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):


[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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New video explains the danger of selection bias in finance

A new video has been produced by the Mathematicians Against Fraudulent Financial and Investment Advice (MAFFIA) group. It explains, in simple terms, how many of the financial strategies and funds available today are based on a statistically dubious foundation, typically rooted in selection bias effects, because the finance world, unlike other fields such as the pharmaceutical industry, has not yet been forced to adopt the necessary rigorous statistical methodology to prevent such problems.

As a result, we often see a “vicious cycle”:

Academic researchers publish a paper describing a new investment strategy, but fail to disclose the fact that they

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Bailey and Lopez de Prado on the crisis of pseudoscience in finance

In a 25 May 2018 Forbes article, Brett Steenbarger interviews David H. Bailey and Marcos Lopez de Prado on the growing crisis of pseudoscience in finance.

Here is an excerpt:

Imagine that a pharmaceutical company develops 1000 drugs and tests these on 1000 groups of volunteer patients. When a few dozen of the tests prove “significant” at the .05 level of chance, those medications are marketed as proven remedies. Believing the “scientific tests”, patients flock to the new wonder drugs, only to find that their conditions become worse as the medications don’t deliver the expected benefit. Some consumers become quite

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Economics, finance and pseudoscience

Beware snake oil salesmen!


Bloomberg columnist Mohamed El-Erian recently lamented that the discipline of economics “is divorced from real-world relevance and has lost credibility.” Among the problems he mentions currently afflicting the field are the following:

The proliferation of simplifying assumptions that lead to an “overreliance on excessively abstract estimation techniques and approaches.” Insufficient consideration of the possibility that financial dislocations can disrupt the economy. Poor and grudging adoption of important insights from behavioral science and other disciplines. An oversimplification of uncertainty. An overemphasis of equilibrium conditions and mean reversion, and an underemphasis on structural changes and tipping

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