The FinanceHub is a community of investment professionals, academics and students in Brazil capable of fostering the research and technology that will help the Brazilian asset management industry cope with its new challenges. Academics from Insper and investment professionals from BWGI were the initial members of this community, but the group now includes participants from many asset managers, banks, pension funds and consulting companies.

Seminars

Participation in the seminars is by invitation only. Please contact us if you would like to attend, but space is limited and we may not be able to welcome you immediately. Each session will have at least two presentations of no more than 45 minutes, including discussion and Q&A. We also welcome original research, but the Seminar coordinator will evaluate whether your research fits the objectives of this Seminar.

Presenter is expected to present the main features of the paper, promote a brief discussion with suggestions for changes or future research and should be able to address questions by all participants. Participants should come prepared for discussion. All are expected to read at least the introduction of all papers. Participants should focus the discussion on the themes related to the paper presentations. The role of the Seminar coordinator is to guide the discussion during the presentations, select topics together with participants and plan sessions. At the end of the session, we welcome general discussions.

Equities (December 2)

Equities Valuation (November 4)

Fixed Income (October 7)

Macroeconomics (September 2)

Behavior Finance (August 5)

Demand Shocks / Derivatives (July 1)

Machine Learning (June 3)

Corporate Bonds (May 6)

Monetary Policy and Asset Prices (April 15)

Factors / Monetary Policy (December 5)

Oprion Factors (November 28)

Financial Technology (November 21)

Political Finane / International Finance (November 7)

Political Finance / International Finance (October 31)

Expectations and Beliefs (October 17)

International Finance / Cross-section (October 10)

Fintech / Demand Based Asset Pricing (September 12)

Macrofinance / Monetary Policy (August 29)

International Finance / Investment Strategies (August 15)

Volatility / Factor Investing (July 25)

Momentum / Household Finance (July 18)

Macrofinance (July 4)

  • Caio Natividade - Global Macro and Intraday Execution

Factor / Individual Assets (June 20)

  • Caio Natividade - Private Equity Replication
  • Fernando Tassinari Moraes - Time Series Variation in the Factor Zoo (Bessembinder et al., 2023)

Macrofinance / Monetary Policy (June 6)

Machine Learning (November 21)

Factors (November 8)

FOMC Announcement (October 25)

Attention (October 4)

Retail Trading (Semptember 19)

Factor Momentum and Correlated Factors (Semptember 5)

Portfolio Optimization and Short Term Reversals (August 22)

Network Diversification / Cryptocurrencies (June 22)

Monetary Policy Surprises (June 6)

Options (May 23)

Currency Momentum (April 25)

Behavior Bias (April 4)

Stock Selection (December 13)

Deep Learning (November 22)

Liquidity Crisis (November 8)

Dynamic Asset Allocation (October 18)

Trends / Monetary Policy (October 18)

Beliefs and Portfolios (August 23)

Interest Rates (August 9)

Inflation / Past Returns (July 26)

Business Cycles / Bubbles (July 12)

Volatility / Bubbles (June 28)

Low Risk Anomalies / Quality (June 14)

Corporate Bonds & Currency Returns (May 3)

Presidential Cycles & Factor Demand (April 19)

ESG & Earnings Seasonality (April 5)

Mutual Funds & Momentum (March 22)

Factors & News (March 8)

Currency & Factor Investing (February 22)

Factors (February 8)

Core Earnings & Harm (December 14)

CIP Violations & Entrepreneurship (November 30)

Cross-Sectional Patterns and Economic Momentum in Currencies (November 16)

Underreaction (November 9)

One-Day Factor Momentum (October 19)

Demand Systems (October 5)

Return Predictability and Exchange Rates (August 24)

International Finance (August 10)

Venture Capital (July 27)

Systemic Risk and Equity-Bond Risk-Return Trade Offs (July 13)

Accounting Information and Term Structure (June 29)

Anomalies (June 15)

Intermediary Asset Pricing and Informed Investors (June 1)

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Ph.D Student Session (January 27)

Value Creation in Funds & Robo-advising (December 2)

Equity Risk Premia (November 11)

Forecasting with Text Data (October 14)

Sovereign Funding Markets (September 23)

Risk Parity (September 9)

Paycheck Frequency & Households’ Decisions (August 19)

  • Filipe Correia - Does Paycheck Frequency Matter for Households’ Decisions? Evidence from Financial Account Data (Baugh et al, 2019)

ML and the Factor Zoo (August 12)

  • Alexandre Rubesam - Searching the Factor Zoo (Hwang & Rubesam, 2019)
  • Alexandre Rubesam - Empirical Asset Pricing via Machine Learning (Gu et al., 2019)

Interest Rate Slope (June 24)

Portfolio Construction (June 3)

Factors (May 20)

Liquidity (May 6)

Financial Intermediaries (April 22)

Machine Learning (April 8)

  • Fernando Moraes - Shrinking the cross section (Kozak et al., 2017)
  • Marcello Paixão - Empirical Asset Pricing via Machine Learning (Gu et al., 2018)

Fixed Income (March 11)

Yield Curve (February 25)

Macro Announcements and Returns (February 12)

Crowded Trades, Momentum and Carry (January 29)

Momentum and Financial Institutions (January 15)

Low Beta & Risk Parity (December 4)

  • Fernando Moraes - Betting Against Beta (Frazzini & Pedersen, 2014)
  • Guido Chagas - Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly (Baker et al., 2011)

ERP and Options (November 21)

Premia (October 23)

Background

There has been an explosion of interest in systematic strategies. Varying in complexity, quantitative ideas are now mainstream and part of any large institutional portfolio.

Top investment firms in the world have moved away from the “secret-sauce” mindset and now co-author research with academics, dedicate resources to education, create new technologies, construct publicly available data sets, and run labs in markets, data science, and technology. They are even bringing open source technology to investing itself. The Brazilian asset management industry is late in this process with very few teams fully dedicated to a systematic approach to investing.

Brazilian portfolios are becoming more international, multi-dimensional and complex. Systematization is a transparent and efficient way of institutional investors to deal with complexity and lack familiarity with certain markets. Our professionals need to be trained in the sciences and technologies of this new approach. We need to start building a more symbiotic relationship with academia. The FinanceHub is an attempt to create a community of investment professionals and academics in Brazil capable of doing just that, fostering the research and technology that will help the Brazilian asset management industry cope with its new challenges.

What We Do

We built a GitHub Repository with codes to support research in finance. Some code helps easily access raw and treated data stored in our servers. Some code implements models used on the analysis of that data. The objective of this branch is to build tools for research which will reduce the cost of data acquisition as well as model set up.

We have lectures on Python programming for finance and financial theory. All of the material is available in our repository in the format of slides and jupyter notebooks, so that everyone can learn python and help build up the community and push the project forward.

We actively discuss and promote research in finance. Among our activities, we organize regular academic seminars on a wide range of finance topics. We also encourage our participants to circulate interesting academic and practitioner research to all members and circulate information on finance seminars at Insper and other institutions.

Where to Find Finance Research

Projects

Tracker Building: Build tracker for all asset classes

Portfolio Construction: Tools for combining assets into a portfolio

Performance Evaluation: Create a library to evaluate the performance of trackers

Factor Building: Build classes that compute factors for all asset classes