股價移動平均真有用嗎？

Are Stock-Price Moving Averages Really Useful?

陳柔君(Jou-Chun Chen) ； 黃敬哲(Jin-Gzhen Huang) ； 蕭朝興(Chaoshin Chiao)

《管理學報》 39卷2期 (2022/06) Pp. 235-263

https://doi.org/10.6504/JMBR.202206_39(2).0005

摘要

本文目的在於探討台灣股票市場中不同期間框架的11種股價移動平均（MA）是否提供投資人獲利的機會？本文利用120個月滾動窗口之Fama-MacBeth迴歸估計MA與股票報酬的關係，再結合主成份分析法，企圖預測樣本外股票報酬並建構投資策略，藉此獲得優異績效。研究結果顯示：第一、使用傳統Fama-MacBeth迴歸，雖然原始個別MA指標對股票報酬的解釋力並不佳，但是它們聯合對股票報酬的預測力卻並不差，這反映迴歸的結果容易受到多種現象（如共線性）所干擾。第二、模型中納入越多的MA指標卻可能無助於提升模型預測報酬的能力，反映預測模型不是越複雜越好。第三、以主成份MA建構樣本外預測投資策略（the out-of-sample forecasted return strategies, OSFRS），並分析比較其他MA混合投資策略，本文確認OSFRS可以獲得較佳的投資績效。

關鍵字

股票價格移動平均 ； Fama-MacBeth迴歸 ； 主成份分析法 ； 樣本外預測

並列摘要

The purpose of this paper is to exploit possible profitability through stock-price moving averages (MA), by using eleven MA at different frequencies as well as the Fama-MacBeth regression with 120 months as a rolling window to derive out-of-sample forecasted returns to individual stocks. Applying principal component analysis to optimize the series of MA helps improve the out-of-sample forecasts on which we form the out-of-sample forecasted return strategies (OSFRS). Evidence shows that, first, individually-employed MA do not well explain the cross-section of stock returns, but their joint predictive power does exist. It follows that the regression estimates could be interfered by certain biases (e.g., multicollinearity). Second, using many MA may not necessarily improve the forecasting power, implying that parts of employed MA are unimportant and generate noisy forecasts. Third, among comparable investment strategies based on hybrid information of MA, OSFRS with the principal components of MA outperforms all others.

並列關鍵字

stock price moving average ； Fama-MacBeth regression ； principal component analysis ； out-of-sample forecast

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