基於新聞情緒建構LSTM神經網路之匯率預測模型與量化交易策略

Abstract

本文透過文字探勘的方法，自Reuters新聞網站爬取與歐元、英鎊及美元相關的外匯新聞，利用情緒分析計算每篇報導的新聞情緒指標，試圖分析新聞情緒與匯率之間的關聯性。首先，本文以迴歸模型探討新聞情緒與匯率報酬率的同期(contemporaneous)關係與預測(predictive)能力；第二，我們建構LSTM神經網路模型預測EUR/USD、GBP/USD的隔日匯率收盤價格(one-step-ahead closing price)，並與傳統的計量模型進行比較。此外，本文也檢視將新聞情緒因子納入LSTM神經網路模型後，是否能提升預測表現，藉此顯示新聞情緒為一項有效的匯率預測因子。最後，對於機構法人或散戶投資人而言，匯率預測的目的不外乎是從交易中獲利，因此我們以LSTM神經網路建構交易策略，並以情緒指標作為進出場決策之濾網。本研究的實證結果發現，在迴歸分析中，新聞情緒與匯率報酬率具有同期的解釋能力，而在預測迴歸式中則不具統計顯著性；匯率預測方面，當LSTM神經網路模型在納入新聞情緒因子後，確實能夠提升預測表現。最後，在LSTM交易策略中，以情緒指標作為濾網能夠大幅提高策略的績效表現，顯示新聞情緒不論是在LSTM模型的預測或交易策略的建構上均為一項具有關鍵性的重要因子。This paper utilizes text-mining methods to crawl forex news related to Euro, British pound, and U.S. dollar from the Reuters news website. We use sentiment analysis for the calculation of news sentiment indicators for each report, attempting to analyze the correlation between news sentiment and foreign exchange rates. First, we create 3 regression models to explore the contemporaneous relationship and the predictive power of news sentiment. Second, we build an LSTM neural network model to predict the one-step-ahead closing price of EUR/USD, GBP/USD exchange rates, and compare it with the traditional econometric model. Furthermore, we also examine whether the inclusion of the news sentiment factor into the LSTM neural network model can enhance the forecasting performance, thereby showing that news sentiment is an effective exchange rate predictor. Last but not least, in terms of institutional or individual investors, the main purpose of exchange rate prediction is nothing more than gaining profits from trading. Therefore, we build trading strategies based on LSTM neural networks and exert sentiment indicators as the trading signal’s filters. The empirical results show that in the regression analysis, there’s a contemporaneous relation between news sentiment and exchange rate return. Nevertheless, for the predictive regression, they are not statistically significant. As for exchange rate forecasting, when the LSTM neural network model incorporates news sentiment factors, it can indeed improve the forecasting performance. Finally, for the LSTM trading strategy, the use of sentiment indicators as the trading signal’s filters can tremendously improve its performance, indicating that news sentiment is a crucial factor both for forecasting the exchange rates movement and for trading strategy.