**Outlook:**iShares Core Canadian Short Term Bond Index ETF assigned short-term B1 & long-term B1 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 12 Dec 2022**for (n+16 weeks)

**Methodology :**Transductive Learning (ML)

## Abstract

The stock market is an interesting industry to study. There are various variations present in it. Many experts have been studying and researching on the various trends that the stock market goes through. One of the major studies has been the attempt to predict the stock prices of various companies based on historical data. Prediction of stock prices will greatly help people to understand where and how to invest so that the risk of losing money is minimized.(Cheng, L.C., Huang, Y.H. and Wu, M.E., 2018, December. Applied attention-based LSTM neural networks in stock prediction. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 4716-4718). IEEE.)** We evaluate iShares Core Canadian Short Term Bond Index ETF prediction models with Transductive Learning (ML) and Logistic Regression ^{1,2,3,4} and conclude that the XSB:TSX stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy**

## Key Points

- Short/Long Term Stocks
- Stock Rating
- How do you decide buy or sell a stock?

## XSB:TSX Target Price Prediction Modeling Methodology

We consider iShares Core Canadian Short Term Bond Index ETF Decision Process with Transductive Learning (ML) where A is the set of discrete actions of XSB: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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Transductive Learning (ML)) X S(n):→ (n+16 weeks) $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

p:Price signals of XSB: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?

## XSB:TSX Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**XSB:TSX iShares Core Canadian Short Term Bond Index ETF

**Time series to forecast n: 12 Dec 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) 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%**

## Adjusted IFRS* Prediction Methods for iShares Core Canadian Short Term Bond Index ETF

- In addition to those hedging relationships specified in paragraph 6.9.1, an entity shall apply the requirements in paragraphs 6.9.11 and 6.9.12 to new hedging relationships in which an alternative benchmark rate is designated as a non-contractually specified risk component (see paragraphs 6.3.7(a) and B6.3.8) when, because of interest rate benchmark reform, that risk component is not separately identifiable at the date it is designated.
- If there is a hedging relationship between a non-derivative monetary asset and a non-derivative monetary liability, changes in the foreign currency component of those financial instruments are presented in profit or loss.
- If the group of items does not have any offsetting risk positions (for example, a group of foreign currency expenses that affect different line items in the statement of profit or loss and other comprehensive income that are hedged for foreign currency risk) then the reclassified hedging instrument gains or losses shall be apportioned to the line items affected by the hedged items. This apportionment shall be done on a systematic and rational basis and shall not result in the grossing up of the net gains or losses arising from a single hedging instrument.
- When determining whether the recognition of lifetime expected credit losses is required, an entity shall consider reasonable and supportable information that is available without undue cost or effort and that may affect the credit risk on a financial instrument in accordance with paragraph 5.5.17(c). An entity need not undertake an exhaustive search for information when determining whether credit risk has increased significantly since initial recognition.

*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

iShares Core Canadian Short Term Bond Index ETF assigned short-term B1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Transductive Learning (ML) with Logistic Regression ^{1,2,3,4} and conclude that the XSB:TSX stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy**

### Financial State Forecast for XSB:TSX iShares Core Canadian Short Term Bond Index ETF Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B1 | B1 |

Operational Risk | 37 | 33 |

Market Risk | 35 | 68 |

Technical Analysis | 84 | 66 |

Fundamental Analysis | 70 | 60 |

Risk Unsystematic | 80 | 51 |

### Prediction Confidence Score

## References

- Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
- Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
- Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
- R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
- Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
- Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
- Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505

## Frequently Asked Questions

Q: What is the prediction methodology for XSB:TSX stock?A: XSB:TSX stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Logistic Regression

Q: Is XSB:TSX stock a buy or sell?

A: The dominant strategy among neural network is to Buy XSB:TSX Stock.

Q: Is iShares Core Canadian Short Term Bond Index ETF stock a good investment?

A: The consensus rating for iShares Core Canadian Short Term Bond Index ETF is Buy and assigned short-term B1 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of XSB:TSX stock?

A: The consensus rating for XSB:TSX is Buy.

Q: What is the prediction period for XSB:TSX stock?

A: The prediction period for XSB:TSX is (n+16 weeks)

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