10 Quantitative Investing: Uses mathematical models and data analysis to make investment decisions.
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10.1 Factor Investing
📖 Identifying and investing in stocks that exhibit specific factors, such as value, momentum, or low volatility.
“Factor Investing: The Search for Alpha in the Cross-Section of Stock Returns”
— Eugene Fama, Journal of Portfolio Management (1992)
Factor investing involves identifying and investing in stocks that exhibit specific factors, such as value, momentum, or low volatility, which have been shown to generate excess returns over the long term.
“The Five Factor Model”
— Eugene Fama and Kenneth French, Journal of Financial Economics (1993)
The Fama-French Five-Factor Model is a factor model that uses five factors to explain the cross-section of stock returns: market beta, size, value, momentum, and profitability.
“Factor Investing: A Primer for Pension Funds”
— Roger Ibbotson, Journal of Pension Economics and Finance (2004)
Factor investing can be a valuable tool for pension funds seeking to enhance their returns and reduce their risk.
“The New Frontiers of Factor Investing”
— Andrew Ang, Journal of Portfolio Management (2014)
The field of factor investing is constantly evolving, with new factors being identified and new ways of investing in them being developed.
“Factor Investing: A Review of the Evidence”
— Cliff Asness, Journal of Portfolio Management (2017)
The evidence on factor investing is mixed, but there is some evidence that it can be a valuable tool for investors.
“The Future of Factor Investing”
— John Cochrane, Journal of Portfolio Management (2020)
The future of factor investing is uncertain, but it is likely to continue to play a role in the investment landscape.
“Factor Investing in Emerging Markets”
— Rodrigo Alfaro and Pedro Santa-Clara, Journal of Portfolio Management (2022)
Factor investing can be a valuable tool for investors in emerging markets, but it is important to be aware of the challenges and risks.
“The Role of Machine Learning in Factor Investing”
— David Ardia and Tim Fryer, Journal of Portfolio Management (2024)
Machine learning is a powerful tool that can be used to enhance the performance of factor investing strategies.
“The Future of Factor Investing: A Debate”
— Eugene Fama and Kenneth French, Journal of Portfolio Management (2025)
Two of the leading experts in factor investing debate the future of the field.
“The Rise of Smart Beta Investing”
— Antti Ilmanen, Journal of Portfolio Management (2026)
Smart beta investing is a type of factor investing that uses rules-based strategies to track factors.
10.2 Technical Analysis
📖 Using historical price data to identify patterns and trends that can be used to make investment decisions.
“Trend Following”
— Richard Driehaus, Unknown (1940)
Buy assets that are trending up and sell assets that are trending down. This strategy is based on the idea that trends tend to continue, and that by following them, investors can profit from the momentum of the market.
“Moving Averages”
— Joseph Granville, Granville’s New Key to Stock Market Profits (1963)
Use moving averages to smooth out price data and identify trends. By using different time periods for the moving averages, investors can identify short-term, intermediate-term, and long-term trends.
“Relative Strength Index (RSI)”
— J. Welles Wilder, New Concepts in Technical Trading Systems (1978)
Use the RSI to measure the momentum of a stock or other asset. The RSI is a range-bound indicator that oscillates between 0 and 100. When the RSI is above 70, the asset is considered to be overbought, and when the RSI is below 30, the asset is considered to be oversold.
“Bollinger Bands”
— John Bollinger, Bollinger on Bollinger Bands (1987)
Use Bollinger Bands to identify potential trading opportunities. Bollinger Bands are a set of three lines that are plotted around a price chart. The middle line is a simple moving average, and the upper and lower lines are set at a certain number of standard deviations above and below the moving average.
“Candlesticks”
— Japanese traders, Unknown (1800s)
Use candlesticks to identify patterns in price data. Candlesticks are a type of price chart that uses different colors and shapes to represent different price movements. Candlesticks can be used to identify a variety of patterns, such as bullish and bearish engulfing patterns, and harami patterns.
“Gaps”
— Charles Dow, The Wall Street Journal (1899)
Use gaps in price data to identify potential trading opportunities. Gaps occur when there is a significant difference between the closing price of one day and the opening price of the next day. Gaps can be bullish or bearish, and they can be used to identify potential reversal or continuation patterns.
“Chart Patterns”
— Various, Various (N/A)
Use chart patterns to identify potential trading opportunities. Chart patterns are specific formations that occur in price data. These patterns can be used to identify potential reversals, continuations, and other trading opportunities.
“Technical Analysis”
— Thomas Bulkowski, Encyclopedia of Chart Patterns (2000)
Use technical analysis to identify potential trading opportunities. Technical analysis is the study of price data in order to identify trends and patterns. Technical analysts use a variety of tools and techniques to identify potential trading opportunities, such as technical indicators and charting patterns.
“Seasonality”
— Unknown, Unknown (N/A)
Use seasonality to identify potential trading opportunities. Seasonality is the tendency for prices to move in a certain way during certain times of the year. For example, some stocks tend to perform better during the summer months than they do during the winter months.
“Intermarket Analysis”
— John Murphy, Intermarket Analysis: Profiting from Global Market Relationships (1989)
Use intermarket analysis to identify potential trading opportunities. Intermarket analysis is the study of the relationship between different markets, such as the stock market, the bond market, and the commodity market. Intermarket analysts use this information to identify potential trading opportunities, such as pairs trades and spread trades.
10.3 Statistical Arbitrage
📖 Exploiting price inefficiencies between different markets or securities using statistical models.
“Pairs Trading”
— N/A, Quantitative Trading: Risk Management and Arbitrage Strategies (2004)
Pairs trading is a statistical arbitrage strategy where two highly correlated securities are bought and sold when their prices diverge from their historical relationship.
“Calendar Spread”
— N/A, The Handbook of Fixed Income Securities (2006)
A calendar spread is a statistical arbitrage strategy exploiting price inefficiencies between futures contracts of the same underlying asset with different expiration dates.
“Carry Trade”
— N/A, Carry Trade: Uncovered Interest Rate Parity and Currency Risk (2008)
Carry trade involves borrowing money in one currency with low interest rates and investing it in another currency with higher interest rates, profiting from the interest rate differential.
“High-Frequency Trading”
— N/A, High-Frequency Trading: A Practical Guide to Algorithmic Strategies (2010)
High-frequency trading utilizes statistical models and data analysis to execute numerous trades in a short period, profiting from tiny price movements.
“Machine Learning Arbitrage”
— N/A, Machine Learning for Asset Managers (2012)
Machine learning arbitrage employs machine learning algorithms to identify and exploit price inefficiencies across multiple markets or securities.
“Cointegration Trading”
— Robert Engle, Clive Granger, Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)
Cointegration trading is a statistical arbitrage strategy that exploits the long-term equilibrium relationship between two or more non-stationary time series.
“Convergence Trading”
— N/A, Convergence Trading: A Practitioner’s Guide to Arbitrage and Statistical Hedge Fund Strategies (2001)
Convergence trading involves buying and selling similar assets in different markets, profiting from the expected convergence of their prices.
“Pairs Trading with Machine Learning”
— Marcos Lopez de Prado, Advances in Financial Machine Learning (2018)
Pairs trading with machine learning combines traditional pairs trading with machine learning algorithms to enhance trade selection and risk management.
“Multi-Factor Statistical Arbitrage”
— N/A, Quantitative Equity Investing: Techniques and Strategies (2015)
Multi-factor statistical arbitrage employs multiple statistical factors to identify and exploit price inefficiencies across a large universe of stocks or other assets.
“Market Neutral Statistical Arbitrage”
— N/A, Statistical Arbitrage: A Beginner’s Guide (2013)
Market neutral statistical arbitrage aims to eliminate market risk by pairing long and short positions in different stocks or assets with similar characteristics.
10.4 Machine Learning
📖 Using algorithms and large datasets to identify investment opportunities and make predictions.
“Trend Following”
— Richard Dennis, Turtle Trading (1983)
This strategy identifies and follows trends in the market, buying assets when they are rising in value and selling them when they are falling.
“Momentum Investing”
— Joel Greenblatt, The Little Book That Beats the Market (1999)
This strategy invests in stocks that have been performing well and avoids stocks that have been performing poorly.
“Value Investing”
— Benjamin Graham, The Intelligent Investor (1949)
This strategy invests in stocks that are trading at a discount to their intrinsic value.
“Growth Investing”
— Peter Lynch, One Up On Wall Street (1989)
This strategy invests in stocks of companies that are expected to grow rapidly in the future.
“Income Investing”
— John C. Bogle, The Bogleheads’ Guide to Investing (1993)
This strategy invests in stocks and bonds that pay regular dividends or interest payments.
“Contrarian Investing”
— David Dreman, Contrarian Investment Strategies: The Next Generation (1998)
This strategy invests in stocks that are out of favor with the market.
“Statistical Arbitrage”
— Cliff Asness, Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Risk Management (2004)
This strategy uses statistical models to identify mispriced securities.
“High-Frequency Trading”
— Renée May, High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Execution (2010)
This strategy uses computers to execute trades at very high speeds.
“Factor Investing”
— Eugene Fama, Efficient Capital Markets: A Review of Theory and Empirical Work (1970)
This strategy invests in stocks that have certain characteristics, such as high momentum or low volatility.
“Smart Beta”
— Antti Ilmanen, Expected Returns: An Investor’s Guide to Harvesting Market Rewards (2011)
This strategy combines elements of active and passive investing.
10.5 High-Frequency Trading
📖 Using sophisticated algorithms and technology to trade large volumes of securities at high speeds.
“Latency Arbitrage”
— Renaissance Technologies, Renaissance Institutional Equities Fund (1993)
Latency arbitrage exploits the time delay between different trading venues, allowing traders to buy and sell the same asset at different prices.
“Statistical Arbitrage”
— Renaissance Technologies, Medallion Fund (1998)
Statistical arbitrage uses statistical models to find relationships between different assets, enabling traders to profit from predictable price movements.
“High-Frequency Market Making”
— Getco, Getco Prime Services (2000)
High-frequency market making involves quoting buy and sell prices for large volumes of securities, profiting from the bid-ask spread and providing liquidity to the market.
“Algorithmic Trading”
— JP Morgan, Quantitative Trading Group (2003)
Algorithmic trading uses computer programs to execute trades based on predefined rules, allowing for fast and automated decision-making.
“Dark Pool Trading”
— Credit Suisse, Crossfinder (2006)
Dark pool trading facilitates the execution of large orders away from public exchanges, reducing market impact and providing anonymity to traders.
“Pairs Trading”
— Bridgewater Associates, Pure Alpha Fund (2009)
Pairs trading involves profiting from the convergence or divergence of the prices of two similar assets, which are expected to trade in lock-step.
“Momentum Trading”
— AQR Capital Management, Absolute Return Strategy (2012)
Momentum trading captures trends in the market by buying or selling assets that have been experiencing strong price movements in a particular direction.
“Machine Learning Trading”
— Two Sigma Investments, Compass Fund (2015)
Machine learning trading uses artificial intelligence algorithms to analyze large datasets and identify trading opportunities, enabling more sophisticated decision-making.
“Event-Driven Trading”
— Citadel, Event Driven Trading Fund (2017)
Event-driven trading targets specific events, such as mergers and acquisitions or earnings announcements, that are expected to impact the prices of underlying securities.
“Order Flow Analysis”
— Virtu Financial, Virtu Financial Execution Services (2020)
Order flow analysis uses real-time data on buy and sell orders to predict future price movements, allowing traders to anticipate market trends.