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    题名: 基本面指標運用於複合預測模型以評估台灣之產業投資組合績效之研究
    Using Combination Forecasts for Accounting Fundamentals to Examine Industry Portfolio Allocation in Taiwan
    作者: 蘇毓涵
    Su, Yu-Han
    贡献者: 郭維裕
    Kuo, Wei-Yu
    蘇毓涵
    Su, Yu-Han
    关键词: 基本面分析
    樣本外預測
    複合預測
    超額報酬率
    多空策略
    投資組合配置
    產業層級
    Fundamental
    Out-of-sample forecast
    Combination forecast
    Industry-level
    Excess return
    Long-short strategy
    Portfolio allocation
    日期: 2020
    上传时间: 2020-08-03 17:22:59 (UTC+8)
    摘要: 本研究針對具有實體商品之產業，以複合迴歸預測模型對樣本外期間之下一期產業層級超額報酬率進行預測，模型中合併帳面市值比(BM)、獲利能力(EARN)、毛利(GP)、投資項目(INV)、應計項目(ACC)分別與各自歷史表現之權重，其對產業層級之季超額報酬率確實具有預測能力。將樣本27類產業的預測結果配合多空策略運用在投資組合配置的選擇上，以預期表現佳的產業作為多頭部位，預期表現差的產業作為空頭部位，以不同權重之策略測試其是否能使投資人在股市上獲利。結果顯示當部位產業數增加，投資組合的報酬率會下降，但Sharpe ratio反而上升，亦即達到分散投資風險的效果；產業數越少，則根據產業預期表現的選擇將越精準，因而報酬率較高。於2014年至2018年底，產業投資組合在130/30、150/50、200/100的配置下，於樣本外期間結束後，獲利可持續勝過標竿(被動買進並持有樣本之27類產業)，甚至分別高於投資大盤之獲利的2倍、3倍、5倍。
    This research examines the predictability of industry-level excess return for the timing t+1 by out-of-sample forecasting combination methods. The five fundamental variables, book-to-market ratio (BM), earnings (EARN), gross profit (GP), investments (INV), and accruals (ACC), are combined with information weight according to individual historical performance. Due to these specific variables, industries without tangible products are excluded in the sample lake. The finding is that these five variables can predict industry-level excess return. Therefore, based on the combination forecast, portfolio allocation strategies rotate into long positions in industries with high expected return, and short positions in industries with low expected return. The portfolio should be rebalanced quarterly. Also, this research examines the profitability of portfolio by setting three kinds of leverage for long-short strategy. Because of risk diversification, when there`re more industries contained in long/short position, return of portfolio would decrease, while Sharpe ratio would increase. After out-of-sample period, from Q1 2014 to Q4 2018, the portfolio can consistently beat a buy-and-hold benchmark portfolio, and investors can get 2 to 5 times payoff compared with market portfolio under 130/30, 150/50, and 200/100 strategies.
    參考文獻: 1.Andrew W.Lo and Pankay N.Patel, (2008). 130/30: The New Long-Only, Journal of Portfolio Management, Vol. 34, Issue 2, pp. 12-38.

    2.Babar Zaheer Butt, Kashif Ur Rehman, M. Aslam Khan and Nadeem Safwan, (2009), Do economic factors influence stock returns? A firm and industry level analysis, African Journal of Business Management, Vol. 4(5), pp. 583-593.

    3.Bruce I. Jacobs and Kenneth N. Levy, (2006). Enhanced Active Equity Strategies, Journal of Portfolio Management, pp. 45-55.

    4.Christopher J. Neely, David E. Rapach, Jun Tu and,Guofu Zhou, (2010). Forecasting the Equity Risk Premium: The Role of Technical Indicators, Management Science, INFORMS, vol. 60(7), pp. 1772-1791.

    5.David E. Rapach, Jack K. Strauss, Guofu Zhou, (2008). Out-of-Sample Equity Premium Prediction: Consistently Beating the Historical Average, Review of Financial Studies, Volume 23, Issue 2, pp. 821–862.

    6.David Hirshleifera, Kewei Hou and Siew Hong Teoh, (2009). Accruals, cash flows, and aggregate stock returns, Journal of Financial Economics, Volume 91, Issue 3, pp. 389-406.

    7.Gil Aharoni, Bruce Grundy and Qi Zeng, (2013). Stock returns and the Miller Modigliani valuation formula: Revisiting the Fama French analysis, Journal of Financial Economics, vol. 110, issue 2, pp. 347-357.

    8.Hemang Desai, Shivaram Rajgopal and Mohan Venkatachalam, (2004). Accounting Review, Vol. 79, No. 2, pp. 355-385.

    9.Jeffrey S. Abarbanell and Brian J. Bushee, (1997). Fundamental analysis, future earnings, and stock prices, Journal of Accounting Research, Vol. 35, No. 1, pp. 1-24.

    10.John Campbell, (1991). A Variance Decomposition for Stock Returns, Economic Journal, vol. 101, issue 405, pp. 157-79.

    11.John Campbell and John Ammer, (1993). What Moves the Stock and Bond Markets? A Variance Decomposition for Long-Term Asset Returns, Journal of Finance, vol. 48, issue 1, pp. 3-37.

    12.John Y. Campbell, Samuel B. Thompson, (2008). Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average, Review of Financial Studies, Volume 21, Issue 4, pp. 1509–1531.

    13.Kevin J. Lansing, Stephen F. LeRoy, and Jun Ma, (2020). Examining the sources of excess return predictability: stochastic volatility or market inefficiency, Federal Reserve Bank of San Francisco Working Paper 2018-14.

    14.Lallemand and Strauss, (2016). Can we count on accounting fundamentals for industry portfolio allocation?, Journal of Portfolio Management, 42 (4) pp. 70-87.

    15.Mark W. Watson & James H. Stock, (2004). Combination forecasts of output growth in a seven-country data set, Journal of Forecasting, vol. 23(6), pp. 405-430.

    16. Morton Pincus, Shivaram Rajgopal and Mohan Venkatachalam, (2007). The Accrual Anomaly: International Evidence, Accounting Review, Vol. 82, No. 1, pp. 169-203.

    17.Richard G. Sloan, (1996). Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings, Accounting Review, Vol. 71, No. 3, pp. 289-315.

    18.Robert Novy-Marx, (2013), The other side of value: The gross profitability premium, Journal of Financial Economics, vol. 108, issue 1, pp. 1-28.

    19.Shmuel Kandel and Robert F. Stambaugh, (1996). On the Predictability of Stock Returns: An Asset-Allocation Perspective, Journal of Finance, Vol. 51, No. 2, pp. 385-424.

    20.Todd Clark, (2004), Can out-of-sample forecast comparisons help prevent overfitting, Journal of Forecasting, vol. 23, issue 2, pp. 115-139.

    21.Tuomo Vuolteenaho, (2002). What Drives Firm-Level Stock Returns, Journal of Finance, 2002, v57, pp. 233-264.
    描述: 碩士
    國立政治大學
    國際經營與貿易學系
    107351004
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0107351004
    数据类型: thesis
    DOI: 10.6814/NCCU202000682
    显示于类别:[國際經營與貿易學系 ] 學位論文

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