Nothing makes a situation better like good timing. Whether it’s getting a promotion, catching the last train after a night out, meeting the love of your life, or joining a quant community – it is many of small consequential gambles of stopping decisions that get us to that triumphant “Yes!”

“Price is what you pay; value is what you get.” B. Graham “In the short run, the market is a voting machine, but in the long run it is a weighing machine.” B. Graham “Value investing is at its core the marriage of a contrarian streak and a calculator.” S. Klarman "Never confuse

Cesar Alvarez from AlvarezQuantTrading.com discusses effective Market Regime techniques to improve strategy performance and reduce risk by only trading in the best market conditions.

I follow Quantocracy on Twitter, and I found Rolling mean correlation in the tidyverse by Robot Wealth. They say to let them know if you’d approach it differently. I would, so I thought it would be interesting to replicate the analysis using tools I’m familiar with: xts and TTR. The xts package

Many in the academic and practitioner research world are arguing that the reported B/P ratio is approaching it’s “expiration date” because of the importance of intangibles, R&D, brand value, and so on. While the determination of true “intrinsic value” remains in the domain of active

There is a negative relationship between aging populations and stock valuations Given that most developed markets are aging, this creates structural headwinds for equities The massive future population declines require investors to rethink traditional asset allocation INTRODUCTION “The curious

With the increasing popularity of machine learning, many traders are looking for ways in which they can “teach” a computer to trade for them. This process is called algorithmic trading (sometimes called algo-trading). Algorithmic trading is a hands off strategy for buying and selling stocks that

As I have shown many times in the past, there isn’t a more reliable time of the year to have a selloff than this upcoming week. I have often referred to is as “The Weakest Week”. Since 1960 the week following the 3rd Friday in September has produced the most bearish results of any week. Below

The tidyverse is a collection of packages that facilitate data science with R. It is particularly powerful for macro trading research because [a] it supports efficient and standardized work with R’s vast universe of econometric models, [b] is well adapted for analyzing data vintages (i.e. data

In our last post, we ran simulations on our 1,000 randomly generated return scenarios to compare the average and risk-adjusted return for satisfactory, naive, and mean-variance optimized (MVO) maximum return and maximum Sharpe ratio portfolios.1 We found that you can shoot for high returns or high

This weekend is the very first episode of the new BST Live show. On the show this week we’re going to discuss how to improve your trading performance and reduce risk by trading only when the market conditions are best for your strategies. I’ve got Cesar Alvarez from Alvarez Quant Trading joining

This is an independent test of Aspect Partners’ flagship tactical asset allocation strategy Risk Managed Momentum (RMM). By tactical standards, RMM is a very active, very aggressive strategy. It has done an excellent job navigating this difficult year so far. Backtested results from 1970 follow.

The price momentum and accruals (the difference between accounting earnings and cash flows—adjustments made for revenue that has been earned but not received, and costs that have been incurred but not paid) anomalies are two well-documented financial phenomena. Recent research has focused on

Trading books set a low bar for the reviewer. 99% are full of facile feel-good advice (don’t fight the trend, always use a protective stop). The 1% that are useful tend to either be dry technical treatments (quants who don’t trade), or sporadically helpful insights from traders who make money

There’s been alot of speculation about what’s next for BST. This video explains all the details you need to know, including what’s happening to BST this Sunday…

A couple of months ago we started a new set of posts designed to keep the QuantStart community aware of what the QuantStart team had been up to in previous month. In last month's post we discussed what we had been working on in July 2020. Articles and Tutorials In August we once again reviewed

We all intuitively know we are better than average drivers, and if we’re investors, well we know we’re better than average there too. Frankly, we all put ourselves easily in the top 10%, at least. Those that are in the know will quote Buffet’s, “The Superivestors of Graham-and-Doddsville,”

Network analysis can provide interesting insights into the dynamics of the market, and the continually changing behaviour. A Minimum Spanning Tree (MST) is a useful method of analyzing complex networks, for aspects such as risk management, portfolio design, and trading strategies. For example,

Just imagine that every time you went into a shop, you had to change your clothes, because they had a different dress code? Say your grocer would insist on only people wearing yellow, whilst your supermarket would require red clothes only.. Ok, that’s a somewhat ridiculous example, which would

This is a test of two recent papers: Momentum Turning Points and Breaking Bad Trends. Learn more about what we do and follow 50+ asset allocation strategies like these in near real-time. Successful trend-following strategies must balance the “speed” of the trading signal. If the signal is too

This paper is unlike any research we’ve shared in the past. Within we dive into the circumstantial evidence surrounding the “weird” behavior many investors believe markets are exhibiting. We tackle narratives such as the impact of central bank intervention, the growing scale of passive /

The usage of alternative data is now a main-stream topic in investment management and algorithmic trading. So let’s together explore the textual analysis of 10-K & 10-Q filings and analyze how these datasets could be used as a profitable part of investment portfolios. We invite you to read

We all became relatively aware of the CAPE ratio when Shiller predicted the 2000 internet bubble in his book “Irrational Exuberance,” then after he added a touch of robustness when he called the 2007 housing crisis 1 we all became intimately aware of it. Since that time CAPE has been utilized to

When investing, we spend plenty of time thinking about which securities should we buy but we rarely wonder how much money should we allocate in each asset. Although it does not seem like an important aspect, it is crucial when defining a strategy, up to the point that it can determine the hole

Can the excess returns of government bonds be predicted? This is a classic research question in finance academic research circles. Sometimes practitioners, who are often buried in the more exciting parts of the financial markets, tend to forget that government bonds (do they even belong in your

There are 6 properties that empirical correlation matrices exhibit that no synthetic generation method has been able to replicate, until now. Enabling researchers to backtest strategies on an abundance of data would make our algorithms and strategies more robust, accurate, and efficient. Since

Volatility hedge funds provided attractive diversification benefits for equity portfolios However, long were preferable over short volatility strategies Some scepticism is required for the hedge fund index performance INTRODUCTION In finance 101, there is usually little doubt on what constitutes the

In my first series of posts, I will give a primer on state space models (SSM) that will lay a foundation in understanding upcoming posts about their variants, usefulness, methods to apply inference and forecasting possibilities. When talking about a state space model (SSM), people usually refer to a

Nowcasting macro-financial indicators requires combining low-frequency and high-frequency time series. Mixed data sampling (MIDAS) regressions explain a low-frequency variable based on high-frequency variables and their lags. For instance, the dependent variable could be quarterly GDP and the

What happens when strong stocks gap down at the open? A well-known trading strategy is to buy the gap, expecting mean reversion. This post uses Zipline to explore down gaps and finds a profitable strategy based on selling, not buying, the gap. Buy the gap? Buying stocks that gap down is a common

Most signals I deal with are noisy, reflecting noise of underlying prices, volume, vol of vol, etc. Many traditional strategies built on such indicators might either: use signal to scale into position such approaches have to deal with noise to avoid thrashing, adjusting position up and down with

This is a blog post about forecasting vol. This is important, since as sensible traders we make forecasts about risk adjusted returns (as in my previous post), which are joint forecasts of return and volatility. We also use forecasted vol to size positions. A better vol forecast should mean we end

For a long while, investors worked under the assumption that the risk and return relationship of a portfolio was linear, meaning that if an investor wanted higher returns, they would have to take on a higher level of risk. This assumption changed when in 1952, Harry Markowitz introduced Modern

Every once in a while it’s effective to challenge the deep-seated ideas that have been utilized to construct your portfolio. Here at Alpha Architect continuously study the literature and conduct research to derive what we believe gives investors the best shot at winning over the long-haul. This

There seems to be a relationship between the Profitability factor and interest rates The most profitable stocks outperformed the least profitable ones when market cap-weighted However, when equal-weighted, the least profitable stocks outperformed INTRODUCTION The gap between theory and reality in

Fire Sale Risk and Expected Stock Returns George O. Aragon (Arizona State U.) and Min S. Kim (Michigan State U.) July 29, 2020 We measure a stock’s exposure to fire sale risk through its ownership links to equity mutual funds that experience outflows during periods of systematic outflows from the

The presidential campaign is becoming hotter as we are moving closer to this year’s election. But we still have enough time to dig deeper into data about the past elections and prepare for the autumn. Therefore, we have prepared a short video recapitulation of our paper on the pre-election drift.

To any investor that has had the opportunity to experience a large collapse in the market, they can tell you that essentially there is nowhere to hide. Correlations of assets that were held to diversify a portfolio suddenly get very correlated in extreme market conditions aka the tails of the

In our last post, we explored mean-variance optimization (MVO) and finally reached the efficient frontier. In the process, we found that different return estimates yielded different frontiers both retrospectively and prospectively. We also introduced the concept of satsificing, originally developed

Our recent 2020 Content Survey highlighted the desire from many of you to study the more advanced mathematics necessary for carrying out applications in quantitative finance. Two of the highlighted areas were Linear Algebra for Deep Learning along with Stochastic Calculus. The latter is the

Risk-neutral probability distributions (RND) are used to compute the fair value of an asset as a discounted conditional expectation of its future payoff. In 1978, Breeden and Litzenberger presented a method to derive this distribution for an underlying asset from observable option prices [1]. The

The world has unquestionably be sent on a wild ride in 2020. We entered the year full of optimism and hope. Markets were at or near all-time highs, unemployment was low, living on easy street was good. Then the impact of COVID-19 ripped through the market and the economy with enough force to make

In the previous article on the topic of artificial neural networks we introduced the concept of the perceptron. We demonstrated that the perceptron was capable of classifying input data via a linear decision boundary. However we postponed a discussion on how to calculate the parameters that govern

The Value factor is often explained as representing a risk premium or a behavioral bias However, financial analysts regard cheap stocks as less risky than expensive ones Data shows that expensive stocks were riskier than cheap ones, which challenges the risk premium theory INTRODUCTION Which of the

Combinations of equity returns and yield-curve changes can be used to classify market-implied underlying macro news. The methodology is structural vector autoregression. Theoretical ‘restrictions’ on unexpected changes to this multivariate linear model allow identifying economically

In the S&C September 2020 article “Tracking Relative Strength In Four Dimensions”, James Garofallou presents a metric for evaluating a security’s strength relative to 11 major market sectors and over several time periods. All this information is squeezed into a single value. Maybe at cost

Editor’s Note: The ability of value investors to adhere to their investment strategy has been put to the greatest test ever. From January 2017 through March 2020, in terms of total returns, the Russell 3000 Growth Index outperformed the Russell 3000 Value Index by 51.7 percentage points (46.7% vs.

Our main goal in Quantpedia is to broaden the horizons of our readers in the field of systematic investing and quantitative trading. We do not aim to sell trading signals but to inspire and give fresh ideas, of how to invest limited time and resources on quantitative research. Clients can adopt

I always dreamed about the machine which tells me to enter long right before the market starts to go up. Might a neural network be this machine? Using Tradesignal and the free Python Neural Net library Pyrenn it is easy to find out… Part one: Classification of data The first step in the process is

Among stock investors, a common strategy/belief held is Value investing — buying stocks that are relative cheaper on price/fundamental ratios. The idea behind why value investing works is that Value stocks are either (1) riskier and/or (2) have been mispriced by the market. In theory, these