Thematic Analysis of a Reading List on Investment Alpha

I recently did a thematic analysis of a reading list on investment alpha, which involves:

 

1. Excess return.

2. Active management.

3. Adjusted risk.

 

The following themes emerged from the reading list, and from also checking the rankings of several hundred books at Amazon.com:

 

1. Excess return: fund type (hedge fund, private equity, venture capital); return drivers (including asset class); and quantitative models.

 

2. Active management: discretionary (human trading, portfolio composition and rebalancing, options, technical analysis) and algorithmic (algorithmic trading; complex event / stream processing; computational intelligence; genetic algorithms; machine learning; neural nets; and software agents).

 

3. Adjusted risk: Bayesian probabilities; investor psychology; market microstructure; and risk management models (such as Monte Carlo simulation, Value at Risk, and systematic risk)

 

This core work suggests the following query line:

 

SELECT return drivers (Bayesian belief network) (multi-asset) (portfolio) (fund)

 

 

WHERE risk (Bayesian probability) (exposures) (exposures – investor decisions) (exposures – market microstructure) AND trade (algorithms)

 

ORDER BY Bayesian (belief network, probability); return drivers (multi-asset); risk (exposures); and trading (algorithms).

 

This thematic analysis will help to focus my post-PhD research on the sociology of finance into the following initial research questions:

 

1. What is the spectrum of possible return drivers in a multi-asset world?

 

A good model for this is David Swensen’s Yale endowment portfolio detailed in Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment (New York: The Free Press, 2009). Antti Ilmanen’s magisterial Expected Returns: An Investor’s Guide to Harvesting Market Rewards (Hoboken, NJ: John Wiley & Sons, 2011) has information on the return drivers of specific asset classes. Matthew Hudson’s recent Funds: Private Equity, Hedge Funds, and All Core Structures (Hoboken, NJ: John Wiley & Sons, 2014) deals with global fund structures.

 

2. What specific risk exposures might these multi-assets face, and under what conditions?

 

Richard C. Grinold and Ronald Kahn’s Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk (New York: McGraw-Hill, 1999) is the classic book on institutional portfolio models. Morton Glantz and Robert Kissell’s Multi-Asset Risk Modeling: Techniques for a Global Economy in an Electronic and Algorithmic Trading Era (San Diego, CA: Academic Press, 2014) is a recent book I will look at. Charles Albert-Lehalle and Sophie Larulle’s Market Microstructure in Practice (Singapore: World Scientific Publishing Company, 2014), and Thierry Foucault, Marco Pagano, and Ailsa Roell’s Market Liquidity: Theory, Evidence, and Policy (New York: Oxford University Press, 2013) deal respectively with the practice and theory of contemporary financial markets. There are many books on behavioural finance and investor psychology: two recent ones are H. Kent Baker and Victor Ricciardi’s collection Investor Behavior: The Psychology of Financial Planning and Investing (Hoboken, NJ: John Wiley & Sons, 2014), and Tim Richards’ Investing Psychology: The Effects of Behavioral Finance on Investment Choice and Bias (Hoboken, NJ: John Wiley & Sons, 2014).

 

3. How can algorithmic trading and computational techniques model the risk-return dynamics of alpha generation?

 

Despite its flaws Rishi K. Narang’s Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading (New York: John Wiley & Sons, 2013) opened my eyes to the structures needed for alpha generation. The Bayesian approach is detailed in David Barber’s Bayesian Reasoning and Machine Learning (New York: Cambridge University Press, 2012). Barry Johnson’s Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies (London: 4Myeloma Press, 2010) and Robert Kissell’s The Science of Algorithmic Trading and Portfolio Management (San Diego, CA: Academic Press, 2013) deal with order types in algorithmic trading. Christian Dunis, Spiros Likothanassis, Andreas Karathanasopoulos, Georgios Sermpinis, and Konstantinos Theofilatos have edited a recent collection on Computational Intelligence Techniques for Trading and Investment (New York: Routledge, 2014). Eugene A. Durenard’s Professional Automated Trading: Theory and Practice (New York: John Wiley & Sons, 2013) covers software agents. For retail trader-oriented applications of data mining, machine learning, and Monte Carlo simulations there is Kevin Davey’s Building Algorithmic Trading Systems: A Trader’s Journey from Data Mining to Monte Carlo Simulation to Live Trading (New York: John Wiley & Sons, 2014), and David Aronson and Timothy Masters’ Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB (CreateSpace, 2013).

 

What this means is that for an investment of about $US1,000 a new researcher can gain some of the core books on institutional, quantitative portfolio and risk management; behavioural finance and market microstructure as potential sources for edges; and some recent practitioner-oriented literature on algorithmic / automated trading that uses computational intelligence.

 

In deference to Mao and McKenzie Wark’s vectoralist class:

 

Let a thousand algorithmic / quantitative micro-funds bloom.

15th June 2013: 10 Books For First Trading System

I’ve spent the past three weeks developing my first trading system: a value-influenced mean reversion strategy that includes causal variables for hedge fund activism and political risk. Here are 10 books I used as part of the development process for my first trading system:

 

1. Jeff Madrick’s Age of Greed: The Triumph of Finance and the Decline of America, 1970 to the Present (Alfred A. Knopf, 2011). Madrick’s overarching history of Wall Street provides detail on central bank, monetary policy, political administration, industry sector, and deal flow variables. It’s also great investigative journalism that gives a deep historical background to what capital and financial markets are really like.

 

2. John Heins & Whitney Tilson’s The Art of Value Investing: How the World’s Best Investors Beat the Market (Hoboken, NJ: John Wiley & Sons, 2013). I started with value investor wisdom for the initial idea development and possible decision rules. Heins & Whitney’s investment newsletter interviews gave me plenty of examples for inductive data coding.

 

3. Anti Ilmanen’s Expected Returns: An Investor’s Guide to Harvesting Market Rewards (Hoboken, NJ: John Wiley & Sons, 2011). Ilmanen was one of the first sources I consulted to understand the historical return drivers of equities as an asset class, and its inter-market relationship with other common asset classes.

 

4. Andrew W. Lo’s Hedge Funds: An Analytic Perspective (rev. ed.) (Princeton, NJ: Princeton University Press, 2011). Hedge fund activism that shapes equities asset prices is a key causal variable for the mean reversion strategy. Lo’s research highlights some hedge fund trading patterns and shows how to draw inferences from databases and market data.

 

5. Keith Fitschen’s Building Reliable Trading Systems: Tradable Strategies That Perform as They Backtest and Meet Your Risk-Reward Goals (Hoboken, NJ: John Wiley & Sons, 2013). I’ve read several books that go back a decade on mechanical, automated and algorithmic trading systems. Fitschen highlights mean reversion and momentum strategies, and the importance of robust backtesting with both in-sample and out-of-sample market data.

 

6. Richard Tortoriello’s Quantitative Strategies for Achieving Alpha (New York: McGraw-Hill, 2009). Tortoriello’s book is essentially a collection of factor models and quantitative screens that uses a Standard & Poor’s rating methodology. Factor models help to isolate the potential alpha of return drivers from the market beta.

 

7. Barry Johnson’s Algorithmic Trading & DMA: An Introduction to Direct Access Trading Strategies (London: 4Myleoma Press, 2010). Johnson covers the importance of market microstructure, early developments in algorithmic and high-frequency trading, and the importance of transaction and execution costs. Several publishers are releasing new books about these topics in the second half of 2013.

 

8. Aaron C. Brown’s Red-Blooded Risk: The Secret History of Wall Street (Hoboken, NJ: John Wiley & Sons, 2012). Brown is a risk manager with AQR Capital Management. One of the many insights I took from this book was the importance of risk ignition in ‘live’ testing of a trading system, and in considering exit signals.

 

9. Ari Kiev’s The Mental Strategies of Top Traders: The Psychological Determinants of Trading Success (Hoboken, NJ: John Wiley & Sons, 2010). The late Ari Kiev was an influential sports performance and trading psychologist who consulted with Steve Cohen’s SAC hedge fund. He has written several best-selling books on trading psychology. This book deals with expectational analysis and variant perception (Michael Steinhardt) which frames the entry signals and filters that a trading system must have.

 

10. John Coates’ The Hour Between Dog And Wolf: Risk-Taking, Gut Feelings, and the Biology of Boom and Bust (London: Fourth Estate, 2012). Coates’ personal research program combines ‘live’ trading experience and neurophysiological studies. Extremely useful information on the human stress response, mental toughness, and risk stressors that can shape ‘live’ trading or the ‘live’ discretionary over-ride of algorithmic trading systems.

 

Hedge fund and trading system architecture is expensive, and beyond the reach of the retail investor. However, investment in the above books (US$345.32 from Amazon.com at the time of original publication) may give a glimpse of what is possible, from initial idea development (value-based mean reversion) to backtesting, ‘live’ trading, and possible algorithm coding. It’s the information you select; the processes you use; and how your alpha return drivers support the trading system that results from research development, backtesting, and ‘live’ trading.