Algorithmic Trading Systems Based on Google Trends

[EN] In this paper we analyze five big data algorithmic trading systems based on artificial intelligence models that uses as predictors stats from Google Trends of dozens of financial terms. The systems were trained using monthly data from 2004 to 2017 and have been tested in a prospective way from January 2017 to February 2018. The performance of this systems shows that Google Trends is a good metric for global Investors’ Mood. Systems for Ibex and Eurostoxx are not profitable but Dow Jones, S&P 500 and Nasdaq systems has been profitable using long and short positions during the period studied. This evidence opens a new field for the investigation of trading systems based on big data instead of Chartism.Gómez Martínez, R.; Prado Román, C.; De La Orden De La Cruz, MDC. (2018). Algorithmic Trading Systems Based on Google Trends. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 11-18. https://doi.org/10.4995/CARMA2018.2018.8295OCS111

Market efficiency analysis using AI models based on Investors’ Mood

The Efficient Market Hypothesis assumes that stock prices in financial markets incorporate all the historical information in any of its forms (weak, semi-strong and strong). The aim of this study is to validate this hypothesis.This study uses artificial intelligence models designed to predict IBEX trends, based on investor mood, extracting information from the big data and using natural language processing algorithms. The results of the study show that the success rate of a system that trains for only 6 months is higher than a system that uses all the available historical information. Investment strategies can also be based on the forecasts of the artificial intelligence models, which can beat the market, by setting up different trading systems for different degrees of risk, depending on the probability threshold provided by the model considered. These results imply that the Spanish financial market has a short-term memory, and does not include older information and therefore does not fulfill the efficient market hypothesis assumptions.KEY WORDS Big data, IBEX, Bayesian Networks, investors’ mood, trading systems,market efficiency.La hipótesis del mercado eficiente asume que los precios de las acciones en los mercados financieros incorporan toda la información histórica en cualquiera de sus formas (débil, semifuerte y fuerte). El objetivo de este estudio es validar esta hipótesis. Este estudio utiliza modelos de inteligencia artificial diseñados para predecir las tendencias del IBEX con base en el estado de ánimo de los inversores, extrayendo información del big data y utilizando algoritmos de procesamiento del lenguaje natural. Los resultados del estudio muestran que la tasa de éxito de un sistema que se prepara para solo 6 meses es mayor que la de un sistema que utiliza toda la información histórica disponible. Las estrategias de inversión también pueden basarse en las previsiones de los modelos de inteligencia artificial, que pueden superar el mercado, estableciendo diferentes sistemas de negociación para distintos grados de riesgo en función del umbral de probabilidad que proporcione el modelo considerado. Estos resultados implican que el mercado financiero español tiene una memoria de corto plazo y no incluye información más antigua, por lo que no cumple los supuestos de la hipótesis de mercado eficiente

Big Data Analytics and Its Applications in Supply Chain Management

In today’s competitive marketplace, development of information technology, rising customer expectations, economic globalization, and the other modern competitive priorities have forced organizations to change. Therefore, competition among enterprises is replaced by competition among enterprises and their supply chains. In current competitive environment, supply chain professionals are struggling in handling the huge data in order to reach integrated, efficient, effective, and agile supply chain. Hence, explosive growth in volume and different types of data throughout the supply chain has created the need to develop technologies that can intelligently and rapidly analyze large volume of data. Big data analytics capability (BDA) is one of the best techniques, which can help organizations to overcome their problem. BDA provides a tool for extracting valuable patterns and information in large volume of data. So, the main purpose of this book chapter is to explore the application of BDA in supply chain management (SCM)

Evaluating VPIN as a trigger for single-stock circuit breakers

We study if VPIN (Easley et al., 2012a) is an efficient advance indicator of toxicity-induced liquidity crises and related sharp price movements. We find that high VPIN readings rarely signal abnormal illiquidity, and very occasionally anticipate large intraday price changes leading to actual trading halts. We find significant differences in illiquidity and price impact between VPIN-identified toxic and non-toxic halts, but they tend to vanish when we control for ex ante realized volatility. We conclude that the capacity of VPIN to anticipate truly toxic events is limited.This work was supported by the Spanish DGICYT projects ECO2010-18567, ECO2011-29751, ECO2013-4409-P, and ECO2014-58434-P. Roberto Pascual also acknowledges the financial support of Fundación BBVA

Analysis of the Tick Rule and Bulk Volume Classification algorithms in the Brazilian stock market

This study aimed to compare the performance of Tick Rule (TR) and Bulk Volume Classification (BVC) models in classifying assets traded on the Brazilian stock exchange (B3) and indicate which one performs better as an investment decision tool. The assets were split into three groups based on their volume, and actual data was used to assess the accuracy of both algorithms. Data from 2018 was used to estimate the parameters that best fit BVC, and transactions from 2019 were used to test the algorithm’s efficiency. Afterward, the Volume-Synchronized Probability of Informed Trading (VPIN) was calculated for each asset using TR and BVC, and the values obtained were compared against VPIN calculated using real data. In conclusion, the TR algorithm shows betters performance than BVC for all three groups of assets. Analysis of the properties of both methods reveals that the base upon which the TR is built holds up in the Brazilian market, whereas BVC mechanics does not reflect the observed reality

Análise dos Algoritmos Tick Rule e Bulk Volume Classification no Mercado Acionário Brasileiro

This study aimed to compare the performance of Tick Rule (TR) and Bulk Volume Classification (BVC) models in classifying assets traded on the Brazilian stock exchange (B3) and indicate which one performs better as an investment decision tool. The assets were split into three groups based on their volume, and actual data was used to assess the accuracy of both algorithms. Data from 2018 was used to estimate the parameters that best fit BVC, and transactions from 2019 were used to test the algorithm’s efficiency. Afterward, the Volume-Synchronized Probability of Informed Trading (VPIN) was calculated for each asset using TR and BVC, and the values obtained were compared against VPIN calculated using real data. In conclusion, the TR algorithm shows betters performance than BVC for all three groups of assets. Analysis of the properties of both methods reveals that the base upon which the TR is built holds up in the Brazilian market, whereas BVC mechanics does not reflect the observed realityO objetivo deste artigo foi comparar o desempenho dos algoritmos Tick Rule (TR) e Bulk Volume Classification (BVC) na classificação de transações de ações negociadas na B3 e, assim, indicar o melhor método como ferramenta de auxílio às decisões de investimento. Os ativos foram separados em três grupos conforme o volume transacionado. Os dados reais foram utilizados para verificar a acurácia dos algoritmos, sendo as informações de 2018 utilizadas para estimar os melhores parâmetros do BVC, e as de 2019, para testar a sua eficiência. Posteriormente, foi calculado o Volume-Synchronized Probability of Informed Trading (VPIN) para cada ação utilizando o TR e o BVC. Esses valores foram comparados com o VPIN apurado com os dados reais. Observou-se que o TR apresenta melhor performance em relação ao BVC para todos os três grupos de ações. As análises das propriedades dos métodos revelaram que a base na qual o TR está calcado se sustenta no mercado brasileiro, enquanto a mecânica do BVC não reflete a realidade

New Trends in the Use of Artificial Intelligence for the Industry 4.0

Industry 4.0 is based on the cyber-physical transformation of processes, systems and methods applied in the manufacturing sector, and on its autonomous and decentralized operation. Industry 4.0 reflects that the industrial world is at the beginning of the so-called Fourth Industrial Revolution, characterized by a massive interconnection of assets and the integration of human operators with the manufacturing environment. In this regard, data analytics and, specifically, the artificial intelligence is the vehicular technology towards the next generation of smart factories.Chapters in this book cover a diversity of current and new developments in the use of artificial intelligence on the industrial sector seen from the fourth industrial revolution point of view, namely, cyber-physical applications, artificial intelligence technologies and tools, Industrial Internet of Things and data analytics. This book contains high-quality chapters containing original research results and literature review of exceptional merit. Thus, it is in the aim of the book to contribute to the literature of the topic in this regard and let the readers know current and new trends in the use of artificial intelligence for the Industry 4.0

Relating Volatility and Jumps between two markets under Directional Change

Directional change (DC) is a new concept in sampling financial market data. Instead of recording the transaction prices at fixed time intervals, as is done in time series, DC lets the data alone decide when to record a transaction. In DC, a data point is recorded when the price has risen or dropped against the current trend by a significant percentage, which is known as the threshold. The magnitude of the threshold is determined by the analyst. Previous studies on DC mainly focus on analysing single price sequences of one market. This thesis focuses on a new path; working on the DC comparative analysis between two markets. We propose a novel data-driven approach to combine the observed DC series of two markets into a single data sequence, which we call the DC combined sequence. This allows us to conduct a comparative analysis between two markets under DC. Based on this approach, we propose a novel indicator that measures the relative volatility between two markets. In addition, we define jumps under DC. Under this measure, we can pinpoint the size, direction, and quantity of DC jumps in a market. Lastly, under the DC comparative analysis, we build a new DC approach to identify co-jumps between two markets

Three essays on the transformative role of technology in financial markets

Financial markets are vital for capital allocation and as a consequence, for the wider economy. They perform two primary functions: liquidity and price discovery. Liquidity refers to the ability to trade large quantities of an instrument quickly, and with relatively little price impact. Therefore, it offers investors the flexibility to make investment decisions. Price discovery encompasses the price formation process in financial markets and is, therefore, critical for efficient capital allocation. Both these functions are linked to the functioning of the wider economy. Over the last decade, financial markets have been transformed with the help of technology and are now a completely different proposition. Specifically, technological advancements, such as high frequency trading (HFT), have altered the structure of financial markets, the strategies of traders, and the liquidity and price discovery processes. These changes and developments have ignited a heated debate among academics and regulators. While some researchers claim that HFTs increase the market efficiency by improving the liquidity and price discovery (see as an example, Brogaard et al., 2014b), others argue that they create adverse selection risks for slow traders and contribute to market instability by exacerbating illiquidity shocks, such as flash crashes (see as an example, Kirilenko et al., 2017). Motivated by these contrasting views, this thesis investigates these issues, and is therefore situated at the intersection of financial markets, technology and regulations. It specifically examines the topical issues around the transformative role of technology in financial markets by adopting novel and unique approaches. In the first study, I present a novel framework illustrating the links between order aggressiveness and flash crashes. My framework involves a trading sequence beginning with significant increases in aggressive sell orders relative to aggressive buy orders until instruments’ prices fall to their lowest levels. Thereafter, a rise in aggressive buy orders propels the prices back to their pre-crash levels. Using a sample of S&P 500 stocks trading during the May 6 2010 flash crash, I show that the framework is correctly specified and provides a basis for linking flash crashes to aggressive strategies, which are found to be more profitable during flash crashes. The second study is a methodological contribution to the financial econometrics literature, in which I propose a state space modelling approach for decomposing a high frequency trading volume into liquidity- and information-driven components. Using a set of high frequency S&P 500 stocks data, I show that the model is empirically relevant, and that informed trading is linked to a reduction in volatility, illiquidity and toxicity/adverse selection. Furthermore, I observe that my estimated informed trading component of volume is a statistically significant predictor of one-second stock returns; however, it is not a significant predictor of one-minute stock returns. I show that this disparity can be explained through the HFT activity, which eliminates pricing inefficiencies at high frequencies. The third study exploits the impact of the international transmission latency on liquidity and volatility by constructing a measure of the transmission latency between exchanges in Frankfurt and London and exploiting speed-inducing technological upgrades. I find that a decrease in the transmission latency increases the liquidity and volatility. In line with the existing theoretical models, I show that the amplification of liquidity and volatility is associated with the variations in adverse selection risk and aggressive trading. I then investigate the net economic effect of high latency, which lead to the finding that the liquidity deterioration effect of high latency dominates its volatility reducing effect. This implies that the liquidity enhancing benefit of increased trading speed in financial markets outweighs its volatility inducing effect

Information Asymmetry and Information Dissemination in High-Frequency Capital Markets

This dissertation is concerned with information asymmetry and information dissemination in high-frequency capital markets. At the intersection of information dissemination and asymmetry with market microstructure, this dissertation pursues three major goals. We propose enhancements to market microstructure methodology to be able to empirically conduct research on information dissemination and asymmetry on recent, high-frequency trading data. Second, we empirically evaluate related microstructure methodology to test its robustness and guide researchers in its application. Third, we employ the proposed methodology to evaluate the efficacy of different information channels, both traditional, legislation-based and new, technology-based channels