本研究主要探討盈餘品質與股票報酬間之關聯性，並以盈餘品質作為交易策略之重要指標。研究假設股市中存在兩種市場週期與公司類型，分別為低波動與高波動，以及價值股與成長股。首先，採用異質性狀態轉換模型(HRSM）將公司區分為價值股與成長股類型，並辨識其所處之市場週期。其次，本研究使用機率單位迴歸模型（Probit Model）以驗證分群效果之顯著性，並以應計品質、持續性和平穩性作為衡量盈餘品質之指標。最後，本研究運用 Panel OLS 迴歸模型，檢驗盈餘品質對下期股票報酬之預測能力，並進一步依市場週期與公司類型之不同組合，分析盈餘品質資訊含量之異質性，同時以歷史資料回測驗證盈餘品質指標於實務交易策略之應用價值。

研究樣本涵蓋 2014 年第一季至2025第三季台灣上市公司，共計 697 間公司（排除金融業）。實證結果顯示，整體樣本下盈餘品質對下期股票報酬之預測力未達統計顯著水準；然而，市場週期本身對下期報酬具有獨立且穩健之正向影響。研究成果與預期相反，盈餘品質指標在價值股中呈現顯著之負向預測力，而在成長股中則不具顯著影響，顯示盈餘品質之資訊含量高度依賴公司所屬類型與所處市場環境。交易策略回測結果顯示，納入市場週期資訊之策略在累積報酬與夏普比率上均優於忽略市場週期之基準策略，且此優勢在高波動市場環境下最為明顯。本研究結果不僅豐富台灣資本市場盈餘資訊之相關研究，亦為投資實務提供結合市場週期與公司特質之策略參考。

This study examines the relationship between earnings quality and stock returns and uses earnings quality as a key indicator for trading strategies. We hypothesize that the stock market has two regimes, low- and high-volatility, and two firm types, value and growth. First, a Heterogeneous Regime Switching Model (HRSM) classifies firms into value and growth types and identifies the prevailing market regime for each firm. Second, a Probit model validates the classification results, with accrual quality, persistence, and smoothness serving as proxies for earnings quality. Finally, a Panel OLS regression tests whether earnings quality predicts next-period stock returns, and examines the heterogeneity of this predictive power across combinations of market regime and firm type. The practical value of earnings quality indicators is validated through an out-of-sample backtest of trading strategies. The sample consists of 697 Taiwan-listed firms, excluding the financial industry, covering Q1 2014 to Q3 2025. The empirical results show a clear contrast: at the fullsample level, earnings quality does not significantly predict next-period stock returns, whereas the market regime itself has an independent and robust positive effect on returns. When firms are further divided by type, earnings quality indicators show a significant negative predictive effect on returns for value stocks but no significant effect for growth stocks, contrary to our hypothesis. This contrast suggests that the informativeness of earnings quality is highly conditional on firm type and market environment. Trading strategies incorporating regime information consistently outperform a regime-agnostic benchmark in both cumulative returns and Sharpe ratios, with the advantage most pronounced during high-volatility periods. These findings contribute to the literature on earnings information in the Taiwan capital market and provide practical guidance for investment strategies that jointly consider market regime and firm characteristics.

第柒章、參考文獻
一、中文文獻
1. 林有志、邱炳雲和何韋霆，2009，盈餘門檻與盈餘管理行為之研究，會計與財金研究，第2卷2期：1-16
2. 陳雅琳，2010，以風險溢酬概念探討台灣股市的最佳投資策略與投資時點，國立臺灣大學管理學院財務金融研究所碩士論文，台北，臺灣
3. 賴梨雯和薛敏正，2009，應計項目組成之波動、盈餘品質與股票報酬，北商學報，第15期：1-22
4. 黃冠騰，2022，狀態轉換模型下的因子投資，國立臺灣師範大學管理學院管理研究所碩士論文，台北，臺灣
5. 黃玉嫺，2007，以報酬為基礎衡量之盈餘品質，國立臺灣大學管理學院會計學研究所碩士論文，台北，臺灣
6. 劉兆倫，2019，VPIN於股價預測應用分析：以決策數與隨機森林模型為例，國立交通大學管理學院資訊管理與財務金融研究所碩士論文，新竹，臺灣
7. 江家瑜、簡凌慧和薛敏正，2014，公司特質對應計與實質盈餘管理選擇的影響，中華會計學刊，第10卷1期：1-32
8. 廖又生，2010，恩隆公司（Enron Corp.）對企業倫理之挑戰， https://www.gaya.org.tw/journal/m51/51-manage.htm，搜尋日期：2026年4月20日
二、英文文獻
1. Sloan, R. G. (1996). Do stock prices fully reflect information in accruals and cash flows about future earnings? The Accounting Review, 71(3): 289–315.
2. Roychowdhury, S. (2006). Earnings management through real activities manipulation. Journal of Accounting and Economics, 42(3): 335–370
3. Demski, J. S. (1998). Performance measure manipulation. Contemporary Accounting Research, 15(3) : 261-285.
4. Baik, B., Choi, S., & Farber, D. B. (in press). Managerial Ability and Income Smoothing. The Accounting Review, https://doi.org/10.2308/accr-52600
5. Habib, A., Ranasinghe, D., & Perera, A. (2024). Business strategy and strategic deviation in accounting, finance, and corporate governance: A review of the empirical literature. Accounting and Finance, 64(1)129-159
6. Koffi, S. P., William, B. W., & Umoh, N. J. (2025). Assessment of environmental, social and governance practices on earnings quality in selected manufacturing firms in Nigeria. SSRN. https://doi.org/10.2139/ssrn.5341395
7. Mitra, R. K. (2016). The association between earnings quality and firm-specific return volatility: Evidence from Japan. Review of Accounting & Finance, 15(3): 294-316
8. Jones, J. (1991). Earnings management during import relief investigations. Journal of Accounting Research, 29(2): 193-228.
9. Dechow, P. M., & Dichev, I. D. (2002). The Quality of Accruals and Earnings: The Role of Accrual Estimation Errors. The Accounting Review, 77(Supplement): 33-59
10. Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting Earning Management. The Accounting Review, 70(2): 193-225
11. Kormendi, R., & Lipe, R. (1987). Earning Innovations, Earnings Persistence, and Stock Returns. The Journal of Business, 60(3): 323-345
12. Lipe, R. (1990). The Relation Between Stock Returns and Accounting Earnings Given Alternative Information. The Accounting Review, 65(1): 49-71
13. Leuz, C., Nanda, D., & Wysocki, P. D. (2003). Earnings management and investor protection: an international comparison. Journal of Financial Economics, 69(3): 505-527
14. Lang, M., & Mark, Maffet. (2011). Transparency and liquidity uncertainty in crisis periods. Journal of Accounting and Economics, 52(2-3): 101-125
15. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2): 357-384.
16. Zhu, X., & Zhu, J. (2013). Predicting stock returns: A regime-switching combination approach and economic links. Journal of Banking & Finance, 37(11) : 4120–4133.
17. Li, K., Mai, F., Shen, R., & Yan, X. (2021). Measuring corporate culture using machine learning. The Review of Financial Studies, 34(7): 3265–3315.
18. Dempster, G. M., & Oliver, N. T. (2019). Financial market pricing of earnings quality: Evidence from a multi-factor return model. Open Journal of Business and Management, 7(1): 312–329.
19. Francis, J., LaFond, R., Olsson, P. M., & Schipper, K. (2004). Costs of equity and earnings attributes. The Accounting Review, 79(4): 967–1010.
20. Isidro, H., & Dias, J. G. (2017). Earnings quality and the heterogeneous relation between earnings and stock returns. Review of Quantitative Finance and Accounting, 49(4): 1143–1165.
21. Tan, H., & Mathews, J. A. (2010). Cyclical industrial dynamics: The case of the global semiconductor industry. Technological Forecasting and Social Change, 77(3): 344–353.