scholarly journals Asset Allocation via Machine Learning and Applications to Equity Portfolio Management

2020 ◽  
Author(s):  
Qing Yang ◽  
Zhenning Hong ◽  
Ruyan Tian ◽  
Tingting Ye ◽  
Liangliang Zhang
Keyword(s):  
Machine Learning ◽  
Portfolio Management ◽  
Asset Allocation

scholarly journals Early Adopters to Early Majority - What's Driving the Artificial Intelligence and Machine Learning Powered Transformation in Financial Services?

2021 ◽  
Vol 12 (4) ◽  
pp. 43
Author(s):  
Srikrishna Chintalapati
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Portfolio Management ◽  
Value Chain ◽  
Financial Services ◽  
Retail Banking ◽  
Early Adopters ◽  
Customer Life Cycle ◽  
Digital Disruption ◽  
The Impact

From retail banking to corporate banking, from property and casualty to personal lines, and from portfolio management to trade processing, the next wave of digital disruption in financial services has been unleashed by the concepts and applications of Artificial Intelligence (AI) and Machine Learning (ML). Together, AI and ML are undoubtedly creating one of the largest technological transformations the world has ever witnessed. Within the advanced streams of research in AI and ML, human intelligence blended with the cognitive reasoning of machines is finally out of the labs and into real-time applications. The Financial Services sector is one of the early adopters of this revolution and arguably much ahead of its leverage compared to other sectors. Built on the conceptual foundations of Innovation diffusion, and a contemporary perspective of enterprise customer life-cycle journey across the AI-value chain defined by McKinsey Global Institute (2017), the current study attempts to highlight the features and use-cases of early-adopters of this transformation. With the theoretical underpinning of technology adoption lifecycle, this paper is an earnest attempt to comment on how AI and ML have been significantly transforming the Financial Services market space from the lens of a domain practitioner. The findings of this study would be of particular relevance to the subject matter experts, Industry analysts, academicians, and researchers focussed on studying the impact of AI and ML in the financial services industry.

Herding and Delegated Portfolio Management: The Impact of Relative Performance Evaluation on Asset Allocation

2011 ◽  
Vol 01 (02) ◽  
pp. 265-292 ◽  
Author(s):  
Ernst Maug ◽  
Narayan Naik
Keyword(s):  
Performance Evaluation ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Relative Performance ◽  
Optimal Contracts ◽  
Fund Managers ◽  
Portfolio Managers ◽  
Delegated Portfolio Management ◽  
The Impact ◽  
Allocation Decisions

This paper investigates the effect of fund managers' performance evaluation on their asset allocation decisions. We derive optimal contracts for delegated portfolio management and show that they always contain relative performance elements. We then show that this biases fund managers to deviate from return-maximizing portfolio allocations and follow those of their benchmark (herding). In many cases, the trustees of the fund who employ the fund manager prefer such a policy. We also show that fund managers in some situations ignore their own superior information and "go with the flow" in order to reduce deviations from their benchmark. We conclude that incentive provisions for portfolio managers are an important factor in their asset allocation decisions.

Dynamic Portfolio Management with Machine Learning

2021 ◽  
Author(s):  
XINYU HUANG ◽  
Massimo Guidolin ◽  
Emmanouil Platanakis ◽  
David Newton
Keyword(s):  
Machine Learning ◽  
Portfolio Management ◽  
Dynamic Portfolio

Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default

2019 ◽  
Vol 22 (03) ◽  
pp. 1950021 ◽  
Author(s):  
Huei-Wen Teng ◽  
Michael Lee
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Credit Risk ◽  
Portfolio Management ◽  
Credit Card ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Credit Risk Management ◽  
Learning Methods ◽  
Machine Learning Methods

Machine learning has successful applications in credit risk management, portfolio management, automatic trading, and fraud detection, to name a few, in the domain of finance technology. Reformulating and solving these topics adequately and accurately is problem specific and challenging along with the availability of complex and voluminous data. In credit risk management, one major problem is to predict the default of credit card holders using real dataset. We review five machine learning methods: the [Formula: see text]-nearest neighbors decision trees, boosting, support vector machine, and neural networks, and apply them to the above problem. In addition, we give explicit Python scripts to conduct analysis using a dataset of 29,999 instances with 23 features collected from a major bank in Taiwan, downloadable in the UC Irvine Machine Learning Repository. We show that the decision tree performs best among others in terms of validation curves.

The Black-Litterman Model for Portfolio Optimization on Vietnam Stock Market

2020 ◽  
Vol 28 (Supp01) ◽  
pp. 99-111
Author(s):  
Bao Quoc Ta ◽  
Thao Vuong
Keyword(s):  
Stock Market ◽  
Portfolio Optimization ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Market Equilibrium ◽  
Management Model ◽  
Optimal Portfolios ◽  
Allocation Model ◽  
Allocation Process ◽  
Allocation Decisions

The Black-Litterman asset allocation model is an extended portfolio management model to construct optimal portfolios by combining the market equilibrium with investor views into asset allocation decisions. In this paper we apply Black-Litterman model for portfolio optimization on Vietnames stock market. We chose ARIMA methodology utilized in financial econonometrics to predict the views of investor which are used as inputs of the Black-Litterman asset allocation process to find optimal portfolio and weights.

Machine learning and asset allocation

2019 ◽  
Vol 48 (4) ◽  
pp. 1069-1094 ◽  
Author(s):  
Bryan R. Routledge
Keyword(s):  
Machine Learning ◽  
Asset Allocation

DRAWDOWN MEASURE IN PORTFOLIO OPTIMIZATION

2005 ◽  
Vol 08 (01) ◽  
pp. 13-58 ◽  
Author(s):  
ALEXEI CHEKHLOV ◽  
STANISLAV URYASEV ◽  
MICHAEL ZABARANKIN
Keyword(s):  
At Risk ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Value At Risk ◽  
Risk Measures ◽  
Sample Path ◽  
Real Life ◽  
Optimization Techniques ◽  
Conditional Value At Risk ◽  
Excess Loss

A new one-parameter family of risk measures called Conditional Drawdown (CDD) has been proposed. These measures of risk are functionals of the portfolio drawdown (underwater) curve considered in active portfolio management. For some value of the tolerance parameter α, in the case of a single sample path, drawdown functional is defined as the mean of the worst (1 - α) * 100% drawdowns. The CDD measure generalizes the notion of the drawdown functional to a multi-scenario case and can be considered as a generalization of deviation measure to a dynamic case. The CDD measure includes the Maximal Drawdown and Average Drawdown as its limiting cases. Mathematical properties of the CDD measure have been studied and efficient optimization techniques for CDD computation and solving asset-allocation problems with a CDD measure have been developed. The CDD family of risk functionals is similar to Conditional Value-at-Risk (CVaR), which is also called Mean Shortfall, Mean Excess Loss, or Tail Value-at-Risk. Some recommendations on how to select the optimal risk functionals for getting practically stable portfolios have been provided. A real-life asset-allocation problem has been solved using the proposed measures. For this particular example, the optimal portfolios for cases of Maximal Drawdown, Average Drawdown, and several intermediate cases between these two have been found.

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