DOI: 10.5061/dryad.80gb5mkx7
title: Semi-coskewnesses and the cross-section of excepted stock returns: Evidence from China

description: We propose an alternative nonlinear semi-risk measure, by decomposing the
 traditional coskewness into four components associated with the signed
 excess market and asset returns, that captures the asymmetries in
 nonlinear markets. We find that the two semi-coskewnesses attributable to
 (positive) negative excess market returns predict significantly
 (lower) higher future returns based on high-frequency data from China’s
 A-share market. After conducting a wide range of implementations, the risk
 premium for negative coskewness stands out as the most
 significant, followed by the premium for mixed negative
 coskewness. In contrast, the results for positive and
 mixed positive coskewnesses are not always significantly
 negative. More importantly from an economically meaningful perspective,
 for a downside risk premium of 25.40% per annum, a 2-standard-deviation
 increase in negative semi-coskewness gives rise to an increase of
 approximately 13.71% in annual expected return.
description: We calculate
 daily realized semi-coskewnesses using 5-minute intraday returns and
 aggregate them to obtain weekly frequency based on all the listed stocks
 of China’s A-share stock market. We also extract market capitalization,
 turnover rate, and book-to-market ratio for each stock from the CRSP
 database and the RESSET Financial Research database respectively. Lastly,
 we use daily returns to compute weekly returns, realized (co)moments,
 realized semi-risk factors, and lagged returns as well as maximum/minimum
 returns over the previous
 month.
description: # **Semi-coskewnesses and the cross-section of excepted stock returns:
 Evidence from China** Our
 empirical data consists of high-frequency intraday data on the opening and
 closing prices of all stocks (shares issued to domestic investors) from
 the China A-share stock market, along with weekly data on equity-related
 indicators. ## Description of the data and file structure The sample
 period selected in this paper spans from April 8, 2005 to April 22, 2022,
 with daily trading hours from 9:30 to 11:30 and 13:00 to 15:00 in GMT+8.
 Since too high data frequency results to larger market microscopic noise,
 and too low data frequency makes it difficult to capture high-frequency
 signals, Liu, Patton and Sheppard (2015) provide evidence that the
 estimations of realized measures based on a 5-minute data frequency
 perform well in terms of prediction accuracy, and a number of studies
 (Patton and Sheppard, 2015; Amaya, Christoffersen, Jacobs, and Vasquez,
 2015; Bollerslev, 2020) adopt this frequency. Therefore, we choose a
 five-minute data frequency as well. We calculate daily realized
 semi-coskewnesses using 5-minute intraday returns and aggregate them to
 obtain weekly frequency. We also extract market capitalization, turnover
 rate, and book-to-market ratio for each stock from the CRSP database and
 the RESSET Financial Research database respectively. Lastly, we use daily
 returns to compute weekly returns, realized (co)moments, realized
 semi-risk factors, and lagged returns as well as maximum/minimum returns
 over the previous month. Each CSV file contains weekly measures for the
 semi-coskewness factors and firm characteristics of each stock over a
 year, e.g., a CSV file named 2005 contains weekly measures for the
 semi-coskewness factors and firm characteristics of each stock during
 2005. In addition, each file's column headers are described as
 follows: * T: each week * ID: stock code * R: the stock return over week
 t. * RVOL: the weekly realized volatility * RVOL+ and RVOL-: the weekly
 realized good and bad volatilities * RSK: the weekly realized skewness *
 RSK+ and RSK-: the weekly realized upside and downside skewensses * RKT:
 the weekly realized kurtosis * CSK: the weekly realized coskewness * CSK+
 and CSK-: the weekly realized upside and downside coskewensses * CSKP,
 CSKM+, CSKN and CSKM-: the four different weekly realized
 semi-coskewnesses (positive, mixed positive, negative, and mixed negative
 coskewnesses) * CKT: the weekly realized kurtosis * R1: the stock return
 over week t+1 * TO: turnover rate * BM: book-to-market ratio * ME: the
 logarithm of the market capitalization of the firm * REV: the previous
 week’s return * MAX: the maximum daily returns over the previous month *
 MIN: the minimum daily returns over the previous month * BETA: the weekly
 realized beta * BETA+ and BETA-: the weekly realized upside and downside
 betas * BETAP, BETAM+, BETAN and BETAM-: the four different realized
 semibetas * IVOL: idiosyncratic volatility. * MOM: momentum ##
 Sharing/Access information Data was derived from the following sources: *
 CRSP database * RESSET Financial Research database ## Code/Software The
 Code folder contains the source code for all empirical study presented in
 the paper. The files containing the string ".m" are implemented
 in MATLAB, while the file containing the string *"*.R" is
 implemented in R.