在本篇論文中，我主要以四種不同的統計模型並且使用非財務變數與財務變數建立新冠肺炎大流行情緒指數。在新的情緒指數中，其中一個影響因素是來自新冠肺炎大流行，此外，我使用了流行病學中被廣泛使用的EPIDERMIC模型。透過此流行病學模型，我可以計算出每日疫情傳播程度的參數，透過此參數的高低可以衡量疫情的嚴重程度。首先，我展示了只包含非財務變數的大流行情緒指數與其他情緒變數具有高度的相關性，證明非財務變數會影響市場投資人情緒。在證明非財務變數具有影響力後，我一樣使用四種的統計模型建立新的投資者情緒指數。我依據相關性檢定與指數的時間趨勢圖進行比較，最後我選擇了以偏最小二乘回歸 (PLS)統計方法架構的模型當作我最終的投資者情緒指數。最後我根據此情緒指數進行樣本內的預測迴歸分析，在回歸的結果中，新的情緒指數與大部分的情緒變數具有統計上的顯著性，並且與超額市場報酬具有負相關的統計顯著水準(P值小於10%)

In this paper, I adopt four methodologies and use the quantitative data to construct a new sentiment index included the non-financial variables and financial variables. From the new sentiment index, one of effect come from the Covid19 pandemic variables; also, I use the EPIDERMIC model which is one of widely used epidemiology model which can do the simulation to know the degree of pandemic. As a result, in my non-financial variables, I can provide the daily variant impact on investor sentiment. I demonstrate that the Covid19 pandemic is positive correlated with VIX and other sentiment proxies. After verifying the non-financial variables have the impacts on investor sentiment, I also use four methodologies to construct the sentiment index. Depending on the correlation test and figure trajectory with VIX, I choose the partial least square model as my new sentiment index. Moreover, I do the predictive regression model as my preliminary in-sample test. The sensitivity for my sentiment index with VIX and other sentiment proxies has the positive coefficient and has the negative coefficient with excess market return following the financial theory that the sentiment and excess market return is negative correlated.

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