Quant Mashup - EP Chan

What is the probability of profit of your next trade? (Introducing PredictNow.Ai) [EP Chan]

What is the probability of profit of your next trade? You would think every trader can answer this simple question. Say you look at your historical trades (live or backtest) and count the winners and losers, and come up with a percentage of winning trades, say 60%. Is the probability of profit of

*- 1 month ago, 7 Aug 2020, 10:42pm -*

US nonfarm employment prediction using RIWI Corp alternative data [EP Chan]

The monthly US nonfarm payroll (NFP) announcement by the United States Bureau of Labor Statistics (BLS) is one of the most closely watched economic indicators, for economists and investors alike. (When I was teaching a class at a well-known proprietary trading firm, the traders suddenly ran out of

*- 9 months ago, 9 Dec 2019, 03:59pm -*

Experiments with GANs for Simulating Returns [EP Chan]

Simulating returns using either the traditional closed-form equations or probabilistic models like Monte Carlo has been the standard practice to match them against empirical observations from stock, bond and other financial time-series data. (See Chan and Ng, 2017 and Lopez de Prado, 2018.) Some of

*- 9 months ago, 5 Dec 2019, 10:53am -*

Is News Sentiment Still Adding Alpha? [EP Chan]

Nowadays it is nearly impossible to step into a quant trading conference without being bombarded with flyers from data vendors and panel discussions on news sentiment. Our team at QTS has made a vigorous effort in the past trying to extract value from such data, with indifferent results. But the

*- 1 year ago, 26 Apr 2019, 05:59am -*

The most overlooked aspect of algorithmic trading [EP Chan]

Many algorithmic traders justifiably worship the legends of our industry, people like Jim Simons, David Shaw, or Peter Muller, but there is one aspect of their greatness most traders have overlooked. They have built their businesses and vast wealth not just by sitting in front of their trading

*- 1 year ago, 5 Apr 2019, 11:40am -*

Loss aversion is not a behavioral bias [EP Chan]

In his famous book "Thinking, Fast and Slow", the Nobel laureate Daniel Kahneman described one common example of a behavioral finance bias: "You are offered a gamble on the toss of a [fair] coin. If the coin shows tails, you lose $100. If the coin shows heads, you win $110. Is this

*- 2 years ago, 29 Jun 2018, 11:33am -*

FX Order Flow as a Predictor [EP Chan]

Order flow is signed trade size, and it has long been known to be predictive of future price changes. (See Lyons, 2001, or Chan, 2017.) The problem, however, is that it is often quite difficult or expensive to obtain such data, whether historical or live. This is especially true for foreign exchange

*- 2 years ago, 2 Feb 2018, 11:50am -*

A novel capital booster: Sports Arbitrage [EP Chan]

As traders, we of course need money to make money, but not everyone has 10-50k of capital lying around to start one's trading journey. Perhaps the starting capital is only 1k or less. This article describes how one can take a small amount of capital and multiply it as much as 10 fold in one

*- 2 years ago, 4 Jan 2018, 09:17am -*

Optimizing trading strategies without overfitting [EP Chan]

Optimizing the parameters of a trading strategy via backtesting has one major problem: there are typically not enough historical trades to achieve statistical significance. Whatever optimal parameters one found are likely to suffer from data snooping bias, and there may be nothing optimal about them

*- 2 years ago, 17 Nov 2017, 09:06am -*

StockTwits Sentiment Analysis [EP Chan]

Exploring alternative datasets to augment financial trading models is currently the hot trend among the quantitative community. With so much social media data out there, its place in financial models has become a popular research discussion. Surely the stock market’s performance influences the

*- 3 years ago, 7 Sep 2017, 09:41am -*

Building an Insider Trading Database and Predicting Future Equity Returns [EP Chan]

I’ve long been interested in the behavior of corporate insiders and how their actions may impact their company’s stock. I had done some research on this in the past, albeit in a very low-tech way using mostly Excel. It’s a highly compelling subject, intuitively aligned with a company’s

*- 3 years ago, 21 Jul 2017, 10:31am -*

Paradox Resolved: Why Risk Decreases Expected Log Return But Not Expected Wealth [EP Chan]

I have been troubled by the following paradox in the past few years. If a stock's log returns (i.e. change in log price per unit time) follow a Gaussian distribution, and if its net returns (i.e. percent change in price per unit time) have mean m and standard distribution s, then many finance

*- 3 years ago, 4 May 2017, 12:00pm -*

More Data or Fewer Predictors: Which is a Better Cure for Overfitting? [EP Chan]

One of the perennial problems in building trading models is the spareness of data and the attendant danger of overfitting. Fortunately, there are systematic methods of dealing with both ends of the problem. These methods are well-known in machine learning, though most traditional machine learning

*- 3 years ago, 3 Mar 2017, 12:45pm -*

Pre-earnings Annoucement Strategies [EP Chan]

Much has been written about the Post-Earnings Announcement Drift (PEAD) strategy (see, for example, my book), but less was written about pre-earnings announcement strategies. That changed recently with the publication of two papers. Just as with PEAD, these pre-announcement strategies do not make

*- 3 years ago, 18 Nov 2016, 08:54am -*

Really, Beware of Low Frequency Data [EP Chan]

I wrote in a previous article about why we should backtest even end-of-day (daily) strategies with intraday quote data. Otherwise, the performance of such strategies can be inflated. Here is another brilliant example that I came across recently. Consider the oil futures ETF USO and its evil twin,

*- 3 years ago, 30 Sep 2016, 10:03am -*

Mean reversion, momentum, and volatility term structure [EP Chan]

Everybody know that volatility depends on the measurement frequency: the standard deviation of 5-minute returns is different from that of daily returns. To be precise, if z is the log price, then volatility, sampled at intervals of τ, is volatility(τ)=√(Var(z(t)-z(t-τ))) where Var means taking

*- 4 years ago, 7 Apr 2016, 03:25pm -*

Predicting volatility [EP Chan]

Predicting volatility is a very old topic. Every finance student has been taught to use the GARCH model for that. But like most things we learned in school, we don't necessarily expect them to be useful in practice, or to work well out-of-sample. (When was the last time you need to use calculus

*- 4 years ago, 27 Nov 2015, 01:50pm -*

Interview with Euan Sinclair [EP Chan]

I have been a big fan of options trader and author Euan Sinclair for a long time. I have cited his highly readable and influential book Option Trading in my own work, and it is always within easy reach from my desk. His more recent book Volatility Trading is another must-read. I ran into him at the

*- 5 years ago, 18 Sep 2015, 11:01am -*

Time series analysis and data gaps [EP Chan]

Most time series techniques such as the ADF test for stationarity, Johansen test for cointegration, or ARIMA model for returns prediction, assume that our data points are collected at regular intervals. In traders' parlance, it assumes bar data with fixed bar length. It is easy to see that this

*- 5 years ago, 2 Jul 2015, 09:13am -*

Beware of Low Frequency Data [EP Chan]

(This post is based on the talk of the same title I gave at Quantopian's NYC conference which commenced at 3.14.15 9:26:54. Do these numbers remind you of something?)A correct backtest of a trading strategy requires accurate historical data. This isn't controversial. Historical data that

*- 5 years ago, 13 Apr 2015, 04:30am -*

Commitments of Traders (COT) strategy on soybean futures [EP Chan]

In our drive to extract alphas from a variety of non-price data, we came across this old-fashioned source: Commitments of Traders (COT) on futures. This indicator is well-known to futures traders since 1923 (see www.cmegroup.com/education/files/COT_FBD_Update_2012-4-26.pdf), but there are often

*- 5 years ago, 28 Feb 2015, 07:36am -*

Trading with Estimize and I/B/E/S earnings estimates data [EP Chan]

By Yang GaoEstimize is an online-community utilizing 'wisdom of crowds' to offer intelligence about market. It contains a wide range of crowd-sourced estimates from over 4,500 buy-side, sell-side and individual analysts. Studies (from Deustche Bank and Rice University among others) show

*- 5 years ago, 8 Jan 2015, 04:06am -*