## Summary

Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend.** We evaluate Taiwan Weighted Index prediction models with Deductive Inference (ML) and Multiple Regression ^{1,2,3,4} and conclude that the Taiwan Weighted Index stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy Taiwan Weighted Index stock.**

## Key Points

- Reaction Function
- What is neural prediction?
- How do you pick a stock?

## Taiwan Weighted Index Target Price Prediction Modeling Methodology

We consider Taiwan Weighted Index Stock Decision Process with Deductive Inference (ML) where A is the set of discrete actions of Taiwan Weighted 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(Multiple 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(Deductive Inference (ML)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of Taiwan Weighted 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?

## Taiwan Weighted Index Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**Taiwan Weighted Index Taiwan Weighted Index

**Time series to forecast n: 27 Nov 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy Taiwan Weighted 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 Taiwan Weighted Index

- However, depending on the nature of the financial instruments and the credit risk information available for particular groups of financial instruments, an entity may not be able to identify significant changes in credit risk for individual financial instruments before the financial instrument becomes past due. This may be the case for financial instruments such as retail loans for which there is little or no updated credit risk information that is routinely obtained and monitored on an individual instrument until a customer breaches the contractual terms. If changes in the credit risk for individual financial instruments are not captured before they become past due, a loss allowance based only on credit information at an individual financial instrument level would not faithfully represent the changes in credit risk since initial recognition.
- Financial assets that are held within a business model whose objective is to hold assets in order to collect contractual cash flows are managed to realise cash flows by collecting contractual payments over the life of the instrument. That is, the entity manages the assets held within the portfolio to collect those particular contractual cash flows (instead of managing the overall return on the portfolio by both holding and selling assets). In determining whether cash flows are going to be realised by collecting the financial assets' contractual cash flows, it is necessary to consider the frequency, value and timing of sales in prior periods, the reasons for those sales and expectations about future sales activity. However sales in themselves do not determine the business model and therefore cannot be considered in isolation. Instead, information about past sales and expectations about future sales provide evidence related to how the entity's stated objective for managing the financial assets is achieved and, specifically, how cash flows are realised. An entity must consider information about past sales within the context of the reasons for those sales and the conditions that existed at that time as compared to current conditions.
- 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
- In some cases, the qualitative and non-statistical quantitative information available may be sufficient to determine that a financial instrument has met the criterion for the recognition of a loss allowance at an amount equal to lifetime expected credit losses. That is, the information does not need to flow through a statistical model or credit ratings process in order to determine whether there has been a significant increase in the credit risk of the financial instrument. In other cases, an entity may need to consider other information, including information from its statistical models or credit ratings processes.

*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

Taiwan Weighted Index assigned short-term Ba3 & long-term B3 forecasted stock rating.** We evaluate the prediction models Deductive Inference (ML) with Multiple Regression ^{1,2,3,4} and conclude that the Taiwan Weighted Index stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy Taiwan Weighted Index stock.**

### Financial State Forecast for Taiwan Weighted Index Taiwan Weighted Index Stock Options & Futures

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

Outlook* | Ba3 | B3 |

Operational Risk | 65 | 65 |

Market Risk | 35 | 30 |

Technical Analysis | 38 | 63 |

Fundamental Analysis | 90 | 30 |

Risk Unsystematic | 87 | 51 |

### Prediction Confidence Score

## References

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## Frequently Asked Questions

Q: What is the prediction methodology for Taiwan Weighted Index stock?A: Taiwan Weighted Index stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Multiple Regression

Q: Is Taiwan Weighted Index stock a buy or sell?

A: The dominant strategy among neural network is to Buy Taiwan Weighted Index Stock.

Q: Is Taiwan Weighted Index stock a good investment?

A: The consensus rating for Taiwan Weighted Index is Buy and assigned short-term Ba3 & long-term B3 forecasted stock rating.

Q: What is the consensus rating of Taiwan Weighted Index stock?

A: The consensus rating for Taiwan Weighted Index is Buy.

Q: What is the prediction period for Taiwan Weighted Index stock?

A: The prediction period for Taiwan Weighted Index is (n+4 weeks)