## Summary

This paper proposes genetic algorithms (GAs) approach to feature discretization and the determination of connection weights for artificial neural networks (ANNs) to predict the stock price index. Previous research proposed many hybrid models of ANN and GA for the method of training the network, feature subset selection, and topology optimization.** We evaluate S&P/TSX Composite Index prediction models with Modular Neural Network (Market News Sentiment Analysis) and Polynomial Regression ^{1,2,3,4} and conclude that the S&P/TSX Composite Index stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold S&P/TSX Composite Index stock.**

## Key Points

- Can we predict stock market using machine learning?
- What is a prediction confidence?
- How can neural networks improve predictions?

## S&P/TSX Composite Index Target Price Prediction Modeling Methodology

We consider S&P/TSX Composite Index Stock Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of S&P/TSX Composite Index 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(Polynomial 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(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+16 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of S&P/TSX Composite Index 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?

## S&P/TSX Composite Index Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**S&P/TSX Composite Index S&P/TSX Composite Index

**Time series to forecast n: 26 Nov 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold S&P/TSX Composite Index 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 S&P/TSX Composite Index

- If a call option right retained by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at fair value, the asset continues to be measured at its fair value. The associated liability is measured at (i) the option exercise price less the time value of the option if the option is in or at the money, or (ii) the fair value of the transferred asset less the time value of the option if the option is out of the money. The adjustment to the measurement of the associated liability ensures that the net carrying amount of the asset and the associated liability is the fair value of the call option right. For example, if the fair value of the underlying asset is CU80, the option exercise price is CU95 and the time value of the option is CU5, the carrying amount of the associated liability is CU75 (CU80 – CU5) and the carrying amount of the transferred asset is CU80 (ie its fair value)
- IFRS 16, issued in January 2016, amended paragraphs 2.1, 5.5.15, B4.3.8, B5.5.34 and B5.5.46. An entity shall apply those amendments when it applies IFRS 16.
- If the holder cannot assess the conditions in paragraph B4.1.21 at initial recognition, the tranche must be measured at fair value through profit or loss. If the underlying pool of instruments can change after initial recognition in such a way that the pool may not meet the conditions in paragraphs B4.1.23–B4.1.24, the tranche does not meet the conditions in paragraph B4.1.21 and must be measured at fair value through profit or loss. However, if the underlying pool includes instruments that are collateralised by assets that do not meet the conditions in paragraphs B4.1.23–B4.1.24, the ability to take possession of such assets shall be disregarded for the purposes of applying this paragraph unless the entity acquired the tranche with the intention of controlling the collateral.
- 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.

*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

S&P/TSX Composite Index assigned short-term B1 & long-term Ba2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Polynomial Regression ^{1,2,3,4} and conclude that the S&P/TSX Composite Index stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold S&P/TSX Composite Index stock.**

### Financial State Forecast for S&P/TSX Composite Index S&P/TSX Composite Index Stock Options & Futures

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

Outlook* | B1 | Ba2 |

Operational Risk | 74 | 52 |

Market Risk | 40 | 51 |

Technical Analysis | 54 | 81 |

Fundamental Analysis | 39 | 78 |

Risk Unsystematic | 88 | 76 |

### Prediction Confidence Score

## References

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## Frequently Asked Questions

Q: What is the prediction methodology for S&P/TSX Composite Index stock?A: S&P/TSX Composite Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Polynomial Regression

Q: Is S&P/TSX Composite Index stock a buy or sell?

A: The dominant strategy among neural network is to Hold S&P/TSX Composite Index Stock.

Q: Is S&P/TSX Composite Index stock a good investment?

A: The consensus rating for S&P/TSX Composite Index is Hold and assigned short-term B1 & long-term Ba2 forecasted stock rating.

Q: What is the consensus rating of S&P/TSX Composite Index stock?

A: The consensus rating for S&P/TSX Composite Index is Hold.

Q: What is the prediction period for S&P/TSX Composite Index stock?

A: The prediction period for S&P/TSX Composite Index is (n+16 weeks)