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
Continue reading New online tool to demonstrate backtest overfitting
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%
Continue reading How financially literate are individual investors?
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
Continue reading Bailey and Borwein give talks on integrity and reproducibility in mathematical finance
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
Continue reading New York Times features story on James Simons
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
Continue reading SEC to propose new rules for high-frequency trading
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
Continue reading Latest DALBAR report underscores poor long-term performance of individual investors
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
Continue reading Do new backtested index ETFs outperform the market?
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 (2012). Scott Patterson, a staff reporter for
Continue reading Review of “Dark Pools” and “Flash Boys”
A recent Globe and Mail blog repeats an oft-cited claim that the U.S. stock market is weaker in mid-term election years (MTEYs). According to this blog, stock markets “have traditionally been weaker than normal during mid-term election years. Price returns during these four-year cycle lows have been atypically negative in January, but then frequently favorable
Continue reading Is the stock market weaker during mid-term election years?
Our recent papers [1,2] on backtest overfitting have attracted significant interest, including several press releases [American Mathematical Society, Science Daily, University of Newcastle] and news articles [Financial Times, Wall Street Journal, Bloomberg, Barron's, Pacific Standard, Morningstar, Seeking Alpha]. The feedback so far has been encouraging, and numerous colleagues have approached us with interesting questions and
Continue reading FAQs on Backtest Overfitting