To answer the sceptics, we use all historical data (62962 observations) on all stock market
indices worldwide to verify the robustness of the so-called Halloween Indicator or Sell in …

Investors try to predict returns of financial assets to make successful investment. Many
quantitative analysts have used machine learning-based methods to find unknown profitable …

From a survey of the papers published in leading accounting journals in 2014, we find that
accounting researchers conduct significance testing almost exclusively at a conventional …

The vast majority of empirical studies in finance employ large enough sample sizes and use
the conventional thresholds for statistical significance. This routine practice can potentially …

We examine the causal links between US industry-wise credits and stock markets. The full
sample bootstrap Granger causality results show that all stock markets Granger cause their …

This paper critically evaluates the significant weather effect on stock return reported in two
seminal studies of investors' mood on stock market. It is found that their research design of …

The use of machine learning in the prediction of financial asset prices is gaining interest in
both academic research and the financial industry. Although these algorithms have been …

The clarion call for causal reduction in the study of capital markets is intensifying. However,
in self-referencing and open systems such as capital markets, the idea of unidirectional …

In this paper, we use the machine learning model to make predictions about the winning bid
rate of apartments nationwide. The winning bid rate for apartments should consider various …

Based on the characteristics of the Chinese futures market, this paper builds a supervised
learning model to predict the trend of futures prices and then designs a trading strategy …