arbitrage - QuantPedia

Systematic Edges in Prediction Markets

November 27, 2025

Prediction markets are financial platforms where participants trade contracts linked to future events, with prices reflecting collective probabilities. While these markets efficiently aggregate information, systematic inefficiencies create trading opportunities. Notable strategies include inter- and intra-market arbitrage, exploiting price differences across platforms or mispricing within a single market. Behavioral biases, such as the longshot bias, lead traders to overvalue underdogs and undervalue favorites, while bookmakers may manipulate odds to mislead naive participants. Experienced traders can exploit these patterns to secure profits. This article reviews common systematic edges in prediction markets, illustrates their practical application, and highlights the potential for profitable trading.

Cryptocurrency as an Investable Asset Class – 10 Lessons

October 24, 2025

Cryptocurrencies have matured from experimental curiosities into a viable investable asset class whose return-generation and risk characteristics merit treatment within empirical asset pricing. A recent paper by Nicola Borri, Yukun Liu, Aleh Tsyvinski, Xi Wu summarizes ten facts from the literature that show cryptocurrencies share important similarities with traditional markets—comparable risk-adjusted performance and a small set of cross-sectional factors—while retaining distinctive features such as frequent large jumps and price signals embedded in blockchain data. Key themes include portfolio diversification, factor structure, market microstructure, and the evolving role of regulation and derivatives in shaping market discovery and stability.

ETF Re-balancing and Hedge Fund Front-Running Trades

September 26, 2024

Uninformed long-term investors provide an easy target for short-term traders, and they often unscrupulously take advantage of them. But ETF investors with long investment time horizons can mitigate some of the front-running costs if they take transactional costs into account to calculate whether it is economically optimal to participate in these “market games” (exchange and broker fees + classical opportunity costs of actively participating in strategy execution). Today, we will turn our attention to the paper “ETF Rebalancing, Hedge Fund Trades, and Capital Market” from Wang, Yao, and Yelekenova to better understand complex relationship between ETFs (their investors) and hedge funds.

What’s the Key Factor Behind the Variation in Anomaly Returns?

October 13, 2023

In a game of poker, it is usually said that when you do not know who the patsy is, you’re the patsy. The world of finance is not different. It is good to know who your counterparties are and which investors/traders drive the return of anomalies you focus on. We discussed that a few months ago in a short blog article called “Which Investors Drive Factor Returns?“. Different sets of investors and their approaches drive different anomalies, and we have one more paper that helps uncover the motivation of investors and traders for trading and their impact on anomaly returns.

Exploration of the Arbitrage Co-movement Effect in ETFs

May 23, 2023

We continue our short series of articles dedicated to the exploration of trading strategies that derive their functionality from the deep understanding of how Exchange Trading Funds (ETFs) work. In our first post, we discussed how we could use the ETF flows to predict subsequent daily ETF performance. In today’s article, we will analyze how we can use the information about the sensitivity of individual stocks to the ETF arbitrage activity to build a profitable equity factor trading strategy.

How to Use ETF Flows to Predict Subsequent Daily ETF Performance

February 3, 2023

Exchange-traded funds (ETFs) are incredibly versatile investment vehicles. They have become more popular in recent years as investors have grown more comfortable with passive investing strategies. But ETFs can be very useful also in active trading strategies, as they can be used to gain exposure to specific markets, sectors, or themes. But when you invest in ETFs or trade them regularly; it’s really good to look under the hood and learn some tricks where to obtain a new source of alpha. And one such possible source or information advantage may be the possibility of analyzing the ETF flows data …

A Study on How Algorithmic Traders Earn Money

September 13, 2022

Our mission here at Quantpedia is to provide both retail and institutional investors with ideas for trading strategies that are easily understandable while based on and backed by quantitative academic research. Today, we present you with the results from a study that we came across. Although it’s not quantitative, but qualitative, it has really held our interest. The paper does not provide any images or figures; it is a study made from various types of surveys with answers from professionals concluded with an attention-grabbing summary table.

Novel Market Structure Insights From Intraday Data

November 19, 2020

In recent years, financial markets have experienced a boom in passive and index-based strategies, which could have caused a change in the trading volume, volatility, beta or correlations. The reason is straightforward: the index investing causes a lot of stocks to move in the same direction. A novel research Shen and Shi (2020), using high-frequency data, suggests that over the last two decades, the patterns mentioned above have changed and the index investing is the cause. Both the trading volume and stock correlations are increased at the end of trading sessions. Betas are firstly dispersed, but in general, converge to one during the rest of the day. Trading volume has high dispersion at the market open, but low dispersion at the market close. Overall, the paper has many important implications for portfolio managers, risk managers and traders as well since it is closely related to the transaction costs, intraday price fluctuations, correlations or liquidity. Moreover, it is full of exciting charts that are worth seeing.

Do Floor Traders Matter?

July 4, 2020

The pandemic of COVID-19 brought many changes for the whole humanity. The financial markets where no exception, but the trading has continued. Nowadays, the order can be placed from anywhere around the world and almost all stock exchanges are electronic and algorithmic. However, there is still one exchange where the floor trading exists – NYSE. During these tough times, NYSE was also purely electronic, the floor trading was closed, and human interaction was not possible. A novel study by Brogaard, Ringgenberg and Roesch examines the role of floor traders in the recent era driven by computers. The conclusion is clear: in the current digital age, floor traders still matter.

Authors: Jonathan Brogaard, Matthew C. Ringgenberg, Dominik Roesch

Title: Does Floor Trading Matter?

Did Automated Trading Resurrect the CAPM?

February 28, 2020

Once upon a time, there was everybody’s favourite finance tool in a town – Capital Asset Pricing Model, which was liked and used by nearly everyone. But a few decades ago, it went out of fashion. Easier accessibility of cheap finance databases allowed a lot of researchers to dig deeper into those data. They uncovered a tremendous amount of evidence for a lot of market anomalies not consistent with CAPM. A new research paper written by Park and Wang shows that CAPM is maybe not completely useless. The rise of automated trading causes individual stocks’ returns to align more closely with the market. Intraday correlation in the equity market is rising, and so is the fraction of firms’ returns that are explained by market returns …

Authors: Park, Wang

Title: Did Trading Bots Resurrect the CAPM?