Prediction markets are financial platforms where participants trade contracts linked to future events, with prices reflecting collective probabilities. While these markets efficiently aggregate information, systematic inefficiencies create trading opportunities. Notable strategies include inter- and intra-market arbitrage, exploiting price differences across platforms or mispricing within a single market. Behavioral biases, such as the longshot bias, lead traders to overvalue underdogs and undervalue favorites, while bookmakers may manipulate odds to mislead naive participants. Experienced traders can exploit these patterns to secure profits. This article reviews common systematic edges in prediction markets, illustrates their practical application, and highlights the potential for profitable trading.

In this article, we explore whether it makes sense to incorporate leveraged ETFs into static and dynamic long-only asset allocation strategies. Leveraged ETFs promise amplified exposure to the underlying asset, offering the potential for significantly higher returns during favorable market conditions. However, this comes at the cost of much higher volatility, path-dependency, and the well-known issue of volatility decay, which can lead to substantial underperformance over longer periods. Our objective is to examine if — and how — leveraged ETFs can be systematically integrated into portfolio construction so that their benefits can be captured while mitigating their inherent risks.

Bitcoin is one of the most widely discussed financial assets of the modern era. Since its inception, it has evolved from a niche digital experiment into a globally recognized investment instrument with institutional adoption and billions in daily trading volume. Despite its inherent volatility, Bitcoin has demonstrated a strong long-term growth trajectory, making it an attractive candidate for trend-based and momentum-oriented trading strategies. In this study, we apply concepts from technical analysis to construct and refine a trend-following strategy for Bitcoin, progressing step by step from a simple MACD setup toward an improved multi-timeframe model.

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– Updated architecture for Quantpedia’s AI Assistant
– 12 new Quantpedia Premium strategies have been added to our database
– 7 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 9 new backtests written in QuantConnect code
– 5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

MicroStrategy has become one of the most polarizing companies in public markets. Once a conventional business intelligence firm, it has transformed into the world’s largest publicly traded Bitcoin proxy, holding over a million BTC on its balance sheet and continuously raising capital to buy more. Supporters praise it as a visionary “Bitcoin ETF with leverage,” while critics argue it is an irrationally overvalued vehicle whose market capitalization regularly trades far above the fair value of its underlying assets. The persistent premium — the gap between MicroStrategy’s equity value and the market value of its Bitcoin holdings — has puzzled analysts, defied traditional valuation logic, and raised the question: why does this spread exist, and why does it not close through arbitrage? A recent academic paper, Valuing MicroStrategy, offers a structural model that explains this phenomenon and sheds light on how the firm’s unique financing mechanics allow its stock price to exceed the value of its assets.

This article examines the impact of major consumer holidays, Thanksgiving and Christmas, on financial markets. Using historical price data from 2004 to 2024, we analyze daily performance trends in the 10 trading days before and after each holiday to determine whether seasonal spending influences asset prices. Our findings suggest that seasonal consumer spending influences financial markets, with Amazon benefiting around Thanksgiving and gold during Christmas.