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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Profits, prophets and pseudoscience

Market prophets

We have all seen articles on financial news sites confidently predicting the future of various markets, often with remarkable specificity. Here are some that have appeared just in the past few weeks (as of 5 November 2018):

The stock market is setting up for another rally, according to Elliott Wave theory. Market strategist urges aggressive buying, says bull market could last decades. Short-seller who warned of ‘unavoidable pain’ earlier this year turns bullish. Stock strategist who saw market correction now predicts 10% to 14% rally — and a fizzle in 2019. Why the Dow Jones Industrial Average

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Hedge fund performance report card

It’s time for another look at the performance of hedge funds. Recent years have not been kind to hedge funds — not only has overall performance been less than stellar (particularly after fees), it has even lagged the S&P 500 index.

But what if we take a longer-term perspective? After all, the past eight years have been a bull market. What has happened in down markets?

A 28-year data comparison

One widely cited index for the hedge fund world is the HFRI Fund Weighted Composite Index (HFRI FWI), which is published by Hedge Fund Research, Inc. of Chicago. It includes

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Doomsayers and market prophets

Prophets of doom

2018 has been another hard year for prophets of doom. Even though some markets have suffered minor corrections (and there are still four months to go!), in most cases broad-market indices in first-world nations are either up or not far from their levels in January. Here are some recent doomsday predictions (note that the author of the blog or article is not always the one making the prediction):

2015

[1 Mar 2015] Stock-market crash of 2016: The countdown begins [17 May 2015] Countdown to the stock-market Crash of 2016 is ticking louder [1 Jul 2015] Recession time

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Mutual fund performance and survivorship bias

Mutual fund performance

As we have noted in previous Mathematical Investor blogs (see this blog for instance), surprisingly few mutual funds beat their respective benchmark (typically some market index). Even fewer consistently beat their benchmark year after year.

A new report from S&P Dow Jones sheds light on this phenomenon. It tabulates, for each year from 2001 through 2017, the percentage of mutual funds in various categories that are out-performed by their respective benchmarks. Here is a brief summary of this performance data.

Table 1: Percentages of U.S. mutual funds beaten by their benchmark

Category Benchmark 2011 2012 2013 2014

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Are target-date funds the answer?

Target-date funds

“Target-date funds” are currently the rage in the finance world. The term refers to a mutual fund that targets a given retirement date, and then steadily shifts the allocation of assets from, say, a 80%/20% mix of stocks and bonds at the start to, say, a 30%/70% mix as the target date approaches.

Vanguard Group, which manages over USD$5 trillion in assets, much of it in employer-offered defined contribution retirement plans, reports that participation in its target date offerings have grown explosively in the past few years. In 2005, when Vanguard started offering target-date funds, only a few

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Are economics and finance “lost in math”?

Is physics “lost in math”?

In a provocative new book, Lost in Math: How Beauty Leads Physics Astray, quantum physicist Sabine Hossenfelder argues that the scientific world in general, and the field of physics in particular, has repeatedly clung to notions that have been rejected by experimental evidence, or has pursued theories far beyond what can be tested by experimentation, mainly because these theories and the mathematics behind them were judged “too beautiful not to be true.” Examples cited by Hossenfelder include:

Supersymmetry. Supersymmetry, the notion that each particle has a “superpartner,” was originally proposed in the 1970s, and

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