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Menampilkan postingan dari Februari, 2009

Applying Corrective AI to Daily Seasonal Forex Trading

  By Sergei Belov, Ernest Chan, Nahid Jetha, and Akshay Nautiyal     ABSTRACT We applied Corrective AI (Chan, 2022) to a trading model that takes advantage of the intraday seasonality of forex returns. Breedon and Ranaldo (2012)   observed that foreign currencies depreciate vs. the US dollar during their local working hours and appreciate during the local working hours of the US dollar. We first backtested the results of Breedon and Ranaldo on recent EURUSD data from September 2021 to January 2023 and then applied Corrective AI to this trading strategy to achieve a significant increase in performance. Breedon and Ranaldo (2012) described a trading strategy that shorted EURUSD during European working hours (3 AM ET to 9 AM ET, where ET denotes the local time in New York, accounting for daylight savings) and bought EURUSD during US working hours (11 AM ET to 3 PM ET). The rationale is that large-scale institutional buying of the US dollar takes place during European working hours to pa
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A new service for retail investors

Here is a new low-cost service called Alerts4All that offers technical trading signals for retail investors. You can, for example, have an alert sent to you every time a "Double bottom" pattern occurs. A much more advanced version of the service will be rolled out soon -- I saw a demo today where you can backtest your strategies online, combining different fundamental and/or technical variables as entry or exit signals. They also have some built-in models for you to adapt (e.g. a model based on The Little Book that Beats the Market by Joel Greenblatt.) More interestingly, you can look at other people's trading models and their historical and/or real-time performance. Matlab or Alphacet it is not, but I think it will be quite useful for many retail traders. It might even be useful to professional traders who want a quick-and-dirty way to test out ideas.
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Trader tax proposal will be the death knell for statistical arbitrage

U.S. Congressman Peter DeFazio, introduced H.R. 1068: “Let Wall Street Pay for Wall Street's Bailout Act of 2009”, which aims to impose a 0.25% transaction tax on the “sale and purchase of financial instruments such as stock, options, and futures. Ladies and gentlemen, 0.25% is 50 basis points round-trip. Few if any statistical arbitrage strategies can survive this transaction tax. And no, this is not "Wall Street paying for Wall Street's Bailout". This is small-time independent trader-entrepreneur like ourselves paying for Wall Street's Bailout. Furthermore, this tax will drain the US market of liquidity, and ultimately will cost every investor, long or short term, a far greater transaction cost than 0.25%. If you want to stop this insanity, please sign this online petition .
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Finding seasonal spreads

I am pleased to introduce guest blogger Paul Teetor for today's article. ---------- Finding Seasonal Spreads By Paul Teetor A seasonal spread is a spread which follows a regular pattern from year to year, such as generally falling in the Spring or generally rising in October. To find seasonal spreads, I've been using ANOVA , which stands for analysis of variance . ANOVA is a well-established statistical technique which, given several groups of data, will determine if the groups have different averages. Importantly, it determines if the differences are statistically significantly . I start with several years of spread data, compute the spread's daily changes, then group the daily changes by their calendar month, giving me 12 groups. The ANOVA analysis tells me if the groups (months) have significantly different averages. If so, I know the spread is seasonal since it is consistently up in certain months and consistently down in others. The beauty is that I can automate
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The limitation of Sharpe ratio

Just as one should not trust VaR completely, one should also beware of high Sharpe ratio strategies. As this Economist article pointed out, a strategy may have a high Sharpe ratio because it has so far been accumulating small gains quite consistently, but it could still be subject to a large loss when black-swan events strike. Personally, I am more comfortable with strategies that do the opposite: those that seldom generate any returns, but always earn a large profit when financial catastrophes occur.
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The peril of VaR

This Quebec pension fund lost some $25 billion due to non-bank asset-backed commercial paper (ABCP). Their Value-at-Risk (VaR) model did not take into account liquidity risk. As usual, the quants got the blame. But can someone tell me a better way to value risk than to run historical simulations? Can we really build risk models on disasters we have not seen before and cannot imagine will happen? (Hat tip: Ray)
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Kelly formula revisited

Some discussions on Kelly's formula with a reader Steven L: Q: "I am more than half way through your book and am stuck at a concept that I can't seem to find an answer in any other forum. I have read Ralph Vince's "Portfolio Management Formulas," which uses Kelly's formula to calculate an optimal "fraction" of the bankroll to bet on each trial. So a trader can calculate a fraction of his total trading account value to risk on each trade. What I am referring to is the so-called "fixed-fractional" trading. There exists an optimal fraction that will maximize the geometric growth rate of the trading equity, in theory anyway. However, in the money management chapter of your book, you use Kelly's formula to derive an optimal "leverage." This seems to be in conflict with what I learned from Ralph Vince, since leverage is usually great than unity and fraction is usually less than unity. I can't seem to make a connection betwe
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