---
title: Data Arbitrage with Proprietary Dividend Forecasts - Historically Precise Updates Led to U.S. Outperformance
url: https://www.ml-quant.com/papers/ssrn/4849100/
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 4849100
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4849100
featured: 2024-06-05
citations: 0
topic: Trading, Microstructure & Execution
---


# Data Arbitrage with Proprietary Dividend Forecasts - Historically Precise Updates Led to U.S. Outperformance

The article highlights the accuracy and promptness of SP Global Market Intelligence’s Dividend Forecasting dataset in capturing dividend revisions, giving investors an edge.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4849100
- Identifier: SSRN 4849100
- Released: 2024-05-30
- First featured: Quant Letter No. 52 (2024-06-05): https://www.ml-quant.com/issues/2024-06-05/
- 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.
- [Assessing the Liquidity Premium in the Italian Bond Market](https://www.ml-quant.com/papers/ssrn/4849208/): A new method for estimating the liquidity premium in Italian government bonds indicates that higher liquidity leads to higher prices, and the liquidity drop during the debt crisis was longer but less severe than during the Covid-19 pandemic.
- [HLOB - Information Persistence and Structure in Limit Order Books](https://www.ml-quant.com/papers/arxiv/2405.18938/): A new deep learning model, HLOB, has been developed for predicting Limit Order Book mid-price changes, outperforming nine other models and offering new insights into information distribution in Limit Order Books.
- [Optimizing Broker Performance Evaluation through Intraday Modeling of Execution Cost](https://www.ml-quant.com/papers/arxiv/2405.18936/): A new method for assessing the efficiency of broker execution algorithms in reducing execution costs for large orders has been introduced, offering a robust framework for choosing the most cost-effective brokers.
- [A Tick-by-Tick Solution for Concentrated Liquidity Provisioning](https://www.ml-quant.com/papers/arxiv/2405.18728/): Early findings suggest that programmable automated market makers with concentrated liquidity capabilities are not most effective when focused solely on the current price.