Insights

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.

During Jensen Huang's speech at National Taiwan University, he revealed a list of NVIDIA's business partners in Taiwan, which led to the viral spread of the term "the world's strongest AI ETF". What impact did this announcement have on the stocks of these business partners? We will use TEJAPI to calculate the Sharpe ratio to determine whether the effect was positive or negative. Additionally, we will visualize the observed results.

For students who want to use TQuant Lab, in addition to the original GitHub installation tutorial, we now offer a faster and simpler way to use it directly on Google Colab. The tutorial on running TQuant Lab in Google Colab eliminates the need to set up a virtual environment, significantly lowering the barrier to entry.