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.

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