Are hedge funds losing their hedge?

Labyrinth at Schonbrunn Garden, Vienna; Credit: Andrea Schaufler and Wikimedia Commons

Origin of long-short hedge funds

“Hedge funds” were pioneered some 70 years ago by Australian financier Alfred Winslow Jones. His idea was to combine a “long” position (i.e., one that profits if the securities go up in price), typically a set of growth stocks, with a “short” position (i.e., one that profits if the securities go down in price) on the other part of the portfolio. Jones argued that this “long-short” strategy is more stable that either of the two parts by themselves — if the overall market

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How safe are cryptocurrency investments?

Credit: Onov3056 and Wikimedia Commons

The mathematics behind cryptocurrency

In a previous Mathematical Investor blog, we presented an overview of the mathematics behind blockchain technology, which is the basis of all cryptocurrency systems. At the present time, most systems are based on the SHA-256 algorithm, which is a specific instance of the Elliptic curve digital signature algorithm.

To attempt to break SHA-256 by brute force computation, one would need to exhaust over 2256 ≈ 1077 possible private keys, which is comparable to the number of atoms in the visible universe. In other words, while the computations necessary to actually

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Active mutual funds underperform passive funds, again

Credit: Mutual Fund Advisors

Introduction

For the vast majority of individuals investors, investing in one or a small number of mutual funds or exchange-traded funds makes more sense than directly owning a set of stocks or bonds. For one thing, investors often sleep better with mutual funds, rather than fretting whenever one of their stock or bond holdings is mentioned in a news report. Also, for many individuals, mutual fund holdings are less likely to run afoul of conflict-of-interest difficulties in their professional work.

In a previous Mathematical Investor blog, we presented data on actively managed versus passive fund

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Backtest overfitting and the post-hoc probability fallacy

A 13-card hand dealt “at random”

Introduction

In several articles on this site (see, for instance, A and B), we have commented on the dangers of backtest overfitting in finance.

By backtest overfitting, we mean the usage of historical market data to develop an investment model, strategy or fund, where many variations are tried on the same fixed dataset. Backtest overfitting, a form of selection bias under multiple testing, has long plagued the field of finance and is now thought to be the leading reason why investments that look great when designed often disappoint when actually fielded to investors.

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How backtest overfitting in finance leads to false discoveries

The present author, together with Marcos López de Prado, has just published the article How backtest overfitting in finance leads to false discoveries in Significance, a journal of the British Statistical Society. The published article is now available at the Significance (Wiley) website.

This article is condensed from the following manuscript, which is freely available from SSRN: Finance is Not Excused: Why Finance Should Not Flout Basic Principles of Statistics.

This paper introduces the problem of backtest overfitting in finance to the general reader who may be trained in the basics of statistics but not necessarily familiar with the application

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The brave new world of probability and statistics

Block function approximation to normal distribution

Introduction

Today, arguably more than ever before, the world is governed by the science of probability and statistics. “Big data” is now the norm in scientific research, with terabytes of data streaming into research centers from satellites and experimental facilities, analyzed by supercomputers. “Data mining” is now an essential part of mathematical finance and business management. Numerous public opinion polls, expertly analyzed, guide the political arena. Covid-19 infection rates, immunization levels and r0 factors are a staple of nightly newscasts.

Yet the public at large remains mostly ignorant of the basic principles

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Can astrology predict financial markets??

Venice astrological circle; credit Wikimedia

Astrology in finance?

In a previous MathInvestor article, we mentioned how absurd it would be if someone offered predictions of stock or bond prices or cryptocurrency rates based on astrological signs.

Consider for a moment that financial market prices are based on a confluence of many thousands of factors worldwide, including developments in science and technology, changes in consumer sentiment and preferences, changes in prices of production, public health emergencies (e.g., Covid-19), political developments, competition with other financial instruments and even changes in weather. These prices are negotiated electronically, on behalf of millions of

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The failure of anomaly indicators in finance

A black swan; credit: Wikimedia

The replicability crisis in science

Recent public reports have underscored a crisis of replicability in numerous fields of science:

In 2012, Amgen researchers reported that they were able to replicate fewer than 10 of 53 cancer studies. In March 2014, physicists announced with fanfare that they had detected evidence of gravitational waves from the “inflation” epoch of the big bang. However, other researchers were unable to verify this conclusion. The current consensus is that the twisting patterns in the data are due to dust in the Milky Way, not inflation. In 2015, in a

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Another miserable year for market forecasters

Venice astrological circle; credit Wikimedia

Pseudoscience and forecasting

Suppose, during a nightly TV weather broadcast, that a reporter presented forecasts by persons, with no credentials in mathematical meteorology, who based their analysis on eyeballing a few charts and graphs. If anyone took such amateur forecasts seriously, when a severe storm was approaching, rather than relying on the consensus of qualified scientists assisted by state-of-the-art supercomputer models, they would risk disaster.

Or suppose that someone suggested that “biorhythms” (a person’s presumed daily, monthly and yearly cycles that supposedly start in sync at birth) could be used to predict the performance

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López de Prado on machine learning in finance

Marcos López de Prado

Introduction

Marcos López de Prado, whom we have featured in previous Math Scholar articles (see Article A, Article B and Article C), has been invited to present a keynote presentation at the ACM Conference on Artificial Intelligence in Finance, to be conducted virtually October 14-16, 2020.

López de Prado is a faculty member of Cornell University and also CEO of True Positive Technologies, LP, a private firm that provides machine learning techniques techniques for finance applications. He is also the author of two books in the field: Advances in Financial Machine Learning, published by Wiley

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