Remember 52 Pick-Up? A dubious sibling, usually older, would ask if you wanted to play a game while holding a deck of cards. Emphatically, you agree, only to be showered with the deck of playing cards and told to pick them up. Wikipedia in its infinite wisdom defines 52 pick up as "a game of

Value investing is an investment philosophy that has been extensively discussed and examined at least since the days of Ben Graham, who popularized it as a discipline in the 20s and 30s. While there are some who are dismissive of its advantages as a long-term strategy, the historical evidence is

Over the last ten years the subject of deep learning has been one of the most discussed fields in machine learning and artificial intelligence. It has produced state-of-the-art results in areas as diverse as computer vision, image recognition, natural language processing and speech recognition.

In exploratory data analysis, we often need to visualize our data in different formats, in order to gain more understanding about the numbers and the relationship between the parameters. One such wonderful and informative representation is the ‘Heatmap’, which is basically a colored image, the

This post will demonstrate how to take into account turnover when dealing with returns-based data using PerformanceAnalytics and the Return.Portfolio function in R. It will demonstrate this on a basic strategy on the nine sector SPDRs. So, first off, this is in response to a question posed by one

The state of trend following was negative last month, as it was in March. The index is now just above the zero-line for the year, back from nearly the +20% mark a month and a half ago. Please check below for more details. Detailed Results The figures for the month are: April return: -2.35% YTD

Everyone seems to want to time factors. Often the first question after an initial discussion of factors is “ok, what’s the current outlook?” And the common answer, “the same as usual,” is often unsatisfying. There is powerful incentive to oversell timing ability. Factor investing is often

My first post on using machine learning for financial prediction took an in-depth look at various feature selection methods as a data pre-processing step in the quest to mine financial data for profitable patterns. I looked at various methods to identify predictive features including Maximal

Blogging is hard. Quant blogging is even harder. I sometimes hear from bloggers in our community that their motivation to blog has faded. Most begin writing with little expectation, just happy to take a break from crunching numbers to interact with actual humans. Sometimes that optimism wanes

We investigate the biases in the backtested performance of “alternative beta” strategies using a sample of 215 commercially promoted trading strategies across five asset classes. Our results lend support to the cautions in recent literature regarding backtest overfitting and lack of robustness

This blog post is available for download here. Summary When a manager outperforms, it implies that other investors have underperformed. In understanding an investment process, we believe it is critical to understand the source of this outperformance to determine whether it is sustainable or not. We

The increasing popularity of index investing forces us to ask the following questions: What necessary functions does active management perform in a financial system? What is the optimal amount of active management to have in such a system, to ensure that those functions are carried out? If the size

Almost at the very moment I published a post featuring some interesting research by Glabadanidis (“Market Timing With Moving Averages” (2015), International Review of Finance, Volume 15, Number 13, Pages 387-425 – see Yes, You Can Time the Market. How it Works, And Why), several readers wrote

The best quant mashup links for the two weeks ending Saturday, 05/07 as voted by our readers: Get Rich Slowly [Financial Hacker] Backtesting Strategies with R (h/t Algotrading Reddit) [Tim Trice] Quantopian Paper About In vs Out-of-Sample Performance of Trading Algorithms [Quantpedia] The Impact of

I. Trading Strategy Developer: Larry Connors (The 2-Period RSI Trading Strategy), Welles Wilder (RSI Momentum Oscillator). Source: (i) Connors, L., Alvarez, C. (2009). Short Term Trading Strategies That Work. Jersey City, NJ: Trading Markets; (ii) Wilder, J. W. (1978). New Concepts in Technical

In the last article in the series we looked at the foundational courses that are often taken in a four-year undergraduate mathematics course. We saw that the major courses were Linear Algebra, Ordinary Differential Equations, Real Analysis and Probability. In the "second year" of our

What does it mean to be the “best” research? For me, this means the most influential in changing my view on the world. So the below list of “best” research represents the research that 1) changed my view of the world 2) helped sharpen my thinking. For context, I’ve been reading source

A big guest on the podcast this week—a man who for many won’t need any introduction, he is; Eric Scott Hunsader. Eric started out as an algorithmic trader in the early 80’s, soon after became a self-taught programmer, and since then he’s written many software applications for financial data.

A reader sent me a link to presentation, Robert Frey – 180 years of Market Drawdowns, about drawdowns and the time that a strategy is underwater. I highly suggest you watch it. I wanted to perform my own analysis on how often a buy and hold strategy on the S&P500 index is making new equity

In recent conversations with current and potential clients, we have seen an uptick in the use of indexed annuities as a tool for risk management. For the uninitiated, Fidelity succinctly described an indexed annuity in a recent blog post: "An indexed annuity is a contract issued and guaranteed

Vanguard founder John Bogle gave a powerful speech last month at the Q Group’s Spring Seminar that lays out the case (again) for favoring basic indexing and shunning complexity in matters of portfolio design and management. As Morningstar’s John Rekenthaler points out, Bogle wields the weapon of

When automated trading strategies are developed and evaluated using backtests on historical pricing data, there exists a tendency to overfit to the past. Using a unique dataset of 888 algorithmic trading strategies developed and backtested on the Quantopian platform with at least 6 months of

The prior piece received a much stronger reaction than I expected. The topic is complicated, with ideas that are difficult to adequately express in words, so I’m going use this piece as a follow-up. I’m going to look at the specific example of the Tech Bubble, which is a clear case in which the

This book is designed to not only produce statistics on many of the most common technical patterns in the stock market, but to show actual trades in such scenarios. Test a strategy; reject if results are not promising Apply a range of parameters to strategies for optimization Attempt to kill any

Most trading systems are of the get-rich-quick type. They exploit temporary market inefficiencies and aim for annual returns in the 100% area. They require maintenance, supervision, and regular adaption to market conditions. Their expiration is often accompanied by large losses. But what if you’ve

Another down month for the index, with a slight loss. Two mild down months after two strong up months keep the index in positive territory for 2016. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for April: WSTF-201604-Index And the 12-month

Especially in economics/econometrics, modellers do not believe their models reflect reality as it is. No, the yield curve does NOT follow a three factor Nelson-Siegel model, the relation between a stock and its underlying factors is NOT linear, and volatility does NOT follow a Garch(1,1) process,

U.S. equity index funds have grown dramatically in recent decades, from a negligible $500MM in assets in the early 1980s to a staggering $4T today. The consensus view in the investment community is that this growth is unsustainable. Indexing, after all, is a form of free-riding, and a market can

Because I have been an investment professional for more than 40 years, I sometimes get asked my opinion about the markets. These questions usually come from those who invest without a systematic approach toward investing. Here are some typical questions and answers: Question: How much do you think

Investors love to chase after the “next big thing,” as investment strategies and styles come in and go out of vogue. The latest object of investor infatuation may be a revitalized interest in merger arbitrage. Consider this bloomberg headline: “Hedge Fund Investors Have Fallen in Love with

It is known that data mining algorithms are not perfect and they can fail under certain conditions. K-Means is an example of that triviality but there is a good alternative, K-Medoids. In a previous post, “Machine Learning: A Brief Breakdown” we already mentioned that K-Means is the cluster

In an earlier post I was looking at distance measures for clustering. In a still earlier post I had referred to analyzing hedge fund regulatory data using clustering to try to put the funds into groups by inferred strategy. I had to solve a problem with clustering that has being bothering me for a

The concept of diversification is based on the concept that a trader can reduce his risk exposure by entering several positions at the same time. The success of a traders portfolio is therefore based on reducing risk rather than maximizing returns. A trader should be able to withstand a string of

A few weeks back i wrote a post including the source code for a Monte Carlo simulation function in R. The idea was to randomly sample daily returns produced by a backtest and build a confidence interval distribution of the middle 50% and 90% of returns. Since then Brian Peterson got in touch with me

Conventional momentum strategies rely on 12 months of past returns for portfolio formation. Novy-Marx (2012) shows that the intermediate return momentum strategy formed using only twelve to seven months of returns prior to portfolio formation significantly outperforms the recent return momentum

Summary Many tactically risk-managed strategies use trend following to manage the risk of severe drawdowns, but in sideways markets, like those experienced in 2011 and 2015, trend following ends up lagging the market by buying high and selling low. As with insurance policies or static allocations to

This paper documents a new empirical fact. Long-short anomaly returns are strongly related to the day of the week. Anomalies for which the speculative leg is the short (long) leg experience the highest (lowest) strategy returns on Monday. The exact opposite pattern is observed on Fridays. The

What is measurement error bias? Errors-in-variables, or measurement error situation happens when your right hand side variable(s); your x in a y_t = \alpha + \beta x_t + \varepsilon_t model is measured with error. If x represents the price of a liquid stock, then it is accurately measured because

The best quant mashup links for the two weeks ending Saturday, 04/23 as voted by our readers: Lossless Compression Algorithms and Market Efficiency? [Turing Finance] You can’t beat all the chimps [Following the Trend] My Year-Long Experience as the Fastest Form-4 Trader [Greg Harris] Are 3-year

Mad Men watchers may recognize the name Bernbach from the quote above. Bernbach is referred to in the second season as the innovative competitor firm that challenges Sterling Cooper's orthodoxy. Bill Bernbach was the brain behind several successful campaigns, including Avis' We Try Harder

Some weekend reading for trend-followers who want to question their beliefs. Valeriy Zakamulin is an animal when it comes to generating research on moving averages. We’ve done a lot of the same work, but we’re too lazy to tabulate the results in an academic paper format. king of ma The king of

The Market Portfolio is NOT Efficient: Evidences, Consequences and Easy to Avoid Errors Pablo Fernandez (University of Navarra), et al. March 16, 2016 The Market Portfolio is not an efficient portfolio. There are many evidences that tell us that: the equal weighted indexes have beaten their

fidlr is an RSutio addin designed to simplify the financial data downloading process from various providers. This initial version is a wrapper around the getSymbols function in the quantmod package and only Yahoo, Google, FRED and Oanda are supported. I will probably add functionalities over time.

If history is any guide, we’re standing at the edge of 40%–96% returns over the next two years. This isn’t wishful thinking or wild speculation. I’m not selling anything. Rather, I’m just reporting historical gains from a market set-up that’s repeating itself right now. So what’s going

This paper considers several popular portfolio implementation techniques that maximize exposure to value and/or momentum stocks while taking into account transaction costs. Our analysis of long-only strategies illustrates how a strategy that simultaneously incor- porates both value and momentum

R/Finance 2016 is less than a month away and, as always, I am very much looking forward to it. In past years, I have elaborated on what puts it among my favorite conferences even though I am not a finance guy. R/Finance is small, single track and intense with almost no fluff. And scattered among the

In this post we discuss sentiment analysis in brief and then present a basic sentiment analysis model in R. Sentiment analysis is the analysis of the feelings (i.e. attitudes, emotions and opinions) which are expressed in the news reports/blog posts/twitter messages etc., using natural language

The trend following model by Kaufman says that trading by the direction of the trend is a conservative approach to the markets. Kaufman’s Market Efficient Model states that longer trends are the most reliable but they respond rather slowly to changing market conditions. The main argument of the

I apologize in advance for this rather "wonkish" post, which is aimed chiefly at the high frequency fraternity, or those at least who trade intra-day, in the equity markets. Such minutiae are the lot of those engaged in high frequency trading. I promise that my next post will be of more

Bagging and Boosting are both ensemble methods in Machine Learning, but what is the key behind them? Bagging and Boosting are similar as they are both ensemble techniques, where a set of weak learners are combined to create a strong learner that obtains better performance than a single one. So,