---
title: "當AI遇上財經-利用Graph Neural Network分析財經市場 When AI Meets Finance: Using Graph Neural Network to Analyze Financial Market - William Chang"
tags: PyConTW2023, 2023-organize, 2023-共筆
---

# 當AI遇上財經-利用Graph Neural Network分析財經市場 When AI Meets Finance: Using Graph Neural Network to Analyze Financial Market - William Chang

{%hackmd H6-2BguNT8iE7ZUrnoG1Tg %}

<iframe src=https://app.sli.do/event/vxoyxhDW9L6KHG7QhMUws2 height=450 width=100%></iframe>

> Collaborative writing start from below
> 從這裡開始共筆

## Time Series

X 軸是時間的資料
+ Non-stationarity
+ Stationarity
+ Distribution does not change over time
+ First-order stationarity
+ Second-order stationarity
+ As for analysis of time series data, the data has to be at lease first-order stationarity

### Why 財經市場難分析？
+ **Autocorrelation**
+ corr over time
+ no 2nd stationarity
+ **Exogeneity**
+ Big env, industry
+ eg. industry chain, global eco
+ **Volatility**
+ no 1st stationatiry
+ Unexpected events
+ e.g. pandemic

除了最後一個外，都有方法解決

### Autocorrelation & Volatility
#### Feature
+ 資料取Lag(one-period diff)
+ Normalization(Min-max scalar)

#### Model Selection
+ CNN
+ LSTM
+ Forget Gate: tackles gradient ...

### Exogeneity
#### Feature
+ Economy indices
+ 例如可以從FRED取得額外的一些information

#### Model Selection
+ Graph Neural Network:
+ Graph

## Graph Neural Network (GNN)
### Graph
+ Objects (**Nodes**) and ther relation (**Edges**)
+ A node in a graph Gt represents an object's present value
+ In our case, a stock's/index's value at time t

### GNN Variants

+ **Aggregation**:
+ taking mean/max/min of the nodes over a graph
+ **Combination**:
+ Weights of the edges
+ Homogeneous v.s. Heterogeneous
+ 常見變種
+ GCN (Graph Convolutional Network):
+ GraphSAGE
+ weighted mean(Aggregation + Combination)
+ GAT(Graph Attention Network)
+ Mean-pooling
+ self-learning heterogeneous edge weight

## Data
> data preparation and feature extraction

### Train/Test Split

- Rolling window (better)
- Wxpanding window

### Metrics

- Mean Percentage Error
- Mean Percentage Absolute Error

## Model

Below is the part that speaker updated the talk/tutorial after speech
講者於演講後有更新或勘誤投影片的部份

Slide 連結：
https://docs.google.com/presentation/d/1OK_G9lLmiYelaii3hq35xomRYOiVGJW_SZNuI6DuRx4/edit?usp=sharing