The Rise of Computerized High Frequency Trading: Use and Controversy

Over the last decade, there has been a dramatic shift in how securities are traded in the capital markets. Utilizing supercomputers and complex algorithms that pick up on breaking news, company/stock/economic information and price and volume movements, many institutions now make trades in a matter of microseconds, through a practice known as high frequency trading. Today, high frequency traders have virtually phased out the dinosaur floor-traders and average investors of the past. With the recent attempted robbery of one of these high frequency trading platforms from Goldman Sachs this past summer, this rise of the machines has become front page news, generating vast controversy and discourse over this largely secretive and ultra-lucrative practice. Because of this phenomenon, those of us on Main Street are faced with a variety of questions: What exactly is high frequency trading? How does it work? How long has this been going on for? Should it be banned or curtailed? What is the end-game, and how will this shape the future of securities trading and its regulation? This iBrief explores the answers to these questions

A survey on financial applications of metaheuristics

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program (E-2015-36)

How effective is 'relationship marketing' in gaining customer loyalty to securities brokerages?

Relationship marketing (RM) is widely acknowledged as a useful tool in gaining customer loyalty in various sectors. However, to date, there had been no research on how RM impacts customer loyalty in the securities brokerage firm industry in The Stock Exchange of Thailand. This study employs an inductive research approach to explore RM in securities brokerage firms in Thailand’s financial services sector and gain an understanding of customers’ and other stakeholders’ views of RM activities and loyalty to brokerages in an emerging market. Multiple data collection methods were employed, including semi-structured interviews as the main collection method and participant observations in a supporting role. Qualitative content analysis and coding techniques were used for analysing the data. This pioneering research provides new theoretical and practice knowledge and delivers a far more subtle and nuanced analysis of the dynamics at play between customer loyalty, various RM strategies and different customer types – compared to the current literature. The study found that securities brokerage firms in Thailand implemented RM practice but with differences in relationship marketing strategies, depending on the types of customers being targeted. The study identified the main factors impacting on customer loyalty to both local and international securities brokerage firms. Finally, the research confirmed that RM had a demonstrable impact in gaining customer loyalty to securities brokerage firms in The Stock Exchange of Thailand (SET), but with intriguing characteristics, for example, RM’s positive impact on individual short-term investors’ loyalty, not to brokerages, but to particular staff

E-finance-lab at the House of Finance : about us

The financial services industry is believed to be on the verge of a dramatic [r]evolution. A substantial redesign of its value chains aimed at reducing costs, providing more efficient and flexible services and enabling new products and revenue streams is imminent. But there seems to be no clear migration path nor goal which can cast light on the question where the finance industry and its various players will be and should be in a decade from now. The mission of the E-Finance Lab is the development and application of research methodologies in the financial industry that promote and assess how business strategies and structures are shared and supported by strategies and structures of information systems. Important challenges include the design of smart production infrastructures, the development and evaluation of advantageous sourcing strategies and smart selling concepts to enable new revenue streams for financial service providers in the future. Overall, our goal is to contribute methods and views to the realignment of the E-Finance value chain. ..

Entrepreneurship in the Netherlands; New economy: new entrepreneurs!

Dit onderzoek beschrijft de belangrijkheid van bestaande en startende bedrijven voor de Nederlandse economie. In drie bijdragen wordt uit verschillende invalshoeken ingegaan op de relatie tussen ondernemerschap en de nieuwe economie. De effecten van ICT op het economische proces worden behandeld. Daarnaast wordt ingegaan op de vraag of de 21ste eeuw een nieuwe gouden eeuw zal zijn voor ondernemerschap. Tot slot komt de rol van kennisgerichte bedrijven in de nieuwe economie uit macro-economisch oogpunt aan de orde.

Algorithms in future capital markets: A survey on AI, ML and associated algorithms in capital markets

This paper reviews Artificial Intelligence (AI), Machine Learning (ML) and associated algorithms in future Capital Markets. New AI algorithms are constantly emerging, with each 'strain' mimicking a new form of human learning, reasoning, knowledge, and decisionmaking. The current main disrupting forms of learning include Deep Learning, Adversarial Learning, Transfer and Meta Learning. Albeit these modes of learning have been in the AI/ML field more than a decade, they now are more applicable due to the availability of data, computing power and infrastructure. These forms of learning have produced new models (e.g., Long Short-Term Memory, Generative Adversarial Networks) and leverage important applications (e.g., Natural Language Processing, Adversarial Examples, Deep Fakes, etc.). These new models and applications will drive changes in future Capital Markets, so it is important to understand their computational strengths and weaknesses. Since ML algorithms effectively self-program and evolve dynamically, financial institutions and regulators are becoming increasingly concerned with ensuring there remains a modicum of human control, focusing on Algorithmic Interpretability/Explainability, Robustness and Legality. For example, the concern is that, in the future, an ecology of trading algorithms across different institutions may 'conspire' and become unintentionally fraudulent (cf. LIBOR) or subject to subversion through compromised datasets (e.g. Microsoft Tay). New and unique forms of systemic risks can emerge, potentially coming from excessive algorithmic complexity. The contribution of this paper is to review AI, ML and associated algorithms, their computational strengths and weaknesses, and discuss their future impact on the Capital Markets

Digital Disruption in the Financial Services Industry: The Emergence of Fintech

Financial services, traditionally consisting of banking, asset management, lending, insurance, and payment systems, are facing a perfect storm brought on by technological innovation and shifting customer expectations. Such changes have exposed a gap in the financial services space between those who have access and those who do not. Traditional financial institutions have been hindered in pursuing strategies of financial inclusion to large underserved and underbanked populations. There is a major opportunity for financial technology companies to alter the competitive dynamics in an industry that has long been dominated by major banks and other large financial institutions. ‘Fintech’, as it is called, represents technology-enabled business models seeking innovation to drive more efficient process, use, and delivery of financial services to consumers (Mention, 2019). This paper explores the factors leading to the rise of fintech, its applications and the impacts it has had on the financial services industry in both the developed and developing worlds

FORECASTING OF THE STOCK RATE OF LEADING WORLD COMPANIES USING ECONOMETRIC METHODS AND DCF ANALYSIS

In this article, we will cover various models for forecasting the stock price of global companies, namely the DCF model, with well-reasoned financial analysis and the ARIMA model, an integrated model of autoregression − moving average, as an econometric mechanism for point and interval forecasting. The main goal is to compare the obtained forecasting results and evaluate their real accuracy. The article is based on forecasting stock prices of two companies: Coca-Cola HBC AG (CCHGY) and Nestle S.A. (NSRGF). At the moment, it is not determined which approach is better for predicting the stock price − the analysis of financial indicators or the use of econometric data analysis methods.In this article, we will cover various models for forecasting the stock price of global companies, namely the DCF model, with well-reasoned financial analysis and the ARIMA model, an integrated model of autoregression − moving average, as an econometric mechanism for point and interval forecasting. The main goal is to compare the obtained forecasting results and evaluate their real accuracy. The article is based on forecasting stock prices of two companies: Coca-Cola HBC AG (CCHGY) and Nestle S.A. (NSRGF). At the moment, it is not determined which approach is better for predicting the stock price − the analysis of financial indicators or the use of econometric data analysis methods