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Cevi Herdian
Universitas Padjajaran
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Learning Nonlinear Temporal Patterns in Ethereum Prices Via LSTM Networks Cevi Herdian
Journal of World Science Vol. 5 No. 2 (2026): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v5i2.1630

Abstract

A Long Short-Term Memory (LSTM) neural network trained on hourly ETH/USDT market data from the Binance exchange is used in this study to examine short-term Ethereum price behavior. The proposed model emphasizes learning temporal dependencies and momentum-driven structures rather than relying on conventional linear forecasting assumptions, acknowledging the highly nonlinear and noise-dominated nature of cryptocurrency markets. The daily high price of Ethereum is selected as the target variable in the forecasting task, which is defined as a univariate regression problem. To ensure realistic predictive assessment, model performance is evaluated using a strictly out-of-sample testing methodology. Empirical findings demonstrate that the LSTM model achieves a strong statistical fit despite significant market volatility. The obtained results—RMSE of 127.33, MAE of 98.76, MSE of 16,213.76, MAPE of 2.73%, and an R² of 0.96—indicate that a substantial portion of short-term price volatility is effectively captured by the nonlinear architecture. Even in a noise-dominated market, the low MAPE and high coefficient of determination suggest robust predictive alignment. Forecasts over the next five days reveal a recurring short-term directional pattern accompanied by widening prediction intervals, which reflect increasing uncertainty as the forecast horizon extends. This pattern underscores the intrinsic difficulty of achieving accurate price-level forecasts in highly volatile cryptocurrency markets. Overall, when applied to short-term cryptocurrency price dynamics, the results indicate that LSTM models are well-suited for capturing trend persistence and regime-related signals, affirming their usefulness as risk-aware decision-support tools rather than deterministic forecasting systems.
Modeling Short-Term Bitcoin Price Dynamics Using Long Short-Term Memory Networks Cevi Herdian
Jurnal Sosial Teknologi Vol. 6 No. 2 (2026): Jurnal Sosial dan Teknologi
Publisher : CV. Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jurnalsostech.v6i2.32702

Abstract

A Long Short-Term Memory (LSTM) neural network trained on daily BTC/USDT data from the Binance exchange is used in this study to investigate short-term Bitcoin price dynamics. Instead of relying on linear forecasting assumptions, the model is designed to capture temporal dependencies and momentum patterns to address the nonlinear and noise-dominated nature of cryptocurrency markets. A strictly out-of-sample framework is employed to evaluate the prediction task, which is defined as a univariate regression problem with the daily high price as the target variable. According to empirical findings, the LSTM model demonstrates strong statistical performance despite significant market volatility, with an RMSE of USD 3,619.74, an MAE of USD 2,989.73, a MAPE of 2.82%, and an R² of 0.90. Forecasts for the next five days reveal a consistent short-term bearish trend, with broad prediction intervals of approximately USD 6,000, indicating considerable uncertainty and expected prices declining from USD 88,425.62 to USD 85,497.88. The results suggest that LSTM models can extract meaningful trend and regime information, making them suitable as risk-aware decision-support tools rather than deterministic forecasting systems, even though precise short-term price-level prediction remains constrained.
An LSTM-Based Framework For Short-Term Solana Price Prediction Cevi Herdian
Jurnal Sosial Teknologi Vol. 6 No. 2 (2026): Jurnal Sosial dan Teknologi
Publisher : CV. Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jurnalsostech.v6i2.32715

Abstract

This study examines short-term Bitcoin price dynamics using a Long Short-Term Memory (LSTM) neural network trained on hourly SOL/USDT data from the Binance exchange. To address the nonlinear and noise-dominated nature of cryptocurrency markets, the model is designed to capture momentum patterns and temporal dependencies rather than rely on linear forecasting assumptions. The prediction task, framed as a univariate regression problem with the daily high price as the target variable, is evaluated using a strictly out-of-sample framework. Empirical results show that despite considerable market volatility, the LSTM model demonstrates strong statistical performance. The model’s RMSE of 10.53, MAE of 8.76, MSE of 110.92, MAPE of 6.09%, and R² of 0.78 indicate that its nonlinear architecture captures a substantial portion of price volatility. In terms of absolute price, the model achieved an RMSE of USD 3,619.74, an MAE of USD 2,989.73, a MAPE of 2.82%, and an R² of 0.90, demonstrating its robustness in both normalized and real-scale assessments. Forecasts suggest that SOL prices are likely to decline from USD 88,425.62 to USD 85,497.88 over the next five days, reflecting a persistent short-term bearish trend with wide prediction intervals of around USD 6,000, indicating substantial uncertainty. These findings imply that LSTM models can effectively extract trend and regime-related information, making them valuable as risk-aware decision-support tools rather than deterministic forecasting systems—even though accurate short-term price-level prediction remains challenging.
Adaptive Layered Buying Strategy Using Mode and Standard Deviation of Daily Price Range: Evidence from BBRI Indonesia Stock Market Cevi Herdian; Rama Pramasandy
Jurnal Indonesia Sosial Sains Vol. 7 No. 8 (2026): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jiss.v7i8.2467

Abstract

The Indonesian stock market exhibits substantial price volatility, making fixed-interval averaging strategies less effective under changing market conditions. This study proposes an adaptive averaging strategy based on the historical distribution of daily price ranges. Daily open, high, low, and close (OHLC) price data for Bank Rakyat Indonesia (BBRI), covering the period from November 2003 to July 2026 and comprising 5,602 observations, were analyzed. The adaptive buying interval was estimated using the mode of non-zero daily price ranges combined with two standard deviations. The resulting interval was then used to construct a layered buying strategy with exponential position sizing. Simulation results indicated that the proposed strategy substantially reduced the average acquisition cost while maintaining manageable capital requirements. Under a seven-level buying strategy, total capital deployment reached IDR 23.68 million, assuming purchases began in January 2025. Furthermore, the simulated portfolio generated a positive unrealized return of 59.28% under the historical price scenario. These findings suggest that statistical measures of price volatility provide a practical basis for determining adaptive averaging intervals in long-term equity investment. The proposed framework offers a simple yet robust alternative to conventional fixed-interval averaging strategies.
Co-Authors Rama Pramasandy