Aptose Biosciences Inc. Research Report

## Summary

Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. We evaluate Aptose Biosciences Inc. prediction models with Modular Neural Network (DNN Layer) and Independent T-Test1,2,3,4 and conclude that the APS:TSX 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 Buy APS:TSX stock.

## Key Points

1. Short/Long Term Stocks
2. Reaction Function

## APS:TSX Target Price Prediction Modeling Methodology

We consider Aptose Biosciences Inc. Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of APS:TSX 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(Independent T-Test)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+6 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of APS:TSX 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?

## APS:TSX Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: APS:TSX Aptose Biosciences Inc.
Time series to forecast n: 03 Dec 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy APS:TSX 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 Aptose Biosciences Inc.

1. As with all fair value measurements, an entity's measurement method for determining the portion of the change in the liability's fair value that is attributable to changes in its credit risk must make maximum use of relevant observable inputs and minimum use of unobservable inputs.
2. IFRS 7 defines credit risk as 'the risk that one party to a financial instrument will cause a financial loss for the other party by failing to discharge an obligation'. The requirement in paragraph 5.7.7(a) relates to the risk that the issuer will fail to perform on that particular liability. It does not necessarily relate to the creditworthiness of the issuer. For example, if an entity issues a collateralised liability and a non-collateralised liability that are otherwise identical, the credit risk of those two liabilities will be different, even though they are issued by the same entity. The credit risk on the collateralised liability will be less than the credit risk of the non-collateralised liability. The credit risk for a collateralised liability may be close to zero.
3. A hedge of a firm commitment (for example, a hedge of the change in fuel price relating to an unrecognised contractual commitment by an electric utility to purchase fuel at a fixed price) is a hedge of an exposure to a change in fair value. Accordingly, such a hedge is a fair value hedge. However, in accordance with paragraph 6.5.4, a hedge of the foreign currency risk of a firm commitment could alternatively be accounted for as a cash flow hedge.
4. Compared to a business model whose objective is to hold financial assets to collect contractual cash flows, this business model will typically involve greater frequency and value of sales. This is because selling financial assets is integral to achieving the business model's objective instead of being only incidental to it. However, there is no threshold for the frequency or value of sales that must occur in this business model because both collecting contractual cash flows and selling financial assets are integral to achieving its objective.

*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

Aptose Biosciences Inc. assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Independent T-Test1,2,3,4 and conclude that the APS:TSX 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 Buy APS:TSX stock.

### Financial State Forecast for APS:TSX Aptose Biosciences Inc. Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 4178
Market Risk5240
Technical Analysis6443
Fundamental Analysis6463
Risk Unsystematic8678

### Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 474 signals.

## References

1. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
2. 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.
3. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
4. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
5. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
6. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
7. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
Frequently Asked QuestionsQ: What is the prediction methodology for APS:TSX stock?
A: APS:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Independent T-Test
Q: Is APS:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy APS:TSX Stock.
Q: Is Aptose Biosciences Inc. stock a good investment?
A: The consensus rating for Aptose Biosciences Inc. is Buy and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of APS:TSX stock?
A: The consensus rating for APS:TSX is Buy.
Q: What is the prediction period for APS:TSX stock?
A: The prediction period for APS:TSX is (n+6 month)