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

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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