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Index investing: “Confidence in the mathematics”

One central difficulty of investing, both in the U.S. and internationally, is that most individual investors are not sufficiently well-informed on financial matters (or else are not sufficiently disciplined in their approach), and thus often make less-than-optimal choices in managing their long-term savings. The 2014 DALBAR report, for instance, concluded that over the past 20 years, individual U.S. stock fund investors achieved only a 5.02% average annual return, which is considerably less than the 9.22% they could have achieved simply by investing in a S&P500 index fund. Results for other asset classes are similar.

In fact, analyst Richard Bernstein has

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Is “cherry picking” a factor in hedge fund performance?

Challenging times for hedge funds

Recently attention has been drawn to the fact that the advantage enjoyed by hedge funds over more conventional investment vehicles has been eroding. For example, the annualized “excess return” of the HFRI equity hedge fund index (adjusted for certain factors, 60 month rolling average) has declined from approximately 15% in 2000 to less than 2% in 2010, and actually has been negative over the past two years. In particular, the average year-to-date hedge fund return (as of September 2014) is only 2%, compared to the 7.27% rise in the S&P500 index. Similarly, only 23% of

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New online tool to demonstrate backtest overfitting

Introduction

We are pleased to announce the availability of a new online tool to demonstrate and analyze the phenomenon of backtest overfitting. It is available HERE. It was developed by researchers at the Scientific Data Management Group at Lawrence Berkeley National Laboratory, with contributions and suggestions from several other persons. A complete list of contributors is given below.

In finance, “backtest overfitting” means using historical market data (i.e., a “backtest”) to develop an investment strategy, where too many variations of the strategy are tried, relative to the amount of data available. Overfit strategies typically work well when tested against

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How financially literate are individual investors?

Introduction

A June 2014 study released by the Employee Benefit Research Institute concluded that many U.S. Baby Boomer and Gen Xer households are expected to run short of money in retirement (assuming 35 years in retirement): 83% of those in the lowest income quartile, 47% in the second quartile, 28% in the third, and 13% even in the highest income quartile. Another study concluded that more than half of future U.S. retirees will rely on Social Security for at least 50% of their income.

Part of the difficulty stems from the fact that many workers, both in the U.S. and

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Bailey and Borwein give talks on integrity and reproducibility in mathematical finance

On 12 July 2014, David H. Bailey and Jonathan M. Borwein (two of the bloggers on this site) presented the talk Scientific Integrity in Mathematical Finance at the Workshop on Optimization, Nonlinear Analysis, Randomness and Risk, held at the Centre for Computer-Assisted Research Mathematics and its Applications (CARMA), University of Newcastle, Australia. The viewgraphs for the talk are available here.

In this talk, Bailey and Borwein summarize research outlined in their paper (co-authored with Marcos Lopez de Prado and Qiji Jim Zhu), Pseudo-mathematics and financial charlatanism: The effects of backtest overfitting on out-of-sample performance. The talk also includes a series

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New York Times features story on James Simons

On 7 July 2014, the New York Times ran a feature story on James H. Simons, the well-known geometer, hedge fund founder, billionaire and philanthropist. Here are some of the fascinating facts uncovered in the Times story and elsewhere:

Simons was born in 1938 in Newton, Massachusetts, the son of a shoe factory owner. Simons graduated from the Massachusetts Institute of Technology in three years, then received his Ph.D. in mathematics from U.C. Berkeley in three more years, finishing at the age of 23. Simons worked on cryptographic mathematics at the Institute for Defense Analyses in Princeton, New Jersey, but

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SEC to propose new rules for high-frequency trading

On June 5, Mary Jo White, Chair of the U.S. Securities and Exchange Commission, sketched some proposed changes to regulate high-frequency trading (HFT). Her full speech is available from the SEC website. Some analysis can be read in the New York Times and Bloomberg News.

Synopsis of White’s comments

White surprised many observers by stating that investors are doing better in the algorithmic trading regime today than they did in the “old manual markets.” She noted that for institutional investors, the cost of executing a large order is roughly 10% lower than in 2006, and the spreads between bid and

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Latest DALBAR report underscores poor long-term performance of individual investors

As we emphasized in a December 2013 Mathematical Investor blog, individual investors are not very well equipped, and certainly not very effective, in managing their own investment portfolios.

This is unfortunate, because fewer workers than in the past, particularly in the U.S., are covered by a “defined-benefit” retirement system, namely a pension that guarantees a certain proportion of one’s income at retirement, based on the number of years in service, in perpetuity until one’s death. Instead, the majority of the growing army of American baby boomers (according to the Population Reference Bureau, 76.4 million Americans were born in the period

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Do new backtested index ETFs outperform the market?

Many investors, individual and institutional, have come to the conclusion that index-linked investments are a rational and, in the long term, profitable investment strategy.

It is certainly true that many individual investors could do far worse that merely investing, say, in a S&P500 index fund or exchange-traded fund (ETF). As we described in a previous Mathematical Investor blog, the typical U.S. equity investor has significantly underperformed the S&P500, with similarly dismal results in other asset categories. In particular, the average equity investor has a 20-year return of 4.25% per annum, compared with a 8.21% average return of the S&P500, for

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Review of “Dark Pools” and “Flash Boys”

Recently two books have appeared that highlight “dark pools” (i.e., new trading venues that permit one to keep trading activity relatively private, at least for a limited time), and “high-frequency trading” (i.e., trading performed by computer algorithms and keyed to very fine-grained time intervals):

Dark Pools

Dark Pools (2012). Scott Patterson, a staff reporter for the Wall Street Journal, introduces the reader to computerized trading algorithms, then recounts the history of the emergence and proliferation of independent trading venues and computerized trading. We learn about the many small firms that rose to prominence — e.g., Island, Instinet, Archipelago, Datek, Getco,

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