The seven reasons most econometric investments fail

Marcos Lopez de Prado, recently named 2019 Quant of the Year by the Journal of Portfolio Management, has released a presentation entitled The seven reasons most econometric investments fail.

Lopez de Prado’s overall point is that many widely used econometric approaches in finance either rely on misleading p-value statistics, or else rely on strong assumptions that are typically not satisfied by financial phenomena. Also, most econometric methods in finance do not pay sufficient attention to overfitting, either in the training set or in the testing set. These tools were, for the most part, developed in scientific fields such as

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Ray Dalio on why capitalism must be reformed

Credit: Ray Dalio

Dalio’s chronicle of capitalism’s ills

Ray Dalio, the founder of the highly successful hedge fund Bridgewater Associates, has written a detailed paper on why present-day capitalism, especially as it exists in the U.S., is in serious trouble and must be reformed, or else society risks increasingly serious social discord and economic dysfunction.

Dalio’s essay contains a treasure-trove of data, statistics and charts documenting the difficulties U.S. society in particular faces in the wake of increasing economic inequality, especially the manifold stresses faced by the less-well-off, and likely outcomes if nothing is done.

Here are some of

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What is the best training for finance PhDs?

A field day for machine learning and artificial intelligence PhDs

In a 27 March 2019 Bloomberg op-ed, Stony Brook University professor Noah Smith describes the quest by many technology and finance companies to hire top-tier PhD graduates, particularly in machine learning (ML) and artificial intelligence (AI). A 2017 Paysa study found that 35% of listed jobs in ML and AI required a PhD.

All of the major tech firms are aggressively expanding their staffs in the ML and AI arenas. Google has more than tripled its number of machine learning researchers in the past few years. Amazon is hiring

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Majority of highest-earning hedge fund managers and traders are at quant firms

Forbes’ list of highest-earning hedge fund managers and traders

Forbes has released their 2019 list of the twenty highest-earning hedge fund managers and traders (see also this Forbes analysis).

Here is a synopsis of the results:

Manager Rank 2017 earnings Company Type: Q* or D* Jim Simons 1 $1.6 B Renaissance Technologies Corp. Q Michael Platt 2 $1.2 B BlueCrest Capital Management D Ray Dalio 3 $870 M Bridgewater Associates Q Ken Griffin 4 $870 M Citadel LLC Q John Overdeck 5 $700 M Two Sigma Investments Q David Siegel 5 $700 M Two Sigma Investments Q Israel Englander

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Mutual fund report card: February 2019

Introduction

In a previous Mathematical Investor blog, we presented data on hedge fund performance, covering the 28-year period from 1990 through August 2018. We found that while the HFRI Fund Weighted Composite Index (HFRI FWI) has nearly identical long-term performance growth as the S&P 500 index, the past eight years or so have not been favorable to the hedge funds. Indeed, some of the leading hedge funds have suffered the largest losses.

Along this line, we noted that Warren Buffett recently won his ten-year bet with a hedge fund manager — an S&P 500 index fund bested a basket of

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Two news reports cite work by Marcos Lopez de Prado

In a previous blog, we mentioned that Marcos Lopez de Prado has been named “2019 Quant of the Year” by the Journal of Portfolio Management (see this previous blog for more details). Today (6 February 2019), Lopez de Prado was cited in two financial news reports.

In the first report, from the Financial Times, Lopez de Prado argues that the “black box” paradigm for artificial intelligence (AI), as is used by Amazon, Google, Netflix and others, is poorly suited to finance. Instead, he recommends the “causality” paradigm, which is used more by large scientific laboratories such as the Lawrence Berkeley

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Marcos Lopez de Prado named “2019 Quant of the Year” by The Journal of Portfolio Management

Marcos Lopez de Prado has been named “2019 Quant of the Year” by The Journal of Portfolio Management. Here are some excerpts from their announcement and more detailed press release:

The Journal of Portfolio Management (JPM) has named Marcos Lopez de Prado ‘Quant of the Year’ for 2019. JPM has instituted the annual Quant of the Year Award to recognize a researcher’s history of outstanding contributions to the field of quantitative portfolio theory. It complements the Bernstein Fabozzi/Jacobs Levy Award, which JPM established in 1999 to acknowledge the most innovative research paper published in a given year by JPM.

Machine

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Jack Bogle: The apostle of index investing

John C. Bogle; credit NY Times

Jack Bogle, founder of Vanguard Funds and a life-long apostle of index investing, died on 16 January 2019. Vanguard CEO Tim Buckley summarized his career in these terms: “Jack Bogle made an impact on not only the entire investment industry, but more importantly, on the lives of countless individuals saving for their futures or their children’s futures.”

J.C. de Swaan, lecturer at Princeton University and partner at Cornwall Capital, recalls that for the past several years Bogle has frequently visited de Swaan’s class of first-year students at Princeton. Bogle’s deep voice and high

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How bad is the problem of data misuse in finance research papers?

[Editor’s note: This guest post is by Zachary David, an analyst who advises institutional clients on trading practices (website: http://zacharydavid.com). The article below is adapted from this blog.]

Spurious results are the norm

Having done a healthy share of paper replications over the past decade, and having been consistently disappointed when the models or techniques broke down on data shortly after (or even before) the authors’ sample periods, I would say that data misuse is a gigantic problem — spurious results are the norm. But also over those years, the granularity of market data available to researchers has become finer,

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The Problem With Financial Oracles

Machine learning in finance

In recent years, machine learning techniques and big-data facilities have become quite popular in the finance and investment world. In the wake of this success, numerous machine learning researchers have decided to found their own asset management companies, hoping to capitalize on this trend.

This begs the question: Are large amounts of data and computing power all that is needed to tame the markets? In this article we delve into the uses and misuses of machine learning (ML) in finance.

The two kinds of machine learning

To the neophyte, all ML might look like the

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