Do Solana Exchange Flows Matter? Hypothesis-Driven Evidence from Short-Term SOL Direction Forecasting

This study examines whether centralized-exchange flows improve one-day-ahead forecasts of SOL return direction. Daily on-chain analysis (OCA) predictors are constructed from more than 41 million transfers above 1 SOL involving 101 labeled centralized-exchange hot wallets. Seven feature sets are evaluated using CatBoost, XGBoost, Random Forest, LSTM, and BiLSTM across four expanding-origin holdouts, with Elastic Net Logistic Regression as a linear benchmark. Technical analysis (TA) achieves the strongest average classification and transaction-cost-adjusted trading performance. OCA performs weakly alone and provides no stable average improvement when added to Baseline or TA. Selected flow- and volume-related subblocks outperform unrestricted OCA on individual descriptive metrics, but none achieves positive average trading performance. Observable Solana exchange flows therefore provide no robust standalone or incremental forecasting advantage beyond conventional predictors.

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Publication Details

Journal
Forecasting
Published
2026-07-30
DOI
https://doi.org/10.3390/forecast8040065
Primary Topic
Financial Markets and Investment Strategies
Type
article
Field-Weighted Citation Impact
0.00
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article

Do Solana Exchange Flows Matter? Hypothesis-Driven Evidence from Short-Term SOL Direction Forecasting

Dmitry Grigoriev, Yan Egorov, Anastasia Grigorieva, Daniil Zharikov
Forecasting
Financial Markets and Investment Strategies
article

Do Solana Exchange Flows Matter? Hypothesis-Driven Evidence from Short-Term SOL Direction Forecasting

Dmitry Grigoriev, Yan Egorov, Anastasia Grigorieva, Daniil Zharikov
article en

Abstract

This study examines whether centralized-exchange flows improve one-day-ahead forecasts of SOL return direction. Daily on-chain analysis (OCA) predictors are constructed from more than 41 million transfers above 1 SOL involving 101 labeled centralized-exchange hot wallets. Seven feature sets are evaluated using CatBoost, XGBoost, Random Forest, LSTM, and BiLSTM across four expanding-origin holdouts, with Elastic Net Logistic Regression as a linear benchmark. Technical analysis (TA) achieves the strongest average classification and transaction-cost-adjusted trading performance. OCA performs weakly alone and provides no stable average improvement when added to Baseline or TA. Selected flow- and volume-related subblocks outperform unrestricted OCA on individual descriptive metrics, but none achieves positive average trading performance. Observable Solana exchange flows therefore provide no robust standalone or incremental forecasting advantage beyond conventional predictors.

ForecastingVol. 8(4)
St Petersburg University (RU)
Openalex Percentile: Top 6%
Financial Markets and Investment Strategies
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