## Abstract

**Outlook:**iShares S&P/TSX 60 Index ETF assigned short-term B1 & long-term B2 forecasted stock rating.

**Rating:**Buy

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted.(Akhtar, M.M., Zamani, A.S., Khan, S., Shatat, A.S.A., Dilshad, S. and Samdani, F., 2022. Stock market prediction based on statistical data using machine learning algorithms. Journal of King Saud University-Science, 34(4), p.101940.)** We evaluate iShares S&P/TSX 60 Index ETF prediction models with Transfer Learning (ML) and Factor ^{1,2,3,4} and conclude that the XIU: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 XIU:TSX stock.**

## Key Points

- How do you pick a stock?
- How do you decide buy or sell a stock?
- Is it better to buy and sell or hold?

## XIU:TSX Target Price Prediction Modeling Methodology

We consider iShares S&P/TSX 60 Index ETF Decision Process with Transfer Learning (ML) where A is the set of discrete actions of XIU: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(Factor)

^{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(Transfer Learning (ML)) X S(n):→ (n+6 month) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of XIU: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?

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

**Sample Set:**Neural Network

**Stock/Index:**XIU:TSX iShares S&P/TSX 60 Index ETF

**Time series to forecast n: 05 Dec 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy XIU: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 iShares S&P/TSX 60 Index ETF

- 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.
- The purpose of estimating expected credit losses is neither to estimate a worstcase scenario nor to estimate the best-case scenario. Instead, an estimate of expected credit losses shall always reflect the possibility that a credit loss occurs and the possibility that no credit loss occurs even if the most likely outcome is no credit loss.
- If items are hedged together as a group in a cash flow hedge, they might affect different line items in the statement of profit or loss and other comprehensive income. The presentation of hedging gains or losses in that statement depends on the group of items
- An entity need not undertake an exhaustive search for information but shall consider all reasonable and supportable information that is available without undue cost or effort and that is relevant to the estimate of expected credit losses, including the effect of expected prepayments. The information used shall include factors that are specific to the borrower, general economic conditions and an assessment of both the current as well as the forecast direction of conditions at the reporting date. An entity may use various sources of data, that may be both internal (entity-specific) and external. Possible data sources include internal historical credit loss experience, internal ratings, credit loss experience of other entities and external ratings, reports and statistics. Entities that have no, or insufficient, sources of entityspecific data may use peer group experience for the comparable financial instrument (or groups of financial instruments).

*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 S&P/TSX 60 Index ETF assigned short-term B1 & long-term B2 forecasted stock rating.** We evaluate the prediction models Transfer Learning (ML) with Factor ^{1,2,3,4} and conclude that the XIU: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 XIU:TSX stock.**

### Financial State Forecast for XIU:TSX iShares S&P/TSX 60 Index ETF Options & Futures

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

Outlook* | B1 | B2 |

Operational Risk | 39 | 42 |

Market Risk | 81 | 74 |

Technical Analysis | 69 | 51 |

Fundamental Analysis | 67 | 39 |

Risk Unsystematic | 37 | 67 |

### Prediction Confidence Score

## References

- Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
- S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
- Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
- 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
- Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
- Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
- Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.

## Frequently Asked Questions

Q: What is the prediction methodology for XIU:TSX stock?A: XIU:TSX stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Factor

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

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

Q: Is iShares S&P/TSX 60 Index ETF stock a good investment?

A: The consensus rating for iShares S&P/TSX 60 Index ETF is Buy and assigned short-term B1 & long-term B2 forecasted stock rating.

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

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

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

A: The prediction period for XIU:TSX is (n+6 month)