Algorithmic Finance

Philip Maymin

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We study the effect of latency arbitrage on allocative efficiency and liquidity in fragmented financial markets. We employ a simple model of latency arbitrage in which a single security is traded on two exchanges, with price quotes available to regular traders only after some delay. An infinitely fast arbitrageur reaps profits when the two markets diverge due to this latency in cross-market communication. Using an agent-based approach, we simulate interactions between high-frequency and zero-intelligence trading agents. From simulation data over a large space of strategy combinations, we estimate game models and compute strategic equilibria in a variety of market environments. We then evaluate allocative efficiency and market liquidity in equilibrium, and we find that market fragmentation and the presence of a latency arbitrageur reduces total surplus and negatively impacts liquidity. By replacing continuous-time markets with periodic call markets, we eliminate latency arbitrage opportunities and achieve further efficiency gains through the aggregation of orders over short time periods.

Elaine Wah

Michael P. Wellman

Latency arbitrage in fragmented markets: A strategic agent-based analysis

碎片化市场中的延迟套利：基于策略代理的分析

Determining the causes of instability and contagion in financial networks is necessary to inform policy and avoid future financial collapse.In the American Economic Review, Elliott, Golub and Jackson proposed a simple model for capturing the dynamics of complex financial networks.In Elliott, Golub and Jackson's model, the institutions in the network are connected by linear dependencies (cross-holdings) and if any institution's value drops below a critical threshold, its value suffers an additional failure cost.This work shows that even in this simple model there are fundamental barriers to understanding the risks that are inherent in a network.First, if institutions are not required to maintain a minimum amount of self-holdings, any change in investments by a single institution can have an arbitrarily magnified influence on the net worth of the institutions in the system.This implies that if institutions have small self-holdings, then estimating the market value of an institution requires almost perfect information about every cross-holding in the system.Second, even if a regulator has complete information about all cross-holdings in the system, it may be computationally intractable to estimate the number of failures that could be caused by a small shock to the system.

Brett Hemenway

Sanjeev Khanna

Sensitivity and computational complexity in financial networks

金融网络中的敏感性与计算复杂度

We build the network of the top 190 Italian quoted companies during the two financial crises of 2008-2009 (US credit crisis) and 2010-2011 (European sovereign debt crisis) and compare its structure to the pre-crises years, using both minimum spanning trees and the full network with thresholds.We also analyze the centrality and compactness of industry sectors.We find a general contraction of the network during the crises, both numerically due to stronger correlation as well as topologically, with the appearance of central dominant companies which attract the other ones into a very large cluster, dominated by financial institutions (commercial banks and insurance companies).In particular, we note the role of insurance behemoth Assicurazioni Generali, which rises from a pre-crises subordinate role to become the central company in the minimum spanning tree after the crises period.The few sectors which maintain compactness before and during the crises are utilities, publishing, and construction.

Paolo Coletti

Maurizio Murgia

The network of the Italian stock market during the 2008–2011 financial crises

2008-2011年金融危机期间意大利股市网络

Technical analysis is the art of identifying patterns in historical data with the belief that certain patterns foretell future price movements. An empirical evaluation of the effectiveness of technical analysis is confounded by the subjectivity involved in identifying patterns. This work presents a robust framework for pattern identification using probabilistic neural networks (PNN). The thirty components of the Dow Jones Industrial Average and a set of ten indices are considered. Fourteen patterns are analyzed. In order to test the possibility that technical patterns are more predictable in certain market environments, the period under study (1990 – 2015) is partitioned into bull and bear markets and the statistical significance of profits earned by identified patterns observed in each environment is analyzed. A range of holding periods from 10 to 50 trading days is considered and a simple model of transaction costs is added. The study reveals that no pattern produces statistically and economically significant profits for a cross-section of stocks and indices analyzed, though a few patterns are more successful predictors. Bullish (bearish) patterns are more reliable predictors in bullish (bearish) market environments. These observations can be explained by the Adaptive Market Hypothesis with certain patterns becoming more accurate predictors in specific market environments.

Samit Ahlawat

Empirical evaluation of price-based technical patterns using probabilistic neural networks

基于概率神经网络的价格技术形态实证评估

This paper contributes to the existing stock market anomaly literature by being the first to analyze the benefits of combining two distinct anomalies; specifically, the low-volatility and mean-reversion anomalies. Our results show that on a long-only basis, these two time-varying anomalies could be combined into a double-sort investment strategy that includes some desirable characteristics from each of them, thereby making the portfolio return accumulation more stable over time. As the added-value of low-volatility investing stems mostly from the risk-reduction side, while contrarian stocks are generally highly volatile with remarkable upside potential, the use of the double-sort portfolio-formation in which the contrarian stocks are picked from the sub-set of below-median volatility stocks can shorten the below-market performance periods that have occasionally materialized for plain low-volatility or plain contrarian investors.

Eero Pätäri

Sheraz Ahmed

Tuomas Lankinen

Julian Scott Yeomans

Combining low-volatility and mean-reversion anomalies: Better together?

结合低波动性和均值回归异常现象：1+1>2？

In this paper, a shorter and more publication focused version of our recent article “A Bottom-Up Approach to the financial Markets” (Mahdavi-Damghani, &amp; Roberts, S. 2019.) is presented. More specifically we propose a new approach to studying the financial markets using the Bottom-Up approach instead of the traditional Top-Down. We achieve this shift in perspective, by re-introducing the High Frequency Trading Ecosystem (HFTE) model Mahdavi-Damghani, B. 2017. More specifically we specify an approach in which agents in Neural Network format designed to address the complexity demands of most common financial strategies interact through an Order-Book. We introduce in that context concepts such as the Path of Interaction in order to study our Ecosystem of strategies through time. We show how a Particle Filter methodology can then be used in order to track the market ecosystem through time. Finally, we take this opportunity to explore how to build a realistic market simulator which objective would be to test real market impact without incurring any research costs.

Babak Mahdavi-Damghani

Stephen Roberts

Guidelines for building a realistic algorithmic trading market simulator for backtesting while incorporating market impact

构建包含市场影响的回测算法交易市场模拟器指南

We present an algorithmic trading strategy based upon a graph version of the dynamic mode decomposition (DMD) model. Unlike the traditional DMD model which tries to characterize a stock’s dynamics based on all other stocks in a universe, the proposed model characterizes a stock’s dynamics based only on stocks that are deemed relevant to the stock in question. The relevance between each pair of stocks in a universe is represented as a directed graph and is updated dynamically. The incorporation of a graph model into DMD effects a model reduction that avoids overfitting of data and improves the quality of the trend predictions. We show that, in a practical setting, the precision and recall rate of the proposed model are significantly better than the traditional DMD and the benchmarks. The proposed model yields portfolios that have more stable returns in most of the universes we backtested.

Andy Yip

William Ng

Ka-Wai Siu

Albert C. Cheung

Michael K. Ng

Graph embedded dynamic mode decomposition for stock price prediction

图嵌入动态模式分解用于股票价格预测

We obtain the bond price formula for the fractional Cox-Ingersoll-Ross model. Then we obtain option price formula for the bond. Finally we apply it to derive option price formula in fractional Heston model.

Jaya P.N. Bishwal

Interest rate derivatives for the fractional Cox-Ingersoll-Ross model

分数Cox-Ingersoll-Ross模型的利率衍生品

Smart Beta Investing has revolutionized investment management field with the ability to offer higher returns with lower costs. The momentum factor in the Smart Beta universe often outperforms other popular factors, besides being well documented in the literature, it is found to be pervasive across different geographies and asset classes. In this paper, we implement a long-only momentum based investment strategy for the Indian equity markets that delivers superior risk-adjusted performance, derived upon comparing multiple strategies across time frames. Based on these tests, we find that the lagged 6-months’ compounded returns indicator with quarterly rebalancing can be used to generate the highest risk-adjusted performance.The paper also tests a related phenomenon called the Accelerated Effect of momentum as documented by Ardila et. al. (2021) for the Indian equity market, and finds that the accelerated momentum effect underperforms the traditional momentum both on an absolute and risk-adjusted basis.

Apurv Nigam

Piyush Pandey

How smart is a momentum strategy? An empirical study of Indian equities

动量策略有多聪明？印度股市的实证研究

短名	Algorithmic Finance
Journal Impact	0.27
国际分区	BUSINESS, FINANCE(Q4)

ISSN	2157-6203, 2158-5571
h-index	11

涉及主题	经济业务计算机科学数学财务金融经济学计量经济学统计工程类生物人工智能宏观经济学机器学习地理物理历史哲学考古地质学程序设计语言货币经济学古生物学材料科学机械工程量子力学
出版信息	出版商: IOS Press BV，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2011，原创研究文献占比: 100.00%，自引率:0.00%， Gold OA占比: 0.00%

Algorithmic Finance

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