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This study demonstrates whether analysts' sentiments toward individual stocks are useful for stock investment strategies. This is achieved by using natural language processing to create a polarity index from textual information in analyst reports. In this study, we performed time series forecasting for the created polarity index using deep learning...
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Citations
... In financial markets, a considerable quantity of documental data can be accessed via the internet, such as news articles, social media, earning reports and answers given to the inquiry of securities. It may range from the MACD line in conventional stock charts to the volume of traded shares (Taguchi et al., 2022). Traded prices and volume of shares in American stock data include such information. ...
Analysis and evaluation of return opportunities presented by equity markets have always been in the core of investors' interest. As technology advances and markets evolve, new ways to approach the task of straightforward valuation emerge. Judging companies and their assets by traditional yardsticks has kept its relevance throughout decades, however, technological change brings into existence new markets and industries whose value is uncertain and difficult to measure with traditional tools. Because of this, investors striving to make informed decisions need to engage with a growing complexity of financial and technological tools at their disposal.
... Regimes in finance theory are the invisible market states, such as expansion, recession, bulls, and bears. Some studies attempted to capture market alpha by incorporating these regime changes into investment strategies [3,4]. We will delve one step further and focus on measuring future regime changes. ...
... Studies in this area include EcoRevs and a new architecture using long short-term memory (LSTM) [9], along with a study of polarity index creation using newspaper articles, economic trend surveys, and bi-directional LSTM [10]. In addition, research has been conducted on the creation of economic indexes using rate information from analyst reports [4,[11][12][13]. The novelty of this study is that the masked language model (MLM) score was used to create a polarity index and index the tone of financial texts. ...
... Regimes in finance theory refer to invisible states of the market, such as expansion, recession, bull, and bear. Some studies have attempted to capture market alpha by incorporating these regime changes into investment strategies [4][5][6]. ...
... There is also research on using text data to predict stock closing prices [18,19]. In addition, research has been conducted on the creation of an economic index using rate information from analyst reports [6,10]. Several studies use text mining of analyst reports to predict stock prices [20,21]. ...
... Daily financial news was used, but not necessarily only when textual information is issued with high frequency. For example, Taguchi et al. [6] predicted the polarity index once using Bidirectional LSTM as an alternative and simulated the investment after regime splitting. If individual polarity indices are created from quarterly earnings reports or analyst reports that are published irregularly, or if a combination of these texts is used to create polarity indices, the method of predicting a polarity index once and then using it may be effective in some extent. ...
This study demonstrates whether financial text is useful for the tactical asset allocation method using stocks. This can be achieved using natural language processing to create polarity indexes in financial news. We perform clustering of the created polarity indexes using the change point detection algorithm. In addition, we construct a stock portfolio and rebalanced it at each change point using an optimization algorithm. Consequently, the proposed asset allocation method outperforms the comparative approach. This result suggests that the polarity index is useful for constructing the equity asset allocation method.
... Wang [23] tested a combination optimization model using BiLSTM and BIGRUs for simultaneous prediction on four stock groups, demonstrating superior performance compared to other benchmark models. Taguchi [24] simulated stock investments in a proposed portfolio strategy, and found that BIGRU had the lowest error, although its generalizability varied for different datasets and given tasks. Liu [25] proposed an EMD-BIGRU model for predicting missing data, and an evaluation of the model's performance indicated that data decomposition improved the prediction accuracy of BIGRUs and enabled capturing more data variations. ...
... Spiral update of position: The distance between the whale position and the prey position is first calculated, and then a spiral equation is created to mimic the humpback whale approaching the prey in a spiral pattern. The equations are shown in Equations (24) and (25). ...
In order to obtain better prediction results, this paper combines improved complete ensemble EMD (ICEEMDAN) and the whale algorithm of multi-objective optimization (MOWOA) to improve the bidirectional gated recurrent unit (BIGRU), which makes full use of original complex stock price time series data and improves the hyperparameters of the BIGRU network. To address the problem that BIGRU cannot make full use of the stationary data, the original sequence data are processed using the ICEEMDAN decomposition algorithm to derive the non-stationary and stationary parts of the data and modeled with the BIGRU and the autoregressive integrated moving average model (ARIMA), respectively. The modeling process introduces a whale algorithm for multi-objective optimization, which improves the probability of finding the best combination of parameter vectors. The R2, MAPE, MSE, MAE, and RMSE values of the BIGRU algorithm, ICEEMDAN-BIGRU algorithm, MOWOA-BIGRU algorithm, and the improved algorithm were compared. An average improvement of 14.4% over the original algorithm’s goodness-of-fit value will greatly improve the accuracy of stock price predictions.
