從股票的長期投資觀點來看，台灣加權股價指數 (大盤) 通常會維持一段時間的上漲或下跌趨勢。在整體大盤上漲趨勢 (簡稱牛市) 時，投資者只要跟隨市場主流類股進行買進，通常都能獲得一定程度的正向報酬。然而在大盤下跌趨勢 (簡稱熊市) 時，是否能找出一種投資策略，透過觀察公司的財務指標變化，評估公司的財務健康狀況和未來潛力以保有正向的報酬，這對投資人而言是一種相對簡單的方式。本研究以 2019 年至 2022 年間發生財務危機的台灣上市櫃公司 (簡稱地雷股) 為研究對象，選取 14 家財務危機公司 (7 家上市 7 家上櫃公司) 與 42 家相同產業且資產規模相近的財務正常公司進行分析。運用兩樣本平均數差異 t-檢定，分析財務正常公司與財務危機公司之間的各項財務率比率是否具顯著差異，找出可能發生財務危機的公司。接著，再運用 Harding and Pagan (2002) 模型尋找市場轉折點，藉此認定出股票市場中的轉折點 (即牛、熊市時點)，分析大盤牛、熊市發生的日期區間，並在熊市發生時，對可能發生財務危機的公司進行放空交易，以獲取超額報酬。以上市公司的地雷股為例，當偵測財務危機公司之財務比率較正常公司產生顯著差異時，在大盤處於熊市時放空地雷股，其表現全數都較大盤產生超跌的現象。因此，執行放空交易策略時的報酬率最少為 9.86%、最高達 24.20% 之超額報酬。若以上櫃公司地雷股為樣本時，熊市股價表現僅 3 檔有超跌的現象，執行相同放空策略分別獲取 3.46%、3.79%及 19.66% 之超額報酬。
關鍵詞：財務比率分析、地雷股、放空交易策略、超額報酬

From the perspective of long-term investment in stocks, the Taiwan Weighted Stock Index (TAIEX) usually maintains a period of upward or downward trend. During a bull market, investors can typically obtain positive returns by following the market's mainstream sectors. However, during a bear market, it may be possible for investors to identify an investment strategy that can generate positive returns by observing changes in a company's financial indicators, evaluating its financial health and future potential. This study analyzed 14 financially distressed companies (7 listed and 7 OTC companies) that experienced financial crises between 2019 and 2022 (referred to as “landmine stocks”), and compared them with 42 financially normal companies in the same industry with similar asset sizes. Using a two-sample t-test, the study analyzed whether there were significant differences in various financial ratios between financially normal and distressed companies to identify potential crisis-prone companies. The Harding and Pagan (2002) model was then used to identify market turning points, and to analyze the date range of bull and bear markets in the stock market. When a bear market occurred, the study implemented a short-selling strategy on the potential crisis-prone companies to obtain excess returns. For example, in the case of listed landmine stocks, when the financial ratios of the crisis-prone companies showed significant differences from those of the financially normal companies, shorting the landmine stocks during a bear market resulted in higher returns than those of the stock market. The excess returns of the short-selling strategy ranged from 9.86% to 24.20%. For OTC landmine stocks, only 3 of them showed oversold phenomena during a bear market, and executing the same short-selling strategy resulted in excess returns of 3.46%, 3.79%, and 19.66%, respectively.
Keywords: Excess Returns, Financial Ratio Analysis, Landmine Stocks, Short-Selling Strategy

1. 李佳蓉 (2022)，「異常報酬、盈餘管理與財務危機之關聯性研究－以台灣上市櫃公司為例」，碩士論文，台南應用科技大學國際企業經營系。
2. 吳淑貞 (2022)，「股市波動性是否引發熊市？」，中山管理評論，30卷，4期：673-704。
3. 金士為 (2023)，「國安基金進場時機與護盤期間交易策略之分析」，清華大學財務金融系未出版論文。
4. 黃瓊瑩 (2021)，「高階管理者更迭、多角化與財務危機之關聯性研究－以台灣上市櫃公司為例」，碩士論文，台南應用科技大學國際企業經營系。
5. 楊皓丞 (2021)，「景氣循環下的投資策略–以台灣股市為例」，碩士論文，臺灣大學財務金融學研究所。
6. 陳明雪 (2022)，「運用現金流量指標建構財務危機預警模型之有效性研究」，碩士論文，國立臺北大學國際財務金融碩士在職專班。
7. 陳家偉 (2021)，「經理人過度自信與企業發生財務危機的關係 – 以學歷專業度為調節效果」，碩士論文，國立臺灣大學財務金融學研究所。
8. 游斯郁 (2011)，「預測熊市: 各國實證研究分析」，碩士論文，國立暨南國際大學國際企業學系。
9. 潘宥君 (2021)，「財報重編與企業財務危機之關聯性分析」，碩士論文，國立臺灣科技大學財務金融研究所。
10. 謝佩綺 (2016)，「企業資訊揭露、公司治理與財務危機預警之實證研究-以台灣上市上櫃電子業為例」，碩士論文，朝陽科技大學財務金融系。
11. 簡詣勳 (2017)，「公司治理、資本結構與財務危機之關聯性研究」，碩士論文，東吳大學國際經營與貿易學系。
12. Altman, E. I. (1968). “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy,” The Journal of Finance, 23, 589-609.
13. Agrawal, A., and Knoeber, C. R. (1996). “Firm performance and mechanisms to control agency problems between managers and shareholders,” Journal of Financial and Quantitative Analysis, 31, 377-397.
14. Atiya, A. F. (2001). “Bankruptcy prediction for credit risk using neural networks: A survey and new results,” IEEE Transactions on Neural Networks, 12, 929-935.
15. Burns, A. F. and W. C. Mitchell (1946), “Measuring Business Cycles,” New York, NBER.
16. Beaver, W. H. (1966). “Financial ratios as predictors of failure,” Journal of Accounting Research, 4, 71-111.
17. Bry, G. and C. Boschan (1971). Cyclical Analysis of Time Series: Procedures and Computer Programs, New York, NBER.
18. Bartov, E., and Mohanram, P. (2004). Private information, earnings manipulations, and executive stock-option exercises,” The Accounting Review, 79, 889-920.
19. Harding, D., and Pagan, A. (2002). “Dissecting the cycle: a methodological investigation,” Journal of Monetary Economics, 49, 365-381.
20. Malmendier, U., and Tate, G. (2008). “Who makes acquisitions? CEO overconfidence and the market's reaction,” Journal of Financial Economics, 89, 20-43.
21. Ohlson, J. A. (1980). “Financial ratios and the probabilistic prediction of bankruptcy,” Journal of Accounting Research, 18,109-131.
22. Pagan, A. R., and Sossounov, K. A. (2003). “A simple framework for analysing bull and bear markets,” Journal of Applied Econometrics, 18, 23-46.
23. Shumway, T. (2001). “Forecasting bankruptcy more accurately: A simple hazard model,” The Journal of Business, 74, 101-124.
24. Taffler, R. J. (1982). “Forecasting company failure in the UK using discriminant analysis and financial ratio data,” Journal of the Royal Statistical Society: Series A (General), 145, 342-358.
25. Zmijewski, M. E. (1984). “Methodological issues related to the estimation of financial distress prediction models,” Journal of Accounting Research, 22, 59-82.