Timing the market: the economic value of price extremes

Abstract

By decomposing asset returns into potential maximum gain (PMG) and potential maximum loss (PML) with price extremes, this study empirically investigated the relationships between PMG and PML. We found significant asymmetry between PMG and PML. PML significantly contributed to forecasting PMG but not vice versa. We further explored the power of this asymmetry for predicting asset returns and found it could significantly improve asset return predictability in both in-sample and out-of-sample forecasting. Investors who incorporate this asymmetry into their investment decisions can get substantial utility gains. This asymmetry remains significant even when controlling for macroeconomic variables, technical indicators, market sentiment, and skewness. Moreover, this asymmetry was found to be quite general across different countries.

On extreme value theory in the presence of technical trend: pre and post Covid-19 analysis of cryptocurrency markets

“…The denial of weak-form market efficiency in asset markets means the dominance of technical trading in price forecasting (McGoun, 1990). Technical indicators are informative for short-term return forecasting in financial markets (Jegadeesh and Titman, 2001; Xie and Wang, 2018). In cryptocurrencies markets, technical trading rules offer substantially higher risk-adjusted returns than the simple buy-and-hold strategy (Hudson and Urquhart, 2019).…”

“…GTI is another important technical measure that foretells the price movements in financial markets (Xie and Wang, 2018). In our case, this indicator is the 250-day moving average of cryptocoins.…”

“…The main issue of equation (1) is that it overlooks the intra-day price movements. To ease this problem, we make a decomposition of the returns with the high price extreme according to standard procedure suggested by Xie and Wang (2018) with equation (2): …”

“…In another way, PMG and PML measure cryptocurrency market risk, as they denote the uncertain movement of prices between two extreme values. Following George and Hwang (2004) and Xie and Wang (2018), we consider PMG and PML, respectively, as indicators of good news and bad news. From now onwards, the time-series dynamics of these two measures in our models reveal how good news and bad news embedded in them adjust with cryptocurrency prices or enter the price-discovery process.…”

“…where CR it is the cumulative returns of a specific cryptocoin over the past year, R it is the mean return during the sample period and s it is the annualised standard deviation of returns. GTI is another important technical measure that foretells the price movements in financial markets (Xie and Wang, 2018). In our case, this indicator is the 250-day moving average of cryptocoins.…”