4. Changes in market conditions that give rise to market risk include changes in a benchmark interest rate, the price of another entity's financial instrument, a commodity price, a foreign exchange rate or an index of prices or rates.

*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

AUSWIDE BANK LTD assigned short-term B2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Lasso Regression^1,2,3,4 and conclude that the ABA stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy ABA stock.

Financial State Forecast for ABA AUSWIDE BANK LTD Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba2
Operational Risk	37	76
Market Risk	84	62
Technical Analysis	58	62
Fundamental Analysis	68	87
Risk Unsystematic	39	57

Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 767 signals.

References

1. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
2. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
3. O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.
4. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
5. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
6. Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
7. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier

Frequently Asked Questions

Q: What is the prediction methodology for ABA stock?
A: ABA stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Lasso Regression
Q: Is ABA stock a buy or sell?
A: The dominant strategy among neural network is to Buy ABA Stock.
Q: Is AUSWIDE BANK LTD stock a good investment?
A: The consensus rating for AUSWIDE BANK LTD is Buy and assigned short-term B2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of ABA stock?
A: The consensus rating for ABA is Buy.
Q: What is the prediction period for ABA stock?
A: The prediction period for ABA is (n+3 month)

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