---
title: Filled and Killed: Forecast and Realized Trading Costs across Horizons from Global Equity and Fixed Income Portfolio Trades
url: https://www.ml-quant.com/papers/ssrn/4782032/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4782032
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4782032
featured: 2024-04-03
citations: 0
topic: Trading, Microstructure & Execution
---


# Filled and Killed: Forecast and Realized Trading Costs across Horizons from Global Equity and Fixed Income Portfolio Trades

The research analyzes trading costs, revealing that large, complex trades can be executed affordably and that trade risk value and complexity extend trade horizons.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4782032
- Identifier: SSRN 4782032
- Released: 2024-04-02
- First featured: Quant Letter No. 43 (2024-04-03): https://www.ml-quant.com/issues/2024-04-03/
- Citations (Semantic Scholar): 0
- Published in: not yet
- Topic: Trading, Microstructure & Execution

## Related

- [FAST: Efficient Action Tokenization for Vision-Language-Action Models](https://www.ml-quant.com/papers/arxiv/2501.09747/): A new tokenization scheme, Frequency-space Action Sequence Tokenization (FAST), has been proposed for robot actions, facilitating the training of vision-language action policies for complex and high-frequency tasks.
- [Deep Reinforcement Learning for Active High Frequency Trading](https://www.ml-quant.com/papers/arxiv/2101.07107/): A new Deep Reinforcement Learning framework has been developed for high frequency stock trading, showing potential for profitable long-term strategies.
- [The Pricing of Liquidity Risk Factors](https://www.ml-quant.com/papers/ssrn/4779075/): The research investigates the pricing of liquidity factors in the US stock market, demonstrating that models with a liquidity factor outperform those with a size factor.
- [Improving Deep Learning of Alpha Term Structures from the Order Book](https://www.ml-quant.com/papers/ssrn/4770476/): The article evaluates the efficiency of four deep learning models in predicting high-frequency returns in equities, emphasizing the role of network structure, input choice, and time inclusion.
- [Social media emotions and market behavior](https://www.ml-quant.com/papers/arxiv/2404.03792/): The research finds that investor emotions expressed on social media can predict daily asset price movements, particularly in low liquidity or high short interest situations.
- [StockGPT: A GenAI Model for Stock Prediction and Trading](https://www.ml-quant.com/papers/arxiv/2404.05101/): The study introduces StockGPT, a model that predicts stock return dynamics using AI, showcasing the potential of AI in complex financial investment decisions.