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Ledger Works Introduces Advanced Inference Model for Cryptocurrency Risk Management

By Advos

TL;DR

Ledger Works' innovative inference model provides 90% accurate asset return distributions, giving a significant advantage in cryptocurrency portfolio risk management.

Ledger Works' approach estimates asset return distributions up to four months in advance with 90% accuracy, using walk-forward backtests and advanced statistical techniques.

Ledger Works' groundbreaking inference model sets a new benchmark for backtesting machine learning and generative models, enhancing risk management strategies in the DeFi space.

Ledger Works' methodology offers valuable insights for professionals in the DeFi space, providing a more accurate fit for forward asset returns than the normal distribution.

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Ledger Works Introduces Advanced Inference Model for Cryptocurrency Risk Management

Ledger Works is revolutionizing cryptocurrency portfolio risk management with the introduction of an advanced inference model that estimates asset return distributions with 90% accuracy up to four months in advance. This innovation significantly surpasses the precision of conventional risk management models used by asset managers today.

The company demonstrated the effectiveness of its methodology through walk-forward backtests employing distinct simulation strategies. One strategy is based on a normal distribution, while the other leverages Ledger Works' sophisticated inference approach. Both strategies utilize advanced statistical techniques for parameter estimation, ensuring high precision.

To evaluate the accuracy of these models, Ledger Works compared their forward-looking predictions and goodness of fit against historical data, focusing on several key metrics:

  • Forecasted Confidence Intervals: The range within which the estimated price is expected to fall a certain percentage of the time.
  • Kolmogorov-Smirnov Test: Measures how closely the predicted distribution aligns with the observed data.
  • Kullback-Leibler Divergence: Quantifies the difference between the predicted and actual distributions.
  • Skewness: Assesses the asymmetry of the predicted distribution.

Findings revealed that Ledger Works' approach provided a more accurate fit for forward asset returns than the normal distribution, outperforming it across all key metrics. This outcome not only underscores the superior performance of the Ledger Works approach but also sets a new benchmark for backtesting similar machine learning and generative models in the future.

The full research paper offers an in-depth exploration of the methodology and results, providing valuable insights for professionals in the decentralized finance (DeFi) space. The company's innovative model can enhance risk management strategies, enabling more informed decisions and optimized cryptocurrency investments.

For those interested in delving into the complete research and understanding how this approach can improve their financial strategies, Ledger Works is available for further discussions.

Curated from BlockchainWire

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Advos

Advos

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