Author: Harbourfront Technologies

Time Series vs. Machine Learning: A Systematic Evaluation

Forecasting is important in finance, as it helps investors, analysts, and institutions make informed decisions under uncertainty. Up to now, most forecasting techniques have relied on traditional time series methods, such as ARIMA, GARCH, and exponential smoothing. However, with recent advancements in machine learning and artificial intelligence, these technologies have …

Tail Risk Hedging with Corporate Bond ETFs

Tail risk hedging is a strategy designed to protect portfolios against extreme market moves that occur infrequently but have a significant impact when they do. These “tail events” lie at the far ends of a return distribution and often coincide with financial crises, sharp market crashes, or systemic shocks. A …

Price Fragility, ETF Flows, and Non-Fundamental Shocks

Financial asset fragility refers to the vulnerability of an asset’s price to sudden and disproportionate changes in response to shocks, even if those shocks are relatively small. This fragility often stems from factors like excessive leverage, crowded positioning, liquidity mismatches, or overreliance on certain market assumptions. Reference utilized the …

Interest Rate Sensitivity in Low-Volatility Investing

Low-volatility investing is a strategy that focuses on stocks with historically lower price fluctuations, aiming to achieve strong risk-adjusted returns. Despite conventional finance theory suggesting that higher risk should lead to higher returns, research has shown that low-volatility stocks often outperform their high-volatility counterparts on a risk-adjusted basis. By reducing …

Variational Autoencoders for Arbitrage-Free Volatility Modeling

Machine learning and AI are transforming investing by enabling data-driven decision-making, uncovering hidden patterns, and automating complex strategies. From algorithmic trading and portfolio optimization to risk management and sentiment analysis, AI-driven models process vast amounts of data with speed and precision, identifying opportunities that traditional methods might miss. Most ML …

Stock and Volatility Simulation: A Comparative Study of Stochastic Models

Stress testing and scenario analysis are essential tools in portfolio management, helping portfolio and risk managers assess potential vulnerabilities under extreme market conditions. By simulating adverse scenarios such as financial crises, interest rate shocks, or geopolitical events, these techniques provide insights into how a portfolio might behave under stress and …