Insights

Whether you're a seasoned expert in the stock market or a beginner just starting to invest, you might be curious about where many stock analysts get their "company information." Most of this data is obtained from the Taiwan Stock Exchange (TWSE) website and the Market Observation Post System (MOPS). This includes financial statements, the number of issued shares for listed companies, recent dividend distributions, and more. You can even receive real-time updates on significant announcements from listed companies. It's one of the must-visit websites for investors! As for why the Market Observation Post System exists and how to use its features and functions, let TEJ (Taiwan Economic Journal) explain it all to you!

This article will introduce algorithmic trading with Python, the benefits of using the Python programming language for financial trading, and how to apply it to financial transactions, allowing investors to harness the power of technology to build their ideal investment portfolios.

In the first article—Verifying LSTM Stock Price Prediction Effectiveness Using TQuant Lab (Part 1)—we compared the predicted data with the actual data to conduct an initial evaluation of the performance of two trained models (for stocks 2618 and 8615). The results were promising. For a more detailed analysis, you can click the link above to learn more, as we will not go into further details here due to space constraints.

If you’ve been involved in investing for some time, you’ve likely heard a lot about algorithmic trading and quantitative trading strategies. For beginners with no software or coding background, it might seem daunting at first. However, with the rise of AI in recent years, the ability to automate investment strategies could become an essential tool […]

When market efficiency is low or inefficient, stock prices tend to overreact or underreact to new information. This phenomenon allows investors to achieve significant positive average returns by buying stocks that have performed well in the past or short-selling stocks that have performed poorly (Jegadeesh and Titman, 1993). From a behavioral finance perspective, George and Hwang (2004) pointed out that traders might be reluctant to buy even if there is favorable news when stock prices approach a new high within the past year. This reluctance leads to stock prices reaching new highs driven by positive news, indicating that even professional investors might underreact to new information. Zhang (2006) found from an information asymmetry perspective that in markets with a higher degree of information asymmetry, future returns of stocks following bad or good news tend to be lower or higher, respectively. Momentum strategies perform better in stocks with higher levels of information asymmetry. This study attempts to use the daily reports of brokers branches trading provided by the Taiwan Stock Exchange to derive relevant indicators from an information asymmetry perspective, combining these with momentum factors to identify stocks that have yet to catch the market's attention but are gradually rising in price. The study will conduct overlapping period tests, IC/IR value tests, and factor portfolio backtesting on this composite factor.

This article uses the LSTM time series model for deep learning-based LSTM stock price prediction, utilizing the opening, high, low, and closing prices of the past five days, quarterly ROE, MOM (indicating the magnitude of price trend changes, and the direction of market trends), and RSI indicators to predict the next day's closing price.

Block Trade, a crucial element of the financial markets, refer to the buying and selling of securities in substantial single transactions. These trades, typically carried out by institutional or large investors, involve a multitude of stocks, bonds, or other financial instruments. Their staggering volume and value characterize them and are often conducted outside the open market to mitigate excessive impact on market prices. TEJ will guide you through this crucial aspect of the financial world.

With the vigorous development of big data applications and cloud computing, Cloud Service Providers (CSPs) are actively constructing data centers, driving growth in the server market. Especially after OpenAI launched ChatGPT, opportunities in the GenAI sector have flourished, sparking a wave of AI infrastructure development. AI servers, in particular, have seen significant hardware improvements compared to normal servers, even adopting new technologies that increase integration and testing complexity. Some assembly factories, leveraging their technical expertise, have successfully tapped into the AI boom, opening up a new growth opportunity for the EMS (Electronic Manufacturing Services) industry.

In recent years, as the stock prices of popular AI companies continue to reach new highs, investors are increasingly focused not only on these companies' operational status but also on the trading behavior of their insiders. Company insiders have more information compared to external investors, giving them an informational advantage when trading the company's stock.