Deep Learning in Finance: LSTMs and Transformers for Markets

Module: Fintech AI | Difficulty: Advanced

LSTM for Time Series

Transformer for Finance

Look-Ahead Bias

Evaluation

| Model | IC | Sharpe | |-------|-----|--------| | Linear | 0.03 | 0.8 | | LSTM | 0.05 | 1.2 | | Transformer | 0.06 | 1.5 |

import torch
import torch.nn as nn

class FinancialLSTM(nn.Module):
    def __init__(self, input_dim, hidden_dim=128, n_layers=2):
        super().__init__()
        self.lstm = nn.LSTM(input_dim, hidden_dim, n_layers, batch_first=True)
        self.head = nn.Linear(hidden_dim, 1)
    def forward(self, x):
        out, _ = self.lstm(x)
        return self.head(out[:, -1, :])

Research Insight: Deep learning for finance faces unique challenges: non-stationarity, low signal-to-noise ratio, and look-ahead bias. Walk-forward validation is essential — standard cross-validation overfits because financial time series have temporal dependencies.