## Summary

Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend.** We evaluate PSEi Composite Index prediction models with Transductive Learning (ML) and Lasso Regression ^{1,2,3,4} and conclude that the PSEi Composite Index stock is predictable in the short/long term. **

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold PSEi Composite Index stock.**

## Key Points

- What are main components of Markov decision process?
- Which neural network is best for prediction?
- What is Markov decision process in reinforcement learning?

## PSEi Composite Index Target Price Prediction Modeling Methodology

We consider PSEi Composite Index Stock Decision Process with Transductive Learning (ML) where A is the set of discrete actions of PSEi 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(Lasso 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+3 month) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of PSEi 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?

## PSEi Composite Index Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**PSEi Composite Index PSEi Composite Index

**Time series to forecast n: 21 Nov 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold PSEi 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 PSEi Composite Index

- It would not be acceptable to designate only some of the financial assets and financial liabilities giving rise to the inconsistency as at fair value through profit or loss if to do so would not eliminate or significantly reduce the inconsistency and would therefore not result in more relevant information. However, it would be acceptable to designate only some of a number of similar financial assets or similar financial liabilities if doing so achieves a significant reduction (and possibly a greater reduction than other allowable designations) in the inconsistency. For example, assume an entity has a number of similar financial liabilities that sum to CU100 and a number of similar financial assets that sum to CU50 but are measured on a different basis. The entity may significantly reduce the measurement inconsistency by designating at initial recognition all of the assets but only some of the liabilities (for example, individual liabilities with a combined total of CU45) as at fair value through profit or loss. However, because designation as at fair value through profit or loss can be applied only to the whole of a financial instrument, the entity in this example must designate one or more liabilities in their entirety. It could not designate either a component of a liability (eg changes in value attributable to only one risk, such as changes in a benchmark interest rate) or a proportion (ie percentage) of a liability.
- The underlying pool must contain one or more instruments that have contractual cash flows that are solely payments of principal and interest on the principal amount outstanding
- A regular way purchase or sale gives rise to a fixed price commitment between trade date and settlement date that meets the definition of a derivative. However, because of the short duration of the commitment it is not recognised as a derivative financial instrument. Instead, this Standard provides for special accounting for such regular way contracts (see paragraphs 3.1.2 and B3.1.3–B3.1.6).
- Hedge effectiveness is the extent to which changes in the fair value or the cash flows of the hedging instrument offset changes in the fair value or the cash flows of the hedged item (for example, when the hedged item is a risk component, the relevant change in fair value or cash flows of an item is the one that is attributable to the hedged risk). Hedge ineffectiveness is the extent to which the changes in the fair value or the cash flows of the hedging instrument are greater or less than those on the hedged item.

*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

PSEi Composite Index assigned short-term Baa2 & long-term B3 forecasted stock rating.** We evaluate the prediction models Transductive Learning (ML) with Lasso Regression ^{1,2,3,4} and conclude that the PSEi Composite Index stock is predictable in the short/long term.**

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold PSEi Composite Index stock.**

### Financial State Forecast for PSEi Composite Index PSEi Composite Index Stock Options & Futures

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

Outlook* | Baa2 | B3 |

Operational Risk | 90 | 63 |

Market Risk | 59 | 69 |

Technical Analysis | 65 | 38 |

Fundamental Analysis | 72 | 31 |

Risk Unsystematic | 83 | 34 |

### Prediction Confidence Score

## References

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## Frequently Asked Questions

Q: What is the prediction methodology for PSEi Composite Index stock?A: PSEi Composite Index stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Lasso Regression

Q: Is PSEi Composite Index stock a buy or sell?

A: The dominant strategy among neural network is to Hold PSEi Composite Index Stock.

Q: Is PSEi Composite Index stock a good investment?

A: The consensus rating for PSEi Composite Index is Hold and assigned short-term Baa2 & long-term B3 forecasted stock rating.

Q: What is the consensus rating of PSEi Composite Index stock?

A: The consensus rating for PSEi Composite Index is Hold.

Q: What is the prediction period for PSEi Composite Index stock?

A: The prediction period for PSEi Composite Index is (n+3 month)