Authors: Mahdavi-Damghani, Babak | Roberts, Stephen

Article Type: Research Article

Abstract: In this paper, a shorter and more publication focused version of our recent article “A Bottom-Up Approach to the financial Markets” (Mahdavi-Damghani, & 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. Show more

Keywords: Generative adversarial networks (GANs), high frequency trading ecosystem (HFTE), high frequency financialfunnel (HFFF), multi-target tracking (MTT), stability of financial systems, markov chain monte carlo (MCMC), data analysis and patterns in data, electronic trading, systemic risk, high frequency trading, game theory, machine learning, predator prey models, sequential monte carlo, particle filter

DOI: 10.3233/AF-220356

Citation: Algorithmic Finance, vol. Pre-press, no. Pre-press, pp. 1-25, 2023

Authors: Pätäri, Eero | Ahmed, Sheraz | Lankinen, Tuomas | Yeomans, Julian Scott

Article Type: Research Article

Abstract: 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. Show more

Keywords: Volatility, contrarian, mean reversion, anomaly, portfolio performance, equity investing

DOI: 10.3233/AF-220441

Citation: Algorithmic Finance, vol. Pre-press, no. Pre-press, pp. 1-24, 2023