WCM Educational Recap #9: Intro to Quantitative Investing

Welcome to the Western Capital Markets blog! This week, we dive into quantitative finance and the various strategies used in the industry.

Overview

Quantitative Finance involves programming computers to trade on the markets. By applying mathematical, statistical, and computer science concepts, workers in the quantitative finance industry (quants) develops trading models.

· Buy-side quants focus on strategy development, whereas sell-side quants facilitate orders

Key Tasks:

· Raising Capital: Gathering funds for investments

· Research: Gather data, develop research pipelines

· Strategy Dev: Gathering funds for investments

· Deployment: Assess performance and decay

Pursuit of Alpha

Alpha is an additional return over a naïve forecast.

If market return is 10% and your return is 12%, your alpha is 2%).

Major sources of alpha include information, processing, modeling, and speed. Speed in particular is an important source of alpha, acting in the future relative to other trades is equivalent to predicting the future.

· There are various strategies to pursuing alpha, with common strategies including Market Making, Directional, Relative Value, Model-Based, Arbitrage, and Carry

Market Making

Market makers are individuals or firms that take the position of the counterparty in a trade, providing liquidity to an asset in exchange for profiting off the bid-ask spread. The bid-ask spread is the difference between the bid price — the highest a buyer will pay for security — and the ask price — the lowest a seller will accept for a security.

If the ask price is $12.50 and the bid price is $12.00, the spread is $0.50, and this is what market makers make from this transaction.)

The Necessity of Market Makers:

· Buyers and sellers come to the exchange at different times, leading it to be difficult to buy and sell an asset

· Market makers continuously provide a bid-ask spread to the exchange, enabling greater liquidity as buyers and sellers can transact faster (Liquidity: the ease with which a security (stock) can be converted to cash. Can be thought of as “cashiness”)

· Traders benefit from better prices and smoother flow of their transactions

Explanation of Strategy

Generally, three types of algorithms exist forming a market making strategy.

· Valuation: Valuation algorithms work to find the price of an asset based on exchange data, converting this to buy and sell quotes

· Position Management: Maintaining a running inventory of equities and options to facilitate smooth trading. This deals with two main types of risk: adverse selection risk and inventory risk

· Execution: Carrying out and managing orders while taking market developments into account. This works on a nanosecond latency to execute trades

Other Important Concepts

· Adverse Selection Risk: the possibility that a buyer or seller knows more about an asset than the counterparty (in this case, the market maker). Other parties can use their information edge on the asset to effectively win trades, so position management algorithms are crucial in reducing that risk

· Inventory Risk: By running an inventory of assets to facilitate trading, market makers are exposed to price changes that can make their inventory be worth less. This is managed in a variety of ways; the simplest method is offloading the asset as soon as possible to pocket the spread

Quant Funds: The Good and Bad

Good Example (Renaissance Technologies)

· Founded by Jim Simons, code-breaker and award-winning mathematics researcher

· Known as the most successful quant fund of all time, Medallion Fund has returned 39% net of fees to date

· Adhere strictly to models and heavy emphasis on risk management, humans don’t make investment decisions

Bad Example (Long Term Capital Management)

· Found securities that were mispriced relative to one another, taking long positions in cheap ones and short in expensive (resulting in 50x — 100x leverage)

· Collapsed after the 1997 Asian and 1998 Russian bond defaults, eventually dissolved in 2000

· Collapse attributed to improper risk management, adding to positions that moved against them

Recommended Books

· The Man Who Solved the Market — Gregory Zuckerman

· When Genius Failed — Roger Lowenstein

Quantitative vs. Fundamental Investing

Discipline

Quantitative Strategies involve high levels of discipline as investors can only tweak the “black box”, not the final investment decision. They rely on a set of rules to trade on.

Fundamental Strategies require the investor to be responsible for imposing discipline on their practice; they must commit to trying to remove human emotion, relative to quant strategies where emotion is inherently removed.

Use of Data

Quantitative Strategies rely on various sizes, types, and processing of data. The use of data is what differentiates one fund from another.

Fundamental Strategies rely on human-interpreted data, which includes intuition and assumptions. The analysis of data is what differentiates one fund from another.

Speed

Quantitative Strategies can trade at very high speeds, ranging between milliseconds to months.

Fundamental Strategies tend to trade over long time horizons (a few quarters to a few decades).

Hedge Fund Strategies

Explanation of Strategy

Simple Moving Averages (SMA): an average of the last x number of data points (Ex. SMA 50 is an average of the last 50 days of closing prices)

· Moving averages “smooth” out the curve: the longer the period the less a single day has an effect on the direction

The simplest version of the strategy uses an SMA 50:

· If price is > MA then BUY

· If price is < MA then SELL

Improved versions will use exponential moving average (EMA)

· EMA’s give more importance to recent values and are more reflective of the current price

· More sophisticated models might use different ways to calculate averages, a combination of indicators, etc.

Example Strategy (Obtain Satellite Data of Retail Parking Lots)

· First, you determine the occupation rate of the parking lot

· You compare it with other similar parking lots

· Finally, you understand the customer flow, as well as trends that could impact it

· These parking lots can be used as a proxy to estimate quarterly earnings

*Alpha comes from information asymmetry compared to retail investors

Careers & Recruiting

Requirements and Opportunities

Quantitative finance draws from three major disciplines: mathematics, statistics, and computer science. Having the knowledge on how to leverage each discipline is key to being successful as a quant.

Three roles as a “Quant”:

· Trader: Uses quantitative strategies to find trading possibilities, rely on mathematics

· Developer: Turn mathematical models into efficient code to trade in the markets, rely on computer science

· Researcher: Search for alpha, seek to develop creative ideas on commonly available data to generate an edge, rely on statistics

Skills

· Example courses to take: Real Analysis, Linear Algebra, Timer Series Analytics, Statistics

· Strong fundamental mathematics and probability skills (Ex. Quick mental math, game theory, probability “games”)

· Understanding programming concepts and languages (Ex. data structures & algorithms, Python, C++, R)

Firms Offering Internships

· Akuna Capital, CPP Investment Board, Citadel, IMC

Resources

· QuantStart: Systematic trading techniques and growing your personal account. Includes Books (Simplified and Advanced Algorithms) and Quantcademy (educational videos)

· Textbooks: Introduction to Statistical Learning is a textbook that focuses on the principles of using statistics on datasets to generate signals. Elements of Statistical Learning is more advanced

· Building your own algorithms: Find datasets on websites such as Kaggle. Try to predict events/movements in prices with the utilization of data, and compare to how others do it