Mastering Tokenizers for Hedge Fund and Mutual Fund LLM Applications: 10 Types You Must Know
Step-by-step examples and manual implementations for finance-focused LLMs, with small code and input-output illustrations
Large Language Models (LLMs) are increasingly being leveraged in finance, especially by Hedge Funds and Mutual Fund institutions, to process and analyze unstructured text data from financial news, reports, and market commentary. The first and arguably most crucial step in any LLM pipeline is tokenization the process of converting raw text into a form that the model can understand. In this post, we explore 10 different types of tokenizers, show manual implementations using toy code, illustrate their input-output behavior with sample financial data, and discuss optimization strategies.
Sample Data
For all examples, we’ll use the following mini financial corpus:
sample_data = [
“Stock AAPL surged 5% after earnings report.”,
“Hedge Fund X increased holdings in TSLA.”,
“MF Y reduced exposure to bonds in Q3.”
]
We will implement all tokenizers manually without relying on external libraries.
1. Whitespace Tokenizer
Logic: Split text by spaces. Straightforward for English text.
for line in sample_data:
tokens = line.split()
print(tokens)
Input: “Stock AAPL surged 5% after earnings report.”
Output: [’Stock’, ‘AAPL’, ‘surged’, ‘5%’, ‘after’, ‘earnings’, ‘report.’]
Use in Finance: Quick parsing for basic text counts or keyword frequency.
Challenge: Fails for compound words or punctuation-heavy financial news.
2. Character-Level Tokenizer
Logic: Each character becomes a token.
for line in sample_data:
tokens = list(line)
print(tokens)
Input: “Hedge Fund X increased holdings in TSLA.”
Output: [’H’,’e’,’d’,’g’,’e’,’ ‘,’F’,’u’,’n’,’d’,’ ‘,’X’,’ ‘,’i’,’n’,’c’,’r’,’e’,’a’,’s’,’e’,’d’,’ ‘,...]
Use in Finance: Useful for small models, numeric data, or code; captures every detail.
Challenge: Extremely long sequences; inefficient for large corpora.
3. Subword Tokenizer (Byte Pair Encoding - BPE)
Logic: Merge frequent pairs of characters iteratively.
# Pseudocode
vocab = {’A’,’P’,’L’,’AP’,’PL’}
text=’AAPL’
tokens=[]
while text:
match=max([v for v in vocab if text.startswith(v)], key=len)
tokens.append(match)
text=text[len(match):]
print(tokens)
Input: “AAPL”
Output: [’AP’, ‘PL’]
Use: Captures meaningful subwords in ticker symbols or abbreviations.
Challenge: Needs training merges; may split rare financial terms.
4. WordPiece Tokenizer
Logic: Similar to BPE, splits unknown words into known subwords.
vocab = {’hedge’,’fund’,’##X’}
word=’hedgeX’
tokens=[]
if word in vocab:
tokens.append(word)
else:
tokens.append(’hedge’)
tokens.append(’##X’)
print(tokens)
Input: “hedgeX”
Output: [’hedge’,’##X’]
Use in Finance: Handles new fund names or ticker variations.
Challenge: Rare or non-standard names may fragment excessively.
5. SentencePiece / Unigram Tokenizer
Logic: Assign probabilities to subwords; pick segmentation with highest likelihood.
# Simplified
vocab=[’Stock’,’AAPL’,’surged’,’5’,’%’]
text=’Stock AAPL surged 5%’
tokens=[v for v in vocab if v in text]
print(tokens)
Input: “Stock AAPL surged 5%”
Output: [’Stock’,’AAPL’,’surged’,’5’,’%’]
Use: Handles languages or finance terms with flexible splits.
Challenge: Needs pre-trained probability table; more complex.
6. Regex Tokenizer
Logic: Split text based on regular expressions.
import re
pattern = r’\b\w+\b’
for line in sample_data:
tokens=re.findall(pattern,line)
print(tokens)
Input: “MF Y reduced exposure to bonds in Q3.”
Output: [’MF’,’Y’,’reduced’,’exposure’,’to’,’bonds’,’in’,’Q3’]
Use: Extract numbers, tickers, or keywords.
Challenge: Regex must be carefully designed; may miss multiword phrases.
7. N-gram Tokenizer
Logic: Create overlapping sequences of n words.
n=2
for line in sample_data:
words=line.split()
tokens=[words[i:i+n] for i in range(len(words)-n+1)]
print(tokens)
Input: “Stock AAPL surged 5% after earnings report.”
Output: [[’Stock’,’AAPL’],[’AAPL’,’surged’],[’surged’,’5%’], ...]
Use: Useful for bigram/trigram feature extraction in trading sentiment analysis.
Challenge: Sequence length grows rapidly; memory overhead.
8. Punctuation-Sensitive Tokenizer
Logic: Split words and keep punctuation separate.
for line in sample_data:
tokens=re.findall(r’\w+|[.,%]’,line)
print(tokens)
Input: “Stock AAPL surged 5% after earnings report.”
Output: [’Stock’,’AAPL’,’surged’,’5’,’%’,’after’,’earnings’,’report’,’.’]
Use: Retain important financial symbols like %, $, etc.
Challenge: May increase token count.
9. Numeric Tokenizer
Logic: Separate numeric values from text.
for line in sample_data:
tokens=re.findall(r’\d+|\w+’,line)
print(tokens)
Input: “Stock AAPL surged 5% after earnings report.”
Output: [’Stock’,’AAPL’,’surged’,’5’,’after’,’earnings’,’report’]
Use: Analyze price movements, percentages, or volumes in finance.
Challenge: Loses ‘%’ unless combined with punctuation tokenizer.
10. Hybrid / Custom Tokenizer
Logic: Combine multiple tokenization rules (punctuation + numeric + subword).
for line in sample_data:
tokens=re.findall(r’\w+|[.,%]|[A-Z]{2,}’,line)
print(tokens)
Input: “Hedge Fund X increased holdings in TSLA.”
Output: [’Hedge’,’Fund’,’X’,’increased’,’holdings’,’in’,’TSLA’,’.’]
Use: Industry finance: hedge funds, mutual funds, ticker symbols, percentages, financial terms.
Challenge: Complexity grows; must tune rules.
How to Optimize Tokenizer Uses with Model in Finance
Align tokenizer and model vocab size: Embedding matrices depend on tokenizer vocab.
Preserve special symbols: Tickers, %, $, $, and fund names are critical.
Select algorithm per domain: BPE / WordPiece for abbreviations, numeric tokenizer for market data.
Experiment with sequence length: Hedge fund news may require longer sequences.
Monitor fragmentation: Rare fund names may need custom subword merges.
Combine strategies: Hybrid tokenizers often perform best.
Failures & Challenges:
Misaligned tokenizers → embedding mismatch.
Truncated numeric data → wrong price analysis.
Excessively large vocab → memory overhead.
Over-splitting tickers or fund names → model confuses entities.
One-Sentence Summary
Choosing the right tokenizer type and carefully aligning it with model architecture is crucial for accurate, efficient, and domain-specific LLM applications in finance.
Interpretation & Conclusion
In finance-focused LLM applications such as hedge fund trading or mutual fund investment analysis, manual understanding and experimentation with different tokenizer types allows practitioners to better capture domain-specific entities like ticker symbols, percentages, fund names, and numeric data. While libraries provide convenience, manual implementations grant full control over tokenization, embedding alignment, and preprocessing rules, reducing errors during training and inference. By testing all 10 types and understanding their input-output behavior, practitioners can make informed decisions on which tokenizer strategy optimizes performance, handles rare entities, and balances sequence length and embedding size.
References & Sources
Hugging Face Tokenizers Documentation
Sennrich et al., Neural Machine Translation of Rare Words with Subword Units, 2016.
Kudo, Taku, and John Richardson. SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing, 2018.
Tao et al., Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies, 2024.
Introducing “How Transformer LLMs Work,” created with
and Maarten Grootendorst, authors of the “Hands-On Large Language Models” book.