@article {Harvey1, author = {Campbell R. Harvey and Yan Liu}, editor = {Moore, Howard}, title = {Practical Applications of Backtesting}, volume = {3}, number = {4}, pages = {1--4}, year = {2016}, doi = {10.3905/pa.2016.3.4.143}, publisher = {Institutional Investor Journals Umbrella}, abstract = {Backtesting Campbell R Harvey Yan Liu The claimed performance of new trading strategies often looks too good to be true{\textemdash}and indeed, in many cases, the good performance is a result of data mining. When implementing the strategy in the real world, practitioners routinely make some corrections to the backtests by haircutting the Sharpe ratio by 50\%.If a large number of strategies have been tested and a modest Sharpe ratio resulted, one should haircut the result to zero. But if a strategy is truly outstanding, why decrease the Sharpe ratio by a full 50\%? {\textquotedblleft}In that case, it seems more reasonable to take just a little off the top,{\textquotedblright} Cam Harvey says in an interview with Institutional Investor Journals .TOPICS: Statistical methods, portfolio management/multi-asset allocation}, issn = {2329-0196}, URL = {https://pa.pm-research.com/content/3/4/1.2}, eprint = {https://pa.pm-research.com/content/3/4/1.2.full.pdf}, journal = {Practical Applications} }