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Introduction The yield curve is often seen as a signal of the economy. It reflects expectations about growth, inflation, and interest rates, and its shape changes as market conditions evolve. A natural question is whether these signals can be used to make better investment decisions. This project explores how to turn yield curve signals into a simple, data-driven strategy. Understanding Market Signals One of the most important signals comes from the spread between long-term a

Can We Predict Stock Returns with Data? Introduction Stock returns are often considered unpredictable, especially in the short term. Prices move quickly, and daily changes are often noisy and random. In this project, I explore a key question: Can we use data to predict whether stock prices will go up or down? To answer this, I combined price data and company fundamentals, and built machine learning models to test predictability across different time horizons. Step1: Building

Introduction In today’s global financial system, countries are closely connected. When one country faces financial stress, the impact can quickly spread to others. This makes it important to understand how risk moves across countries and whether we can predict it. In this project, I analyze sovereign credit risk using CDS (Credit Default Swap) data from multiple countries. My goal is to understand how risks are connected and how they change over time. How Risk Moves Across Co

Source: Liberty Times (財經自由時報) In today’s global and highly competitive market, companies must continuously seek growth opportunities to remain competitive. While organic growth can be slow and uncertain, mergers and acquisitions (M&A) have become one of the most effective strategies for rapid expansion. Through M&A, firms can acquire advanced technologies, access new markets, and strengthen their competitive advantage while reducing the time and cost required for internal d