---
title: Making Work Meaningful
url: https://www.ml-quant.com/papers/ssrn/5223106/
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 5223106
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5223106
featured: 2025-04-23
citations: unknown
topic: Econometrics & Forecasting
---


# Making Work Meaningful

The research identifies social impact as the key factor in making work meaningful, based on an analysis of panel data.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5223106
- Identifier: SSRN 5223106
- Released: 2025-04-19
- First featured: Quant Letter No. 94 (2025-04-23): https://www.ml-quant.com/issues/2025-04-23/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

## Related

- [TKAN: Temporal Kolmogorov-Arnold Networks](https://www.ml-quant.com/papers/ssrn/4825654/): The article presents Temporal Kolomogorov-Arnold Networks (TKANs), a new neural network design that merges the benefits of Recurrent Neural Networks and Long Short-Term Memory for improved multistep time series forecasting.
- [Robust agents learn causal world models](https://www.ml-quant.com/papers/arxiv/2402.10877/): The research suggests that intelligent agents must learn an approximate causal model to generalize to new domains, impacting fields like transfer learning and causal inference.
- [The heterogeneous causal effects of the EU's Cohesion Fund](https://www.ml-quant.com/papers/arxiv/2504.13223/): A study measures the impact of the EU's Cohesion Fund on regional output and investment, showing its peak effect within the first seven years, particularly in poorer regions.
- [Cross-Modal Temporal Fusion for Financial Market Forecasting](https://www.ml-quant.com/papers/arxiv/2504.13522/): The article presents Cross-Modal Temporal Fusion (CMTF), a new transformer-based system that uses diverse financial data to enhance the precision of financial market predictions.
- [Machine Learning Algorithms For Accurate Stock Market Forecasting: Case Studies 2012](https://www.ml-quant.com/papers/ssrn/5228711/): The article explores the use of machine learning in predicting stock market movements and timing market entry, addressing challenges in financial data mining.
- [Bridging Short- and Long-Term Dependencies: A CNN-Transformer Hybrid for Financial Time Series Forecasting](https://www.ml-quant.com/papers/arxiv/2504.19309/): The paper suggests a hybrid architecture combining Convolutional Neural Networks and Transformers for efficient short- and long-term financial time series data modeling, showing improved performance in intraday stock price prediction.