Market Research

In the previous article, we introduced TESG’s scoring mechanism in detail and its rating ratio among popular ETFs in China. This article will teach readers how to further use the ESG rating provided by TESG, and use Python to construct a concurrent An investment portfolio with sustainable operations and financial growth potential.

The business opportunities incurred by cybersecurity crises are much larger than you imagine. But before putting your cash in, do you understand the cybersecurity industry chain and its components?

In 2014, the CFA Institute in the US began discussing the incorporation of ESG (Environmental, Social, and Governance) factors into investment decision-making and business valuation. However, the measurement involved in each rating varies greatly, thus confusing users. Given this, TEJ released the “TESG Rating Index (TESG Rating) ” on March 31, 2022, a tool for reference in investment and credit. What’s more, we use the TESG Rating to explore its correlation with commonly-used valuation multiples, such as the price-to-book (P/B) ratio and price-to-earnings (P/E) ratio.

Not all companies can utilize ChatGPT for AI-driven transformation, and ignoring information security risks may affect their sustainability.

In recent years, various asset management companies have launched ESG-related ETFs, such as the pioneering Fubon Corporate Governance ETF (00692), which focuses on the top 100 companies in Taiwan in terms of corporate governance, and the Cathay Sustainable High Dividend ETF (00878), which combines high dividend yields with sustainability criteria. The common thread among these ESG ETFs is their tracking of ESG indices introduced by domestic and international index providers. While each ESG ETF specializes in different areas, they all rely on ESG screening criteria, emphasizing sustainable business practices among their constituent stocks, allowing investors to invest in assets that balance environmental sustainability and robust growth potential.

In the previous article, we used an LSTM model to predict stock price trends by using the past 10 days' opening prices, highest prices, lowest prices, closing prices, and trading volumes to predict the closing price for the next day. However, we observed that the model's performance was not very satisfactory when relying solely on yesterday's stock price to predict tomorrow's price. Therefore, we have decided to change our approach. This time, we aim to use the model to help us identify buy and sell points and formulate a trading strategy. We have also incorporated eight new feature indicators, with four being technical indicators and four being macroeconomic indicators, in the hope of improving our prediction results using these two facets of feature values.

In this article, we will show you how to export mutilated data with the “Transfer Data” function. Then, we will apply the data in the Margin Trading database to help users select fundamental indicators.

his chapter, [Margin Trading]will be divided into three parts. The first part will guide readers in a simple and unburdened way to learn how to operate the [Margin Trading] database.

In the last two articles, we’ve introduced the three major institutional investors in Taiwan. Now we are heading to share the calculation methods of Dealers’ shareholding ratio.