Important Research Papers for Quants
A list of foundational research papers that every aspiring and practicing quant should read.
Why Research Papers?
Unlike many other disciplines within the umbrella of finance, quantitative finance tends to be very academic in nature. This means that a majority of the modern techniques and practices used within this field have arisen from innovations in research labs at universities and other academic institutions. Therefore, reading research papers that have been published by the premier quantitative finance universities is a worthwhile pursuit.
If you're just interested in finding out the most recent quant research papers that have been published you can find a great list of them on arxiv or srrn. However, if you're looking for a curated list of some of the most important quant finance research papers to start, that's what we'll cover in this article. We'll share the most seminal papers in the field, including those that introduced the French Fama model all the way to the Black Scholes model.
Paper #1 - What Happened To The Quants in August 2007?
This paper covers the remarkable events that unfolded during the week of August 6th that shook up the hedge fund industry. During this week, many quantitative hedge funds experienced unprecedented losses, which could be attributed to their use of long/short equity strategies (the use of short-selling). During this week, there was hypothesized to be a sudden liquidation of a series of quantitative portfolios which in return caused increased pressure on the long/short strategies. Further research of this event revealed that systemic risk associated with the quant industry may be increasing over recent years.
Paper #2 - The Cross-Section of Expected Stock Returns
In this paper, Fama reveals how leveraging size and book-to-market equity can capture the cross-sectional variation in average stock returns. This is demonstrated through the use of multiple linear regressions, which highlight that stock risks are multidimensional. The significance of this is that it can be leveraged by investors to understand how varying characteristics can be used to estimate a stock's expected return.
Paper #3 - A Five-Factor Asset Pricing Model
In this paper, Fama reveals a new financial model that aims to be an improvement on the three-factor model introduced in 1993. This model aims to capture size, value, quality, profitability, and investment patterns in average stock returns. The overall model not only better explains stock return but also decreases the unexplained variance of the predictions. While the model is an improvement over its predecessor, its minor flaw is that it fails to capture the low average returns on small stocks. Overall, this paper was very important because it gave future quants a framework for approaching average return modeling.
Paper #4 - The Statistics of Sharpe Ratios
The Sharpe ratio is a popular metric used to evaluate the performance of a portfolio. In essence, the Sharpe ratio compares the return on investment with its underlying risk. In this paper, Lo analyzes the statistical distribution of Sharpe ratios to see whether they are being measured accurately. In doing so, Lo finds that the annual Sharpe ratio for a hedge fund can be overstated by as much as 65 percent because of autocorrelation with monthly returns. Furthermore, adjusting the calculation of the Sharpe ratio can significantly alter the rankings of various portfolio strategies.
Paper #5 - Optimal Execution of Portfolio Transactions
This paper showcases one of the preliminary attempts at portfolio optimization. In it, Almgren and Chriss highlight the execution of portfolio transactions that aim to minimize volatility risk and transaction costs that arise from market impact. Portfolio transactions refer to transactions that move a portfolio from a given state to a new state over a defined period of time. This strategy is also commonly associated with minimizing Value at Risk (VAR) and maximizing the expected revenue of trading.
This paper first introduced the famous Black-Scholes model - a mathematical model for estimating the underlying price of an option based on other investment instruments and factors. The idea for this model comes from the observation that if options are being correctly priced, it should not be possible for long/short positions to be profitable. The model takes in five inputs: strike price, current stock price, time to expiration, risk-free rate, and volatility.
This paper introduced the GARCH model - a volatility estimator that factors in periods of high, low, open, and close prices in historical time series. One of the great aspects of this model is that it allows the user to factor in more real-world context when predicting the expected return for a financial instrument. Not only has this model been demonstrated to improve the accuracy of predictions in comparison to classical estimators, but has also been shown to have the smallest variance in its predictions.
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