The Fractal Market Hypothesis (FMH) is increasingly studied and applied by both finance academics and practitioners. We previously discussed the use of Detrended Fluctuation Analysis to estimate the Hurst exponent for major cryptocurrencies. Continuing this line of research, Reference applies Multifractal Detrended Fluctuation Analysis (MFDFA) to examine cryptocurrency, commodity, …

Predicting market direction is challenging, and forecasting market crashes is even more difficult, yet this remains a growing area of research. We previously discussed market correction prediction, and Reference continues this line of inquiry by examining how machine learning can be used to predict market crashes within the Adaptive …

Hedging is an important topic in portfolio and risk management; however, relatively little research has been conducted in this area. Many questions remain open, such as when to hedge, how frequently to hedge, and which volatility measure should be used in hedging decisions. We have previously discussed whether hedging should …

In these days of big data, machine learning, and AI, many researchers are showing growing interest in sophisticated models for stock price prediction or to refine basic models of stock dynamics. Reference takes the opposite approach. It uses a classical model for stock price dynamics—the Geometric Brownian Motion (GBM)—and …

Artificial intelligence (AI) is advancing rapidly, and traders and investors are finding ways to leverage this progress to gain an additional edge. Reference examines the effectiveness of AI—ChatGPT, in particular—in personal finance. Unlike previous studies that focus on quantitative aspects, the paper evaluates AI performance in a qualitative way. …

As cryptocurrencies become mainstream, researchers have begun examining their statistical properties, particularly volatility, which represents the second moment of the return distribution. However, limited attention has been given to higher-order moments, specifically skewness and kurtosis. Given that cryptocurrencies are highly volatile and exhibit heavy-tail risks, their return distributions are not …

Probabilistic AI is a branch of artificial intelligence that models uncertainty explicitly, allowing systems to reason and make predictions even when data is incomplete or noisy. Instead of producing single-point estimates, it generates probability distributions over possible outcomes, capturing both what is known and how confident the model is. Reference …

As cryptocurrencies, particularly Bitcoin (BTC), become more mainstream, investors have noticed an increasing correlation between BTC and the stock market. Some investors even argue that BTC now functions as a substitute risk asset for the technology sector. Reference presents a quantitative study on the correlation between BTC and the …

Artificial intelligence (AI) has become an integral part of modern finance, transforming how institutions analyze data, manage risk, and execute trades. By leveraging machine learning algorithms and natural language processing, AI systems can identify complex patterns in large financial datasets, forecast market movements, and detect anomalies that might signal fraud …

Dollar-cost averaging (DCA) is an investment strategy where you invest a fixed amount of money at regular intervals, regardless of market conditions. By consistently buying over time, you smooth out entry prices, reduce the impact of short-term volatility, and avoid the risk of mistiming the market with a single large …