Technical, Fundamental, and Combined Information for Separating Winners from Losers

Hong-Yi Chen, Cheng Few Lee and Wei-Kang Shih

Chapter 95 in Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning:(In 4 Volumes), 2020, pp 3319-3365 from World Scientific Publishing Co. Pte. Ltd.

Abstract: This study examines how fundamental accounting information can be used to supplement technical information to separate momentum winners from losers. We first introduce a ratio of liquidity buy volume to liquidity sell volume (BOS ratio) to proxy the level of information asymmetry for stocks and show that the BOS momentum strategy can enhance the profits of momentum strategy. We further propose a unified framework, produced by incorporating two fundamental indicators — the FSCORE (Piotroski, 2000) and the GSCORE (Mohanram, 2005) — into momentum strategy. The empirical results show that the combined investment strategy includes stocks with a larger information content that the market cannot reflect in time, and therefore, the combined investment strategy outperforms momentum strategy by generating significantly higher returns.

Keywords: Financial Econometrics; Financial Mathematics; Financial Statistics; Financial Technology; Machine Learning; Covariance Regression; Cluster Effect; Option Bound; Dynamic Capital Budgeting; Big Data (search for similar items in EconPapers)
JEL-codes: C01 C1 G32 (search for similar items in EconPapers)
Date: 2020
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Journal Article: Technical, fundamental, and combined information for separating winners from losers (2016)
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