In this post I am going to be looking at portfolio optimisation methods, touching on both the use of Monte Carlo, “brute force” style optimisation and then the use of Scipy’s “optimize” function for “minimizing (or maximizing) objective functions, possibly subject to constraints”, as

Researchers often devote a significant amount of time trying to determine the optimal, or best performing, configuration of a trading model. With the prolif­eration of data and advances in high-performance computing, it is trivial to optimize millions, even billions, of trading models and parameter

Most factors generated positive returns in 1H 2019 Low Volatility produced the best and Value the worst performance Factor performance is comparable in the US & Europe, but markedly different in Japan INTRODUCTION We present the performance of five well-known factors on an annual basis for the

What are the research questions? The authors present 4 MYTHs regarding stock buybacks popular in the financial press. MYTH 1: Companies are self-liquidating using share repurchases at a historically high rate. MYTH 2: Share repurchases have come at the expense of profitable investment. MYTH 3: The

We continue our exploration of quantitative signals in fixed income. We use a measure of credit curve steepness as a valuation signal for timing exposure between corporate bonds and U.S. Treasuries. The value signal generates a 0.84% annualized return from 1950 to 2019 but is highly regime dependent

1. Crashes and Low Returns (link) Static asset allocation locks in the “Two Risks that Ruin Investing” - crashes and low returns. If you accept a static asset allocation strategy, you accept its history repeating in the future. For example, a 60/40 strategy drawdown of -63% in the 1930’s. 2.

I published a bitcoin swing trading strategy in 2015 over here (German only). Time to review the methodology of swing trading and have a look on the performance. Can a rational strategy get an edge in an irrational market? Have a look and be surprised! Swing Point Trading Technique Swing trading is

Positive month for the Wizards which lifts the YTD performance further up in the positive territory at the halfway mark. Please check below for more details. Detailed Results The figures for the month are: June return: 1.46% YTD return: 4.52% Below is the chart displaying individual system results

Economists use machine learning (ML) to study asset prices in two different ways. Approach #1: use these techniques to predict the cross-section of expected returns—i.e., to predict which stocks are most likely to have high or low future returns. e.g., see here, here, or here. Approach #2: use

Bad beta means market exposure that is expensive to hedge. Good beta is market exposure that is cheap to hedge. Distinguishing between these is crucial for FX trading strategies. The market sensitivity of FX positions can be decomposed into a risk premium beta (‘bad beta’) and a real rate beta

This is part 2 of the Ichimoku Strategy creation and backtest – with part 1 having dealt with the calculation and creation of the individual Ichimoku elements (which can be found here), we now move onto creating the actual trading strategy logic and subsequent backtest. The Ichimoku approach

In the previous post (which should definitely be read first!) we explored how graphs can be used to represent a currency market, and how we might use shortest-path algorithms to discover arbitrage opportunities. Today, we will apply this to real-world data. It should be noted that we are not

One of the recurring themes we see in our research is the concept of “no pain; no gain.” Or as Corey Hoffstein says, “No pain, no premium.” Cliff Asness may put it best when he says that some strategies require that you hold on to them “like grim death.” Bottom line: nothing is easy in

In the great book and series Game of Thrones, the inhabitants of the Iron Islands have a saying “What is Dead May Never Die” which is to be replied with “But rises again harder and stronger.” I am reminded of this saying as more and more market commentators and practitioners declare that

I thought it was about time for another blog post, and this time I have decided to take a look at the “Ichimoku Kinko Hyo” trading strategy, or just “Ichimoku” strategy for short. The Ichimoku system is a Japanese charting and technical analysis method and was published in 1969 by a reporter

TL;DR: We developed a strategy using Augmento sentiment signals, and backtested it on Bitmex XBTUSD to generate a positive return between 2017 and 2019. Creating algorithms to trade Bitcoin is hard, and finding good data that is independent of the price but still correlated with the market is even

This is a follow-up post to a recent post in which we discussed how to generate 1-dimensional financial time series with Generative Adversarial Networks. If you haven’t read that post yet we suggest you to do so, since it introduces the building blocks used in this one. Here we will go over the

With a 5-star rating on iTunes, we are proud to say that Season 1 of our podcast – Flirting with Models – received a tremendously warm welcome. And so we’re happy to announce that Season 2 is now available! You can listen to the new season on: iTunes Stitcher Google Play TuneIn Android The

There is consistency in the performance of the Value factor across markets and asset classes Allows to create a coherent framework of how to think about Value Suggests a global driver of factor performance INTRODUCTION Our research aims to educate investors by bridging the gap between academic

fter you have trained a neural network (NN), you would want to save it for future calculation and eventually deploying to production. So, what is a Tensorflow model? Tensorflow model contains the network design or graph and values of the network parameters that we have trained. Important Note: I

In March I discussed how the weeks following options expiration in March, June, and September have been the worst 3 weeks of the year. Below I have updated the June stats and profit, which I also showed last June. 2019-06-23 The strong, steady downslope and bearish numbers suggest we are entering a

Since my last post I have been working on the process noise covariance matrix Q, with a view to optimising both the Q and R matrices for an Extended Kalman filter to model the cyclic component of price action as a Sine wave. However, my work to date has produced unsatisfactory results and I have

The Federal Reserve publishes the yield-to-maturity of US Treasury bonds. However, the actual returns earned by investors are not publicly available. Nor are they readily and intuitively discerned from historical yields, since “a bond’s return equals its yield only if its yield stays constant

In this article, we will understand how natural language processing, sentiment analysis and social media play a role in the share markets with the help of Python. This would be explained with respect to the trading in China markets A-share stocks. This article is the final project submitted by the

When dealing with mathematical modeling, choosing the right scale to frame the equations can make the difference between a successful and lasting model, or poor description of reality. In today’s post, we explore two important scaling procedures that arise in finance: the annualisation of returns

We are now closer to applying our knowledge of neural networks (NN) to our trading systems. But, we still have to tune our rudiments a bit on TensorFlow. If you are not yet familiar with our supervised machine learning flowchart, take a look at the first two posts in this series. DeepTrading with

We introduce triangle plots for visualising long-term investment metrics. Return triangles are well suited to showcase the performance of a strategy or asset for a huge number of possible subperiods. Sensitivity analysis with respect to the length of the holding period as well as the start and end

Before you commit your precious time to read this blog post, I need to warn you that this is one of those posts that market nerds like myself will get a kick out of, but which probably won’t add much of practical value to your trading. The purpose of this post is to scratch the surface of the

What are the research questions? Do the most prominent long/short factors — value, momentum, carry, and defensive — survive out of sample? Can long/short factors be timed? What are the Academic Insights? YES. All of the factors exist out of sample, albeit their magnitudes are generally muted.(1)

We expand last week’s commentary to explore momentum, carry, value, and long-term reversal signals in a time-series context. Using these signals, we generate long/short portfolios for each asset class. We use a sub-sampling methodology to bootstrap and annualized return distribution. We find that

Investors can access alternative strategies via mutual funds and ETFs Most of these show moderate to high correlations to equities, which is concerning Bonds would have been a better diversifier in recent years INTRODUCTION Investing is challenging as it is complex and complicated, which requires

News and comments are major drivers for asset prices, maybe more so than conventional price and economic data. Yet it is impossible for any financial professional to read and analyse the vast and growing flow of written information. This is becoming the domain of natural language processing; a

One commonality in my strategies is the inclusion of a market timing component. This could be a signal to go into cash or reduce position size or enter a ‘safe’ ETF. This applies to my swing trading strategies, my monthly rotation strategies and my Tactical Assert Allocation strategies. As a

We’ve written a lot here and on our sister site BetterBuyAndHold.com about the threat of rising interest rates. You can read some of our past work projecting returns for Treasury ETFs and other interest rate sensitive assets here and here. In a nutshell, assuming that we’re near the tail end of

We built a simple tool recently to review so-called value spreads over time. (1) This tool maps out the median valuations for the top decile and bottom decile “cheap stock” portfolios (as measured by EBIT/TEV). Why might this be useful? This tool allows one to identify the “valuation” spread

As a non-academic finance person, I was never really exposed to academic research until I started working on articles for Alpha Architect. Fortunately (or unfortunately, depending on your perspective), I am now very familiar with the so-called “cross-section of expected returns” debates.

When you build your portfolio you must decide what is your risk profile. A pension fund’s risk profile is different than that of a hedge fund, which is different than that of a family office. Everyone’s goal is to maximize returns given the risk. Sinfully but commonly risk is defined as the

In this commentary we explore the application of several quantitative signals to a broad set of fixed income exposures. Specifically, we explore value, momentum, carry, long-term reversals, and volatility signals. We find that value, 3-month momentum, carry, and 3-year reversals all create

Alternative Title: How to Avoid Bad Manager Timing Let’s look at the two types of risks in most investments: Strategy Risk: If you own a black-box ‘go-anywhere’ hedge fund that invests long and short and uses futures and derivatives at any frequencies, you are mostly exposed to the strategy

The performance of the Size factor in the US was positive since 1926, but not particularly attractive Returns in Europe were more favorable, but not in Japan Alternative metrics to market capitalization would not have resulted in better performance SMALL VERSUS LARGE STOCKS In the David vs. Goliath

Strong result in May for the Trend Following index, taking the Year-to-Date performance in positive territory. Please check below for more details. Detailed Results The figures for the month are: May return: 5.01% YTD return: 2.94% Below is the chart displaying individual system results throughout

Join experts from banks, buy-side, Silicon Valley and academia to meet, network and share ideas at America's leading quant finance event. 3 key themes shaping the agenda: 1. Innovations in machine learning, HFT, AI and data 2. Quant techniques in investment and trading 3. Advances in option

Mean-reverting portfolio construction is an exciting area that involves a wide range of forecasting and optimisation techniques. In Part 1 of the setries, I demonstrate the approach to the construction of optimal mean reverting portfolios satisfying sparsity and volatility constraints.

Hedge funds’ capital structure is vulnerable to market shocks because most of them offer high liquidity to loss-sensitive investors. Moreover, hedge fund managers form expectations about each other based on market prices and investor flows. When industry-wide position liquidations become a

In an interview published at the Enterprising Investor blog, Frank Fabozzi, a well-known researcher and author in the mathematical finance field, has sharply criticized the current state of academic economics and finance. Here are some highlights: The “rational models” constructed in economics

An important part of getting a Kalman filter to work well is tuning the process noise covariance matrix Q and the measurement noise covariance matrix R. This post is about obtaining the R matrix, with a post about the Q matrix to come in due course. In my last post about the alternative version

The underperformance of value stocks over the past 10 years has received much attention from the financial media and led at least some investors to conclude that value investing is dead. From 2009 through March 2019, while the S&P 500 Index returned 14.2 percent per annum (total cumulative

This is a summary of the recent performance of a wide range of excellent Tactical Asset Allocation (TAA) strategies, net of transaction costs. These strategies are sourced from books, academic papers, and other publications. While we don’t (yet) include every published TAA model, these strategies

In this commentary we explore tactical credit strategies that switch between high yield bonds and core fixed income exposures. We find that short-term momentum signals generate statistically significant annualized excess returns. We use a cross-section of statistically significant strategy

Last week’s talk by Edward Altman at the 50-year anniversary of Altman’s Z-score event at the CFA New York inspired me to compile an expanded list of memorable inventions in equity analysis. Each one is a successful blend of quantitative and fundamental thinking - which is increasingly being