Quant Mashup - Alex Chinco

Many explanations for the same fact [Alex Chinco]

Asset-pricing research consistently produces many different explanations for the same empirical facts. As a rule of thumb, you should expect asset-pricing researchers to wildly overachieve. Behavioral researchers can typically point to several psychological biases which might explain the same

*- 4 months ago, 19 Jun 2021, 10:22am -*

Factor Models, Little Green Men, And Machine Learning [Alex Chinco]

Economists use machine learning (ML) to study asset prices in two different ways. Approach #1: use these techniques to predict the cross-section of expected returns—i.e., to predict which stocks are most likely to have high or low future returns. e.g., see here, here, or here. Approach #2: use

*- 2 years ago, 29 Jun 2019, 11:08am -*

Risk-Factor Identification: A Critique [Alex Chinco]

In standard cross-sectional asset-pricing models, expected returns are governed by exposure to aggregate risk factors in a market populated by fully rational investors. Here’s how these models work. Because investors are fully rational, they correctly anticipate which assets are most likely to

*- 2 years ago, 27 May 2019, 03:05am -*

The Basic Recipe For Rationalizing Errors In Belief [Alex Chinco]

Behavioral-finance models are often written down so that, although each individual trader holds incorrect beliefs, market events nevertheless unfold in such a way that traders can rationalize their own errors. e.g., consider the model in Scheinkman and Xiong (2003). In this model, each individual

*- 2 years ago, 4 Feb 2019, 01:29am -*

The Existence Of A Bubble vs. The Timing Of Its Crash [Alex Chinco]

Journalists love to talk about bubbles. The Wall Street Journal has hinted at bubbles in both the Chinese stock market and the market for Bitcoin during the past month alone. But, financial economists are much more reluctant to call something a bubble. There’s debate about whether bubbles even

*- 2 years ago, 1 Nov 2018, 10:05am -*

How Bad Are False Positives, Really? [Alex Chinco]

Imagine you’re looking for variables that predict the cross-section of expected returns. No search process is perfect. So, as you work, you will inevitably uncover both tradable anomalies as well as spurious correlations. To figure out which are which, you regress returns on each variables that

*- 3 years ago, 10 Jan 2018, 11:14am -*

A Tell-Tale Sign of Short-Run Trading [Alex Chinco]

Motivation Trading has gotten a lot faster over the last two decades. The term “short-run trader” used to refer to people who traded multiple times a day. Now, it refers to algorithms that trade multiple times a second. Some people are worried about this new breed of short-run trader making

*- 4 years ago, 27 Jun 2017, 11:52am -*

How Many Assets Are Needed To Test a K-Factor Model? [Alex Chinco]

Imagine you’re a financial economist who thinks that some risk factor,{\color{white}i}f_t, explains the cross-section of expected returns. And, you decide to test your hunch. First, you regress the realized returns of N different assets on{\color{white}i}f_t to estimate each asset’s exposure to

*- 4 years ago, 24 May 2017, 01:55pm -*

The Tension Between Learning and Predicting [Alex Chinco]

Imagine we’re traders in a market where the cross-section of returns is related to V \geq 1 variables: In the equation above, \alpha^\star denotes the mean return, and each \beta_v^\star captures the relationship between returns and the vth right-hand-side variable. Some notation: I’m going to

*- 4 years ago, 24 Jan 2017, 09:29pm -*

Why Bayesian Variable Selection Doesn’t Scale [Alex Chinco]

Traders are constantly looking for variables that predict returns. If x is the only candidate variable traders are considering, then it’s easy to use the Bayesian information criterion to check whether x predicts returns. Previously, I showed that using the univariate version of the Bayesian

*- 4 years ago, 19 Jan 2017, 11:03pm -*

The Bayesian Information Criterion [Alex Chinco]

Imagine that we’re trying to predict the cross-section of expected returns, and we’ve got a sneaking suspicion that x might be a good predictor. So, we regress today’s returns on x to see if our hunch is right, \begin{align*} r_{n,t} = \hat{\mu}_{\text{OLS}} + \hat{\beta}_{\text{OLS}} \cdot

*- 4 years ago, 4 Jan 2017, 02:52am -*

Intuition Behind the Bayesian LASSO [Alex Chinco]

Imagine you’ve just seen Apple’s most recent return, r, which is Apple’s long-run expected return, \mu^\star, plus some random noise, \epsilon \overset{\scriptscriptstyle \mathrm{iid}}{\sim} \mathrm{N}(0, \, 1): (1) \begin{align*} r &= \mu^\star + \epsilon. \end{align*} You want to use

*- 5 years ago, 25 Sep 2016, 07:00pm -*

Inferring Trader Horizons from Trading Volume [Alex Chinco]

1. Motivating Example This post shows that, if traders face convex transaction costs (i.e., it costs them more per share to buy 2 shares of stock than to buy 1 share of stock), then it is possible to infer traders’ investment horizons from trading-volume data. To see why, imagine you are a trader

*- 5 years ago, 13 Jul 2016, 12:03pm -*

Asset-Pricing Implications of Dimensional Analysis [Alex Chinco]

I have been trying to use dimensional analysis to understand asset-pricing problems. In many hard physical problems, it is possible to gain some insight about the functional form of the solution by examining the dimensions of the relevant input variables. In the canonical example of this brand of

*- 5 years ago, 16 May 2016, 02:11am -*

ETF-Rebalancing Cascades [Alex Chinco]

This post looks at the consequences of ETF rebalancing. These funds follow pre-announced rules that involve discrete thresholds. The well-known SPDR tracks the S&P 500, but there are over 1400 different ETFs tracking a wide variety of different underlying indexes. When any of these underlying

*- 5 years ago, 7 Apr 2016, 01:27am -*

Using the LASSO to Forecast Returns [Alex Chinco]

1. Motivating Example A Popular Goal. Financial economists have been looking for variables that predict stock returns for as long as there have been financial economists. For some recent examples, think about Jegadeesh and Titman (1993), which shows that a stock’s current returns are predicted by

*- 5 years ago, 5 Dec 2015, 03:24pm -*

Screening Using False-Discovery Rates [Alex Chinco]

1. Motivating Example Jegadeesh and Titman (1993) show that, if you rank stocks according to their returns over the previous 12 months, then the past winners will outperform the past losers by 1.5{\scriptstyle \%} per month over the next 3 months. But, the authors don’t just test this particular

*- 5 years ago, 6 Nov 2015, 03:46pm -*

Multiscale Noisy-Rational-Expectations Equilibrium [Alex Chinco]

1. Motivation Evolutionarily Slow. In modern financial markets, people simultaneously trade the exact same assets on vastly different timescales. For example, a Jegadeesh and Titman (1993)-style momentum portfolio turns over half its holdings once every 6 months. By contrast, Kirilenko, Kyle,

*- 6 years ago, 29 Aug 2015, 09:38am -*

Risk Aversion, Information Choice, and Price Impact [Alex Chinco]

Kyle (1985) introduces an information-based asset-pricing model where informed traders keep trading until the marginal benefit of holding one additional share of the asset is exactly offset by the marginal cost of this last trade’s price impact. This model has really nice intuition, but it also

*- 6 years ago, 26 Jun 2015, 04:31am -*

Comparing Kyle and Grossman-Stiglitz [Alex Chinco]

1. Motivation New information-based asset-pricing models are often extensions of either Kyle (1985) or Grossman-Stiglitz (1980). At first glance, these two canonical models look quite similar. Both price an asset with an unknown payout, like a stock or bond, and both analyze the strategic behavior

*- 6 years ago, 16 Jun 2015, 02:33am -*