7,882 research outputs found
A conceptual framework for changes in Fund Management and Accountability relative to ESG issues
Major developments in socially responsible investment (SRI) and in environmental, social and governance (ESG) issues for fund managers (FMs) have occurred in the past decade. Much positive change has occurred but problems of disclosure, transparency and accountability remain. This article argues that trustees, FM investors and investee companies all require shared knowledge to overcome, in part, these problems. This involves clear concepts of accountability, and knowledge of fund management and of the associated âchain of accountabilityâ to enhance visibility and transparency. Dealing with the problems also requires development of an analytic framework based on relevant literature and theory. These empirical and analytic constructs combine to form a novel conceptual framework that is used to identify a clear set of areas to change FM investment decision making in a coherent way relative to ESG issues. The constructs and the change strategy are also used together to analyse how one can create favourable conditions for enhanced accountability. Ethical problems and climate change issues will be used as the main examples of ESG issues. The article has policy implications for the UK âStewardship Codeâ (2010), the legal responsibilities of key players and for the âCarbon Disclosure Projectâ
ALGA: Automatic Logic Gate Annotator for Building Financial News Events Detectors
We present a new automatic data labelling framework called ALGA - Automatic Logic Gate Annotator. The framework helps to create large amounts of annotated data for training domain-specific financial news events detection classifiers quicker. ALGA framework implements a rules-based approach to annotate a training dataset. This method has following advantages: 1) unlike traditional data labelling methods, it helps to filter relevant news articles from noise; 2) allows easier transferability to other domains and better interpretability of models trained on automatically labelled data. To create this framework, we focus on the U.S.-based companies that operate in the Apparel and Footwear industry. We show that event detection classifiers trained on the data generated by our framework can achieve state-of-the-art performance in the domain-specific financial events detection task. Besides, we create a domain-specific events synonyms dictionary
Bayesian Networks for Asset Management and Financial Risk
This thesis explores the use of Bayesian networks to develop âviewsâ for a Black-Litterman asset allocation model, and determines whether they can help in the creation of better investment portfolios. Views represent an investorâs expectations of the future performance of a companyâs shares: an estimate of expected return, and a measure of the uncertainty of this estimate. This thesis aims to automate the creation of views and to pioneer intelligent portfolio construction as part of an algorithmic asset management process
Reinforcement Learning Applied to Trading Systems: A Survey
Financial domain tasks, such as trading in market exchanges, are challenging
and have long attracted researchers. The recent achievements and the consequent
notoriety of Reinforcement Learning (RL) have also increased its adoption in
trading tasks. RL uses a framework with well-established formal concepts, which
raises its attractiveness in learning profitable trading strategies. However,
RL use without due attention in the financial area can prevent new researchers
from following standards or failing to adopt relevant conceptual guidelines. In
this work, we embrace the seminal RL technical fundamentals, concepts, and
recommendations to perform a unified, theoretically-grounded examination and
comparison of previous research that could serve as a structuring guide for the
field of study. A selection of twenty-nine articles was reviewed under our
classification that considers RL's most common formulations and design patterns
from a large volume of available studies. This classification allowed for
precise inspection of the most relevant aspects regarding data input,
preprocessing, state and action composition, adopted RL techniques, evaluation
setups, and overall results. Our analysis approach organized around fundamental
RL concepts allowed for a clear identification of current system design best
practices, gaps that require further investigation, and promising research
opportunities. Finally, this review attempts to promote the development of this
field of study by facilitating researchers' commitment to standards adherence
and helping them to avoid straying away from the RL constructs' firm ground.Comment: 38 page
Enhancing portfolio management using artificial intelligence: literature review
Building an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization
Portfolio management is a fundamental problem in finance. It involves
periodic reallocations of assets to maximize the expected returns within an
appropriate level of risk exposure. Deep reinforcement learning (RL) has been
considered a promising approach to solving this problem owing to its strong
capability in sequential decision making. However, due to the non-stationary
nature of financial markets, applying RL techniques to portfolio optimization
remains a challenging problem. Extracting trading knowledge from various expert
strategies could be helpful for agents to accommodate the changing markets. In
this paper, we propose MetaTrader, a novel two-stage RL-based approach for
portfolio management, which learns to integrate diverse trading policies to
adapt to various market conditions. In the first stage, MetaTrader incorporates
an imitation learning objective into the reinforcement learning framework.
Through imitating different expert demonstrations, MetaTrader acquires a set of
trading policies with great diversity. In the second stage, MetaTrader learns a
meta-policy to recognize the market conditions and decide on the most proper
learned policy to follow. We evaluate the proposed approach on three real-world
index datasets and compare it to state-of-the-art baselines. The empirical
results demonstrate that MetaTrader significantly outperforms those baselines
in balancing profits and risks. Furthermore, thorough ablation studies validate
the effectiveness of the components in the proposed approach
A Conceptual Model of Investor Behavior
Based on a survey of behavioral finance literature, this paper presents a descriptive model of individual investor behavior in which investment decisions are seen as an iterative process of interactions between the investor and the investment environment. This investment process is influenced by a number of interdependent variables and driven by dual mental systems, the interplay of which contributes to boundedly rational behavior where investors use various heuristics and may exhibit behavioral biases. In the modeling tradition of cognitive science and intelligent systems, the investor is seen as a learning, adapting, and evolving entity that perceives the environment, processes information, acts upon it, and updates his or her internal states. This conceptual model can be used to build stylized representations of (classes of) individual investors, and further studied using the paradigm of agent-based artificial financial markets. By allowing us to implement individual investor behavior, to choose various market mechanisms, and to analyze the obtained asset prices, agent-based models can bridge the gap between the micro level of individual investor behavior and the macro level of aggregate market phenomena. It has been recognized, yet not fully explored, that these models could be used as a tool to generate or test various behavioral hypothesis.behavioral finance;financial decision making;agent-based artificial financial markets;cognitive modeling;investor behavior
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