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In-depth research and data-driven insights on quantitative finance, factor investing, risk, and ESG from the TEJ research team.
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Fundamental Factor Research: Monthly Revenue Information – part1
The Taiwan equity market possesses a rare institutional advantage globally: under the Securities and Exchange Act, listed companies are required to announce and report their operational results for the preceding month by the 10th of each month (Exception: starting from FY2026, insurance companies and entities with insurance subsidiaries may extend their disclosure deadline to the 15th of each month). This is commonly referred to in the market as "Monthly Revenue".
Factor Research – Tracking Smart Money Footprints via Foreign Institutional Concentration – QFII Part 1
Track QFII ‘smart money’ footprints in Taiwan large-cap stocks! Learn how the Foreign-Institutional Trading Concentration (conc_qfii) factor predicts returns.
Empirical Research on Behavioral Factors in the Taiwan Stock Market: A Case Study of the Share Distribution
In an AI-driven Taiwan stock market, mastering chip distribution (ownership structure) is the key to profitability. This study delves into the Share Distribution data from the TDCC, transforming 15 tiers of shareholding data into behavioral finance factors such as investor attention, opinion dispersion, and retail speculation. By utilizing Fama–MacBeth two-stage regression and the alphalens-tej quantitative tool, we precisely validate the predictive power of psychological biases on stock returns, providing investors with actionable Alpha strategies and robust risk management solutions.
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Quant Data Science
Implementation of Deviation Rate Trading Strategy using TEJAPI and LLM
This article explores how the combination of LLM and TEJAPI enhances the efficiency and precision of stock market analysis. It elucidates how this integration contributes to identifying market trends, analyzing stock performance, discovering key information in market news, and providing third-party investment insights. This synergy not only aids professional traders and investors but also offers ordinary investors more ways to grasp the dynamics of the market.
Quant Data Science
TQuant Lab Aroon Up Down Trading Strategy
Aroon Indicator, developed by Tushar Chande in 1995, is typically for measuring market tendency. It consists of two lines - Aroon Up and Aroon Down.
Quant Data Science
TQuant Lab Momentum Trade
In recent years, momentum trading has become a frequent topic of discussion in stock market strategies. In the stock market, we often hear discussions about the price-volume relationship, where price is considered a leading indicator of volume, among other concepts. This article aims to explore the back-testing effects of increasing trading volume as an entry strategy.
Quant Data Science
TQuant Lab Price Deviation Ratio Trading Strategy
The Price Deviation Ratio is a common technical indicator that compares the current stock price to the N-day moving average price, reflecting whether the current price is relatively high or low compared to its historical values. Generally, when the stock price consistently exceeds the moving average price, it’s called a ‘positive deviation.’ Conversely, it’s called’ negative deviation’ when it consistently falls below the moving average price.’ Therefore, when positive or negative deviation expands, it is interpreted as a sustained overbought or oversold condition in the market, serving as a basis for entry and exit decisions. However, using only the Price Deviation Ratio can generate too many trading signals. Hence, we include the highest and lowest prices over the past N days as a second filter. The actual strategy is as follows:
Quant Data Science
TQuant Lab Bollinger Bands Trading Strategy
The Bollinger Bands is a technical indicator invented by John Bollinger in the 1980s. It combines the concepts of moving averages and statistical standard deviation to construct a trading strategy based on statistical analysis. This article will demonstrate how to deploy this strategy on the TQuant Lab back testing platform.
Quant Data Science
TQuant Lab MACD Trading Strategy
MACD, which stands for Moving Average Convergence Divergence, is a commonly used tool in technical analysis for measuring the trend changes and momentum of an asset.
Quant Data Science
TQuant Lab Rookie Manual
TQuant Lab offers a robust quantitative back-testing system with high precision performance and risk calculations, top-quality data sources, and a highly realistic simulated trading environment. It aids users in swiftly deploying a wide range of trading strategies. Feel free to click into the article to learn more information.
Quant Data Science
How to avoid common mistakes during trading – Loss Avoidance
“Loss avoidance” is a crucial topic in investing, whether for novice investors or experienced experts. As we pursue investment returns, the risk of losses is ever-present. Therefore, adopting effective loss avoidance strategies is vital to protect our capital and enhance the chances of investment success. In this article, we will use Python and the tejapi to fetch stock price data to examine the differences between implementing loss avoidance and without loss avoidance measures. By understanding and applying loss avoidance, we will be better equipped to protect our investments, reduce potential losses, and enhance long-term returns.
Quant Data Science
Options Pricing with Monte Carlo Simulation
Monte Carlo simulation has been widely adopted in the field of financial research. In 【Quant(19)】Prediction of Portfolio Performance, we have introduced how to use Monte Carlo simulation for stock price prediction. In today`s article, we will extend the application to more complex options pricing. In 【Quant】CRR Model and 【Quant】Black Scholes model and Greeks, we respectively used the principles of binomial trees and the Black-Scholes formula to calculate theoretical prices of options. These articles also explained many fundamental concepts related to options. For those who have a limited understanding of options, it is recommended to read these two articles first before continuing with the current one. In the subsequent sections, we will demonstrate how to use Monte Carlo simulation to predict stock prices. We will then extend the discussion to pricing European options and finally introduce some variance reduction techniques to assist us in using Monte Carlo simulation.
Quant Data Science
Employee Turnover Rate Prediction
Employee turnover rate refers to the fluctuation in human resources within a company during a specific period due to employee departures and new hires. This metric is a crucial concept for assessing the stability of both the organizational structure and the workforce within a company. A lower turnover rate indicates that there are relatively fewer personnel changes, reflecting stability and continuity within the organization. Conversely, a higher turnover rate may imply organizational issues, job dissatisfaction, or other factors that can have a negative impact on company operations and the work environment. Monitoring employee turnover rates helps companies understand and evaluate the effectiveness of their human resource management strategies. It enables them to take appropriate measures to improve employee retention and satisfaction, ensuring long-term stability and growth for the organization. Predicting turnover rates allows companies to better plan and manage their human resources, reduce costs, increase talent retention, and enhance organizational effectiveness.
Quant Data Science
【Quant】CRR Model
Programming CRR model for calculating options theoretical price. Keyword: CRR model, Options, Call, Put Highlight Preface In our previous article — 【Quant】Black Scholes model and Greeks, we introduce how to program the Black Scholes model. However, Black Scholes has its disadvantages and can not calculate the theoretical price for American options. Therefore, three years after […]
Quant Data Science
【Quant】Black Scholes model and Greeks
In 1997, Robert Merton and Myron Scholes won the Nobel Prize in Economics for their Black-Scholes options pricing formula, beating out many other contenders. The Black-Scholes model is still a widely-used option pricing model in the financial industry and by investors due to its excellent mathematical properties, simplicity, and ease of use. Today, we will focus on programming this model and Greeks derived from Black Scholes model.