backtesting

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.

This time, the Williams %R strategy was used for backtesting. The Williams indicator is also called the Williams index, wmsr, or W%R. Its English name is The Williams Percent Range. It was created by the famous American trader Larry Williams in 1973. It is a standard indicator in technical analysis. One. The KD line and other indicators commonly used by investors to judge overbought or oversold are developed based on the William indicator.

The Ichimoku Kinko Hyo strategy employed in this simulation utilizes the concept of the three-line conversion in the Ichimoku Cloud chart for backtesting, coupled with trailing stop-loss testing for profitability. Under the pen name Ichimoku Sanjin (いちもくさんじん / Ichimoku Sanjin), Goichi Hosoda authored seven series of works detailing the philosophy behind this indicator and its trading system.

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.

  Highlights Difficulties：★☆☆☆☆ Using the Moving Average and standard deviation to construct a Bollinger Band, determine when to buy and sell. Preface Bollinger Band is a technical indicator that John Bollinger invents in the 1980s. Bollinger Bands consist of the concepts of statistics and moving averages. The moving Average(MA) is the average closing price of […]

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.