Here's the curriculum I've put together for Quantitative Finance students.

It starts at a fairly basic level (introduction to diversification, trend following, and relative momentum) and culminates with advanced reading in academic papers.

### Prerequisites

- A deep-seated interest in how financial markets work
- An A grade in either precalculus or high school statistics (AP Calculus AB or AP Statistics preferred)
- Ability to commit a minimum of six hours per week to finance homework

## Introductory Books

**The Ivy Portfolio (Meb Faber)**

I've screened dozens of books and have chosen this one because of its readability and balanced introduction to a variety of topics.

Part I discusses how the well-known Yale and Harvard endowments invest and how individual investors can replicate those strategies using index funds.

Part II provides a brief introduction to private equity and hedge fund strategies.

Part III introduces a simple trend-following system that has historically increased risk-adjusted returns while cutting the size of large losses in half.

For example, adding a 10-month moving average filter to the S&P 500 increased its return from 9.3% to 10.6% and lowered its volatility from 15.6% to 11.9%, nearly doubling the risk-adjusted return. The maximum drawdown (largest peak-to-trough loss) was cut from 45% to 23%.

The book also discusses a 13F strategy that can be used to track the stocks that hedge funds buy, cloning their exposures without paying fees.

**The Way of the Turtle (Curtis Faith)**

Curtis Faith's book is the most accessible introduction to building trading strategies that I've seen.

Chapters 1 and 2 cover the trader's greatest enemy: himself and his own biases. Trading rules-based systems based on their historical performance eliminates most of these problems.

Chapters 3-8 introduces trend following trading, risk management, and basic statistical metrics that are used to evaluate strategies.

Chapters 9-10 discuss specific indicators that are used to build trend-following trading systems.

Chapters 11-14 go into some of the pitfalls of building systems, including overfitting, small sample sizes, and under-diversification.

The epilogue presents the full-fledged trend trading system that the author, one of the original Turtle traders, used when he was in his twenties working for Richard Dennis.

## Strategy Research and Paper Trading

To practice screening for stocks, students initially sign up for StockRover's free plan.

When they're ready to start testing to start testing strategies, they'll need to sign up for Portfolio123. Students that are serious about doing their own research will find the Ultimate plan to be most helpful, as it will allow them to use variable position sizing (as opposed to equally weighting all the stocks) when they run their tests.

When they're ready to start testing to start testing strategies, they'll need to sign up for Portfolio123. Students that are serious about doing their own research will find the Ultimate plan to be most helpful, as it will allow them to use variable position sizing (as opposed to equally weighting all the stocks) when they run their tests.

Paper trading for stocks and options can be done for free through TD Ameritrade's ThinkorSwim platform. (It's the paperMoney feature.) Your broker probably also offers a paper-trading system, though it may not be as sophisticated as Thinkorswim's.

## Academic Research (organized by topic)

Within each topic, I've sorted the research so that the easiest material is at the top of the list.

### Momentum

*AQR addresses criticisms of momentum by pointing to its strong premium, pervasive evidence (both through time and across markets), and negative correlation to value.*

*Estimates of parameters for portfolio optimization based on long-term observed average values are inferior to estimates based on observations over much shorter time frames.*

*NOTE #1: You can play around with Portfolio Visualizer's mean-variance optimizer if you want to get a hands-on idea of what the paper is talking about.*

*NOTE #2: This is one of the papers featured in my $10,000 financial adviser offer.*

Volatility-Adjusted Momentum

*Incorporating volatility estimates in constructing stock momentum leads to a Sharpe ratio increase (0.34 to 1.14) and strongly reduced crash risk. Similar improvements are found in corporate bonds.*

### Trend Following

*We show that the returns of Managed Futures funds and CTAs can be explained by time series momentum strategies and we discuss the economic intuition behind these strategies. Time series momentum strategies produce large correlations and high-R-squares with Managed Futures indices and individual manager returns, including the largest and most successful managers. While the largest Managed Futures managers have realized significant alphas to traditional long-only benchmarks, controlling for time series momentum strategies drives the alphas of most managers to zero. We consider a number of implementation issues relevant to time series momentum strategies, including risk management, risk allocation across asset classes and trend horizons, portfolio re-balancing frequency, transaction costs, and fees.*

*AQR examines out-of-sample evidence for trend following in factors and alternative assets. They find that it works, provides substantial diversification, and isn't explained by vol targeting.*

Enduring Effect of Time-Series Momentum on Stock Returns Over Nearly 100 Years

*Trend following works in individual U.S. (1927-2014) and international (1975-2014) stocks without January losses or momentum crashes.*

*Dual Momentum – A Craftsman’s Perspective*

*Our objectives were twofold:*

*1.Verify the strength and robustness of the Dual Momentum concept and specifically the Global Equity Momentum strategy*

*2.Describe how to use ensemble methods to preserve expected performance while minimizing the probability of adverse outcomes*

*Glitch*

*The convexity profile for a short-term trend following system is most significant when measured over weekly or monthly horizons. Long-term systems, on the other hand, exhibit negative or insignificant convexity profiles over these horizons.*

*Cross-Sectional and Time-Series Tests of Return Predictability: What Is the Difference?*

*The difference between the performances of TS and CS strategies is largely due to a time-varying net-long investment in risky assets.*

### Value

Fact, Fiction, and Value Investing*With characteristic wit, AQR corrects misconceptions. Topics include concentration, profitability, momentum, value in other asset classes, composite metrics, and large caps.*

*Equal-Weight Benchmarking: Raising the Monkey Bars*

*In the period from 1969 to 2011, if you had picked stocks at random, there is a 99.9% chance you would have beaten the market. It is certainly remarkable that, at a time when the vast majority of hypothetical monkeys flinging darts at the financial pages outperformed, less than half of active managers managed to do so.*

*NOTE: This is the paper featured in my Introduction to Quantitative Finance page.*

*Combination Metric Backtest and Comparison of Value Deciles (1951 to 2013)*

*Over the long run, and with some regularity, cheap stocks tend to outperform more expensive stocks. The PB, PE and PCF ratios are all very useful metrics for sorting cheap stocks from expensive stocks, but we can’t know which will be the better bet at any given point in time. The combo spreads the risk of underperformance relative to any single metric, and, in doing so, generates reliable investment performance over the full period without lagging far behind the front-runner at any point.*

*Do 'Dogs of the World' Bark or Bite? Evidence From Single-Country ETFs*

*Mean reversion in financial markets is commonly accepted as a powerful force. This paper examines the performance of a simple mean-reversion-based strategy -- Dogs of the World -- designed to take advantage of return reversals in national equity markets. Both a simulated application of the strategy using indexes since 1971 and application using single-country ETFs since 1997 produces higher compounded average returns than those of a comparable market index. Although the Dogs strategy also produces higher volatility than the index, the information ratio for the strategy suggests that the return more than compensates. An advantage of this strategy is that its implementation using single-country ETFs is straightforward and inexpensive. [NOTE: This paper is no longer available at the SSRN link provided.]*

Industry Long-Term Return Reversal

*Long-term reversals (3-10 year formations): all U.S. stocks are grouped into 48 industries, and the industries are ranked and traded L/S against each other.*

### Betting-Against-Beta

*We present a model with leverage and margin constraints that vary across investors and time. We find evidence consistent with each of the model’s five central predictions: (1) Since constrained investors bid up high-beta assets, high beta is associated with low alpha, as we find empirically for U.S. equities, 20 international equity markets, Treasury bonds, corporate bonds, and futures; (2) A betting-against-beta (BAB) factor, which is long leveraged low beta assets and short high-beta assets, produces significant positive risk-adjusted returns; (3) When funding constraints tighten, the return of the BAB factor is low; (4) Increased funding liquidity risk compresses betas toward one; (5) More constrained investors hold riskier assets.*

*What drives BAB - leverage constraints or behavioral effects? AQR digs in by dividing BAB into B.A.Correlation and B.A.Volatility factors.*

### Quality

*Higher-quality stocks have higher prices (P/B) on average, but not by a very large margin. A quality-minus-junk (QMJ) factor earns significant risk-adjusted returns in the U.S. and globally.*

### Size

*In the earliest days of empirical work in academic finance, the size effect was the first market anomaly to challenge the standard asset pricing model and prompt debates about market efficiency. The notion that small stocks have higher average returns than large stocks, even after risk-adjustment, was a pathbreaking discovery, one that for decades has been taken as an unwavering fact of financial markets. In practice, the discovery of the size effect fueled a crowd of small cap indices and active funds to a point where the investment landscape is now segmented into large and small stock universes. Despite its long and illustrious history in academia and its commonplace acceptance in practice, there is still confusion and debate about the size effect. We examine many claims about the size effect and aim to clarify some of the misunderstanding surrounding it by performing simple tests using publicly available data.*

### Short-Term Reversal

*Only the component of short-term reversal which isolates reaction to recent “nonfundamental” price changes is significant and positive; it generates a risk-adjusted return 3x as large.*

### Seasonality

*Halloween and turn-of-the month (TOM) are the strongest calendar effects, fully diminishing the other three (the January effect, weekend effect and holiday effect) to zero.*

Using a sample of 97 stock return anomalies, we find that returns are 6 times higher on earnings announcement days. Results are consistent with biased expectations being at least partially corrected.

### Multi-Factor

Buffett's Alpha*AQR finds that Berkshire Hathaway's large 13% CAPM alpha and 0.79 Sharpe ratio become insignificant after controlling for BAB and QMJ. Warren Buffett may have been the first multifactor investor.*

*NOTE #1: Here's a thread about Berkshire Hathaway's factor loadings. You can also check them yourself at Portfolio Visualizer.*

*NOTE #2: This is one of the papers featured in my $10,000 financial adviser offer.*

*Ignored Risks of Factor Investing*

*The risks of factor investing are usually understated (perhaps, severely so), and the diversification benefits tend to be overstated.*

Factor Momentum and the Momentum Factor

*Factors are positively autocorrelated [and time-series momentum strategies work on them]. Momentum is not a distinct risk factor; it aggregates the autocorrelations found in other factors.*

### Craftsmanship Alpha

Long-Only Style Investing: Don't Just Mix, Integrate*AQR compares "mixing" styles via stand-alone portfolios and "integrating" by combining signals during the stock selection process. Integrating is better b/c it minimizes the impact of constraints.*

*Role of Shorting, Firm Size, and Time on Market Anomalies*

*AQR dives into the sources of alpha for L/S size, value, and momentum and whether the alphas have weakened over time.*

### Tax Alpha

*The authors construct basic value and momentum strategies. The long-short portfolio has a tax BENEFIT of 0.3%, which can be increased to 6.1% through tax-aware stock selection and timing.*

*Relaxing the long-only constraint results in a large increase in their tax benefits in particular due to an increase in the character benefit.*

### Volatility Targeting

*The authors look at a strategy that uses volatility to adjust the leverage of individual factors (SMB, HML, Mom, RMW, CMA, Carry) as well as country equity indices.*

*This paper looks at volatility targeting over equities, futures and bonds. Volatility is generally autocorrelated, BUT targeting works better for equity and credit than Treasuries and commodities.*

### Options

Options as a Strategic Investment (Lawrence McMillan)

*Selling a call against stock does NOT provide a reliable cushion against declines. The cushion comes from the call's negative delta and its exposure to the volatility risk premium, not from the premium that you initially collect.*

*Delta-hedged covered calls outperform unhedged covered calls. This is the case in equity indices throughout the world. The VRP appears to add more diversification than the equity component of the strategy.*

*Equity index covered calls have historically provided attractive risk-adjusted returns largely because they collect equity and volatility risk premia. However, they also embed exposure to an uncompensated risk, a naïve equity market reversal strategy.*

*(1) Long-short trend-following and (2) covered calls each have higher Sharpes than buy-and-hold but negative correlations to each other. [This paper is no longer available at the original SSRN download page.]*

*Diagonal collars on QQQ, 3/1999-3/2008, generated higher returns, lower volatility, lower drawdowns, and lower kurtosis than QQQ itself. Purchases were made at ask and sales at bid.*

*Embedded Leverage*

*Many financial instruments are designed with embedded leverage such as options and leveraged exchange traded funds (ETFs). Embedded leverage alleviates investors' leverage constraints and, therefore, we hypothesize that embedded leverage lowers required returns. Consistent with this hypothesis, we find that asset classes with embedded leverage offer low risk-adjusted returns and, in the cross-section, higher embedded leverage is associated with lower returns. A portfolio which is long low-embedded-leverage securities and short high-embedded-leverage securities earns large abnormal returns, with t-statistics of 8.6 for equity options, 6.3 for index options, and 2.5 for ETFs. We provide extensive robustness tests and discuss the broader implications of embedded leverage for financial economics.*

Volatility Trading (Euan Sinclair)

## More Strategies

I stay abreast of current research via the FinTwit community and maintain lists of the more interesting developments below (organized by topic):- Benchmarks
- Value, Long-Term Reversal, Low Volatility, and Quality
- Trend-Following, Cross-Sectional Momentum, and Carry
- Volatility Risk Premium
- Calendar and Diagonal Spreads
- Real Estate, Commodities, and Bonds
- Seasonality, Sentiment, Macro, and Short-Term Mean Reversion
- Craftsmanship Alpha
- Tax Alpha
- Sarlacc Pits (things to avoid buying)
- Market Psychology and Bubbles

If you're interested in learning more, please use the form below to contact me. I look forward to meeting you!

## 0 comments:

## Post a Comment