# Bias İlgili Makaleler

HTX Haber Merkezi, kripto endüstrisindeki piyasa trendleri, proje güncellemeleri, teknoloji gelişmeleri ve düzenleyici politikaları kapsayan "Bias" hakkında en son makaleleri ve derinlemesine analizleri sunmaktadır.

Prediction Markets Under Bias

The article "Prediction Markets Under Bias" by Jeff Park of Bitwise argues against the common media portrayal of prediction markets as mere gambling or a social ill. It distinguishes between gambling and investment based on whether a participant's strategy has a positive expected value (+EV), not the market's structure. The author contends that prediction markets, like poker, are skill-based and can be a legitimate form of investment, offering individuals autonomy, truth discovery, and decentralized value. The piece critiques the common conflation of prediction markets with casino gambling, highlighting their role in risk hedging and capital efficiency, similar to insurance and securitization. A key differentiator is their precision and finite expiration, which makes prices directly anchor to factual outcomes, rewarding deep research and information advantage rather than punishing the uninformed. The author concludes that media opposition often stems from a structural bias, as these markets challenge traditional information gatekeepers and promote a more democratic, transparent system for pricing truth. The real debate is not about information having a price, but about who controls and profits from it.

marsbit04/20 12:08

Your Backtest Is Lying: Why You Must Use Point-in-Time Data

The article warns that backtests using revised historical data are misleading due to look-ahead bias. It illustrates this with a hypothetical trading strategy based on Bitcoin exchange outflows from Binance: entering the market when the 5-day moving average of BTC balance falls below the 14-day average, and exiting on the reverse. Initial backtest results using current data appeared reasonably successful, even comparable to buy-and-hold at times. However, the author argues this is deceptive because metrics like exchange balances are often revised retroactively as new data arrives, meaning a backtest uses information that wasn't available in real-time. Re-running the identical strategy using Point-in-Time (PiT) data—which is immutable and reflects only what was known when the data was first published—yielded significantly worse performance. The PiT-based strategy failed to capture key market upswings as effectively, resulting in meaningfully lower cumulative returns. The key takeaway is that only immutable, Point-in-Time data ensures a backtest accurately replays history without look-ahead bias, and that using revised data will produce lying backtests.

insights.glassnode03/13 14:12

Your Backtest Is Lying: Why You Must Use Point-in-Time Data

This article warns against a common pitfall in backtesting trading strategies: look-ahead bias caused by using revised historical data. It illustrates this with a hypothetical Bitcoin strategy based on exchange outflows from Binance. The strategy is built on the premise that sustained outflows (when the 5-day moving average of BTC balance falls below the 14-day average) are bullish, while inflows signal a sell-off. An initial backtest using standard, revised data shows the strategy performing comparably to a simple buy-and-hold approach. However, the author argues these results are misleading because the data has been updated with information that wasn't available in real-time. This data mutation creates an unfair advantage in the backtest. To demonstrate, the test is rerun using Point-in-Time (PiT) data—an immutable, append-only record that reflects only what was known on any given day. The results are significantly worse, as the PiT-based strategy misses key profitable moves. The key takeaway is that accurate backtesting requires immutable Point-in-Time data to avoid look-ahead bias and replay history honestly.

insights.glassnode03/13 14:12

a16z: AI Makes Everyone 10x More Efficient, But No Company Becomes 10x More Valuable

a16z investor George Sivulka argues that while AI has dramatically increased individual productivity by 10x, it hasn’t translated into a 10x increase in company value. The core issue is not the technology itself, but the failure to redesign organizations around it—much like factories in the 1890s initially replaced steam engines with electric motors but didn’t see real gains until they fully redesigned assembly lines decades later. Sivulka distinguishes between “Personal AI” (e.g., ChatGPT) and “Organizational AI,” outlining seven key dimensions where they differ: 1. **Coordination:** Personal AI creates chaos; Organizational AI coordinates teams and agents toward unified goals. 2. **Signal:** Personal AI generates noise and low-quality output; Organizational AI filters noise to find valuable signals. 3. **Bias:** Personal AI reinforces user bias; Organizational AI introduces objectivity and challenges assumptions. 4. **Edge Advantage:** Personal AI optimizes for general usage; Organizational AI leverages domain-specific expertise for competitive advantage. 5. **Outcome:** Personal AI saves time; Organizational AI drives revenue growth. 6. **Enablement:** Personal AI gives a tool; Organizational AI embeds processes and enables organizational change. 7. **Promptless:** Personal AI requires human prompts; Organizational AI acts autonomously without human intervention. True value, Sivulka concludes, will come from rebuilding organizations and processes around AI—not just adopting the technology. The future belongs to companies that build “Organizational AI” systems that integrate deeply with institutional workflows.

marsbit03/13 04:40

a16z: AI Makes Everyone 10x More Efficient, But No Company Becomes 10x More Valuable

marsbit03/13 04:40

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