Some things in life are naturally made for each other. Some examples include the following: Peanut Butter and Jelly Starsky & Hutch Value and Momentum So my ears perked up when the idea of combining VIX levels and Trend Following started making the rounds on finance twitter. Like any geek, I(...)

Successful investing requires translating sound investment concepts into actual trading strategies. We study many of the implementation details that portfolio managers need to pay attention to; such choices range from portfolio construction to execution. While these kinds of decisions apply to any(...)

Does Bitcoin behave like some other asset class? To investigate, we calculate daily and monthly return correlations between Bitcoin and each of 34 exchange-traded products encompassing eight used in “Simple Asset Class ETF Momentum Strategy ” (SACEMS), 24 considered in “SACEMS Portfolio-Asset(...)

In this article series Imanol Pérez, a PhD researcher in Mathematics at Oxford University, and an expert guest contributor to QuantStart outlines the basics of high-frequency trading. In this article Imanol uses the theory of stochastic optimal control to optimally execute a large trade order. It(...)

Calculating returns on a price series is one of the most basic calculations in finance, but it can become a headache when we want to do aggregations for weeks, months, years, etc. In python the Pandas library makes this aggregation is very easy to do, but if we don’t pay attention we could still(...)

Note: this is Part two of a two-part article series. Please see article one here. Michael Edesess’ article, The Trend that is Ruining Finance Research, makes the case that financial research is flawed. In this two-part article series, weexamine the points that Edesess raised in some detail. His(...)

SUMMARY It’s difficult to rationalise why there should be excess returns from high quality stocks The Quality factor needs to be constructed beta-neutral to achieve positive returns Exposure to the Quality factor is an attractive hedge for an equity-centric portfolio INTRODUCTION The concept of(...)

The current return expectations for core U.S. equities and bonds paint a grim picture for the success of the 4% rule in retirement portfolios. While varying the allocation to equities throughout the retirement horizon can provide better results, employing tactical strategies to systematically(...)

The benefits of country diversification are well established. This article shows that the same benefits extend to equity factors, such as value, size, momentum, investment, and profitability. Specifically, country factor portfolios reflect both common variation, which we define as the global(...)

Now that the Federal Reserve has begun the process of raising interest rates, and has announced their intention to begin to unwind their policy of quantitative easing (reducing the amount of bonds in their portfolio, either by selling holdings or allowing holdings to mature), investors may be(...)

It has been about 5 months since my last blog post and in this time I have been working away from home, been on summer holiday and spent some time mucking about on boats, so I have not been able to devote as much time to my blog as I would have liked. However, that has now changed, and this blog(...)

This is a test of the Trinity Portfolio from Meb Faber and Cambria Investments, so named for the three key elements of the strategy: (1) a globally diversified mix of assets, (2) a tilt towards the value and momentum factors, and (3) exposure to momentum and trend-following. We’ve titled our test(...)

Why don’t machines rule the financial world? In his September 2017 presentation entitled “The 7 Reasons Most Machine Learning Funds Fail”, Marcos Lopez de Prado explores causes of the high failure rate of quantitative finance firms, particularly those employing machine learning. He then outlines(...)

We are back again with another post on Python. Our last post, “Basic Operations on Stock data using Python” was well received and we are glad to see the number of likes & shares for the post on various quant trading and Python forums. Keep them coming! Financial market data is a very critical(...)

I recently gave a presentation on Sector trading using the 200-day moving average at the Northwest Traders and Technical Analysts. Some questions asked were: What if we only trade this monthly? What if we used weekly bars to trade only weekly? Wat if we used weekly bars to trade monthly? The reason(...)

Motivation Support Vector Machines (SVM) are among the most popular Supervised Learning techniques for classification and regression, due to their ease in usage to find non-linear patterns. They work by separating data by finding an optimal threshold – known as a decision boundary or hyperplane, to(...)

Michael Edesses’ article, “The Trend that is Ruining Finance Research” makes the case that financial research is flawed. In this two-part article series, we will examine the points that Michael raises in some detail. We find his arguments have some merit. Importantly however, his article fails to(...)

Equal-weight and volatility-weighted allocations are two common factor allocation frameworks Risk-return ratios are not higher with volatility-weighted allocations Different reasons can explain the superiority of equal-weight allocations INTRODUCTION In July we published a research report “Factors(...)

Averages are often used to summarize data: but sometimes fitting for the average means fitting nothing at all. Expected returns are a meaningful input to portfolio construction, but are unlikely to be the returns actually realized. Reality rarely looks average. The world is dynamic and forecasts(...)

Asset Allocation in a Low Yield Environment John Huss (AQR Capital Mgt.), et al. August 17, 2017 The year 2016 saw bond yields fall to unprecedented low levels in major developed markets, with nominal yields on 10-year German and Japanese government bonds even turning negative. While yields have(...)

From a seasonality standpoint, there isn’t a more reliable time of the year to have a selloff than this upcoming week. In the past I have referred to is as “The Weakest Week”. Since 1961 the week following the 3rd Friday in September has produced the most bearish results of any week. Below is a(...)

The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become(...)

Smart Portfolios is about building and maintaining smart investment portfolios. At its heart are the three key questions every investor needs to answer: 1. What to invest in. 2. How much to invest. 3. When to make changes to a portfolio. Author Robert Carver addresses these three areas by providing(...)

As we have mentioned before, here, here and here, there is overwhelming evidence that the number of stock anomalies in the universe is much lower than originally thought. Most of the previous research papers attempt to filter out past anomalies in the literature (generally over 300+) by applying(...)

Equity factors can be valued using fundamental metrics Value and Size are cheap while Low Volatility and Growth are expensive Likely more meaningful for medium- to long-term than short-term investors INTRODUCTION The term “Factor Investing” reached an all-time high this year according to Google(...)

In the first three articles of our Dynamic Asset Allocation for Practitioners series (article 1, article 2, article 3), we focused on the first half of the total process. We specified a universe of global asset classes and sorted it on relative strength with 21 different raw and risk-adjusted(...)

In this article series Imanol Pérez, a PhD researcher in Mathematics at Oxford University, and an expert guest contributor to QuantStart continues the discussion of high-frequency trading via the introduction of the limit order book. As we saw in the in the first article of the series, the(...)

Prof. Richard Price, an old friend, co-author, and Alpha Architect advisory board member, is working on some cool new co-authored research that requires audience participation! Dr. Price, alongside Professors Dipankar Ghosh, and Atul Rai, are conducting research to better understand what factors(...)

What are the research questions? Research has shown that dividends constitute a greater contribution to the returns of a value versus a growth strategy. However, the question remains as to whether or not dividends matter within a style category. For style investors, does dividend policy determine(...)

Continuing a previous post, in which we modeled the relationship in the levels of the VIX Index and the Year 1 and Year 2 CBOE Correlation Indices, we next turn our attention to modeling changes in the VIX index. In case you missed it, the post can be found here: Correlation Cointegration We saw(...)

In a world of anemic asset returns, tax management may help significantly contribute to improving portfolio returns. Ideally, asset location decisions would be made with full investor information, including goals, risk tolerances, tax rates, and distribution of wealth among account types. Without(...)

Correlations between Quality and Growth factors are currently elevated Value is more negatively correlated than usual to Quality, Growth and Low Volatility Monitoring correlations is important for maximising diversification benefits INTRODUCTION The rise of ETFs is often associated with higher(...)

Hello all, last week I wrote a guest post featured on Dr. Ernest Chan’s blog which highlighted some of my research while working with QTS Capital Management on social media sentiment analysis and its place in financial models. The focus of this research was on how to derive sentiment signals from(...)

Notes on Part-2 The Data Bid-Ask Spread Analysis How Do Aggregate Bid-Ask Spreads Vary with Days To Expiration? How Do Bid-Ask Spreads Vary with Volume? How Do Bid-Ask Spreads Vary with Volatility? Summary Conclusions Notes on Part-2 Some astute readers in the comments noted that analysis based on(...)

In our final blog post, that finishes the trend-following series, we briefly review the results of the forward-tests of the profitability of various trend following rules in different financial markets: stocks, bonds, currencies, and commodities. The results of these tests allow us to better(...)

Following on from my recent posts about trading volatility over weekends, I’m now going to briefly look at trading options overnight. Option traders have always complained when they were too long options overnight, expecting to usually lose money on overnight longs. This doesn’t make sense in a(...)

There has been a recent popularity regarding time windows and it is one I completely agree with! There are certain structural changes that happen throughout the 24 hour session and as a trader it is important to take note of these when designing a system or strategy (or just placing trades). For(...)

This paper develops a portfolio allocation framework to study the benefits of style integration and to compare the effectiveness of alternative integration methods in commodity markets. The framework is flexible enough to be applicable to any asset class for either long-short, long- or short-only(...)

My latest post at http://quantsjourney.blogspot.co.uk/2017/09/time-of-day-effects-in-fx.html was on time of days effects in FX and I was claiming that you can actually make money with simple strategies depending on time of day. Below you will find 2 very simple strategies you can play with and make(...)

Exploring alternative datasets to augment financial trading models is currently the hot trend among the quantitative community. With so much social media data out there, its place in financial models has become a popular research discussion. Surely the stock market’s performance influences the(...)

One question that I am asked frequently is which operating system to use for quantitative trading research and implementation. The short answer, as of the writing date of this article, is if you want to carry out any serious/mathematical quant trading research (machine learning/deep learning) you(...)

I recently had someone email me about the performance of a strategy I created back in late 2005/early 2006 and traded for a few years. I remember the strategy being a daily mean reversion set up with an intraday pullback entry. I figured it probably had not done well over the last decade. I stopped(...)

Time of day is critical for trading, it is even possible building trading strategies solely depending on time of day (I will keep this for another post) I will be using the concept of quality and define a high quality market, from an intraday timing perspective, as a market when trading range and(...)

There are no silver bullets for profiling risk, but drawdown’s properties arguably give this metric a leg up over most of the competition. The combination of an intuitive framework, simplicity, and sharp focus on how markets actually behave is a tough act to beat. Perhaps the strongest argument in(...)

This is another comparison between R and MATLAB (Python also in the mix this time). In previous rounds we discussed the differences in 3d visualization, differences in syntax and input-output differences. Today is about computational speed. Spoiler alert: MATLAB wins by a knockout. A genuinely fair(...)

Everybody would like to see the future. If you’re a portfolio manager, you’d definitely love to see the future. Many posts here on QuantDare deal with the challenge of predicting the future (with Prophet, Random Forests, Lasso, etc). This time, we talk about something different: imagine we are able(...)

In the previous article we looked at how one can use Neural Networks to detect jumps present in returns of a particular stock. In this blog post, we build on the thinking established in the previous article and use a Random Forest to detect jumps present in stock market returns. I have build an(...)

If you’re interested in using artificial neural networks (ANNs) for algorithmic trading, but don’t know where to start, then this article is for you. Normally if you want to learn about neural networks, you need to be reasonably well versed in matrix and vector operations – the world of linear(...)

Volume Filters: Part 1 | Trading Strategy (Entry & Exit) I. Trading Strategy Developer: R. D. Edwards, J. Magee (Volume Filters); R. D. Donchian (Price Breakout Channels). Concept: Trading strategy based on price breakouts confirmed by volume filters (i.e. volume breakouts). Research Question:(...)

Our firm is growing rapidly and we’re looking to hire new teammates (one initially, possibly another down the road). If you are passionate about investor education and helping us deliver affordable alpha, please reach out! We just posted a new job for an execution trader/researcher role. Jack and(...)