Tolga Cenesizoglu

Market participants who need to trade a significant number of securities within a given period can face high transaction costs. In this paper, we document how improvements in intraday liquidity forecasts can help reduce total transaction costs. We compare various approaches for forecasting intraday transaction costs, including autoregressive and machine learning models, using comprehensive ultra‐high‐frequency limit order book data for a sample of NYSE stocks from 2002 to 2012. Our results indicate that improved liquidity forecasts can significantly decrease total transaction costs. Simple models capturing seasonality in market liquidity tend to outperform alternative models.

We reassess Boehmer et al. (2021, BJZZ)'s seminal work on the predictive power of retail order imbalance (ROI) for future stock returns. First, we replicate their 2010-2015 analysis in the more recent 2016-2021 period. We find that the ROI's predictive power weakens significantly. Specifically, past ROI can no longer predict weekly returns on large-cap stocks, and the long-short strategy based on past ROI is no longer profitable. Second, we analyze the effect of using the alternative quote midpoint (QMP) method to identify and sign retail trades on their main conclusions. While the results based on the QMP method align with BJZZ's findings in 2010-2015, the two methods provide different conclusions in 2016-2021. Our study shows that BJZZ's original findings are sensitive to the sample period and the approach to identify ROIs.

This paper analyzes whether the Fed had the ability through its conventional monetary policy to affect key economic and financial variables, and, in particular, the term structure of interest rates, during the recent financial crisis. This departs from the empirical literature that focuses mainly on the effectiveness of unconventional monetary policies during this episode, although these policies are appropriate only to the extent that the conventional policy was ineffective in the first place. Our identification strategy based on the conditional heteroskedasticity of the structural innovations allows us to specify a flexible structural vector auto-regressive process that relaxes the identifying assumptions commonly used in earlier studies. Comparing our results obtained from samples excluding and including the financial crisis, we find that the conventional monetary policy has lost its effectiveness shortly after the beginning of the financial turmoil. This result suggests that the Fed's use of unconventional policies was appropriate, at least, with the objective of changing the term structure of interest rates.