Quantocracy’s Daily Wrap for 04/22/2019

Bonds are key to a well-diversified portfolio; theyve provided both consistent returns and consistent diversification against riskier asset classes like stocks and real estate. But bonds face stiff headwinds in the coming years. Thats not prognostication, its a mathematical certainty. Its imperative that our portfolio designs account for this less optimistic future. Failing to do so

mlfinlab is a living and breathing project in the sense that it is continually enhanced with new code from the chapters in the Advanced Financial Machine Learning book. We have built this on lean principles with the goal of providing the greatest value to the quantitative community. Currently the package contains code from the following chapters: Chapter 2: Financial Data structures: The

The Perfect Withdrawal Rate (PWR) is the rate of regular portfolio withdrawals that leads to a zero balance over a given time frame. 4% is the commonly accepted lower bound for safe withdrawal rates, but this is only based on one realization of history and the actual risk investors take on by using this number may be uncertain. Using simulation techniques, we aim to explore how different

Only showing the latest backtest versions without disclosing their out-of-sample degradation Backtesting todays static holdings (managers, asset allocations, sub-asset-classes) into the past – filled with look-ahead bias Charging fees that are on par with the tracking error of the strategy Asking candidates at job interviews to reveal interesting new factors and data-sets Publishing quant

In this weeks post, we discuss two posts. The first post, written by Elisabetta, examines a new method attempting to directly measure aggregate investor overconfidence. The second post, written by Larry Swedroe, examines issues that plague Factor Investing.