# Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies

arXiv: https://arxiv.org/abs/2309.10546 · HTML: https://mlsanity.com/paper/2309.10546/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 1 of 1)

- [Deep Learning for Stock Selection Based on High Frequency Price-Volume Data](https://mlsanity.com/paper/1911.02502/) (2019)

## Built on by (most cited first, 6 of 6)

- [Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting](https://mlsanity.com/paper/2608.01857/) (2026)
- [Forecasting VIX using interpretable Kolmogorov-Arnold networks](https://mlsanity.com/paper/2502.00980/) (2025)
- [Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data](https://mlsanity.com/paper/2503.18096/) (2025)
- [Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting](https://mlsanity.com/paper/2606.00060/) (2026)
- [Alternative Loss Function in Evaluation of Transformer Models](https://mlsanity.com/paper/2507.16548/) (2025)
- [DPAL: A Test-Time Adaptation Loss for Financial Time Series Forecasting](https://mlsanity.com/paper/2610.14376/) (2026)

## Abstract (arXiv)

This paper investigates the issue of an adequate loss function in the optimization of machine learning models used in the forecasting of financial time series for the purpose of algorithmic investment strategies (AIS) construction. We propose the Mean Absolute Directional Loss (MADL) function, solving important problems of classical forecast error functions in extracting information from forecasts to create efficient buy/sell signals in algorithmic investment strategies. Finally, based on the data from two different asset classes (cryptocurrencies: Bitcoin and commodities: Crude Oil), we show that the new loss function enables us to select better hyperparameters for the LSTM model and obtain more efficient investment strategies, with regard to risk-adjusted return metrics on the out-of-sample data.