Multi-region investment strategies, including developed and emerging markets.
This paper shows that investors with limited ability to borrow tend to chase high-risk assets. As a result, low-risk (low-beta) assets offer better risk-adjusted returns. A strategy that goes long low-beta assets and shorts high-beta ones—called “Betting Against Beta”—consistently beats the market.
This paper reveals that high-frequency trading (HFT) is not one big strategy—but three distinct types. It shows that when HFTs compete in market-making, volatility actually goes down, and smaller exchanges become more viable.
This paper shows that in high-frequency trading (HFT), milliseconds matter. The fastest firms earn the majority of profits, not because they make better trades—but because they make more of them, faster. Speed wins.
High-frequency traders (HFTs) often compete with large institutional trades, raising execution costs for informed orders. This paper shows that when HFT speed is restricted, price impact drops—especially for skilled institutional traders.
This paper shows that high-frequency traders use their speed to crowd out slower traders from the most profitable limit order executions. They exploit fleeting imbalances in the order book and withdraw when risk increases—leaving slower traders with poor fills.
Bitcoin’s extreme volatility comes with an upside: low correlation with other assets. This paper shows that even a small allocation to Bitcoin can significantly boost the Sharpe ratio of a diversified portfolio.
This paper shows that Initial Coin Offerings (ICOs) can help startups grow by giving them access to funding and liquidity. Projects that make good disclosures, use tokens with real utility, and get listed on exchanges are more likely to survive and hire more employees later.
This paper shows that cryptocurrency prices can be much higher in some countries than others. These price gaps happen because of restrictions on moving money between countries, not because of problems with the cryptocurrencies themselves.
Crypto returns aren’t driven by mining costs or fiat correlations. Instead, they respond to network adoption, momentum, and investor attention. This paper identifies the key factors that predict crypto returns and provides a foundation for data-driven crypto investing.
During financial crises, hedge funds and commodity index traders (CITs) offload positions as volatility rises, forcing commercial hedgers to absorb risk. This “risk convection” disrupts markets, creating trading opportunities based on liquidity shifts.
Performance * Basis-momentum is the strongest predictor of commodity returns, outperforming traditional factors like basis and momentum. * A long-short strategy based on basis-momentum earns an 18.38% annualized return with a Sharpe ratio of 0.9. * The strategy captures both spot
Gold’s returns are predictable using volatility and risk factors, but it doesn’t always act as a safe haven or inflation hedge. Instead, gold moves with stocks and bonds, though its actual returns can diverge in crises.