Systematic Trading Research Articles

An announcement effect in reverse? Evidence from cash‐settled convertible bonds

AbstractWe analyze the trading activity of underlying shares at the maturity of convertible bonds with cash‐settlement provisions. Our findings indicate that arbitrageurs and option dealers engage in systematic trading that leads to an increase in trading volumes and changes in the level of short interest in the affected underlying equities. We find significantly positive abnormal returns for the affected stocks if convertible bond‐related trading is not contaminated by opposing option‐related flows. This finding supports the notion that arbitrageurs can create a substantial market impact on shares underlying convertible bonds even without any relevant news dissemination.

Enhancing Sustainable Finance through Green Hydrogen Equity Investments: A Multifaceted Risk-Return Analysis

Amidst the global push for decarbonization, green hydrogen has gained recognition as a versatile and clean energy carrier, prompting the financial sector to introduce specialized investment instruments like Green Hydrogen Exchange-Traded Funds (ETFs). Despite the nascent nature of research on green hydrogen portfolio performance, this study examines two key green hydrogen ETFs (i.e., HJEN and HDRO) from April 2021–May 2023, aiming at conducting a multifaceted exploration of their performance, isolating and measuring their sensitivity to the primary market factor, and assessing the capabilities of systematic trading strategies to preserve capital and minimize losses during market downturns. The results spotlight lower returns and higher risks in green hydrogen investments compared to conventional equity (proxied by ETFs offering exposure to developed markets—EFA and emerging markets—EEM) and green energy portfolios (proxied by the ETF ICLN). To comprehensively evaluate performance, an array of risk-adjusted metrics, including Std Sharpe, ES Sharpe, VaR Sharpe, Information ratio, Sortino ratio, Treynor ratio, and various downside risk metrics (historical VaR, modified VaR, Expected Shortfall, loss deviation, downside deviation, and maximum drawdown) are employed, offering a nuanced understanding of the investment landscape. Moreover, single-factor models highlight significant systematic market risk, reflected in notably high beta coefficients, negative alphas, and active premia, underscoring the sensitivity of green hydrogen investments to market fluctuations. Despite these challenges, a silver lining emerges as the study demonstrates the efficacy of implementing straightforward Dual Moving Average Crossover (DMAC) trading strategies. These strategies significantly enhance the risk-return profile of green hydrogen portfolios, offering investors a pathway to align financial and social objectives within their equity portfolios. This research is motivated by the need to provide market players, policymakers, and stakeholders with valuable insights into the benefits and risks associated with green hydrogen investment, considering its potential to reshape the global energy landscape.

An overview of technical analysis in systematic trading strategies returns and a novel systematic strategy yielding positive significant returns

This paper contributes to the literature on systematic trading strategies, in particular technical analysis profitability. We measure the profitability and forecasting power of a trend following strategy implemented in Python on a wide perimeter (205 European stocks, 11 industries, 7 major stock exchanges) over 8 years: from 2015 to 2022. The strategy signal is based on 4 moving averages and a trailing stop loss. We also introduce a mechanism based on trailing upper and lower price bounds to avoid false signals and limit transaction costs during lateral movements. We calibrate the iper-parameters to all stocks belonging to the same industry. The returns of the strategy applied to the constituents of the top performing industries provides a total return of 20% net of transaction costs, with an annualized Sharpe ratio of 0.54, in the out of sample time window from 2020 to 2022.

Are polluters shunned? A study on the institutional ownership and returns of polluter stocks

AbstractWe hypothesise that societal norms cause institutional investors to discriminate against polluters. Consistent with this hypothesis, we document relatively lower institutional ownership for the most polluting firms in the United States. Institutional discrimination against polluters increases over 1987–2018, in line with increasing environmental awareness. Institutions are primarily averse to investments in dioxin emitters. Institutions that prioritize portfolio performance are less sensitive to societal norms against polluters, as are institutions with shorter investment horizons and more aggressive trading strategies. Lastly, we test whether investor discrimination affects polluter equity valuations and creates systematic trading opportunities, but find no convincing evidence of a polluter return premium.

Workhorse or Trojan Horse? The Alternative Risk Premium Conundrum in Multi-Asset Portfolios

Diversified alternative risk premium (ARP) portfolios seek to generate absolute returns using a broad range of systematic trading strategies incorporating multiple investment styles covering all the major asset classes. Against a backdrop of low developed market bond yields and fully valued equities, ARP offered a reasonably priced combination of low correlation with traditional asset classes, attractive expected Sharpe ratio, and reasonable liquidity. Following a period of rapid adoption, disappointing performance over the 2018–2020 period has produced considerable soul searching regarding the role of ARP in institutional portfolios. Should these strategies remain a candidate for multi-asset portfolios, or is the experience of the past years a death knell regarding their usefulness? To examine this very topical issue, in this article, the authors use a unique array of benchmarks leveraging a proprietary database of 2,000 tradable bank indexes. They evaluate whether recent returns are consistent with long-term expectations. In the process, they consider the extent to which unique environmental headwinds and a lack of true breadth across ARP strategies contributed to this outcome.

The Psychological Underpinnings of Why Systematic Trading is Superior to Discretionary Trading

The Psychological Underpinnings of Why Systematic Trading is Superior to Discretionary Trading

Cryptocurrency Sectorization through Clustering and Web-Scraping: Application to Systematic Trading

A prospective study was undertaken to identify clinical, radiographical, haematological and biochemical profiles of severe acute respiratory syndrome (SARS) patients. A prediction rule, which demarcates low from high risk patients for SARS in an outbreak situation was developed. A total of 295 patients with unexplained respiratory illnesses, admitted to Queen Mary Hospital, Hong Kong SAR, China, in March to July 2003, were evaluated for clinical, radiological, haematological and alanine transaminase (ALT) data daily for 3 days after hospitalisation. In total, 44 cases were subsequently confirmed to have SARS by RT-PCR (68.2%) and serology (100%). The scoring system of attributing 11, 10, 3, 3 and 3 points to the presence of independent risk factors, namely: epidemiological link, radiographical deterioration, myalgia, lymphopenia and elevated ALT respectively, generated high and low-risk (total score 11–30 and 0–10, respectively) groups for SARS. The sensitivity and specificity of this prediction rule in positively identifying a SARS patient were 97.7 and 81.3%, respectively. The positive and negative predictive values were 47.8 and 99.5%, respectively. The prediction rule appears to be helpful in assessing suspected patients with severe acute respiratory syndrome at the bedside, and should be further validated in other severe acute respiratory syndrome cohorts.

A market scoring mechanism for trading of German electricity futures

We present a novel systematic commodity trading model utilizing a time series momentum strategy. The main innovation is a scoring mechanism to generate buy and sell signals, including determining the position size. The model is applied to the German electricity futures market and its price drivers: the momentum and volatility of the German front quarter, the Argus/McCloskey’s Coal Price Index (API 2) coal and emissions. We test the systematic model on data from November 2010 to December 2019 for several strategies. The model outperforms a basic short sale model. The application of the model yields improved risk-adjusted returns. This paper provides, to the best of our knowledge, the first description of a systematic trading approach to German electricity futures.

Pricing and Systematic Trading of Municipal Bonds

In this article, the authors propose a systematic approach for pricing and trading municipal bonds, leveraging the feature-rich information available at the individual bond level. Based on the proposed pricing framework, they estimate several models using ridge regression and Kalman filtering. In their empirical work, they show that the models compare favorably in pricing accuracy to those available in the literature. Additionally, the models are able to quickly adapt to changing market conditions. Incorporating the pricing models into relative value trading strategies, the authors demonstrate that the resulting portfolios generate significant excess returns and positive alpha relative to the Vanguard Long-Term Tax-Exempt Fund (VWLTX), one of the largest mutual funds in the municipal space.

Designing All-Weather Overlays — A Study on Option-Based Systematic Strategies

We perform an empirical analysis of systematic trading strategies on options. Namely, we focus on strategies which sell out of the money (OTM) call options to harvest the premium, and buy downside protection through OTM puts. We compare the risk adjusted performance across different choices of strike, maturity and option notional. In this paper we mostly focus on the S&P 500 index over the period 2007–2018. There is also a brief look of the performances of the best strategies during the COVID-19 pandemic in early 2020.

Forecast Optimization via Parameter Tuning: Performance Gain and Overfit

Most predictors used in systematic trading are designed as functions of available data streams. The functional form of these signals involves tuning a number of parameters such as the trading frequency, sample sizes, entry and exit thresholds, stop losses, and so on to improve the performance of the predictor. It is well known that such tuning leads to overfitting, meaning that the constructed signal fits historical returns well but may poorly predict their future behavior. One of the central questions that has not been previously addressed is how much each fitting parameter contributes to the overall performance improvement. In this article, this question is addressed both quantitatively and qualitatively. The author concentrates on a simple but very common in practice fitting technique consisting of smoothing the signal using the weighted moving average. It is shown that the improvement of the Sharpe ratio mainly depends on the signal autocorrelation and its alpha decay parameter. The findings are confirmed by numerical simulations. This result sheds more light on the overfitting phenomenon in the context of investment strategy construction and reveals the sources of performance improvement in forecast design. TOPICS:Big data/machine learning, portfolio construction, simulations, statistical methods Key Findings • Fitting the parameters of return predictors on historical data unavoidably leads to statistical overfitting, which may result in partial or total failure of the signal in the out-of-sample period. • This article introduces a novel framework capable of precisely estimating the level of overfitting or performance gain arising from optimization over every parameter. • An illustration of the power of the new methodology is provided using the ubiquitous example of exponential moving average smoothing of the forecast.

Generative adversarial networks for financial trading strategies fine-tuning and combination

Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environment, an analyst needs to appropriately fine-tune their strategy, or discover how to combine weak signals in novel alpha creating manners. Both aspects, namely fine-tuning and combination, have been extensively researched using several methods, but emerging techniques such as Generative Adversarial Networks can have an impact on such aspects. Therefore, our work proposes the use of Conditional Generative Adversarial Networks (cGANs) for trading strategy calibration and aggregation. To this end, we provide a full methodology on: (i) the training and selection of a cGAN for time series data; (ii) how each sample is used for strategy calibration; and (iii) how all generated samples can be used for ensemble modelling. To provide evidence that our approach is well grounded, we have designed an experiment with multiple trading strategies, encompassing 579 assets. We compared cGAN with an ensemble scheme and model validation methods, both suited for time series. Our results suggest that cGANs are a suitable alternative for strategy calibration and combination, providing outperformance when the traditional techniques fail to generate any alpha.

A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing

The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with smart beta strategies emerging as a trend among institutional investors. Whilst they perform well in the long run, these strategies often suffer from severe short-term drawdown (peak-to-trough decline) with fluctuating performance across cycles. To manage short term risk (cyclicality and underperformance), we build a dynamic asset allocation system using Hidden Markov Models (HMMs). We use a variety of portfolio construction techniques to test our smart beta strategies and the resulting portfolios show an improvement in risk-adjusted returns, especially on more return-oriented portfolios (up to 50% of return in excess of market adjusted by relative risk annually). In addition, we propose a novel smart beta allocation system based on the Feature Saliency HMM (FSHMM) algorithm that performs feature selection simultaneously with the training of the HMM, to improve regime identification. We evaluate our systematic trading system with real life assets using MSCI indices; further, the results (up to 60% of return in excess of market adjusted by relative risk annually) show model performance improvement with respect to portfolios built using full feature HMMs.

Why Commonality Persists?

Studies on commonality in returns, order flows and liquidity find that the first principal component is closely aligned with the market factor. With the increasing presence of high-frequency trading, commonality in returns, order flows, and liquidity can potentially arise from the commonality in the interpretation of real-time signals. In this paper, we go beyond the first factor and show that the other dominant principal components consistently reflects investors' herding behavior, demonstrating the multi-dimensional aspect of commonality. Instead of relating the asset returns to order flows, we take both as endogenous, and provide empirical evidence showing that returns commonality is driven by investors' attention, while order flows commonality is driven by investors' sentiment. We also present a comprehensive longitudinal study of commonality and co-movement over a period in excess of two decades under a unifying market microstructure framework to demonstrate the persistence of commonality over time. Our results not only extend the knowledge about cross-sectional asset behaviors, but can also be used to develop systematic trading strategies.

An Introduction to Algorithmic Trading: Opportunities & Challenges within the Systematic Trading Industry

In what follows we present systematic trading and discuss the benefits. We evaluate contemporary trends, the opportunities arising from machine learning and the operational cost challenges faced, leveraging on the history of the industry to demonstrate why maintaining a competitive edge is paramount to its continued success.

Avoiding Backtesting Overfitting by Covariance-Penalties: An Empirical Investigation of the Ordinary and Total Least Squares Cases

Systematic trading strategies are rule-based procedures that choose portfolios and allocate assets. To attain certain desired return profiles, quantitative strategists must determine a large array of trading parameters. Backtesting, the attempt to identify the appropriate parameters using historical data available, has been highly criticized because of its abundance of misleading results. Hence, there is increasing interest in devising procedures for the assessment and comparison of strategies (i.e., devising schemes for preventing what is known as backtesting overfitting). So far, many financial researchers have proposed different ways to tackle this problem that can be broadly categorized into three types: data snooping, overestimated performance, and cross-validation evaluation. In this article, the authors propose a new approach to dealing with financial overfitting, a covariance-penalty correction, in which a risk metric is lowered given the number of parameters and amount of data used to underpin a trading strategy. They outline the foundation and main results behind the covariance-penalty correction for trading strategies. After that, the authors pursue an empirical investigation and compare its performance with some other approaches in the realm of covariance-penalties across more than 1,300 assets, using ordinary and total least squares. Their results suggest that covariance-penalties are a suitable procedure to avoid backtesting overfitting, and total least squares provides superior performance when compared to ordinary least squares. TOPICS:Statistical methods, simulations, big data/machine learning Key Findings • A literature review of backtest overfitting, putting in perspective the different approaches available. • A new Covariance-Penalty formula to correct Sharpe ratios based on the number of parameters in a model. • An empirical investigation across 1300 assets, using ordinary and total least squares, comparing our technique with others from the Covariance-Penalty literature.

Internet finance investor sentiment and return comovement

Since more and more investors are affected by the Internet finance, this paper focuses on examining whether the systematic trading among investors would lead to stock return comovements beyond the usual risk factors. Internet finance investor sentiment index (IFIS) measures the sentiment related to Internet finance investors' behaviors, which is found to be a systematic factor of stock market returns. To clarify the influence mechanism of IFIS, two groups of portfolios are constructed. First, stocks are sorted into three portfolios according to their degrees of relevance to Internet financial products. IFIS has more significant impact on stocks of firms closely linked to Internet financial products. Second, the role of IFIS on return comovements is further examined using size portfolios. Interestingly, IFIS has significant incremental explanatory power, beyond Fama and French (2015) five factors, on return comovements for stocks with larger market capitalization. This phenomenon is contradictory to the findings by using existing stock investor sentiment in the literature. Our findings have strong implications for research on Internet finance and stock return comovements.

Зовнішній штампований орнамент на аттичній чорнолаковій кераміці з Ольвії

Штампований і врізний орнамент належить до найбільш поширених методів орнаментації чорнолакової кераміки. Ці методи орнаментації використовували з середини V ст. до н.е. до елліністичного періоду, техніка поширилася від Афін до Італії. Штампами могли прикрашати посуд як всередині, так і ззовні. Зовнішні штампи зустрічаються набагато рідше, ніж внутрішні.
 Аналіз чорнолакового посуду з Ольвії показав, що лише декілька відсотків штампованого посуду мають штампи по зовнішній стороні. Проте матеріал репрезентує всі зміни у морфології й орнаментації, що характерні класичних екземплярів. Це ще раз доводить тісні та системні торгівельні зв’язки Ольвійського полісу з Афінами та її округою у класичний період.

Resolution-elastic neutron scattering by correlation techniques

Neutron scattering applications often require discriminating the elastic contribution from the inelastic contribution. For this purpose, correlation spectroscopy offers an effective tool with both pulsed and continuous neutron sources as well as several advantages: the analysis of the neutron velocity distribution can be carried out with a duty factor of 50%, independently on the resolution value; the best statistical accuracy for spectra where the elastic part encompasses most of the integrated intensity is provided. Depending on the statistical chopper position, correlation analysis can be used for both incoming and outgoing neutron velocity determination. Moreover, the correlation technique is very profitable for investigating weak signals in the presence of high background, which is often the case for small samples. To provide instrument flexibility and versatility, an innovative approach comprising tuning resolution by variable Resolution-Elastic Neutron Scattering (RENS) is proposed, offering further benefits by enabling systematic trading of intensity for resolution and vice versa. This study puts into evidence the advantages offered by the use of statistical chopper and of correlation technique for RENS in choosing the best compromise between resolution and beam intensity.

Evaluating the Building Blocks of a Dynamically Adaptive Systematic Trading Strategy

Financial markets change their behaviours abruptly. The mean, variance and correlation patterns of stocks can vary dramatically, triggered by fundamental changes in macroeconomic variables, policies or regulations. A trader needs to adapt her trading style to make the best out of the different phases in the stock markets. Similarly, an investor might want to invest in different asset classes in different market regimes for a stable risk adjusted return profile. Here, we explore the use of State Switching Markov Autoregressive models for identifying and predicting different market regimes loosely modeled on the Wyckoff Price Regimes of accumulation, distribution, advance and decline. We explore the behaviour of various asset classes and market sectors in the identified regimes. We look at the trading strategies like trend following, range trading, retracement trading and breakout trading in the given market regimes and tailor them for the specific regimes. We tie together the best trading strategy and asset allocation for the identified market regimes to come up with a robust dynamically adaptive trading system to outperform simple traditional alphas.