Resources

Insight from the Little Harbor Research Team

Quants’ Quandry: Crossing The Chasm

By Rick Roche

A series of five installments, Little Harbor Advisors’ Managing Director Rick Roche explores the acceptance and diffusion of quantitative investment management and upends erroneous assumptions and media assertions of widespread quant investing. Roche conducted a broad survey of Chartered Financial Analysts and learned that to date, only a small sum of institutional dollars has been allocated to quant strategies.

This free downloadable white paper dispelled conventional wisdom on the widespread use, size of, and reluctance to embrace quantitative investment market as a percentage of overall investment assets.

The $340 Trillion Dollar Problem

By Chris Galizio and Moses Grader

Wealth is created by productivity growth and efficient use of capital – the very dollar system that created temporary wealth this decade is now evolving and turning the basic laws of finance and economics on their heads. As a result, investment managers, who rely on the rational valuation of security cashflows to allocate capital, have underperformed passive investment indexes. Markets have never been more inefficient, nor has there been a better time to be a bottom-up, long-short equity, active manager.

This free downloadable overview explains the ins and outs on how to be strategic with fund management.

Suggested Reading

Inside the Black Box: The Simple Truth About Quantitative Trading

by Rishi K. Narang

Quantitative trading strategies—known to many as “black boxes”—have gained a reputation of being difficult to explain and even harder to understand. Inside the Black Box lifts the veil of mystery surrounding quant trading and strategies in a straight-forward, non-technical style. Whether you’re an institutional investor or high-net worth individual, the lessons learned here will help you gain an edge in today’s turbulent market.

The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It

by Scott Patterson

Former Wall Street Journal staff reporter Scott Patterson shares the gripping tale of four math whizzes who made billions with formulas and high-speed computers. Dubbed the new kings of Wall Street, the “Quants” used a dizzying cocktail of differential calculus, quantum physics, and advanced geometry to reap riches from financial markets with the simple click of a computer mouse. They also sowed the seeds for history’s greatest financial disaster. The Quants is a masterpiece of explanatory journalism, a tale of ambition and hubris, and an ominous warning to Wall Street.

A Man for All Markets: From Las Vegas to Wall Street, How I Beat the Dealer and the Market

by Edward O. Thorp

Legendary mathematician Edward O. Thorp invented card counting and taught the world how to beat the dealer at the blackjack table. Once he shifted his sights to the biggest casino in the world, Wall Street, he ushered in a revolution by devising and deploying mathematical formulas to beat the market.

Third-Party Research

Placid Markets, Uncertain Times

Acadian Asset Management LLC

The financial market in the US is fragile and frequently bends to a broad range of issues. How then can investors maintain confidence amid placid markets, when unintended and undesirable risk exposure slips into portfolios, unnoticed? Click this link to briefly review suggestions for investors.

Rise of the Machines: Landscape and Recent Developments in Quantitative Hedge Fund Strategies, Products and Managers

Barclays Bank

The mystique of Silicon Valley has spread to the hedge fund (HF) industry, and managers and investors are increasingly making investments in both human capital and tools to explore the role of technology in data and analytics as applied to investment and alpha generation objectives. Questions have since been raised as to whether or not machines have indeed surpassed humans in investing. This white paper highlights the systematic landscape in terms of size, strategies and players, and explains shifts in investor sentiment.

Big Data and AI Strategies Machine Learning and Alternative Data Approach to Investing

J.P. Morgan

The financial industry has witnessed profound changes with participants increasingly adopting quantitative investing techniques. This report provides a framework for Machine Learning and Big Data investing including an overview of alternative data types and Machine Learning methods that analyze them. This educational guide spotlights the concepts of Big Data and Machine Learning from the perspective of investors.

Quant Investing – Past, Present, Future

Black Rock

Quantitative or scientific investing applies rigorous and systematic analysis—the scientific method—to investing. Scientific investors use this method to develop return forecasts and then construct portfolios by optimally trading off those expected returns against risk and trading costs. This detailed overview distinguishes practitioner strategies against quantitative investing approaches.

Information Pooling Applied to Financial Time Series Forecasting

P/E Investments

Financial time series data is known to exhibit a low signal to noise ratio. Click this link to read a white paper that helps separate the desired signal from spurious correlation when modeling economic and financial time series data.

Risk Management in a Bayesian Financial Model

P/E Investments

This white paper provides an outline of risk management techniques used in conjunction with the Bayesian methodology, often applied to forecast financial time series data.

Financial Time Series: Forecasting in a Bayesian Framework

P/E Investments

An overview of the Bayesian methodology and tractable online Bayesian framework to forecast financial time series data.

Does Academic Research Destroy Stock Return Predictability?

The Journal of Finance

Can academic studies serve as predictors? This question is answered by leveraging peer-reviewed finance, accounting, and economics journal samples that attribute cross-sectional return predictability to statistical biases, rational pricing, and mispricing. By comparing the results of all samples, return-predictability periods and potential results may share correlations. Click the link above to learn more about market efficiency.

Why Small Data is the New Big Data

Wharton School

The corporate world has become completely blinded by Big Data, yet the power of Small Data and the depth of information it reveals, often gets overlooked. Big Data analyzes the past, whereas Small Data or otherwise innocuous observations, are the “emotional DNA” that, if mined properly, can spotlight highly valuable and actionable correlations in investing, portfolio development and financial planning.