| dc.description.abstract | In the past, most of the literature on the price-volume relationship has focused on two variables, namely price and volume, and seldom controlled for other effects when investigating the price-volume relationship. In addition, the study of asset pricing has always been an important academic topic, and various scholars have tried to find out the variables related to asset prices. Therefore, this paper tries to collect these variables that have been proven to be related to stock returns, and then combine them with machine learning to reduct the dimension of these variables. We collect a total of 46 variables with a sample period from January 2002 to December 2022 and divide the sample period into in-sample and out-of-sample periods, where the in-sample period is from January 2002 to December 2006 and the out-of sample period is from January 2007 to December 2022. First, we reduct the dimension of these variables by supervised/unsupervised dimensionality reduction methods (PCA, PLS, and PQR), and then make out-of-sample predictions of stock returns and compare the predictability of the three models. Finally, this paper refers to the suggestion of Stock and Watson′s (2002) to categorize the 46 variables and then make predictions for stock returns. The result shows that PLS has the best predictability in stock returns, while PQR(0.5) has the second best predictability. Therefore, we finally used PLS and PQR(0.5) to extract the factors for all categories. Next, the above factors are incorporated into the quantile regression model to observe the price-volume relationship during the out-of-sample period, and the regression results are compared to those without controlling for any factors, the result shows that these factors affect the price-volume relationship. In addition, we also use these factors to filter stock returns, and then observe the causality between the filtered returns and the trading volume via Granger causality test. | en_US |