So‐de Shyu, Yi Jeng, W.H. Ton, Kon‐jung Lee and H.M. Chuang
With the development of the modern portfolio theory and the advancement of information technology, the employment of quantitative approaches to practically measure asset risks and…
Abstract
Purpose
With the development of the modern portfolio theory and the advancement of information technology, the employment of quantitative approaches to practically measure asset risks and returns, and the construction of portfolios (even dynamic portfolios) has become possible and popular. Therefore, the purpose of this paper is to construct a multi‐factor model for Taiwan stock universe using fundamental technical descriptors and then to apply the equity market neutral investing using multiple‐factor models as a tool.
Design/methodology/approach
This study constructs a Taiwan equity multi‐factor model using cross‐sectional fundamental technical approach.
Findings
The model involves 28 explanatory factors (including 20 industry factors), and the results of the estimations are satisfactory. The model's explanatory power is 58.6 per cent on average. Furthermore, this multi‐factor model is feasible, modulized, dynamic (i.e. modified over time) and updating.
Originality/value
The multi‐factor model, constructed and utilized in this study, is a useful and feasible tool. It generates important inputs into the applications of building market neutral portfolio.
Purpose
Taiwan OTC market is an electronic, order driven, call market. The purpose of this paper is to gain understanding of whether trade size or number of transaction provides more information on explaining price volatility and market liquidity in this market. The paper also aims to investigate how market condition can affect the relationship between information type and trading activities.
Design/methodology/approach
The paper uses data from the Taiwan OTC market to run the empirical tests. It divides firms into five size groups based on their market capitalization. Regression equations are run to test: whether number of transactions has a more significant impact on price volatility on the Taiwan OTC market; the impact of market information on number of transactions; the relative impact of firm specific and market information on number of transactions; and the impact of number of transaction of bid‐ask spread.
Findings
Findings show that the larger the number of transactions, the higher the price volatility. Smaller firms on the Taiwan OTC market are traded based on firm‐specific information. This relation is further affected by market trends. Especially for the larger firms, when the market is up and the amount of market information increases, number of transactions increases. When the market is down and the amount of market information increases, number of transactions decreases. Finally, it is found spread size is more likely to be influenced by number of transactions, instead of trade size. Overall, based on these empirical results, the information content of number of transactions seems to be higher than that of trade size in the Taiwan OTC market.
Practical implications
Investors now understand that number of transaction actually carry more information than trade size does.
Originality/value
The relation between market information and number of transaction, also that between market information and trade size is influenced by market condition. The paper fills a gap in the literature to show that market condition has an impact on the relation between information type and trader's behavior. A number of transactions are identified that provide more information than trade size does. It is also shown that market conditions can further affect the impact of information on trading activities.