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Low Frequency Algorithmic Trading
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Low Frequency Algorithmic Trading

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematics and Finance".

Deadline for manuscript submissions: closed (1 May 2024) | Viewed by 6090

Special Issue Editor

Dr. Pankaj Topiwala

E-Mail Website
Guest Editor
FastVDO LLC, 3097 Cortona Dr., Melbourne, FL 32940, USA
Interests: investment; trading; algorithm trading; market timing; index trading

Special Issue Information

Dear Colleagues,

Algorithmic trading accounts for over 80% (and growing) of all trades and over 90% of trades by professionals. Much of this work is focused on short-term trading (down to microseconds), also called high-frequency trading, suitable for machine trading by professionals. These algorithms often take advantage of arbitrage opportunities that exist only on very short time scales. Little attention is paid to low-frequency trading—trading that is based on minutes, hours, and days. Such trading methods can be suitable for very long-term performance (e.g., over decades) and can apply to both professionals and small investors alike. This Special Issue aims to remedy this gap, as the majority of those who invest are in fact small investors who seek long-term performance. This puts severe constraints on the complexity and frequency of trades; we suggest limiting trades to once per day and on average less than once per week. Importantly, performance measures specifically suitable to this application space are encouraged as standard measures such as the Sharpe ratio may not be as suitable. 

Dr. Pankaj Topiwala
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • algorithmic trading suitable for small investors
  • index and stock (and bond) trading at relaxed pace
  • long-term investment performance
  • novel investment performance measures
  • quantitative analysis of performance

Published Papers (4 papers)

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Research

18 pages, 2319 KiB  
Article
Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator and William%R: A Case Study on the U.S. and Korean Indices
by Chan Kyu Paik, Jinhee Choi and Ivan Ureta Vaquero
J. Risk Financial Manag. 2024, 17(3), 92; https://doi.org/10.3390/jrfm17030092 - 20 Feb 2024
Viewed by 1157
Abstract
Using stochastics in stock market analysis is widely accepted for index estimation and ultra-high-frequency trading. However, previous studies linking index estimation to actual trading without applying low-frequency trading are limited. This study applied William%R to the existing research and used fixed parameters to [...] Read more.
Using stochastics in stock market analysis is widely accepted for index estimation and ultra-high-frequency trading. However, previous studies linking index estimation to actual trading without applying low-frequency trading are limited. This study applied William%R to the existing research and used fixed parameters to remove noise from stochastics. We propose contributing to stock market stakeholders by finding an easy-to-apply algorithmic trading methodology for individual and pension fund investors. The algorithm constructed two oscillators with fixed parameters to identify when to enter and exit the index and achieved good results against the benchmark. We tested two ETFs, SPY (S&P 500) and EWY (MSCI Korea), from 2010 to 2022. Over the 12-year study period, our model showed it can outperform the benchmark index, having a high hit ratio of over 80%, a maximum drawdown in the low single digits, and a trading frequency of 1.5 trades per year. The results of our empirical research show that this methodology simplifies the process for investors to effectively implement market timing strategies in their investment decisions. Full article
(This article belongs to the Special Issue Low Frequency Algorithmic Trading)
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27 pages, 3402 KiB  
Article
Optimal and Non-Optimal MACD Parameter Values and Their Ranges for Stock-Index Futures: A Comparative Study of Nikkei, Dow Jones, and Nasdaq
by Byung-Kook Kang
J. Risk Financial Manag. 2023, 16(12), 508; https://doi.org/10.3390/jrfm16120508 - 07 Dec 2023
Viewed by 1799
Abstract
This study investigates the optimal and non-optimal parameter values of the MACD (Moving Average Convergence Divergence) technical analysis indicator for three major stock market index futures: the Nikkei 225, the Dow Jones, and the Nasdaq. Using a recently developed methodology, it reveals the [...] Read more.
This study investigates the optimal and non-optimal parameter values of the MACD (Moving Average Convergence Divergence) technical analysis indicator for three major stock market index futures: the Nikkei 225, the Dow Jones, and the Nasdaq. Using a recently developed methodology, it reveals the existence of specific ranges of optimal and non-optimal values for each of the three parameters of the MACD indicator in these indices. Sample models employing the optimal parameter values in the three index futures generated significantly higher returns, outperforming both a non-technical buy-and-hold strategy and a random strategy that did not incorporate any market information. This discovery suggests that the three market indices may not be weak-form efficient. Therefore, this study contributes to the research on market efficiency by verifying inefficiency using a new approach. The highlight of this study is identifying that the ranges of optimal parameter values for the three indices are different from each other, but the optimal parameter value combinations for each of the three indices share a unique characteristic form. This issue and its finding have not been explored in the existing literature. Several interesting findings and valuable insights for market participants and researchers arise from this study. The new methodology is unique in finding optimal and non-optimal parameter values through the analysis of parameter sets used in well-performing and poorly performing sample models. Its validity and reliability have been confirmed by this study, making a useful contribution to the field of technical analysis research, particularly in parameter optimization insight. Full article
(This article belongs to the Special Issue Low Frequency Algorithmic Trading)
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18 pages, 2649 KiB  
Article
Surviving Black Swans III: Timing US Sector Funds
by Pankaj Topiwala
J. Risk Financial Manag. 2023, 16(5), 275; https://doi.org/10.3390/jrfm16050275 - 17 May 2023
Cited by 1 | Viewed by 1074
Abstract
The typical small investor makes on average about 5% a year in investment gains, just half of what the market does. Moreover, most investment funds also underperform compared to the broader market. In two previous papers, we explored how a specific and simple [...] Read more.
The typical small investor makes on average about 5% a year in investment gains, just half of what the market does. Moreover, most investment funds also underperform compared to the broader market. In two previous papers, we explored how a specific and simple approach to algorithmic trading can help both types of investors achieve strong results. For concreteness, we focused attention on investing in a single variable, in our case, a major US-based index such as SPX and IXIC, individually. For illustrative purposes, we also considered some highly traded tech stock examples. In this paper, we extend our work to study the US sector funds, and for the first time in our series, we also consider trading multiple variables at a time to see how that may differ from our single-variable investment strategy. To simplify matters, we consider an initial equal weighted portfolio of several sector funds, selected randomly without any analysis, and assume that each is traded independently. To simplify further, we do no rebalancing in our study, though that is an essential part of money management according to modern portfolio theory. We nevertheless obtain interesting and informative results. We can typically improve on the performance of most sector funds compared to buy-and-hold (hereafter referred to as BnH). Moreover, as an example of portfolio growth, a portfolio of five equal weighted sector funds in BnH achieves 6.5× growth over 20 years (ending in March 2023), whereas our approach achieves 12.4× growth—nearly 2× better, at roughly half the maximum drawdown. That is a strong win for both professional and home investors. Full article
(This article belongs to the Special Issue Low Frequency Algorithmic Trading)
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26 pages, 3532 KiB  
Article
Surviving Black Swans II: Timing the 2020–2022 Roller Coaster
by Pankaj Topiwala
J. Risk Financial Manag. 2023, 16(2), 106; https://doi.org/10.3390/jrfm16020106 - 09 Feb 2023
Cited by 2 | Viewed by 1374
Abstract
Unbeknownst to the public, most investment funds actually underperform the broader market. Yet, millions of individual investors fare even worse, barely treading water. Algorithmic trading now accounts for over 80% of all trades and is the domain of professionals. Can it also help [...] Read more.
Unbeknownst to the public, most investment funds actually underperform the broader market. Yet, millions of individual investors fare even worse, barely treading water. Algorithmic trading now accounts for over 80% of all trades and is the domain of professionals. Can it also help the small investor? In a previous paper, we laid the foundations of a simple algorithmic market timing approach based on the moving average crossover concept which can indeed both outperform the broader market and reduce drawdowns, in a way that even the retail investor can benefit. In this paper, we extend our work to study the recent volatile time period, 2020–2022, and especially the unexpected market roller coaster of 2022, to see how our ideas hold up. While our methods overall would not have made gains in 2022, they would have suffered lessor drawdowns than the market, and made consistent gains over longer periods, including the volatile 2020–2022 period. In addition, at least one of our algorithms would have made handsome profits even in 2022, and can generally negotiate black swans well. Full article
(This article belongs to the Special Issue Low Frequency Algorithmic Trading)
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