Is Bitcoin falling in October 2026, and how low could it go?

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1. 1

   The Bitcoin price and Bitcoin price uncertainty: Evidence of Bitcoin price volatility

   Journal of Futures Markets (Köse et al.)Published Feb 1, 2024Checked Oct 16, 2026

   “This study examines the Bitcoin price by taking into account global factors, including the Chicago Board Options Exchange's Market Volatility Index (VIX), the US dollar index, the gold price, the oil price, and Bitcoin price volatility. The analysis is conducted using the structural vector autoregression (SVAR) model. The variance decomposition findings revealed that the influence of the VIX on the Bitcoin price was initially restricted, but progressively intensified over time. Among the indicators, Bitcoin price volatility had the highest explanatory share in both daily and weekly data analysis. The impulse response functions demonstrated a statistically significant inverse relationship between the VIX and the Bitcoin price. Furthermore, the analysis revealed that the Bitcoin price was mostly impacted by its own volatility. This implies that investing in Bitcoin requires a certain level of risk‐taking.”

2. 2

   The Impact of Digital Currency Innovation: Risk Spillover Effects Between the Cryptocurrency and Traditional Financial Markets.

   Entropy (Basel, Switzerland) (Zhuang & Liu)Published Aug 24, 2026Checked Oct 16, 2026

   “The findings reveal an overall weak and asymmetric bidirectional spillover relationship between the cryptocurrency and traditional financial markets. Volatility in the digital currency market is found to be largely endogenous, with a limited capacity to transmit shocks externally. Conversely, traditional financial markets-particularly the equity market-exert a more pronounced influence on the digital currency market. Critically, under the impact of extreme risk events, the cross-market linkages exhibit structural breaks; the direction and intensity of correlation can strengthen significantly or even reverse, demonstrating a clear state-dependency. This research provides empirical evidence for understanding the functional role of digital assets within the macro-financial system, their risk transmission pathways, and their implications for systemic financial stability. The findings offer valuable theoretical and practical insights for financial regulators in designing robust cross-market risk prevention frameworks and for investors seeking to optimize asset allocation strategies.”

3. 3

   Bitcoin Prices: Configurational Effects of Technological Drivers, Macroeconomic Fundamentals and Economic Agents' Expectation

   International Journal of Finance & Economics (Manickavasagam et al.)Published Aug 13, 2025Checked Oct 16, 2026

   “Findings show that economic agents' expectations such as OECD's Business Confidence Index, Consumer Confidence Index and Composite Leading Indicator emerge as influential variables of Bitcoin, surpassing traditional drivers like Gold and Financial Stress Index. Among these, Business Confidence Index and Composite Leading Indicator exhibit a very large effect on Bitcoin prices, and from the technology variable group, Average Block Size exhibits a very large effect on Bitcoin prices. fsQCA indicates that nine distinct configurations contribute to high Bitcoin prices and eight configurations lead to low Bitcoin prices, thus depicting equifinality in Bitcoin price determination. These insights can provide policymakers and investors with a better understanding of the Bitcoin price dynamic by finding out necessary variables and equifinal pathways towards either high or low prices, thus promoting better risk management activities, as well as regulatory approaches to this highly dynamic asset class.”

4. 4

   Political uncertainty and multi-scale systemic risk spillovers in the cryptocurrency market: A STVAR-based network topology approach.

   PloS one (Wang et al.)Published May 28, 2026Checked Oct 16, 2026

   “To reveal the risk characteristics at different time scales, we use wavelet packet decomposition to decompose the sequence into short-term, medium-term, and long-term components; We also use forecast error variance decomposition to quantify risk spillovers, in order to study the direction and intensity of risk spillovers between different cryptocurrencies. In terms of the responses of cryptocurrency systemic risks to political uncertainty shocks, we find significant asymmetries. The mid- and long-term risk components indicate that most cryptocurrencies exhibit a stronger response during periods of high political uncertainty than during low periods. Moreover, shocks during high political uncertainty periods enlarge the cross-cryptocurrency risk spillovers. Finally, Bitcoin, Ethereum and Monero are stable risk transmitters, while Peercoin and Namecoin are more vulnerable risk receivers. This paper provides some insights into the differential impact of political uncertainty on the stability and interconnectivity of various cryptocurrencies.”

5. 5

   Narrative Research Study: Market Sentiment As A Trigger For Cryptocurrency Volatility

   SENTRALISASI (Hidayatullah & Juniar)Published Dec 7, 2024Checked Oct 16, 2026

   “The analysis employs Structural Equation Modeling - Partial Least Squares to examine the relationship between market sentiment and price volatility. The latent variable "Market Sentiment" is constructed using the NLP indicator, while "Price Volatility" is measured by daily standard deviations and the Average True Range. Additional factors, such as Layer-1, Layer-2, Decentralized Finance, Specialized Computing, Real World Assets, and Decentralized Infrastructure Projects, are also incorporated for a more comprehensive analysis. The results show that positive sentiment significantly increased price volatility, especially for speculative projects like Layer-2 and DeFi, while negative sentiment significantly reduced volatility. Neutral sentiment had no significant effect on price volatility. These findings highlight the important role of social media and news in driving sharp price movements in the cryptocurrency market, providing valuable insights for investors and policymakers in managing and responding to market sentiment.”

6. 6

   Evolution of spatial structure, passing network patterns, and gameplay intensity in elite women's and men's football (2020-2025).

   Scientific reports (Carstens et al.)Published May 14, 2026Checked Oct 18, 2026

   “Drawing on event-level records for 13,018 matches across ten top-tier men's and women's leagues in England, Spain, Germany, Italy, and the United States (2020-2025), we quantify match dynamics through two complementary lenses: conventional performance statistics and pitch-passing networks that track ball movement across spatial regions of the field. Between 2020 and 2025, average passing volume, pass accuracy, and the proportion of passes made under pressure all increased, with the largest year-on-year changes occurring in women's competitions. Network measures reveal that normalized outreach decreased, indicating teams increasingly concentrate ball circulation into shorter-range passing connections rather than wide spatial distribution. These trends are consistent across countries and tiers, yet persistent national differences indicate that stylistic diversity remains. Notably, women's competitions exhibit stronger rates of change across most metrics, consistent with an accelerating professionalization, while the systematic decline in network outreach across all competitions is consistent with a sport-wide shift toward shorter, more concentrated passing structures.”

7. 7

   Are Counterattacks More Effective than Positional Attacks in Soccer? A Comparative Analysis of Influencing Factors.

   Journal of human kinetics (Prieto-González et al.)Published Sep 23, 2025Checked Oct 18, 2026

   “These variables included team performance metrics and tactical indicators. Counterattacks were more efficient than positional attacks across the three leagues, and top teams performed better than intermediate and bottom teams. Serie A showed the highest efficiency. In La Liga and the Premier League, positional attack efficiency was linked to right flank attacks, while counterattacks relied on central-zone efficiency. Serie A showed similar patterns, with the right flank contributing most to positional attack efficiency and central-zone efficiency being the strongest predictor of counterattack efficiency. The superiority of counterattacks over positional attacks underscores the importance of tactical precision and situational execution. Top teams excelled in both strategies, emphasizing the need for efficiency. The findings highlight the significance of adapting strategies to each league's unique trends. Coaches can leverage these insights to refine their approach, focusing on fast transitions, possession play, and wing play to enhance attacking efficiency and overall team performance.”