Why We Need a Statistical Revolution?

This article is written by Mark van der Laan.

He is a Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at the University of California, Berkeley.

https://senseaboutscienceusa.org/super-learning-and-the-revolution-in-knowledge/

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Alternative Sector Classification Methods

Abstract This paper offers two alternative sector classification methods in order to classify companies more accurately. Introduction During the early 1900s, various departments of the US government initiated research and studies on the various industries and their different functions. Due to the lack of set standards, each department ended up using its own methodology. Consolidating information across multiple sources became a challenge. The Standard Industrial Classification (SIC) was hence proposed as a uniform classification system, aimed to represent major industries, sub-class and specific function/product, and was formally adopted in 1937. However, SIC was facing a challenge because of the change in the economic environment. After that, the Global Industry Classification System (GICS) was launched by Standard & Poor's (S&P) and Morgan Stanley (MSCI) in August 1999. The standard provides a comprehensive, globally consistent definition of economic sectors and industr

Deflated Sharpe Ratio can reduce false discovery

Introduction With the recent advent of large financial datasets, machine learning, and high-performance computing, analysts can backtest millions of alternative investment strategies. Backtest optimizers search for combinations of parameters to maximize a strategy's simulated historical performance, leading to backtest overfitting. The performance inflation problem goes beyond backtesting. More generally, researchers and investment managers tend to report only positive results, a phenomenon is known as selection bias. Failure to control the number of tests involved in a given finding can lead to overly optimistic performance expectations. The Deflated Sharpe Ratio (DSR) corrects for two major sources of performance inflation: selection bias under multiple tests and non-normally distributed returns . By doing so, DSR helps separate legitimate empirical results from statistical deception. Backtesting is a good example. Backtesting is a historical simulation of how a particular inv

Why safe assets, such as gold and treasury bond cannot hedge market risk?

In early 2020, SPY (SPDR S&P500 ETF Trust), GLD (SPDR Gold Trust), and IEF (iShares 7-10 Year Treasury Bond ETF) fell simultaneously. Most people think that Gold and US treasury bond should rise while the stock market falls as they are the safe assets. Here are some reasons to explain why they cannot hedge market risk. Most hedge funds have a hedged portfolio. Their portfolios appear to have gone through a significant amount of deleveraging in March of 2020. Multi-strategy funds suffered losses so that they needed to rebalance the portfolio by unwinding their equity market neutral books. Most hedge funds use leverage to increase profit. In normal conditions, leverage allows portfolios to be more flexible and less constrained. Leverage gives long/short equity managers the flexibility to tune their books to the desired size, and add securities without resorting to exiting positions in other parts of the book to fund those purchases. The entire concept of risk parity relies on using

Matroid Evolved: Research

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