From 05a8650558dac682497435b4e9c3af5eb3eeae13 Mon Sep 17 00:00:00 2001 From: Oleksii Lialka Date: Tue, 29 Jun 2021 16:13:01 +0300 Subject: [PATCH] Added IPRE advanced analytics white paper --- .../report/google-ipre-ml-report.tex | 172 ------------------ .../report/google-ipre-white-paper.pdf | Bin 0 -> 336443 bytes .../report/media/irp-feature-importance.png | Bin 29397 -> 0 bytes .../report/media/irp-heatmap.png | Bin 56709 -> 0 bytes .../report/media/irp-pairplot.png | Bin 171800 -> 0 bytes .../report/media/returns-lineplot.png | Bin 41295 -> 0 bytes 6 files changed, 172 deletions(-) delete mode 100644 advanced-analytics/report/google-ipre-ml-report.tex create mode 100644 advanced-analytics/report/google-ipre-white-paper.pdf delete mode 100644 advanced-analytics/report/media/irp-feature-importance.png delete mode 100644 advanced-analytics/report/media/irp-heatmap.png delete mode 100644 advanced-analytics/report/media/irp-pairplot.png delete mode 100644 advanced-analytics/report/media/returns-lineplot.png diff --git a/advanced-analytics/report/google-ipre-ml-report.tex b/advanced-analytics/report/google-ipre-ml-report.tex deleted file mode 100644 index 8b122e1..0000000 --- a/advanced-analytics/report/google-ipre-ml-report.tex +++ /dev/null @@ -1,172 +0,0 @@ -\documentclass[twocolumn]{article} -\usepackage{url} -\usepackage{graphicx} - -\title{ - Google Analytics Reference Pattern \\ - Investment Products Recommendation Engine \\ - Advanced Analytics White Paper -} -\date{\today} -\author{ - Oleksii Lialka - \thanks{Staff Data Scientist, SoftServe Inc., olial@softserveinc.com} -} - -\begin{document} -\maketitle - -\begin{abstract} - This document provides an overview of the advanced analytics services and solutions of the \emph{Investment Products Recommendation Engine} (IPRE) reference pattern. - It contains sections dedicated to data pipelines and ETL, advanced analytics and machine learning methodology, principles of the recommendation service. - The GitHub public repository\footnote{[TBD]: link to GitHub repo} hosts the source code of the IPRE reference pattern. -\end{abstract} - -\section{Solution overview} - The IPRE is a comprehensive investment banking solution, which builds a bridge between retail investors and the complexity of the capital markets. - The AI/ML products assist clients in optimal portfolio construction, wealth allocation and discovering key performance metrics of the investment. - An ensemble of ML solutions produces highly personalized recommendations and investment advice based on an individuals' risk preferences and wealth management goals. - - The reference pattern is designed to be highly reproducible, with the minimal manual effort required to set all services up. - \emph{Terraform} script builds and deploys all components of the IPRE advanced analytics. - -\subsection{Project structure} - The source code of the advanced analytics services is nested under the \verb|advanced-analytics/| directory. - Scripts content follows a modular structure according to the objectives of ETL, portfolio optimization, investment analytics services. - - Directory \verb|data-pipelines/| contains Cloud Functions scripts for ETL, generating datasets, writing data to Cloud Storage. - Modules for \emph{capital markets} and \emph{investor risk preferences} datasets have dedicated Cloud Functions for invoking training and inference jobs of BigQuery AutoML and ARIMA models. - Directory \verb|recommendation-engine/| provides scripts for convex optimization, investment analytics. - -\section{Data pipelines} - The IPRE service relies on multiple data sources, both internal and external. - The solution implements scalable data pipelines with the Big Data technologies, such as \emph{BigQuery}, \emph{Cloud Storage}, \emph{Dataflow}. - Data pipelines follow the \emph{schema-on-read} data lake convention. - - All external raw data streams are aggregated in dedicated Cloud Storage buckets. - The Cloud Functions trigger minor pre-processing scripts. - Writing an object to the Cloud Storage bucket triggers Dataflow job for adding new data to BigQuery. - - Such architecture makes an ETL pipeline resilient to corrupt data and scalable to multiple data sources. - The Cloud Functions provide clean, cost-effective solution for migrating massive datasets from data lake to DWH. - -\subsection{Capital markets data} - Historical market data is a crucial element for the recommendation service. - A dedicated data pipeline job collects quotes of the select securities from \emph{YahooFinance}\footnote{Modular interface of data pipelines permits integration of any other data provider}. - Securities of interest include the Big Tech stocks, FX, ETF, and others. - All select assets vary in return and risk. - It allows IPRE to construct a wide range of portfolios to meet diverse investors' preferences. - - \begin{figure} - \includegraphics[width=0.49\textwidth]{media/returns-lineplot.png} - \caption{Cumulative monthly returns of select stocks.} - \end{figure} - - After minor preprocessing, daily historical quotes ($q$) are turned into periodic \emph{returns}. - - \begin{equation} - {r_{t, t + dt} = \frac{q_{t + dt} - q_{t}}{q_{t}}} - \end{equation} - - \emph{Returns} observations with a unique timestamp are written to Cloud Storage. - It allows to reduce egress and ensures that BigQuery does not receive duplicate data. - During the first run of the script, all observations starting from 2017 will make it to BigQuery. - Subsequent runs provide incremental observations of the "unseen" data. - - % Can remove par - In the final stage of ETL, the processed data is written to BigQuery. - Aggregating data in BigQuery allows other services to retrieve the data in a cost-effective way. - -\subsection{Investors risk preferences} - The \emph{investor risk preferences} (IRP) is a synthetic dataset containing historical records of 1000 existing retail investors\footnote{The dataset mimics customer data FSI companies typically collect. It can be easily replaced by real clients' data.}. - This dataset is a crucial component for making personalized recommendations based on an individual's investment preferences. - - \begin{figure} - \includegraphics[width=0.49\textwidth]{media/irp-pairplot.png} - \caption{Pairplot of investor risk preferences select features.} - \end{figure} - - The \emph{risk aversion} is a target variable of interest. - \emph{Average monthly income, education, loans, deposits} are among 15 independent variables. - Investors' attributes are generated using different continuous variable distribution functions: \emph{Gamma, Gumbel, Gaussian, R-distributed and others}. - A script produces monthly snapshots of investors' attributes, resulting in 48'000 data points. - - The Cloud Function triggers a generation of the dataset upon the first launch of IPRE. - Dataflow migrates generated dataset from Cloud Storage to BigQuery. - -\section{Advanced analytics} - This section describes the use of BigQuery built-in tools for ML and forecasting. - The data stored in BigQuery is used for producing factors for the recommendation service. - BigQuery ML and forecasting tools can also be used for leveraging massive datasets to produce deep customer insights and business analytics. - -\subsection{Risk aversion} - Predicting the risk aversion of prospect investors is a cornerstone IPRE feature. - The risk aversion factor is a proxy of an investors' risk preference. - Individuals' risk preference corresponds to the level of risk an investor is comfortable within the long term. - Modern financial theory and behavioural finance studies advocate that the risk aversion factor shapes the investment decisions of an individual\footnote{Björk, T., Murgoci, A. and Zhou, X.Y. (2014). Mathematical Finance, 24: 1-24.}. - - \begin{figure} - \includegraphics[width=0.49\textwidth]{media/irp-feature-importance.png} - \caption{Feature importance of the investor risk aversion model.} - \end{figure} - - Inferring the risk aversion from individuals' attributes is a supervised machine learning problem. - The Cloud Function invokes script for training BigQuery AutoML model on the IRP data stored in BigQuery. - BigQuery ML tools handle the entire model training pipeline, from data preparation to hyperparameter tuning. - - \begin{figure} - \includegraphics[width=0.49\textwidth]{media/irp-heatmap.png} - \caption{Correlation map of investor risk preferences select features.} - \end{figure} - - Once training is complete, an inference script predicts risk aversion for existing clients of the bank, who are not yet active investors. - The mean objective of the IPRE is to provide non-finance professionals with an insight into individual's risk preferences. - -\subsection{Expected returns} - The IPRE utilizes portfolio optimization technique for producing a recommendation. - The expected returns vector ($\mu$) is a predicted return on standalone assets (stocks, indices) in a foreseeable future. - The precision of the recommendation depends on the accuracy of expected returns. - - Processed historical market quotes turned into periodic returns and stored in BigQuery. - The Cloud Function triggers the BigQuery forecasting tool to train models for each time series stream. - Invocation of multi-threaded BigQuery jobs reduces overall training time. - BigQuery uses an advanced version of \emph{ARIMA} for time series forecasting. - The BigQuery implementation of \emph{ARIMA} is a good fit for foracasting noisy, non-stationary financial time series data. - - Models make a one-step-ahead prediction of the expected returns and store results in a dedicated Cloud Storage bucket. - -\section{Recommendation service} - The IPRE service takes user ID as an argument, retrieves predicted risk aversion factor, and makes optimal portfolio recommendation. - The risk aversion factor is inferred from the clients' data using the BigQuery AutoML model. - The user can select a preferred level of risk with the \emph{UI} to obtain a recommendation, which fits them best. - The IPRE returns a recommendation on the capital market assets, expected risk, return, portfolio performance metrics. - -\subsection{Portfolio optimization} - The objective of the portfolio optimization is to find a combination of assets, which maximizes the difference between expected return ($\mu$) and a risk ($\Sigma$). - As far as different investors have different risk preferences, the risk aversion ($\lambda$) introduces a penalty to an objective function. - - \begin{equation} - \max \: \mu \omega^{T} - \frac{\lambda}{2} \omega^{T} \Sigma \omega \, : \, \forall \: \omega \in \Re - \label{formula:portfolio} - \end{equation} - - The higher the risk aversion, the higher the penalty for including risky assets into a portfolio\footnote{MATHEWS, T. (2004). Portfolio Selection with Quadratic Utility Revisited. The Geneva Papers on Risk and Insurance Theory, 29(2), 137-144.} - Portfolio optimization produces optimal asset weights in the portfolio given risk aversion level. - User can select a risk aversion factor in the \emph{UI} to obtain a recommendation, which fit them best. - -\subsection{Investment analytics} - The IPRE service computes key performance metrics: expected return, risk and the \emph{Sharpe Ratio}. - It allows user to make informed decisions on the investments, as well as compare portfolios of different composition. - As far as high returns are accompanied by high risk, an investor can select a portfolio, which fits their risk appetite. - -\section{Implications} - A risk aversion factor of existing investors can be inferred analytically by solving the first-order condition of the equation (\ref{formula:portfolio}). - The IPRE can scale to produce recommendations on the OTC assets and structured investment products offered by financial companies to retail investors. - The BigQuery managed tools for advanced analytics, ML and forecasting allow cost-effectively leverage massive datasets with lower manual effort and maintenance overhead compared to an in-house solution. - - The IPRE unlocks new dimension of \emph{proactive} recommendation engagement. - Serving professional investment advice via web and mobile interface is a financial services industry differentiator. - It provides a competitive advantage to a company seeking to increase retail investors' conversion and onboarding rates. - Providing content-aware, highly personalized recommendations increases the trading activity of existing and prospective retail investors. - -\end{document} diff --git a/advanced-analytics/report/google-ipre-white-paper.pdf b/advanced-analytics/report/google-ipre-white-paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..9eae6159c35d8deeaca9a306411da4822184b297 GIT binary patch literal 336443 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