This study evaluates the risk-adjusted consequences of large-weight equity–cryptocurrency substitution and examines whether these effects differ systematically across equity styles (Growth vs. Value) and regions (Asia, Europe, and the Americas). Using daily data from January 1, 2021 to December 31, 2023, the analysis constructs style-segmented MSCI country equity indices and compares annual Sharpe ratios under four constant-mix, daily rebalanced strategies: (S1) 100% equity; (S2) 50% equity / 50% cryptocurrency; (S3) 50% equity / 50% global bonds; and (S4) 33.33% equity / 33.33% global bonds / 33.33% cryptocurrency. Five major non-stablecoin cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Solana (SOL), and Binance Coin (BNB)—are evaluated individually to isolate coin-specific substitution effects. Performance is assessed annually and compared across Growth and Value portfolios within identical country–year–cryptocurrency environments to identify style-dependent outcomes. The results show that cryptocurrency substitution generally improves Sharpe ratios, but the effects are benchmark-, coin-, style-, and region-dependent. Improvements are more heterogeneous under direct 50% equity–crypto substitution, especially for Value portfolios, but become uniformly positive when crypto is introduced within an equity–bond benchmark. SOL provides the largest and most consistent improvements, while ETH and BNB are frequently strong and BTC is the least consistent. Growth portfolios benefit more than Value portfolios in Asia and the Americas, whereas Europe shows more style-neutral effects. Because cryptocurrency shocks are common within a given coin-year, statistical inference is interpreted as cross-market evidence rather than fully independent observations.
A Universal, Options-Free, One-Byte Market-State Primitive: Cross-Asset Transfer, Distributional-Novelty Detection, and Privacy-Preserving Federation Randolph James Ferlic, M.D. and Kimberly Kate Ferlic (Fieldstone Analytics, LLC, Austin, TX, USA) Preprint · Zenodo DOI: 10.5281/zenodo.22116173 · CC-BY 4.0 · Community: spiral-domain-encoder-campaign Abstract A companion study established that a fixed class-discriminant encoder — which reduces a window of a multivariate stream to a single 8-bit token — is a near-lossless detector of market stress across asset classes, but a taxed classifier and a null forecaster. This study asks, under strict pre-registration, whether that detector becomes a *universal, deployable primitive*, and finds that it does, along five axes, with an honest boundary preserved throughout. (1) The codebook is universal: fit on equities alone, it detects stress zero-shot across cryptocurrency, commodities, emerging-market funds, credit, and foreign exchange at a median AUC of 0.84, within 0.01 of a bespoke per-asset codebook, because the calm-to-stress discriminant direction is nearly identical across asset classes (median cross-asset cosine 0.94). (2) The token detects distributional regime-change that volatility structurally misses — the one place it beats volatility — and is an orthogonal channel that improves a volatility-only stack by +0.18 AUC on a mixed target. (3) It generalizes to credit and rates (pooled AUC 0.91) and to currency crosses (0.80), including markets with no options index. (4) It is cheap at scale and low-data: a genuine one-byte-on-the-wire firm-wide dashboard detects systemic stress at 0.92 at 200× compression — a 42-instrument universe fits in ~10 kilobytes per year — and a new deployment is usable after ~3 months of calm history. (5) The same one-byte token supports privacy-preserving federation: institutions each sharing one token per day match full-data systemic-detection utility while their individual positions are unrecoverable even to a non-linear, colluding, market-informed adversary (incremental reconstruction R² ≈ 0.01). All headline results survive a permutation-placebo leakage test, moving-block bootstrap confidence intervals, and robustness to split, seed, stress-label, and a strict recent holdout. Every negative is reported, and two honest scope refinements are disclosed: a formal differential-privacy guarantee is event-level, not stream-level, and portfolio membership is coarsely inferable while positions are not. Highlights · Universal codebook — one equity-fit codebook detects stress zero-shot across every asset class (median 0.84, transfer loss ~0.01, 22/23 targets); mechanism = a shared cross-asset stress direction (cosine 0.94). · A capability volatility lacks — the token detects distributional (shape) regime-change where trailing volatility is at or below chance; an orthogonal channel that complements any GARCH/VIX stack. · Coverage — credit/rates (pooled 0.91) and FX/carry white-space (0.80), options-free, at one byte per instrument. · Compression-at-scale — a 1-byte-on-the-wire firm-wide dashboard at AUC 0.92, 200× compression, ~10 KB/instrument-year; detection-sufficient encoder ≈ 590 multiply-accumulates and < 3 KB (microcontroller-class). · Low-data cold-start — usable after ~3 months of calm data; saturates within ~2 years; train on a trailing window. · Privacy-preserving federation — full-data-equivalent systemic detection with individual positions non-invertible even under a strong adversary; optional event-level differential privacy; manipulation-robust median aggregation; per-channel attribution; a yield-curve-shape token. · Information-theoretic characterization — detection is a ~1-bit decision while classification is high-bit; competitive with principled two-sample/OOD tests at O(1) online cost. · Honest negatives and hardening — network topology, temporal-token, cross-sectional ranking, and tail-edge results are all reported null; every headline result is leakage-tested (permutation placebo → chance) and confidence-bounded. What this record contains · Manuscript_Paper40.pdf — the manuscript (single-column preprint). · PAPER_40_ZENODO_ARCHIVE.zip — the reproducibility archive: seven pre-registration scopes (frozen outcome bands) covering studies FIN-15 … FIN-45; the deterministic per-study runners and the shared frozen encoder/loader and federation modules; the per-study JSON result summaries behind every figure and table value; the figure-rebuild script; and the eight figures. All paths and identifiers are scrubbed and leak-scanned per the campaign deposit discipline; no raw market data is redistributed. Cite as R. J. Ferlic and K. K. Ferlic, "A universal, options-free, one-byte market-state primitive: cross-asset transfer, distributional-novelty detection, and privacy-preserving federation," Zenodo, 2026, doi: 10.5281/zenodo.22116173. License and patent notice Released under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). Consistent with that license, no patent, patent-application, or other intellectual-property right of the authors is licensed, waived, granted, or otherwise conveyed by this deposit; the methods described — including the single-token class-discriminant encoder, its multi-token product-quantization variant, its unsupervised nearest-centroid-distance monitoring mode, and the privacy-preserving multi-party aggregation of its tokens — are the subject of filed and pending U.S. patent applications. Licensing inquiries: randolphf@fieldstoneanalyticsllc.com. Companion deposits (spiral-domain-encoder-campaign) · Single-token financial market-state monitor (companion study): doi:10.5281/zenodo.22101085 · Single-token industrial sensor substrate: doi:10.5281/zenodo.20854722 · Hardening and generality characterization: doi:10.5281/zenodo.20802759 · Deterministic multi-token token ladder: doi:10.5281/zenodo.22003179 Keywords universal codebook; decision-preserving compression; distributional novelty; federated privacy; differential privacy; systemic risk; market-stress detection; class-discriminant codebook; cross-asset transfer; edge computing; anomaly detection; pre-registration; honest negatives; financial time series; regime detection; credit risk; yield curve; volatility regime; VIX; market surveillance
A One-Byte, Options-Free Market-State Monitor: Detection-Preserving Compression of Financial Data Streams with a Class-Discriminant Token Randolph James Ferlic, M.D., and Kimberly Kate Ferlic — Fieldstone Analytics, LLC Correspondence: randolphf@fieldstoneanalyticsllc.com Preprint · Zenodo DOI: 10.5281/zenodo.22101085 · CC-BY 4.0 Abstract A previously described class-discriminant encoder reduces a window of a multivariate stream to a single 8-bit token (statistical features projected onto a shrinkage-regularized linear-discriminant and principal-component subspace, quantized to a k-means centroid, read by a lightweight head), trading thousands-fold compression for a preserved decision. We ask, under strict pre-registration, how much of that property survives on financial data — the hardest domain for naive machine learning. Across five asset classes (equities, foreign exchange, rates, cryptocurrency, commodities) and thousands of trading days of real daily price/volume data, we find a sharp, consistent boundary. As a detector, the token is near-lossless: an unsupervised codebook fit on calm windows only flags market-stress windows by nearest-centroid distance at a mean AUC of about 0.90, within 0.01–0.04 of a full 50-feature detector, at roughly 500× compression and with no labels. Pooled across a thirteen-name basket, a one-byte-per-instrument, options-free Token Market-State Index tracks the VIX volatility index (Spearman 0.62) and detects market stress that neither a full-feature detector nor VIX statistically outperforms at this sample size — a result that survives purged, embargoed walk-forward validation and block-bootstrap confidence intervals across 2010–2026, and generalizes across all five asset classes, to intraday (hourly) frequency, and to a distinct cross-asset macro risk-off state. A second, complementary detector built from the same tokens — the cross-sectional co-movement of the per-instrument token distances — flags correlation and contagion regimes, adding information beyond volatility (a +0.11 AUC point-estimate increment) and rivaling the absorption-ratio systemic-risk measure computed from the full return covariance. As a classifier or forecaster, the same token is honestly limited: it pays a real 0.07–0.16 AUC tax on supervised volatility-regime and market-state classification (only partly recovered by multi-token product quantization), it is coincident rather than leading, and it shows no directional-return skill at any horizon. The unifying regularity is that the encoder retains what a detector needs and discards what a classifier or forecaster needs — the same compression boundary observed for physiological and industrial signals, now mapped in finance. We report all negatives, including two pre-registered red flags that caught bugs in our own code before they became false results. Highlights · The single 8-bit token is a strong coincident detector of market stress: an unsupervised calm-fit codebook flags stress windows at a mean AUC of ~0.90, within 0.01–0.04 of a full 50-feature detector, at ~500× — indeed as few as two bits — compression, with no labels. · A pooled, options-free, one-byte-per-instrument Token Market-State Index tracks VIX (Spearman 0.62) and detects market stress that neither a full-feature detector nor VIX statistically outperforms at this sample size — surviving purged/embargoed walk-forward and block-bootstrap intervals across 2010–2026, and generalizing across equities, FX, rates, cryptocurrency and commodities, to intraday frequency, and to a distinct macro risk-off state. · A second detector from the same tokens — cross-sectional co-movement — flags contagion / correlation regimes, adding information beyond volatility (a +0.11 AUC point-estimate increment) and rivaling the absorption-ratio systemic-risk measure, from compressed tokens rather than the full covariance. · Honest limits, fully reported: the token pays a real 0.07–0.16 AUC classification tax, is coincident, not leading, and shows no directional-return skill at any horizon; a coincident de-risking rule reduces drawdown but is presented explicitly not as a trading strategy. Two self-caught bugs (a lookahead label and a zeroed factor) are disclosed. · No new method is claimed: the contribution is a pre-registered map of where an extreme-compression class-discriminant token is a detector and where it is not, on real public market data, with all outcomes — including those that missed their bands — reported verbatim. Statistical candor is explicit: only the market-state result carries bootstrap confidence intervals; the other comparative figures are single-split point estimates, pre-registered but multiplicity-uncorrected. What this record contains · The manuscript (PDF), with a fourteen-study summary table and eight figures. · A reproducibility archive (`PAPER_39_ZENODO_ARCHIVE.zip`): the eight pre-registration scopes with frozen outcome bands, the deterministic per-study runners (FIN-1 through FIN-14), the per-study JSON result summaries behind every figure and table value, and the figures. All data are public daily and hourly OHLCV series and the CBOE Volatility Index (VIX); no raw data is redistributed — the loaders fetch the public source at run time. Cite as R. J. Ferlic and K. K. Ferlic, "A one-byte, options-free market-state monitor: detection-preserving compression of financial data streams with a class-discriminant token," Zenodo, 2026, doi: 10.5281/zenodo.22101085. License and patent notice Released under CC-BY 4.0. Consistent with Section 2(b) of that license, no patent, patent-application, or other intellectual-property right of the authors is licensed, waived, granted, or otherwise conveyed by this publication or by any reuse of it; the methods described herein — including the single-token class-discriminant encoder, its multi-token product-quantization variant, and its unsupervised nearest-centroid-distance monitoring mode — are the subject of filed and pending U.S. patent applications, and attribution under CC-BY does not extend to those rights. Inquiries regarding licensing of the encoding methods may be directed to randolphf@fieldstoneanalyticsllc.com. Companion deposits Part of the single-token class-discriminant codebook family on Zenodo (community: spiral-domain-encoder-campaign), which includes the single-token industrial sensor substrate (doi: 10.5281/zenodo.20854722), the hardening-and-generality characterization (doi: 10.5281/zenodo.20802759), and the deterministic multi-token token-ladder (doi: 10.5281/zenodo.22003179). This deposit applies the same previously described encoding method to a new input domain — financial data streams. Keywords decision-preserving compression, class-discriminant codebook, market-stress detection, contagion and correlation regime, systemic risk, volatility regime, VIX, anomaly detection, edge computing, pre-registration, honest negatives.
Abstract Dynamic exposure rules can appear effective simply because they reduce risky participation, not because they time exposure well. This study evaluates Pogi, a recursive FULL/PARTIAL/NONE controller that separates portfolio composition from total risky exposure and uses a non-executed shadow path to observe recovery during defensive states. Using daily cryptocurrency data from 2014–2026, eight chronological test folds, recursive transaction costs, and CRRA certainty-equivalent welfare, Pogi is compared with static scaling, volatility targeting, CPPI, drawdown throttling, moving-average control, fractional Kelly scaling, and an exact ex-post exposure-matched diagnostic. The analysis also tests initialization and memory sensitivity, timing nulls, search capacity, selection-aware inference, and external validation using U.S. industry portfolios and frozen cross-market transfer. The completed evaluation did not establish robust welfare superiority for Pogi or support a broader methodological contribution under the pre-specified evidence criteria. The results instead show why dynamic exposure rules should be judged against exposure-matched benchmarks, model-search controls, recursive-state diagnostics, and genuinely external validation.
The stability of markets hosting leveraged exchange-traded products is governed not by any single product's loop gain but by the spectral radius of a loop-gain matrix, and scalar per-product monitoring underestimates system feedback by construction. Recent work measures the self-reinforcement of a leveraged fund's daily close rebalancing through a scalar loop gain and treats cross-asset spillovers as bias. We model complexes on correlated underlyings as a coupled feedback system with matrix gain L and show that scalar monitoring has two blind spots: (i) cycle amplification, since rho(L) >= max_i l_ii for nonnegative coupling, strict under two-way coupling; and (ii) transmitted displacement, which arises already under one-way coupling and is invisible to the receiver's own gain. We give a reduced-form estimator of L requiring only prices and public fund assets -- no signed order flow -- via cross-asset overnight reversals, reporting its measurement-convention sensitivity explicitly. In simulation the spectral radius is recovered with RMSE 0.005 at T=250, a lead-lag confounder yields a 2% false-alarm rate, and in a calibrated blind-spot configuration the scalar monitor reports "safe" and the matrix monitor "unsafe" on 100% of paths. In the 2026 Korean single-stock LETF episode we detect transmission from the SK Hynix complex into Samsung Electronics' closing price (DiD z=-2.82; exact randomization p=0.0055 against 182 control pairs), scaling with the sender's rebalancing capital; conservatively, about 41% of Samsung's closing displacement variance is imported -- invisible to its own "moderate" gain of 0.24. The same estimator returns nulls for the U.S. MSTR-Bitcoin-Coinbase complex, whose capital is comparable but whose closing venue is far deeper. Monitoring should be organized around the (complex x venue) matrix, not around products.
Prediction markets such as Polymarket are increasingly cited as real-time probability estimates for financial outcomes, yet it remains unknown whether their prices are consistent with the risk-neutral probabilities implied by options markets pricing the same events. Using 2,671 daily observations across 24 Gold and Silver CME futures threshold contracts over a six-month period, this paper finds that Polymarket systematically overprices the upside relative to Black-76 implied probabilities by 8.9 percentage points for Gold and 5.3 percentage points for Silver, a finding robust to seven independent checks and consistent in direction with a contemporaneous independent study on Bitcoin threshold contracts. The divergence exhibits AR(1) half-lives under three days, narrows significantly as expiry approaches, and cannot be fully explained by transaction costs or the commodity risk premium. These results suggest systematic mispricing in prediction market binary threshold contracts, though the observed magnitudes should be interpreted as upper bounds on behavioural mispricing given the structural wedge between risk-neutral and real-world probability measures.
The paper presents a new hybrid approach (LSTM-RF-SLSQP) that acts as an advanced decision-support tool for institutional crypto portfolio management. The methodology includes the use of LSTM neural networks to detect non-linear temporal patterns and RF algorithms to detect structural market noise. The ability to predict future prices based on LSTM-RF model has been extensively verified out-of-sample using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), and shown to have smaller prediction error than standalone algorithms on very volatile assets. Then, based on robust return and risk forecasts, SLSQP optimization algorithm allocates asset weights aiming at maximizing Sharpe ratio under specific institutional constraints, applying buy-and-hold approach with quarterly rebalance. The empirical study is performed for a portfolio of ten major cryptocurrencies (BTC, ETH, SOL, BNB, XRP, ADA, DOGE, TRX, LINK, DOT) using data provided by KuCoin exchange from January 2024 to January 2026. The numerical experiments demonstrated the significant superiority of the suggested framework compared to all benchmarks. Namely, the LSTM-RF-SLSQP approach provides the impressive annual return of 107.40%, Sharpe ratio of 2.04, with maximum drawdown equal to -3.80%.
Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can vary across assets, decision fields, and market conditions. Existing LLM-based trading systems either coordinate human-defined external experts or adopt conventional internal Mixture-of-Experts (MoE) routers that do not directly evaluate how individual experts contribute to trading decisions. Moreover, these routers receive no direct signal indicating when an inactive expert has become more suitable as market conditions change. We find that native router scores poorly reflect how much individual experts improve trading decisions, frequently leaving better alternatives unselected. We further reveal that token-specific expert usefulness exhibits a compact low-dimensional structure. Based on these findings, we propose TradingMoE, a trading-oriented sparse MoE that augments a frozen dense LLM with lightweight residual experts. We introduce a Query-Key router that represents the expertise required by each token under the current market context as a low-dimensional query and matches it with learnable expert keys. We further propose a sparse expert selection update mechanism that samples a few inactive experts during training and estimates whether they should replace the weakest expert in the current Top-k route. This mechanism enables the router to update expert selection as market conditions change while preserving sparse computation. Experiments against 22 baselines on stock and cryptocurrency markets show that TradingMoE improves cumulative return over the best-performing baselines by 30.89% and 30.7%, respectively. Rolling paper-trading experiments further demonstrate that its advantage persists under forward-only deployment.
Crypto currency is one of most interesting financial innovation of 21st century. Crypto currency trading not only involve financial literacy while trading but also there are psychological factors affecting the decision of traders. Keeping in view the psychological factors and investors’ decision, this research study is designed to investigate the complex interplay between psychological triggers and market dynamics in the cryptocurrency sector in Pakistan, specifically examining how these elements coalesce to drive investor behavior and market volatility. While traditional financial models often attribute asset fluctuations to technological or fundamental shifts, this study posits that cryptocurrency markets are fundamentally driven by human perception and emotional reactivity. Utilizing a quantitative methodological approach, data was collected from a sample of 175 experienced traders to analyze the impact of emotional states, market sentiment, and behavioral discipline on trading outcomes. The empirical results, derived through multiple linear regression analysis, reveal that the model possesses a high level of explanatory power, accounting for 56% of the variance in emotional trading behavior (R2=0.56R2=0.56). Market sentiment emerged as the primary determinant of impulsive trading (β=0.48β=0.48), demonstrating that external social cues often exert a stronger influence on decision-making than internal emotional states. Among specific psychological variables, Fear, Uncertainty, and Doubt (FUD) were identified as the most significant predictors of rash choices (β=0.34β=0.34), while the Fear of Missing Out (FOMO) also demonstrated a substantial, though secondary, effect (β=0.21β=0.21). Conversely, the study found that trading experience and the application of systematic strategies serve as vital moderating factors that decrease emotional reactivity and enhance behavioral stability (β=−0.19β=−0.19). The findings contribute to the fields of behavioral finance and digital economics by illustrating that the volatility inherent in digital assets is a systemic byproduct of individual psychological biases aggregated through digital narratives. The research concludes that achieving a sustainable financial ecosystem requires moving beyond purely technical regulations. Instead, it advocates for the implementation of behaviorally-informed safeguards, such as algorithmic "cooling-off" periods and sentiment-aware trading tools, to mitigate the risks associated with reactive investing. Ultimately, this work provides a blueprint for a more resilient digital financial future by prioritizing human factors in market governance.
This study investigates whether the macroscopic statistical maturity of cryptocurrencies implies dynamical equivalence with traditional equity markets. We analyze high-frequency data (2020--2025) using the Complexity--Entropy Causality Plane (CECP) and directed horizontal visibility graphs (directed HVG) to uncover complex temporal patterns and time-directed structures in the return series. While conventional stylized facts show striking convergence across all assets, structural diagnostics reveal a compelling paradox: cryptocurrencies appear more locally random than the equity benchmark during ordinary periods, yet exhibit significantly stronger directional time-irreversibility around high-visibility return events. The absolute-return results show that large cryptocurrency fluctuations tend to begin abruptly and remain elevated afterward. Separate analyses of positive returns and negative-return magnitudes show that this pattern is shared across cryptocurrencies on the upside but varies across assets on the downside. We conclude that statistical maturity is only skin-deep; the underlying dynamical processes of mature cryptocurrencies remain fundamentally distinct from traditional benchmarks.
Volatility control converts risk estimates into portfolio exposure, yet existing approaches often rely on a fixed volatility estimator or a pre-defined control rule that may not adapt to changing market conditions. We propose VolRouter, a modular framework that formulates volatility control as state-conditioned routing over estimator-controller pairs. VolRouter first summarizes market conditions into a control-relevant state profile and then performs routing through three stages: state inference, switch review, and pair selection. The Router can be implemented using rule-based, learnable, or LLM-based decision modules, while portfolio actions remain generated by predefined control policies. We evaluate VolRouter across S&P 500, Multi-Asset, Bitcoin, and USDT volatility-control settings. VolRouter achieves the highest Sharpe ratio in three of four settings. On S&P 500, it improves Sharpe from 0.952 for RV + Naive Scaling to 1.222 while reducing maximum drawdown from 15.10% to 12.58% and daily CVaR from 1.76% to 1.32%. On Multi-Asset, it improves Sharpe from 1.498 to 1.540 and reduces CVaR from 1.56% to 1.18%. Bitcoin shows similar improvements in risk-adjusted performance, while USDT provides a boundary case where simpler state-aware selectors remain competitive. Ablation and sensitivity analyses show that the improvement comes from relative policy evaluation and selective persistent switching rather than simply expanding the policy library. These results suggest that volatility control can be viewed as a policy-selection problem when risk management requirements vary across market states.
Velian Prapatoni, Rizky Parlika, Firza Prima Aditiawan
Cryptocurrency markets are characterized by high volatility, rapid price fluctuations, and substantial uncertainty, creating challenges for investment risk interpretation. This study develops a descriptive risk-interpretation framework, rather than a price-prediction or decision-optimization model, by integrating multi-scale volatility analysis with technical indicators. A quantitative descriptive design was applied to approximately one year of historical hourly price data for Bitcoin and Ethereum, covering open, high, low, close, volume, and percentage change attributes. The data were chronologically sorted, numerically cleaned and normalized, transformed into log returns, and analyzed through rolling standard deviation. Volatility was estimated across three explicitly defined horizons: short-term 7-period, medium-term 30-period, and long-term 90-period rolling windows. Moving Average (MA), Relative Strength Index (RSI), and Average True Range (ATR) were then incorporated to contextualize trend direction, momentum, and fluctuation intensity. The results show that volatility is strongly horizon-dependent: short-term movements responded more sharply to market shocks, whereas longer horizons produced smoother risk patterns. Across the analyzed Bitcoin and Ethereum hourly series, the reported 42.3% short-term and 21.7% medium-term increases were calculated as relative differences against long-term rolling volatility during identified high-uncertainty intervals, not as predictive accuracy measures. These findings indicate that combining rolling volatility with MA, RSI, and ATR can improve the transparency of descriptive cryptocurrency risk assessment. The framework may support preliminary interpretation for novice or risk-averse investors, although it does not empirically test investor comprehension or subsequent decision quality.
Cryptocurrencies have emerged as a prominent asset class characterized by rapid price fluctuations, growing institutional participation, and continuing debate over whether their price movements are random or predictable. This study examines the randomness and weak-form market efficiency of the top ten cryptocurrencies by market capitalization—Bitcoin, Ethereum, Tether, Binance Coin, XRP, USD Coin, Solana, TRON, Dogecoin, and Hype liquid—using daily closing price data from April 2016 to March 2026 (subject to data availability for each coin). Daily log returns were tested using Descriptive Statistics, the Jarque–Bera test of normality, the Wald–Wolfowitz Run Test, and the Autocorrelation Test. The results show that daily returns for all selected cryptocurrencies are non-normally distributed, exhibiting excess kurtosis and skewness. The Run Test results indicate that seven of the ten cryptocurrencies—Bitcoin, Ethereum, Tether, Binance Coin, XRP, USD Coin, and Dogecoin—do not follow a random walk, while Solana, TRON, and Hype liquid exhibit randomness consistent with weak-form efficiency. However, the Autocorrelation Test reveals strong positive serial correlation across all ten cryptocurrencies, indicating that the market falls short of weak-form efficiency. The study concludes that the cryptocurrency market provides mixed and largely inefficient evidence with respect to the Random Walk Hypothesis, implying that historical price information may retain some predictive value for investors.