TD1 Stock: A Sinking Ship?

Outlook: TALI DIGITAL LIMITED is assigned short-term B2 & long-term B2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Hold
Time series to forecast n: for Weeks²
Methodology : Multi-Task Learning (ML)
Hypothesis Testing : Wilcoxon Rank-Sum Test
Surveillance : Major exchange and OTC

¹The accuracy of the model is being monitored on a regular basis.(15-minute period)

²Time series is updated based on short-term trends.

Summary

TALI DIGITAL LIMITED prediction model is evaluated with Multi-Task Learning (ML) and Wilcoxon Rank-Sum Test^1,2,3,4 and it is concluded that the TD1 stock is predictable in the short/long term. Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Hold

Key Points

1. What is a prediction confidence?
2. How do you decide buy or sell a stock?
3. How do predictive algorithms actually work?

TD1 Target Price Prediction Modeling Methodology

We consider TALI DIGITAL LIMITED Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of TD1 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(Wilcoxon Rank-Sum Test)^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(Multi-Task Learning (ML)) X S(n):→ 1 Year $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of TD1 stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Multi-Task Learning (ML)

Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently.

Wilcoxon Rank-Sum Test

The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.

 

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?

TD1 Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: TD1 TALI DIGITAL LIMITED
Time series to forecast: 1 Year

According to price forecasts, the dominant strategy among neural network is: Hold

Strategic Interaction Table Legend:

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 (Grey to Black): *Technical Analysis%

Financial Data Adjustments for Multi-Task Learning (ML) based TD1 Stock Prediction Model

1. For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the contract.
2. Measurement of a financial asset or financial liability and classification of recognised changes in its value are determined by the item's classification and whether the item is part of a designated hedging relationship. Those requirements can create a measurement or recognition inconsistency (sometimes referred to as an 'accounting mismatch') when, for example, in the absence of designation as at fair value through profit or loss, a financial asset would be classified as subsequently measured at fair value through profit or loss and a liability the entity considers related would be subsequently measured at amortised cost (with changes in fair value not recognised). In such circumstances, an entity may conclude that its financial statements would provide more relevant information if both the asset and the liability were measured as at fair value through profit or loss.
3. However, in some cases, the time value of money element may be modified (ie imperfect). That would be the case, for example, if a financial asset's interest rate is periodically reset but the frequency of that reset does not match the tenor of the interest rate (for example, the interest rate resets every month to a one-year rate) or if a financial asset's interest rate is periodically reset to an average of particular short- and long-term interest rates. In such cases, an entity must assess the modification to determine whether the contractual cash flows represent solely payments of principal and interest on the principal amount outstanding. In some circumstances, the entity may be able to make that determination by performing a qualitative assessment of the time value of money element whereas, in other circumstances, it may be necessary to perform a quantitative assessment.
4. Because the hedge accounting model is based on a general notion of offset between gains and losses on the hedging instrument and the hedged item, hedge effectiveness is determined not only by the economic relationship between those items (ie the changes in their underlyings) but also by the effect of credit risk on the value of both the hedging instrument and the hedged item. The effect of credit risk means that even if there is an economic relationship between the hedging instrument and the hedged item, the level of offset might become erratic. This can result from a change in the credit risk of either the hedging instrument or the hedged item that is of such a magnitude that the credit risk dominates the value changes that result from the economic relationship (ie the effect of the changes in the underlyings). A level of magnitude that gives rise to dominance is one that would result in the loss (or gain) from credit risk frustrating the effect of changes in the underlyings on the value of the hedging instrument or the hedged item, even if those changes were significant.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

TD1 TALI DIGITAL LIMITED Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B2
Income Statement	C	Caa2
Balance Sheet	C	B1
Leverage Ratios	B2	Caa2
Cash Flow	Baa2	Caa2
Rates of Return and Profitability	B2	Baa2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Conclusions

TALI DIGITAL LIMITED is assigned short-term B2 & long-term B2 estimated rating. TALI DIGITAL LIMITED prediction model is evaluated with Multi-Task Learning (ML) and Wilcoxon Rank-Sum Test^1,2,3,4 and it is concluded that the TD1 stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Hold

Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 577 signals.

References

1. Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
2. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
3. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
4. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
5. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
6. Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
7. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014

Frequently Asked Questions

Q: What is the prediction methodology for TD1 stock?
A: TD1 stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is TD1 stock a buy or sell?
A: The dominant strategy among neural network is to Hold TD1 Stock.
Q: Is TALI DIGITAL LIMITED stock a good investment?
A: The consensus rating for TALI DIGITAL LIMITED is Hold and is assigned short-term B2 & long-term B2 estimated rating.
Q: What is the consensus rating of TD1 stock?
A: The consensus rating for TD1 is Hold.
Q: What is the prediction period for TD1 stock?
A: The prediction period for TD1 is 1 Year

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