Financial Robo-Advisor: Learning from Academic Literature

Financial Robo-Advisor is the technology that integrates machine learning and self-identification to determine investment decisions. This study explores the financial robo-advisor based on bibliometric analysis and a systematic literature review. The method used three steps: determining the keyword, bibliometric analysis of literature metadata using VOSviewer, then collecting and analysing the articles. The bibliometric analysis results show five cluster keywords defined with different colors. In the network visualization, the robo-advisor connects to other keywords: investment, fintech, and artificial intelligence. Furthermore, the systematic literature review shows that the articles are divided into seven research objectives: (1) Law, Regulation, and Policy; (2) Investment Literate and Education; (3) Offered Services; (4) Present Risk-Portfolio Matching Technology; (5) Optimal Portfolio Methods; (6) Human-Robo Interaction; (7) Theoretical Design and Gap. Furthermore, this study can be used by academicians and practitioners to find out about robo-advisors based on an academic perspective

Asset management as a digital platform industry: a global financial network perspective

While contemporary technological disruption is increasingly conceptualized in terms of the logic and paradoxes of the digital platform economy, discussions of “FinTech” have only engaged to a limited extent with these debates—particularly from an economic geographic standpoint. Here we fill this gap by proposing an adapted Global Financial Network (GFN) framework for conceptualizing the organizational and geographic logic of the digital platform economy in finance, and applying it to examine the impact of the digital platform model on asset management. As we will show, asset management is being profoundly disrupted by what we dub digital asset management platforms—or DAMPs—which encompass services including index fund and ETF provision, robo-advising, and analytics and trading support. Like other digital platforms, DAMPs do not so much leverage technology to enhance their competitiveness within markets, as to radically restructure the market itself. Also, like other platforms, their rise has produced a winner-take-all paradox of centralization through democratization that defies predictions of technology-enabled industry decentralization. However, the logic and implications of the rise of DAMPs diverges, in other respects, from non-financial digital platforms, as finance has long possessed an informational intensity and regulatory and organizational fluidity characteristic of the digital platform economy. Consequently, the digital platform model has mostly developed endogenously in asset management through incremental innovation by major financial firms—in a process that has reinforced the position of leading incumbent asset management centers, and above all New York—rather than being introduced from the outside by upstart technology firms and clusters

Personalized Finance Advisory through Case-based Recommender Systems and Diversification Strategies

Recommendation of financial investment strategies is a complex and knowledge-intensive task. Typically, financial advisors have to discuss at length with their wealthy clients and have to sift through several investment proposals before finding one able to completely meet investors' needs and constraints. As a consequence, a recent trend in wealth management is to improve the advisory process by exploiting recommendation technologies. This paper proposes a framework for recommendation of asset allocation strategies which combines case-based reasoning with a novel diversification strategy to support financial advisors in the task of proposing diverse and personalized investment portfolios. The performance of the framework has been evaluated by means of an experimental session conducted against 1172 real users, and results show that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings while meeting the preferred risk profile. Furthermore, our diversification strategy shows promising results in terms of both diversity and average yield

Constructing a Shariah Document Screening Prototype Based on Serverless Architecture

The aim of this research is to discuss the groundwork of building an Islamic Banking Document Screening Prototype based on a serverless architecture framework. This research first forms an algorithm for document matching based Vector Space Model (VCM) and adopts Levenshtein Distance for similarity setting. Product proposals will become a query, and policy documents by the central bank will be a corpus or database for document matching. Both the query and corpus went through preprocessing stage prior to similarity analysis. One set of queries with two sets of corpora is tested in this research to compare similarity values. Finally, a prototype of Shariah Document Screening is built based on a serverless architecture framework and ReactJS interface. This research is the first attempt to introduce a Shariah document screening prototype based on a serverless architecture technology that would be useful to the Islamic financial industry towards achieving a Shariah-compliant business. Given the development of Fintech, the output of this research study would be a complement to the existing Fintech applications, which focus on ensuring the Islamic nature of the businesses

Engineering Adaptive Interfaces – Enhancement of Comprehension and Decision-Making

The role of information systems is growing steadily and permeating more and more all levels of our society. Meanwhile, information systems have to support different user groups in various decision situations simultaneously. Hence, the existing design approach to creat- ing a unified user interface is reaching its limits. This work examines adaptive information system design by investigating user-adaptive information visualization and situation-aware nudging. An exploratory eye-tracking study investigates participants’ perception and comprehension of different financial visualizations and shows that none of them can be preferred across the board. Moreover, it reveals expertise knowledge as the research direction for visualization recommendations. Afterward, two empirical studies are conducted to relate different visualizations to participants’ domain-specific knowledge. The first study, conducted with a broad sample of the population, shows that financial and graphical literacy increases participants’ financial decision-making competency with certain visualizations. The second study, conducted with a more specific sample and an additional visualization, underlines a large part of the first study’s results. Additionally, it identifies statistical literacy as an increasing factor in financial decision-making. Both studies are demonstrating that different visualizations cause different cognitive loads despite the same amount of information. After all, the results are used to derive visualization recommendations based on domain-specific knowledge and cognitive load. This work also investigates the situation-aware effectiveness of nudging with the example of decision inertia. In a preliminary study, an experimental task is systematically transferred to different situational contexts by observing situational user characteristics. The identified contexts are examined in a subsequent large-scale empirical study with different nudges to reduce decision inertia. The results show gender-specific differences in decision inertia across the context. Hence, information system design has to adapt to gender and situational user characteristics to support users in their decision-making. Moreover, the study delivers empirical evidence for the contextual effectiveness of nudg- ing. Future nudging research has to incorporate situational user characteristics to provide effective nudges in different situational contexts. Especially, further fundamental research is needed to understand the situational effectiveness of nudging. The study identifies in- dividual situational preferences as one promising research stream

Essays in information relaxations and scenario analysis for partially observable settings

This dissertation consists of three main essays in which we study important problems in engineering and finance. In the first part of this dissertation, we study the use of Information Relaxations to obtain dual bounds in the context of Partially Observable Markov Decision Processes (POMDPs). POMDPs are in general intractable problems and the best we can do is obtain suboptimal policies. To evaluate these policies, we investigate and extend the information relaxation approach developed originally for Markov Decision Processes. The use of information relaxation duality for POMDPs presents important challenges, and we show how change-of-measure arguments can be used to overcome them. As a second contribution, we show that many value function approximations for POMDPs are supersolutions. By constructing penalties from supersolutions we are able to achieve significant variance reduction when estimating the duality gap directly, and the resulting dual bounds are guaranteed to provide tighter bounds than those provided by the supersolutions themselves. Applications in robotic navigation and telecommunications are given in Chapter 2. A further application of this approach is provided in Chapter 5 in the context of personalized medicine. In the second part of this dissertation, we discuss a number of weaknesses inherent in traditional scenario analysis. For instance, the standard approach to scenario analysis aims to compute the P&L of a portfolio resulting from joint stresses to underlying risk factors, leaving all unstressed risk factors set to zero. This approach ignores thereby the conditional distribution of the unstressed risk factors given the stressed risk factors. We address these weaknesses by embedding the scenario analysis within a dynamic factor model for the underlying risk factors. We recur to multivariate state-space models that allow the modeling of real-world behavior of financial markets, like volatility clustering for example. Additionally, these models are sufficiently tractable to permit the computation (or simulation from) the conditional distribution of unstressed risk factors. Our approach permits the use of observable and unobservable risk factors. We provide applications to fixed income and options portfolios, where we are able to show the degree in which the two scenario analysis approaches can lead to dramatic differences. In the third part, we propose a framework to study a Human-Machine interaction system within the context of financial Robo-advising. In this setting, based on risk-sensitive dynamic games, the robo-advisor adaptively learns the preferences of the investor as the investor makes decisions that optimize her risk-sensitive criterion. The investor and machine's objectives are aligned but the presence of asymmetric information makes this joint optimization process a game with strategic interactions. By considering an investor with mean-variance risk preferences we are able to reduce the game to a POMDP. The human-machine interaction protocol features a trade-off between allowing the robo-advisor to learn the investors preferences through costly communications and optimizing the investor's objective relying on outdated information

A Function-based Relevance Model for Making Sense of Technological Change in the Context of a Firm

Industrial Engineerin

Artificial Intelligence in Asset Management

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways