Quantitative Analysis

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.

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.

As the name suggests, high dividends refer to companies distributing higher profits to investors in the form of cash dividends. There is no specific definition of how much dividend constitutes a high dividend. This article will utilize TQuant Lab to conduct a backtesting performance analysis of buying and holding High Dividend ETF.

The KD Indicator is a practical and widely used tool in technical analysis. It's primarily used to determine the short-term strength of stock prices and potential reversal timing.

This momentum factor strategy utilizes the concept of momentum factor: "stocks with good past performance are likely to perform well in the future, and vice versa" to simulate backtesting and to verify the profitability of "the strong get stronger, and the weak get weaker" in the Taiwan stock market.

This article selects five critical stock selection criteria from Buffett’s Business Investment Rules. From utilizing TejToolAPI to fetch financial data and screen targets to using TQuant Lab to backtest investment risks and performance, we will explore Buffett’s investment style and appreciate the ability of Business Investment Rules to generate excess returns.

Creating a convergence strategy with the RSI moving average strategy. The RSI is an oscillating technical indicator representing the comparative strength between buyers and sellers in the market.

During the election period, the internet is full of election-related news; some even make relevant expectations on the stock market, affecting investor's sentiment. However, does the so-called "election market trend" really exist? In this article, we will conduct a quantitative analysis on Taiwan stock index in previous presidential elections using TEJ API.

In previous tutorials, we learned how to create our own STREAMLIT App. For more details, you can refer to this article. In this article, we will use the TEJ API database to connect with the STREAMLIT package and implement a grid trading strategy. We will use tools such as date selection, dropdown menus, and numerical selectors to interact with charts and tables, making the data an interactive app. Grid trading is a trading strategy that selects a range by setting two parameters, the upper bound and the lower bound. We divide the stock price into grid intervals, buying stocks when the price falls and touches the lower grid, and selling stocks when the price rises and exceeds the upper grid. This strategy is a lazy strategy that doesn’t require much manual operation. It can also profit from price fluctuations. However, there are a few points to note which is the efficiency of capital utilization will be lower than manual trading.

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.

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:

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.