金融議題一向備受大眾所關注，無數投資人前仆後繼地探查潛在的獲利機會，並有企業管理階層和政府持續關注股票市場的動態變化，以便提前應對危機狀況，而若可針對各大產業之類股指數走勢進行預測，提前獲悉未來產業波動，對於每一名參與或關注市場的決策者而言，此類的預測資訊將會是一大瑰寶。然而，考量到市場中存在許多內、外生變數和干擾因子，致使過往研究的預測結果難以與實際走勢完全吻合，或僅用於特定時段或特定標的，因而使類股預測成為一道難題。近年來，受益於類神經網路的蓬勃發展，其良好的分類及預測性能引發各界廣大迴響，許多人開始以類神經網路預測股票市場。本研究認為，若能選擇適當的類股相關資料作為預測因子，並以類神經網路精確分析資料與未來走勢間的關係，類股預測的進一步突破將指日可待。因此，本研究建立一套類股預測系統，以效率市場假說和行為金融學作為理論基礎，選定技術指標、基本面指標和社群媒體，建立多樣化的輸入資料組合以增加預測因子之代表性；另外，本研究擬憑藉類神經網路卓越的運算能力，以此作為核心基礎，分別設計資料分析模型和走勢預測模型，前者旨在從原始資料集中萃取關鍵特徵資料，後者則整合特徵資料，並以此預測未來類股走勢方向和幅度，使類股預測系統具備揭示金融市場中特定產業未來波動變化的用途，以提供有品質的預測結果供所有決策者參考。最終研究結果揭示，在所有類神經網路中，時間卷積網路無論在特徵萃取或走勢預測上均表現最佳，因此作為本研究建立類股預測系統的基底模型，並在所有情境下皆擁有突出表現，顯示本研究在類股走勢預測上得以克服過往預測方法之不足，藉由將系統拆分為兩階段模型，提供更加有效的類股走勢預測結果，同時也證實多樣化的輸入資料組合可進一步提升預測準確度。

Accurate prediction of sector index direction has always been important and invaluable to every decision-maker in the financial market. Consequently, investors, corporate management and government officials continuously monitor the stock market, trying to identify trends to meet their respective needs. However, due to the endogenous and exogenous variables, past research often fails to correctly forecast trends because of the use of inappropriate data and inaccurate methods for combining predictive factors. In this study, we develop a forecasting system that integrates three critical predictive factors: technical indicators, fundamental indicators, and social media data, to predict the movement direction of S&P 500 Information Technology sector index. The selection of these factors is based on efficient market hypothesis and behavioral finance to explain stock market behavior and enhance the representativeness of predictive factors. Additionally, we applied several neural networks to build up a data analysis model and a trend prediction model. The former accurately analyze the relationship between raw data and sector index trends, extracting key features, while the latter integrates these features to forecast future sector index trends. The results indicate that across all scenarios, our system outperforms XGBoost, SVM, Random Forest, and DNN, consistently demonstrating outstanding performance according to accuracy, MCC, and MSE. This method overcomes the limitations of previous approaches, offering more effective sector index trend predictions by splitting the system into two-stage models. Moreover, we also confirm that a diversified data set can further enhance prediction accuracy.

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