Quantitative Analysis

This guide will introduce the types of data analysis used in quantitative research, then discuss relevant examples and applications in the finance industry.

The article delves into the performance of momentum strategy under different take-profit and stop-loss conditions, analyzing how to balance risk and reward in a highly volatile market using actual backtesting data.

The SuperTrend Indicator is a technical analysis tool used to identify trends in financial markets. It assists investors in determining relative high and low points during market swings, helping them make buy and sell decisions. However, one drawback of the SuperTrend Indicator is its tendency to be less effective during consolidation phases. To address this, we will use the Average Directional Index (ADX) to optimize the SuperTrend strategy in this article.

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.

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 article selects disposal stocks as investment targets and uses TQuant Lab to conduct backtest performance analysis of buy, sell, and hold targets to confirm whether the investment strategy of popular stocks is a profitable and well-performing target.

In the Taiwan stock market, retail investors account for the highest proportion and have the highest trading volume in the market. This article uses factor analysis to delve into the relationship between retail investor trading behavior and stock expected returns.

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.

The Average True Range (ATR) is designed to assess the extent of price fluctuations within a specific period. ATR is commonly utilized as a tool in technical analysis, assisting traders to determine entry points, exit points, and stop-loss levels for loss aversion purpose.

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.

Quantitative Investing requires historical data to perform backtesting, examining the feasibility of our investing strategy. Therefore, it is pivotal to ensure that the data used for analyzing always aligns with the information the investors have when making their decisions. For instance, in order to pursue correctness, accounting databases update new numbers when financial restatements occur. However, using the data after financial restatement for backtesting will prevent investors from accurately reconstructing the actual market conditions at that time, and will lead to information bias, reducing the credibility of the backtesting result.