ENABLING LIFE-TIME
FINANCIAL PLANNING
FOR EVERYONE

Everyone has various goals in their lives – Some want to save for early retirement, some want to purchase their perfect home, and others want to buy their dream car. But most investors are unsure how achievable their goals are and unclear how to begin their financial planning.

Veranos provides GBI (Goal-based investment) technology for personalized lifetime financial planning that guides each individual to a customized investment plan for achieving his or her financial goals.

Our mission is to help ordinary people maximize their lifetime wealth.

Veranos Advanced GBI™ combines cutting-edge financial engineering modeling (MSGP, Multi-Stochastic Goal Programming) with machine learning and big data analysis techniques for developing a personalized lifetime financial planning solution with theoretical optimality, high accuracy, computational efficiency, and wide applicability.

This allows each investor to optimize a financial plan based on his or her financial goals while also effectively controlling investment risk.

Veranos aims to help everyone make optimal financial decisions by providing personalized financial products and services based on Veranos Advanced GBI™.

01
Expertise

Veranos provides fully personalized lifetime financial planning based on industry-leading research.

Expertise in all core areas of personalized financial planning service.

Personalized financial planning requires financial market modeling, financial optimization, investment and investor analyses. Our research team includes experts in these areas that continuously perform cutting-edge research.

In-depth knowledge in advanced financial techniques including Nobel Prize-winning models.

Veranos Advanced GBI™ is based on financial theory and portfolio optimization that have been theoretically proven and empirically tested for several decades. Veranos develops advanced financial planning models by integrating MSGP, machine learning, big data analysis to the already accepted methods.

Technical skills to reach incomparable computational speed.

Goal-based investing methods generally require computation that takes at least several hours due to extreme number of variables and future scenarios that must be considered for maximizing goal achievement. Veranos Advanced GBI™ finds the optimal financial plan within a few minutes (if not seconds). This allows services that are low cost, fully personalized, mass customized, and easily expandable.

Efficient implementation of personalized financial product/service.

Veranos Advanced GBI™ efficiently computes optimal financial plans that maximize goal achievement even when there are multiple goals across several time periods. Thus, Veranos GBI is suitable for designing various personalized products and services including pension or retirement solutions, goal-based investment products, and investment advisory services.

02
Technology

Veranos is a global leader in personalized lifetime financial planning with expertise in core technology for analyzing clients, modeling financial markets, and optimizing portfolios.

Analyze investment circumstances

Optimize personalized financial plan by analyzing investment circumstances including investors (individual or institution), investment objectives (e.g., managing pension, planning for retirement, or achieving a target return), financial data (e.g., current assets and future contribution), and non-financial data (e.g., age and investment horizon).

Model financial markets

Financial markets are highly volatile and hardly predictable. Instead of attempting to pinpoint future market movements, Veranos incorporates market uncertainty through statistical models and probabilistically optimizes portfolios through possible market scenarios.

Examine investable assets

An important step in constructing efficient portfolios is to select a distinct number of assets among a wide range of investable assets that are most effective for an investor’s financial planning based on investment circumstances and investor’s profile.

Optimize financial plan

Based on investor and market analyses, personalized financial plan that maximizes goal achievement is optimized. Lifetime financial planning provides optimal allocations at each stage of the investment horizon that reflect changes in market conditions.

03
Research

Veranos is dedicated to producing world-leading research in financial engineering and data analysis

Personalized goal-based investment via multi-stage stochastic goal programming
Abstract
This paper proposes a goal-based investing model that is suitable for personalized wealth management, which only requires a few intuitive inputs from individuals such as wealth, investment, and consumption goals. In particular, priority levels can be assigned to consumption goals and our model assures maximum probability of achieving higher priority goals in a holistic approach. Furthermore, our model, which combines multi-stage stochastic programming and goal programming, is formulated as a linear programming problem that efficiently finds the theoretical optimal investment decision. With its simplicity, flexibility, and computational efficiency, the proposed goal-based investing model provides a new framework for automated investment management services.
Author
Woo Chang Kim, Do-Gyun Kwon, Yongjae Lee, Jang Ho Kim, Changle Lin
Personalized Lifetime Asset-Liability Management via Risk-Averse Stochastic Dual Dynamic Programming
Abstract
In this paper, we address an individual asset-liability management problem that makes lifetime asset allocation and consumption decisions. We make decisions annually for a lifetime-long period under piecewise linear utility functions, which may incorporate multiple consumption goals of investors; transaction costs are also considered. The problem can be modeled as a Multistage Stochastic Linear Programing (MSLP) problem. Ordinary MSLP problems are solved by the Sample Average Approximation (SAA) method, which uses scenario trees. Unfortunately, however, our problem cannot be solved by the SAA method alone, because the size of the scenario trees grows exponentially as the number of stages increases. To solve the problem with large-scale scenario trees that have 10100 number of nodes, therefore, we use the Stochastic Dual Dynamic Programming (SDDP) algorithm, which overcomes the curse of dimensionality by assuming stage-wise independence. We provide a risk-neutral and a risk-averse version of model formulations, and empirically determine how individuals should change their risk-averseness as retirement approaches. In addition, we suggest a biased sampling method to enhance the convergence speed of the risk-averse model.
Author
Do-gyun Kwon, Yongjae Lee, Woo Chang Kim
The Journal of Portfolio Management, March 2017
Robust factor-based investing
Abstract
In quantitative portfolio management, combining optimization with estimation causes concern for asset managers because portfolio problems may be sensitive to deviations in their inputs, but obtaining accurate input estimates is a difficult task. Robust factor models address these concerns using factor models for estimating asset returns and worst-case approaches for gaining stability in portfolio performance. Recent studies on robust factor investing explore methods of incorporating factors into robust portfolio construction. In this article, the authors provide a survey that includes theoretical insight, empirical findings from historical data, and experience from practitioners in formulating and executing robust factor-based investment strategies. <Read more>
Author
Jang Ho Kim, Woo Chang Kim, Frank J. Fabozzi
International Journal of Financial Engineering and Risk Management, August 2018
Why Your Smart Beta Portfolio Might Not Work
Abstract
Smart beta, which accounts for rule-based factor-tilting strategies that fall between active and passive investment, has emerged as an alternative to active investment after its major decline since the global financial crisis. In spite of the smart beta's remarkable commercial prosperity, many experts in both industry and academia share some concerns. Some of them believe that the marketing hype might confuse investors while others are concerned about the exposure to unintended risks that smart beta products might bring. In this study, we provide a comprehensive review of diverse perspectives from both practitioners and researchers on smart beta and we perform empirical and theoretical investigations on the efficiency of smart beta (or factor-tilting) strategies as investment building blocks. We find that factor-based investment building blocks may cause inefficiency under the mean-variance framework. <Read more>
Author
Yongjae Lee, Woo Chang Kim
IE Magazine, December 2015
Anatomy of Robo-Advisor : 적용기술의 타당성을 중심으로
Abstract
급변하는 세계금융시장의 상황과 맞물려 최근 들어 금웅과 기술의 결합을 통한 핀테크 산업에 대한 관심이 커지고 있다. 이러한 핀테크 산업의 흐름에서 자산운용업을 담당하는 키워드가 바로 로보어드바이저이다. 로보어드바이저 산업은 은행의 자산관리사인 프라이빗뱅터의 업무 중에서 자동화 될 수 있는 부분만을 공학기술을 통해 제공하는 것으로, 최근 개인투자자들이 접근할 수 있는 투자자산의 다양화, 투자관련정보의 양적 급증, 가속화되는 정보전달속도로 인해 공학적 접근법과 시스템의 필요성이 대두되고 있다. <Read more>
Author
Geum Il Bae, Yongjae Lee, Jieun Kim, Woo Chang Kim, Min Jeong Kim, Jang Ho Kim
대한산업공학회지, December 2017
한국 ETF 시장의 시스템적 리스크 분석 및 최적의 ETF 도입 순서에 대한 연구
Abstract
Traditionally, the private wealth management industry has been dedicated to high-net-worth individuals due to expensive service costs. However, robo-advisors are making sudden rises during the on-going FinTech revolution. Robo-advisors aim to provide personalized wealth management services for everyone by using automated investment management algorithms and online distribution channels. To reduce service costs, robo-advisors mainly use ETFs to construct investment portfolios. Therefore, Korean ETF market should grow in both quantity and quality, in order for robo-advisors to succeed in Korea. In this study, we first analyze how vulnerable the Korean ETF market is to external or internal shocks in comparison with the U.S. ETF market. Then, we derive the optimal introduction sequence of ETFs in the Korean ETF market based on the modern portfolio theory. <Read more>
Author
Beom Hyun Kim, Yongjae Lee, Do-gyun Kwon, Woo Chang Kim
대한산업공학회지, Accepted
국내 인적자본을 고려한 생애주기별 자산관리
Abstract
Lifetime financial planning has become extremely important due to increase in life expectancy and decrease in retirement age. In comparison to short-term investment management, lifetime financial planning must consider life circumstances such as current age, retirement age, and lifestyle. One methodology for representing an individual’s life stage is through human capital. This paper discusses human capital formulations and demonstrates calculations for various industries in Korea. Furthermore, the paper includes analysis on the effect of human capital in lifetime financial planning as oppose to only focusing on financial capital when performing portfolio optimization.
Author
JeaYong Yu, Joohwan Hong, Yongjae Lee, Jang Ho Kim
Four-University Rotating Fintech Conference 2018, April 2018
Multi-stage stochastic goal programming explained: Holistic approach for goal-based investing
Description
‘State of the Art in Robo-Adivising Systems: Financial Technologies for Enhanced Social Security’ 컨퍼런스는 2018년 4월 12일과 13일 서울에서 진행. 해당 컨퍼런스는 KAIST, 미국 프린스턴 대학, 중국 칭화대학교, 프랑스 EDHEC가 공동으로 2017년 부터 매년 개최하는 핀테크(FinTech)관련 국제 컨퍼런스로써, 자산운용산업의 핀테크, 즉 로보 어드바이저 산업에 대해 전 세계의 학계, 산업계, 규제당국의 관계자가 참여하여 논의하는 장이다. 본 컨퍼런스에서 마이클 뎀스터 케임브리지 대학 교수, 김우창 카이스트 교수, 리오넬 마텔리니 EDHEC 교수, 존 멀비 프린스턴 교수 등 세계 최고 전문가들이 참여하여 자산운용산업 전반과 최신 동향에 대해 논의하였다. <Read more>
Presenters
Jang Ho Kim, Yongjae Lee
Four-University Rotating Fintech Conference 2017, April 2017
Goal-based investment via multi-stage stochastic goal programming for robo-advisor services
Description
‘Four-University Rotating FinTech Conference: Wealth Management Systems for Individual Investors’ 컨퍼런스는 2017년 4월 26일과 27일 미국 프린스턴 대학에서 진행. 해당 컨퍼런스는 KAIST, 미국 프린스턴 대학, 중국 칭화대학교, 프랑스 EDHEC가 공동으로 2017년 부터 매년 개최하는 핀테크(FinTech)관련 국제 컨퍼런스로써, 자산운용산업의 핀테크, 즉 로보 어드바이저 산업에 대해 전 세계의 학계, 산업계, 규제당국의 관계자가 참여하여 논의하는 장이다. Andrew Yao (Turing Award 수상자, 칭화대 핀테크 센터 센터장), John Bogle (Vanguard Group 창업자, Bogle Financial Markets Research Center 센터장), 김우창 (KAIST 교수, KAIST 자산운용미래기술센터 센터장), Lionel Martellini (EDHEC-Risk Institute 이사), John Mashey (Bell 연구소/실리콘 밸리 전산학자), John Mulvey (Princeton University 교수, Bendheim Center for Finance 설립 멤버) 등이 로보 어드바이저와 관련된 최신 연구 결과를 발표하며, 알리바바 산하의 앤트 파이낸셜과 메릴 린치 등은 자신의 로보 어드바이저 시스템을 시연하였다. <Read more>
Presenters
Woo Chang Kim
Four-University Rotating Fintech Conference 2017, April 2017
Robust portfolio models
Description
‘Four-University Rotating FinTech Conference: Wealth Management Systems for Individual Investors’ 컨퍼런스는 2017년 4월 26일과 27일 미국 프린스턴 대학에서 진행. 해당 컨퍼런스는 KAIST, 미국 프린스턴 대학, 중국 칭화대학교, 프랑스 EDHEC가 공동으로 2017년 부터 매년 개최하는 핀테크(FinTech)관련 국제 컨퍼런스로써, 자산운용산업의 핀테크, 즉 로보 어드바이저 산업에 대해 전 세계의 학계, 산업계, 규제당국의 관계자가 참여하여 논의하는 장이다. Andrew Yao (Turing Award 수상자, 칭화대 핀테크 센터 센터장), John Bogle (Vanguard Group 창업자, Bogle Financial Markets Research Center 센터장), 김우창 (KAIST 교수, KAIST 자산운용미래기술센터 센터장), Lionel Martellini (EDHEC-Risk Institute 이사), John Mashey (Bell 연구소/실리콘 밸리 전산학자), John Mulvey (Princeton University 교수, Bendheim Center for Finance 설립 멤버) 등이 로보 어드바이저와 관련된 최신 연구 결과를 발표하며, 알리바바 산하의 앤트 파이낸셜과 메릴 린치 등은 자신의 로보 어드바이저 시스템을 시연하였다. <Read more>
Presenters
Jang Ho Kim, Woo Chang Kim
대한산업공학회, November 2015
‘로보 어드바이저’의 현황과 한국시장 도입 가능성에 대한 연구
Description
고비용의 인력 대신 고도화된 알고리즘 기반의 소프트웨어를 활용한 온라인 자산관리인 로보 어드바이저의 2015년 현황을 살펴보고 국내외 대표적인 운용형, 자문형 로보 어드바이저 회사를 비교하였다. 또한, 한국 시장의 현황을 살피고, 국내에 상장되어 있는 ETF를 구분하여 한국 시장을 대표하는 각 지수를 따르는 ETF를 조사하는 것으로 국내에서의 로보 어드바이저 도입 가능성이 있는지 연구한 내용을 발표하였다. <Read more>
Presenters
Jieun Kim, Yongjae Lee, Geum Il Bae, Je-ok Choi, Dongyeol Lee, Woo Chang Kim
대한산업공학회, April 2016
로보 어드바이저(Robo-Advisor) 산업 활성화를 위한 국내 ETF시장의 발전 방향에 대한 연구
Description
Presenters
Beom Hyun Kim, Do-gyun Kwon, Yongjae Lee, Woo Chang Kim
Wiley, December 2015
Robust Equity Portfolio Management + Website: Formulations, Implementations, and Properties using MATLAB
Abstract
Robust Equity Portfolio Management + Website offers the most comprehensive coverage available in this burgeoning field. Beginning with the fundamentals before moving into advanced techniques, this book provides useful coverage for both beginners and advanced readers. MATLAB code is provided to allow readers of all levels to begin implementing robust models immediately, with detailed explanations and applications in the equity market included to help you grasp the real-world use of each technique. The discussion includes the most up-to-date thinking and cutting-edge methods, including a much-needed alternative to the traditional Markowitz mean-variance model. Unparalleled in depth and breadth, this book is an invaluable reference for all risk managers, portfolio managers, and analysts. <Read more>
Author
Woo Chang Kim, Jang Ho Kim, Frank J. Fabozzi
  • Personalized goal-based investment via multi-stage stochastic goal programming
  • Personalized Lifetime Asset-Liability Management via Risk-Averse Stochastic Dual Dynamic Programming
  • Robust factor-based investing
  • Why Your Smart Beta Portfolio Might Not Work
  • Anatomy of Robo-Advisor : 적용기술의 타당성을 중심으로
  • 한국 ETF 시장의 시스템적 리스크 분석 및 최적의 ETF 도입 순서에 대한 연구
  • 국내 인적자본을 고려한 생애주기별 자산관리
  • Multi-stage stochastic goal programming explained: Holistic approach for goal-based investing
  • Goal-based investment via multi-stage stochastic goal programming for robo-advisor services
  • Robust portfolio models
  • ‘로보 어드바이저’의 현황과 한국시장 도입 가능성에 대한 연구
  • 로보 어드바이저(Robo-Advisor) 산업 활성화를 위한 국내 ETF시장의 발전 방향에 대한 연구
  • Robust Equity Portfolio Management + Website: Formulations, Implementations, and Properties using MATLAB
04
Team

Experts in financial optimization, data analysis, and software engineering have joined Veranos with a shared goal of providing personalized financial service for everyone.

Young Long Kim

CEO

Woo Chang Kim

Wonjong Rhee

Doh Hyung Kim

Yeonsik Jang

Jang Ho Kim

Yongjae Lee

Youngrae Song

Joohwan Hong

Doojin Park

Jisun Park

Hye Seob Shim

Hyung Woo Ryoo

Career

Industry leaders in financial technology have joined Veranos

We are waiting for passionate individuals to join, learn, and grow with our team of experts. We look for applicants with knowledge and skills in financial analysis and/or software development. More importantly, we look for individuals that share our vision. We are hiring for all levels, so contact us if interested regardless of your level of experience and expertise.

career@veranostech.com

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