We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market...

In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and...

We examine the potential of ChatGPT, and other large language models, in predicting stock market returns using sentiment analysis of news headlines. We use ChatGPT to indicate whether a given headline is good, bad, or irrelevant news for firms' stock prices. We then compute a numerical score and...

Using text from 200 million pages of 13,000 US local newspapers and machine learning methods, we construct a 170-year-long measure of economic sentiment at the country and state levels, that expands existing measures in both the time series (by more than a century) and the cross-section. Our...

In this study, we comprehensively examine the volatility term structures in commodity markets. We model state-dependent spillovers in principal components (PCs) of the volatility term structures of different commodities, as well as that of the equity market. We detect strong economic links and a...

We examine an array of short-term mean-reversion indicators for global equities. The indicators encompass the most widely known price oscillators from the field of technical analysis along with several modified versions first developed by the author in the 2009- 2010 time frame. Constructing...

We study the performance of many traditional and novel, text-based variables for in-sample and out-of-sample forecasting of oil spot, futures, and energy company stock returns, and changes in oil volatility, production, and inventories. After controlling for small-sample biases, we find evidence...

This study investigates the relationship between expected returns on cryptocurrencies and macroeconomic fundamentals. We employ a dynamic factor model and summarize information as common factors. We find that the common factors are strongly linked to the cryptocurrency expected returns at a...

The study employs the wavelet coherence and the spillover index methodologies to investigate time-varying relationship between green sukuk and selected assets from the crypto-currency and commodity markets between October 2019, and March 2022. The results of wavelet coherence indicated weak...

We systematically re-examine the efficacy of trend-based technical indicators in predicting cryptocurrency market returns at daily, weekly, and monthly horizons. It shows that the price-based signals are more effective than the volume-based signals in the short horizon (daily and weekly), while...