HXT:TSX Horizons S&P/TSX 60 Index ETF

Outlook: Horizons S&P/TSX 60 Index ETF is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 05 Mar 2023 for (n+6 month)
Methodology : Modular Neural Network (Emotional Trigger/Responses Analysis)

Abstract

Horizons S&P/TSX 60 Index ETF prediction model is evaluated with Modular Neural Network (Emotional Trigger/Responses Analysis) and Logistic Regression^1,2,3,4 and it is concluded that the HXT: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: Buy

Key Points

1. How do you pick a stock?
2. How can neural networks improve predictions?
3. Understanding Buy, Sell, and Hold Ratings

HXT:TSX Target Price Prediction Modeling Methodology

We consider Horizons S&P/TSX 60 Index ETF Decision Process with Modular Neural Network (Emotional Trigger/Responses Analysis) where A is the set of discrete actions of HXT: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(Logistic 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(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+6 month) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of HXT: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?

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

Sample Set: Neural Network
Stock/Index: HXT:TSX Horizons S&P/TSX 60 Index ETF
Time series to forecast n: 05 Mar 2023 for (n+6 month)

According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

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%

IFRS Reconciliation Adjustments for Horizons S&P/TSX 60 Index ETF

1. If an entity measures a hybrid contract at fair value in accordance with paragraphs 4.1.2A, 4.1.4 or 4.1.5 but the fair value of the hybrid contract had not been measured in comparative reporting periods, the fair value of the hybrid contract in the comparative reporting periods shall be the sum of the fair values of the components (ie the non-derivative host and the embedded derivative) at the end of each comparative reporting period if the entity restates prior periods (see paragraph 7.2.15).
2. An entity may use practical expedients when measuring expected credit losses if they are consistent with the principles in paragraph 5.5.17. An example of a practical expedient is the calculation of the expected credit losses on trade receivables using a provision matrix. The entity would use its historical credit loss experience (adjusted as appropriate in accordance with paragraphs B5.5.51–B5.5.52) for trade receivables to estimate the 12-month expected credit losses or the lifetime expected credit losses on the financial assets as relevant. A provision matrix might, for example, specify fixed provision rates depending on the number of days that a trade receivable is past due (for example, 1 per cent if not past due, 2 per cent if less than 30 days past due, 3 per cent if more than 30 days but less than 90 days past due, 20 per cent if 90–180 days past due etc). Depending on the diversity of its customer base, the entity would use appropriate groupings if its historical credit loss experience shows significantly different loss patterns for different customer segments. Examples of criteria that might be used to group assets include geographical region, product type, customer rating, collateral or trade credit insurance and type of customer (such as wholesale or retail)
3. If an entity previously accounted for a derivative liability that is linked to, and must be settled by, delivery of an equity instrument that does not have a quoted price in an active market for an identical instrument (ie a Level 1 input) at cost in accordance with IAS 39, it shall measure that derivative liability at fair value at the date of initial application. Any difference between the previous carrying amount and the fair value shall be recognised in the opening retained earnings of the reporting period that includes the date of initial application.
4. The decision of an entity to designate a financial asset or financial liability as at fair value through profit or loss is similar to an accounting policy choice (although, unlike an accounting policy choice, it is not required to be applied consistently to all similar transactions). When an entity has such a choice, paragraph 14(b) of IAS 8 requires the chosen policy to result in the financial statements providing reliable and more relevant information about the effects of transactions, other events and conditions on the entity's financial position, financial performance or cash flows. For example, in the case of designation of a financial liability as at fair value through profit or loss, paragraph 4.2.2 sets out the two circumstances when the requirement for more relevant information will be met. Accordingly, to choose such designation in accordance with paragraph 4.2.2, the entity needs to demonstrate that it falls within one (or both) of these two circumstances.

*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.

Conclusions

Horizons S&P/TSX 60 Index ETF is assigned short-term Ba1 & long-term Ba1 estimated rating. Horizons S&P/TSX 60 Index ETF prediction model is evaluated with Modular Neural Network (Emotional Trigger/Responses Analysis) and Logistic Regression^1,2,3,4 and it is concluded that the HXT: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: Buy

HXT:TSX Horizons S&P/TSX 60 Index ETF Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	B1
Balance Sheet	Baa2	Baa2
Leverage Ratios	Baa2	Caa2
Cash Flow	B1	Baa2
Rates of Return and Profitability	B3	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?

Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 806 signals.

References

1. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
2. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
3. Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
4. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
5. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
6. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
7. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8

Frequently Asked Questions

Q: What is the prediction methodology for HXT:TSX stock?
A: HXT:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Logistic Regression
Q: Is HXT:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy HXT:TSX Stock.
Q: Is Horizons S&P/TSX 60 Index ETF stock a good investment?
A: The consensus rating for Horizons S&P/TSX 60 Index ETF is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of HXT:TSX stock?
A: The consensus rating for HXT:TSX is Buy.
Q: What is the prediction period for HXT:TSX stock?
A: The prediction period for HXT:TSX is (n+6 month)

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