arXiv · arXiv q-fin · 2025
We create a time series model for annual returns of three asset classes: the USA Standard & Poor (S&P) stock index, the international stock index, and the USA Bank of America investment-grade corporate bond index. Using this, we made an online financial app simulating wealth process. This includes options for regular withdrawals and contributions. Four factors are: S&P volatility and earnings, corporate BAA rate, and…
Andrey Sarantsev, Angel Piotrowski, Ian Anderson
arXiv · arXiv q-fin · 2022
We develop a novel technique to extract credit-relevant information from the text of quarterly earnings calls. This information is not spanned by fundamental or market variables and forecasts future credit spread changes. One reason for such forecastability is that our text-based measure predicts future credit spread risk and firm profitability. More firm- and call-level complexity increase the forecasting power of o…
Harry Mamaysky, Yiwen Shen, Hongyu Wu
arXiv · arXiv q-fin · 2024
Earnings release is a key economic event in the financial markets and crucial for predicting stock movements. Earnings data gives a glimpse into how a company is doing financially and can hint at where its stock might go next. However, the irregularity of its release cycle makes it a challenge to incorporate this data in a medium-frequency algorithmic trading model and the usefulness of this data fades fast after it …
Zhengxin Joseph Ye, Bjoern Schuller
arXiv · arXiv q-fin · 2023
Liquidity providers are essential for the function of decentralized exchanges to ensure liquidity takers can be guaranteed a counterparty for their trades. However, liquidity providers investing in liquidity pools face many risks, the most prominent of which is impermanent loss. Currently, analysis of this metric is difficult to conduct due to different market maker algorithms, fee structures and concentrated liquidi…
Rohan Tangri, Peter Yatsyshin, Elisabeth A. Duijnstee, Danilo Mandic
OpenAlex · The Journal of Finance · 1998 · cites 5765
ABSTRACT We propose a theory of securities market under‐ and overreactions based on two well‐known psychological biases: investor overconfidence about the precision of private information; and biased self‐attribution, which causes asymmetric shifts in investors' confidence as a function of their investment outcomes. We show that overconfidence implies negative long‐lag autocorrelations, excess volatility, and, when m…
Kent Daniel, David Hirshleifer, Avanidhar Subrahmanyam
arXiv · arXiv q-fin · 2017
On a daily investment decision in a security market, the price earnings (PE) ratio is one of the most widely applied methods being used as a firm valuation tool by investment experts. Unfortunately, recent academic developments in financial econometrics and machine learning rarely look at this tool. In practice, fundamental PE ratios are often estimated only by subjective expert opinions. The purpose of this research…
Haizhen Wang, Ratthachat Chatpatanasiri, Pairote Sattayatham
arXiv · arXiv q-fin · 2025
Passive liquidity providers (LPs) in automated market makers (AMMs) face losses due to adverse selection (LVR), which static trading fees often fail to offset in practice. We study the key determinants of LP profitability in a dynamic reduced-form model where an AMM operates in parallel with a centralized exchange (CEX), traders route their orders optimally to the venue offering the better price, and arbitrageurs exp…
Steven Campbell, Philippe Bergault, Jason Milionis, Marcel Nutz
arXiv · arXiv q-fin · 2025
We study the problem of optimal liquidity withdrawal for a representative liquidity provider (LP) in an automated market maker (AMM). LPs earn fees from trading activity but are exposed to impermanent loss (IL) due to price fluctuations. While existing work has focused on static provision and exogenous exit strategies, we characterise the optimal exit time as the solution to a stochastic control problem with an endog…
Philippe Bergault, Sébastien Bieber, Leandro Sánchez-Betancourt
arXiv · arXiv q-fin · 2025
An automated market maker (AMM) provides a method for creating a decentralized exchange on the blockchain. For this purpose, individual investors lend liquidity to the AMM pool in exchange for a stream of fees earned from its operations as a market maker. Within this work, we reinterpret the loss-versus-rebalancing as the implied fee stream generated by an AMM so that a risk-neutral investor is indifferent in the dec…
Maxim Bichuch, Zachary Feinstein
arXiv · arXiv q-fin · 2025
Concentrated liquidity automated market makers (AMMs), such as Uniswap v3, enable liquidity providers (LPs) to earn liquidity rewards by depositing tokens into liquidity pools. However, LPs often face significant financial losses driven by poorly selected liquidity provision intervals and high costs associated with frequent liquidity reallocation. To support LPs in achieving more profitable liquidity concentration, w…
Simon Caspar Zeller, Paul-Niklas Ken Kandora, Daniel Kirste, Niclas Kannengießer, Steffen Rebennack
arXiv · arXiv q-fin · 2024
In decentralized finance, any individual can pool their assets into an automated market maker (AMM) -- herein we focus on the constant product market maker (CPMM) -- in exchange for a claim on a fraction of future pool assets and fees earned from the market making operations. This position is represented by a liquidity token, whose prevailing on-chain price is effectively the initial deposited assets. Though this pri…
Maxim Bichuch, Zachary Feinstein
arXiv · arXiv q-fin · 2024
This paper presents the results of a comprehensive empirical study of losses to arbitrageurs (following the formalization of loss-versus-rebalancing by [Milionis et al., 2022]) incurred by liquidity providers on automated market makers (AMMs). We show that those losses exceed the fees earned by liquidity providers across many of the largest AMM liquidity pools (on Uniswap). Remarkably, we also find that the Uniswap v…
Robin Fritsch, Andrea Canidio
arXiv · arXiv q-fin · 2021
Uniswap is a decentralized exchange (DEX) and was first launched on November 2, 2018 on the Ethereum mainnet [1] and is part of an Ecosystem of products in Decentralized Finance (DeFi). It replaces a traditional order book type of trading common on centralized exchanges (CEX) with a deterministic model that swaps currencies (or tokens/assets) along a fixed price function determined by the amount of currencies supplie…
Andreas A. Aigner, Gurvinder Dhaliwal
OpenAlex · The Journal of Financial Research · 2012 · cites 135
Abstract We examine the impact of trading costs on pairs trading profitability in the U.S. equity market, 1963 to 2009. After controlling for commissions, market impact, and short selling fees, pairs trading remains profitable, albeit at much more modest levels. Specifically, we document a risk‐adjusted return of about 30 basis points per month among portfolios of well‐matched pairs that are formed within refined ind…
Binh Do, Robert W. Faff
arXiv · arXiv q-fin · 2022
The stock market offers a platform where people buy and sell shares of publicly listed companies. Generally, stock prices are quite volatile; hence predicting them is a daunting task. There is still much research going to develop more accuracy in stock price prediction. Portfolio construction refers to the allocation of different sector stocks optimally to achieve a maximum return by taking a minimum risk. A good por…
Jaydip Sen, Arpit Awad, Aaditya Raj, Gourav Ray, Pusparna Chakraborty
arXiv · arXiv q-fin · 2021
We propose a novel portfolio trading system, which contains a feature preprocessing module and a trading module. The feature preprocessing module consists of various data processing operations, while in the trading part, we integrate the portfolio weight rebalance function with the trading algorithm and make the trading system fully automated and suitable for individual investors, holding a handful of stocks. The dat…
Lin Li
arXiv · arXiv q-fin · 2019
Unfair stock trading strategies have been shown to be one of the most negative perceptions that customers can have concerning trading and may result in long-term losses for a company. Investment banks usually place trading orders for multiple clients with the same target assets but different order sizes and diverse requirements such as time frame and risk aversion level, thereby total earning and individual earning c…
Wenhang Bao
arXiv · arXiv q-fin · 2014
Although the understanding of and motivation behind individual trading behavior is an important puzzle in finance, little is known about the connection between an investor's portfolio structure and her trading behavior in practice. In this paper, we investigate the relation between what stocks investors hold, and what stocks they buy, and show that investors with similar portfolio structures to a great extent trade i…
Ludvig Bohlin, Martin Rosvall