Asset and Wealth Management Digital, Data and Analytics Transformation Framework - A Business Imperative.

Unrelating Change for Asset and Wealth Management (AWM) Firms: AWM Digital, Data and Analytics Transformation is a business imperative to drive the change. Financial institutions strategically expanded their AWM portfolios after facing a protracted flight-to-liquidity scenario ensuing the 2008 financial crisis. Over a decade of bull run and $100+ trillion global assets under management (AUM) and $43 trillion AUM in North America, the AWM industry stands on the precipice of fundamental shifts.?

The Combined forces of non-bank intermediaries, disruptive digital technologies, demographic shifts, change in investment tactics, economic turmoil & market volatility, ESG consideration, regulatory changes, and classical quantitative finance vs. advanced analytics methods for portfolio construction, asset allocation & optimization are radically reshaping fund & asset management dynamics.

Forces driving the change:

1) Demographic shifts: Tech and finance savvy Millennials and Gen Z, collectively next-gen investors, are aging into market adding wealth pool to all segments such as High & Ultra-High Net Worth Individuals (HNWI & UHNWI), Mass Affluent, and Retail Mass — next-gen investors have longer investment horizon and set to inherit ~$58 trillion over the next 20 years. Growth in retail investors contributed net flows of 4.7% in 2019. These new populations demand transparency and higher returns at a low cost. AWM firms need to develop behavior-based intelligent segmentation and personalized products & services.

2) Rise in non-bank disrupters: Registered Independent Advisors (RIA — 1300+ RIAs are in the marketplace), Multi-Family Offices (MFOs), and platform providers (e.g., OurCrowd, YieldStreet, SoFi, etc.) with bespoke investment alternatives are winning market share from traditional AWM firms. RIAs and MFOs have done this by focusing on personalized financial and superior technology, catering to the evolving investment preferences of HNWI. Platform providers are democratizing next-gen mass-market with innovative products (e.g., SPACs, Pre-IPO shares, lending alternatives — Lombard loans; crowdfunding; financing-basket, etc.)

3) Shift in investment tactics: Next-gen high-tech and high-touch HNWIs/UHNWIs, with ~43% investable wealth and greater appetite for risk, demand data-analytics driven Active portfolio management and transparent investment processes with capital preservation and sustainable Alpha targeting asset classes of both Active-Core (e.g., long-short equities, tailored indexing, corporate debt, money-market, etc.) and High-Value-Alternatives (e.g., hedge funds, private equity, real estate, infrastructure, commodities, private debt, liquid alternative mutual funds, private placements, and bespoke credit). Next-gen HNWIs/UHNWIs are comfortable with independently navigating many elements of their wealth management and expect Advisors to have in-depth knowledge of asset classes, factors impacting risk-return profile, and advanced quantitative methods to explain risk-return paradigm over the investment spectrum; Smart-Beta and Alpha + Beta ETFs with a hybrid model for Mass Affluent; Robo-Advisory and Passive ETFs for Retail Mass; Democratization of Direct Indexing — as investors increasingly seek customized portfolios, there is growing sentiment that direct indexing is a viable alternative to compete with low-cost ETFs.

Millennials and Gen Z investors are aging into market adding wealth pool to all segments -- High/Ultra-High Net Worth, Mass Affluent, and Retail Mass with longer investment horizon and set to inherit ~$58 trillion over the next 20 years; Rise in RAIs and platform providers disrupting AWM marketplace; Shift in investment tactics with next-gen investors demanding low cost sustainable Alpha, and transparent investment processes; Rise in Passive funds, but HNWIs & UHNWIs demand more Active-Core & Active-Alternatives, Mass Affluent for Factor-Based Smart Beta, and Retail Mass for Passive ETFs; Democratization of AWM via Factor tilted Index and crowdfunding alternatives are rapidly growing in entire investment spectrum. Big-data and advanced analytics driven Factor investing is used driving Alpha in all asset classes -- Index funds, Active Alpha, Smart Beta, Active mutual funds, Hedge funds Long-Short equities, and others; Quantitative finance is shifting from classical to advanced techniques.

Call for action — Techs & Ops cost Reduction and Invest in Data and Advanced Analytics:

Modernize AWM platform ecosystem with AI-powered data fabric & data pipeline, RESTful APIs, RPA, data science, and advanced analytics to address customer engagement & risk profiling, wealth planning & portfolio simulation, proposal development, closing & on-boarding, and analytical capabilities to manage investments & rebalance asset allocation; Design and implement GPU-powered cloud computing data science platform for quantitative and factor (e.g., fundamental factors, momentum factors, macroeconomic, and statistical factors) investing portfolio models.

Build your Alpha Universe collecting and processing wide array of data sets: 1) structured: internal portfolio positions & transactions, security master, price master, reference data, and real-time market data feeds; 2) vendor data: Bloomberg, FactSet, Refinitiv, S&P, Moody’s, ICE data services, Morningstar, MSCI, and Dow Jones Factiva; 3) unstructured: SEC filings, investors presentation, street transcripts, market events, analyst feedback, broker research, news feeds, etc. 4) Factor universe (e.g., Fundamental, Momentum, Statistical, ?Macroeconomics, Cyclical, Sector, Geopolitical, and ESG, etc.): Fundamental and statistical factors: Balance sheet, cash flows, income statements, financial rations, R-Squared, correlation, implied volatility, etc.; Style factors: Value, Size, Growth, Low Volatility, Quality, Yield, Momentum, and others; ESG factors for sustainable finance: (E) Carbon Emissions, GHG Emissions, Renewable Energy, Toxic Air Emissions, Water Efficiency, Climate Change, etc.; (S) Benefits, Diversity and Inclusion, Employee Training, Human Capital, Operational Performance, Product Quality and Safety; (G) Board Profile, Board Skillset, Business Ethics, Compensation, Ownership and Control, and Sustainability.

Classical quant-finance that assumed efficient market tends to equilibrium, risk neutrality, risk free price arbitrage, normality in asset returns, and?constant covariance, etc., fell short in managing portfolio risk-return as evidenced by multitude of financial crisis and corporate collapses. AWM firms now can explore large number of factors and use GPU-powered cloud computing, data science & advanced analytics techniques for risk-return modeling, asset allocation and portfolio optimization.?ML/DL models are more effective.

Practice advanced structured analytics: Classical quantitative finance, that assumed efficient market, investors are rational & risk-averse, and returns are normally distributed, fell short in the wake of multitude financial crisis. Market reflexivity distorted underlying fundamentals triggered by investors’ reaction and behavior. AWM firms began exploring risk factors that could explain risk-return profile and help driving Alpha. Today factor modeling is used tilt all index funds, smart beta, factor Alpha portfolios. AWM firms modeling factor signals on expected return from linear combination of risk factors. However, securities exposures to factors are not linear – practice a combination of linear functions, ensemble & deep learning, and stochastic methods model factor exposure, return, spread, and risk.

From a productive usage and value creation perspective AI/ML methods and Generative Modeling such as generative adversarial network (GAN) using as Generator & Discriminator, Long short-term memory (LSTM) recurrent neural network (RNN), multi-layer perceptron (MLP), stochastic discount factor (SDF -- pricing kernel), and simulation engine etc. can significantly reduce noise on time-series asset returns and generate much 95% to 99% accurate future returns compare to traditional ARIMA or GARCH models. Asset Managers and Investors that target for excess Alpha outperforming benchmark indices can now use AI/ML new methods for active asset management. With high performance GPU led cloud computing, FIs can process wide array of data to engineer large number of features & apply AI/ML methods to glean patterns in trade signals for excess alpha. See the AWM advanced analytics (e.g., Generative AI) framework below -- for asset return, allocation, and portfolio optimization methods.

Factor investing is dominating asset and investment management strategies: Factor investing has become a widely discussed part of today’s asset management practices influencing entire spectrum asset classes and investment methods. In the realm of investing, a factor is any underlying characteristic that helps explain the long-term risk and return performance of an asset. Risk factors are the building block of?factor investing. Through a factor lens, each security has an array of exposures that explain its risk and return. The risk factor models were contemplated, empirically researched, accepted, and developed in the wake of academic and Wall Street Pundits unequivocally debunking the efficient market hypothesis (EMH) that states asset prices reflect all available information and consistent alpha generation is impossible.?See bellow an illustrative factor investing framework (source: MSCI)

Practice advanced structured analytics: AWM industry has been using underlying factor models to explain risk-return profile and help driving Alpha with parsimonious linear models. However, factors are not linear – practice a combination of linear function, ensemble & deep learning, and stochastic methods to model factor exposure, return, and risk: [?? = ???? + ??].

1. Factors Loading: Beta, Momentum, Size, Short & Long-Term Reversal, Residual Volatility, Liquidity, Earnings Yield, Book-to-Price, Leverage, Dividend Yield, Profitability, Seasonality, Growth, Quality, News & Analyst Sentiment.
2. Factor scoring and rank ordering: Use K-Means clustering to cluster stocks by factors, Principal Component Analysis (PCA), Least Absolute Shrinkage and Selection Operator (LASSO), Cost function, and Z-Scores for feature engineering and importance analysis to reduce dimensions and eliminate sample biases; Model individual factor exposure, return, spread with random forest, boosted tree, and neural network with historical returns, predict expected return, estimate Z-score; ANOVA for factor interaction analysis and rank-order with weighted return.?
3. Multiperiod Factor Return Time-Series: Use Fourier noise reduction, ARIMA, GARCH, and Generative Adversarial?Network (GAN) with LSTM-RNN (Long Short-Term Memory) as Generator & Discriminator; Use ARIMA/GARCH as an input feature into the LSTM for better accuracy. See an illustrative deep learning analytics for factor return:

1. Stochastic methods to Optimize, back & stress test and risk management: Use Resampling MVO & Utility Functions to Optimize portfolio, VaR and Conditional VaR (CVaR) for portfolio risk, expected shortfall, back & stress testing, estimate Sharp Ratio and other metrics for risk-return profiling.?See an illustrative stochastic method to stress asset return:

In conclusion, AWM must practice advanced structured analytics as the classical quantitative finance, that assumed efficient market, investors are rational & risk-averse, and returns are normally distributed, fell short in the wake of multitude financial crisis. Market reflexivity distorted underlying fundamentals triggered by investors’ reaction and behavior. AWM firms began exploring risk factors that could explain risk-return profile and help driving Alpha. Today factor modeling is used tilt all index funds, smart beta, factor Alpha portfolios. AWM firms modeling factor signals on expected return from linear combination of risk factors. However, securities exposures to factors are not linear – practice a combination of linear functions, ensemble & deep learning, and stochastic methods model factor exposure, return, spread, and risk.

Author: Saroj Das held leadership roles with PwC, KPMG, EY, and IBM. He is a seasoned Banking & Capital Market risk practitioner focused on data and analytics. Over 25 years, he has assisted banks with portfolio data and analytics transformation programs spanning Retail, Mortgage, Wholesale, CIB, Capital Market, Asset & Wealth Management, and Treasury & Payments. Saroj works with business owners to solve complex use cases using next-gen technologies including Big-data, data science (AI/ML/Deep Learning) & predictive analytics, cloud computing, Blockchain, and automation.