Insights

Taiwan’s stock market is a shallow dish market. A shallow dish means it has no depth and is susceptible to external shocks. In contrast, the stock market is easily disturbed by external forces. It has significant ups and downs, such as being influenced by news or large purchases of a single stock by large investors, which can significantly affect stock price performance. This article uses several chip data to explore the impact on stock prices when chips are concentrated in stock and presents them in an interactive chart.

In financial markets, technical analysis has long been a crucial tool for investors to assess market trends and develop trading strategies. Among these techniques, the Ichimoku Kinko Hyo (Ichimoku Cloud) utilizes five moving averages and a cloud structure to analyze stock support, resistance, and trend changes, enabling traders to quickly identify bullish or bearish signals. However, traditional Ichimoku strategies rely on fixed parameters (9-26-52) and visual interpretation, making them inflexible in adapting to different market conditions. Additionally, in choppy market conditions, Ichimoku-based signals can generate false positives, leading to erroneous trades and capital drawdowns. With the advancement of machine learning, XGBoost (Extreme Gradient Boosting) has become one of the most widely used models in quantitative trading. By leveraging Gradient Boosting Decision Trees (GBDT), XGBoost learns complex high-dimensional relationships between different data points and enhances the filtering and decision-making process of trading signals. Compared to purely relying on technical indicators, XGBoost can integrate various market variables—such as price momentum, volume changes, and market sentiment—to uncover relationships between data and future stock returns. This ultimately improves the accuracy and robustness of trading strategies.

In the world of investing, accurate predictions and systematic analysis are key to achieving long-term success. Strategists who can decipher market trends become sought-after figures for investors eager to learn. Michael Sivy is undoubtedly one of the best. With precise market forecasts and unique investment insights, he has built a stellar reputation in the U.S. investment community. From warning of the 1987 stock market crash to predicting a market turnaround in 1995 and foreseeing the bursting of the tech bubble in 1999, Sivy has demonstrated exceptional market foresight through decades of research and hands-on experience. His book, The Rule of 72, has become a must-read classic, offering systematic analysis and clear guidelines for investors of all types.

Dividend arbitrage focuses on the price inefficiencies between dividend payouts and option pricing. This article will explore its execution process, examples, and challenges.

Dividend arbitrage focuses on the price inefficiencies between dividend payouts and option pricing. This article will explore its execution process, examples, and challenges.

Dividend arbitrage focuses on the price inefficiencies between dividend payouts and option pricing. This article will explore its execution process, examples, and challenges.

Quantitative data collection methods include surveys, interviews, observations, and dataset reviews. Explore the techniques, pros, and cons of each method.

In investment decision-making, price-volume factors are essential for investors to gain insights into market behavior. The relationship between price and trading volume supply and demand dynamics of an asset also reveals capital flows and shifts in market sentiment. These factors play a crucial role in capturing short-term opportunities and identifying potential risks in asset allocation.

Dividend arbitrage focuses on the price inefficiencies between dividend payouts and option pricing. This article will explore its execution process, examples, and challenges.