Bollinger Bands have been a popular indicator by traders since they were invented in the early 1980’s. They’re calculated in four, easy steps and are intended to provide traders an idea of the price range of a security. We can use these to develop a number of different algorithmic strategies to

Although linear systems are pretty convenient at many levels, many real world applications cannot rely in this assumption. The Extended Kalman Filter can deal with these nonlinearities in a simple way. Learn how in this post. Introduction In the 1960s, Rufold E. Kalman codeveloped one of the most

Are you looking to get stock market data and analyse the historical data in Python? You have come to right place. After reading this, you will be able to: Get historical data for stocks Plot the stock market data and analyse the performance Get the fundamental, futures and options data For easy

Despite government bonds being one of the major asset classes invested in global portfolios, 30% of overall market capitalization according to Doeswijk, et al. (2020), little work has been done to investigate whether factors are present in the sovereign bond market. (Here is a deep dive into fixed

By the third year of my PhD program, I was impatient. I had endured 8 years of lectures, exams, and keeping close watch over my bank account’s balance. Meanwhile, my colleagues from undergrad had embarked on interesting projects with big potential, and were getting paid well to do so. Their lives

Equities generated attractive nominal returns across all inflation regimes However, real returns were zero when inflation was above 10% Energy and materials performed best, consumer-facing sectors worst INTRODUCTION “I came of age and studied economics in the 1970s and I remember what that

Welcome to Part 2 of the series of posts dealing with how to build your own python based personal portfolio /wealth simulation model. At the end of the first post (which can be found here), we got to the point where we had modelled some inflows, some outflows, we had applied an annual salary raise

Nowadays, we see an increasing number of machine learning based strategies and other related financial analyses. But can the machines replace us? Undoubtedly, AI algorithms have greater capacities to “digest” big data, but as always in the markets, everything is not rational. Cao et al. (2021)

Prospect theory was developed by Daniel Kahneman and Amos Tversky in 1979. The theory starts with the concept of loss aversion—the observation that people react differently between potential losses and potential gains. Thus, people make decisions based on the potential gain or loss relative to

Forecasting the Long-Term Equity Premium for Asset Allocation Athanasios Sakkas (U. of Nottingham) and Nikolaos Tessaromatis (EDHEC) July 12, 2021 Long-term country equity premium forecasts based on a cross-sectional global factor model (CS-GFM), where factors represent compensation for risks

In 2006, Meb Faber wrote a highly influential paper on tactical asset allocation and market timing. The strategy was particularly attractive in part because of its simplicity: Buy when monthly price > 10-month SMA Sell and move to cash when monthly price By applying this simple, mechanical

In recent posts I reviewed several basic applications for generating fair-value estimates for the 10-year Treasury yield, which can be used as a proxy for projecting return. Let’s expand this effort by forecasting performance for the US equity market over a 10-year window. The goal is developing a

We will examine the relationship between annual returns and largest annual drop. Let’s use some well known Select Sector SPDRs and the SPDR S&P 500 Trust (SPY). Using prices from 1999-01-01 to 2021-06-30 we calculate the annual returns and the biggest drop for each year. For example, if we

Probably like a lot of you, I am an indicator junkie. Whenever I read about an indicator I have not tested and makes some sense, I got to try it out. Now, most of the time they turn out to not be useful for my strategies. While reading the April 2021 Technical Analysis of Stocks & Commodities, I

Features are inputs to supervised machine learning (ML) models. In traditional finance, they are typically called “factors”, and they are used in linear regression models to either explain or predict returns. In the former usage, the factors are contemporaneous with the target returns, while in

Compared to plain indicators, bands have the advantage that they look more colorful on charts. And they offer more lines to trigger trade signals. In this way, bands beat any old single-line indicator hands down. This was also noticed by Vitali Apirine, who invented in the Stocks&Commodities

Social media is replete with examples of people showing how much money they made going all-in on Bitcoin, Tesla, Gamestop, Dogecoin or whatever new fad is out there. These are the lucky ones. Most people bet too large and eventually blow up their account, losing years of hard work and wealth in the

Generally, a quant is a professional in the financial technology industry who designs complex algorithms with the help of quantitative analysis. Quants are skilled in mathematics, finance and computer skills - a blend which is rare. In the trading domain, quants design and implement the algorithms

Long volatility strategies can be built without using options Our systematic approach has used exclusively currencies and bonds Investors can achieve attractive diversification benefits with such strategies INTRODUCTION The insurance policy is one of the game-changing products of our civilization as

Risk parity is a portfolio construction technique that seeks to equalize risk contributions from the different components of the portfolio. Risk parity with respect to uncorrelated risk sources maximizes diversification. Simple risk parity rules are based on the inverses of market beta, price

In a 2020 paper, the authors explore the side effects of applying ESG screens to a passive portfolio. While the ESG scores or tilt was improved and Sharpe ratios increased, significant regional, sector, and conventional risk factor exposures were magnified. While investors may be attracted and

The sentence that surprised me most while reading Fischer and Krauss’ paper ‘Deep learning with long short-term memory networks for financial market predictions’ was as follows: “To our knowledge, there has been no previous attempt to deploy LSTM networks on a large, liquid, and survivor

As we have promised, we present a short article as an introduction into the methodology of the Quantpedia Pro CPPI reports. Quantpedia Pro clients can use the model portfolio built in the Portfolio Manager as a risky asset to test various variants – Basic CPPI, Drawdown Based CPPI and Dynamic

We track more than 60 tactical asset allocation strategies. In this post we look at which of those strategies are most often recommended by common portfolio optimization techniques, and why strategies that “play well with others” are not always the best strategies. First, a little background for

The Q1 2021 factor rotation into Value and Size has reversed Value is the only factor with positive performance in 1H 2021 Momentum has generated the most negative returns INTRODUCTION We present the performance of five well-known factors on an annual basis for the last 10 years. Specifically, we

The SPX, Dow, and NASDQ all closed at all-time highs on Friday. Just 10 days ago the Dow closed at a 50-day low. That quick of a move from low to high is quite an accomplishment. Over the last 101 years this just the 21st time the Dow has managed to move from a 50-day closing low to a 50-day closing

Over the last few posts I've been grappling with the difficulties of trading futures with a retail sized account. I've tried a couple of things so far - a complex dynamic optimisation (here and here) where I try and optimise the portfolio every day in the knowledge that I can only take

Two of the more interesting puzzles in finance are the high beta anomaly (high beta stocks have lower returns) and the IVOL anomaly (stocks with greater idiosyncratic volatility have produced lower returns). These are anomalies because both beta and IVOL are viewed as risk factors and should be

Nowadays, social media are involved in fields that were unimaginable in the past. Among others, the world of finance, trading and investing is no exception. For example, Stocktwits is a strong community in this area, Seeking Alpha connects (non)professional analysts, and Twitter connects

There are many types of approaches you can use in pairs trading, but the Distance Approach is one of the most widely used because of its simplicity. The basic concept is as follows: Using Euclidean squared distance on the normalized price time series, n closest pairs of assets are chosen as pairs. S

In a couple of recent posts (here and here) I explored the idea of using dynamic optimisation to deal with the following problem: diversification across markets is good, but requires more capital. That didn't work out so well! I can also appreciate that this is *way* beyond most peoples idea of

The online trading platform is growing bigger day by day. More people are entering this industry, and they are trying to come up with unique trading strategies. But automated trading has taken the industry by storm. Automated trading is the best thing an independent trader can ask for. But

The research (and the theory) has convincingly shown that mutual funds should and do underperform a passive index by an amount approximately equal to fees. However, no one has actually tried to construct the active mutual fund dominating passive strategy using commercially available products. It

This is a test of a tactical bond strategy from Paul Novell of Investing for a Living. It rotates between credit bond ETFs and defensive assets based on the same rules as his popular SPY-COMP strategy. Backtested results from 1970 follow. Results are net of transaction costs – see backtest

Regression analysis is frequently subject to multicollinearity Independent variables can be residualized Using residualized variables in a factor exposure analysis identifies different drivers DISCLAIMER “The worth of an econometrics textbook tends to be inversely related to the technical material

This post is inspired by Leo Breiman’s opinion piece “No Bayesians in foxholes”. The saying “there are no atheists in foxholes” refers to the fact that if you are in the foxhole (being bombarded..), you pray! Leo’s paraphrase indicates that when complex, real problems are present, there

SPX managed to make an intraday high for the 5th day in a row on Friday. An interesting study from the Quantifinder looked at the possible impact of 5 higher highs occurring. The studies examined the impact of the position of the market when the 5 higher highs occurred. I broke it down again over

A new proxy formula for equity market-to-book ratios suggests that (the logarithm of) such a ratio is equal to the discounted expected value of (i) differences between return on equity and market returns and (ii) the net value added from share issuance or repurchases. A firm with a higher

This is part two in a series of posts about using optimisation to get the best possible portfolio given a relatively small amount of capital. Part one is here (where I discussed the idea). You should read that now, if you haven't already done so. In this post I show you and explain the code and

Equity Tail Risk in the Treasury Bond Market Mirco Rubin (EDHEC) and Dario Ruzzi (Bank of Italy) December 23, 2020 This paper quantifies the effects of equity tail risk on the US government bond market. We estimate equity tail risk as the option-implied stock market volatility that stems from large

Pairs selection is the first crucial step to building a pairs trading strategy. And it is no surprise, to perform it correctly, one must diligently examine, compare and contrast numerous test results, graphs and characteristics. For example, cointegration analysis alone can be performed in one of

Recently I ran across an interesting paper published by National Bureau of Economic Research entitled “Return Based Measue of Firm Quality”. I happen to have a suitable data and thought why not reproduce it on data from polish stock exchange in the free time. It turned out not so bad and thanks

In the previous blogs, we examined supervised learning algorithms like linear regression in detail. In this blog, we look at what unsupervised learning is and how it differs from supervised learning. Then, we move on to discuss some use cases of unsupervised learning in investment and trading. We

Last year when bitcoin had its fourth drawdown of 80% in the past ten years, I thought It might be a good time to reenter that market. Having traded digital assets in 2017, I was familiar with the reasons for owning bitcoin. I won’t reiterate them here. You can find information on bitcoin,

The J.P. Morgan Efficiente 5 Index is a tactical asset allocation strategy designed by J.P. Morgan based on a broad universe of 13 ETFs. This post will illustrate how to replicate this strategy with Google Sheets. Notes: A fully functional spreadsheet corresponding to this post is available here.

Cryptocurrencies are a hot topic in the investing world, but is it possible to create an investment methodology combining modern Reinforcement Learning with classical indicators? Along this blog we have covered topics such as how to automate cryptocurrencies investment or whether reinforcement

The concept of pairs trading is pretty straightforward. As described in [Gatev et al. (2006)], we first find two stocks that have moved together historically and then monitor the spread between these stocks. If the prices of the two stocks diverge, we short the winner and go long on the loser,

As the following table demonstrates, since its inception in the 1970s, the private equity industry has grown significantly. According to Preqin data, there are now more than 18,000 private equity funds, with assets under management exceeding $4 trillion. Source: NACUBO endowment studies, FY

While portfolio optimization is well known in the Equity space, in the Fixed Income industry, the subject is less discussed although it has very specific needs and it can be more complex compared to its Equity counterparts. One key difference between the two of them is the trading lot size. In

Catastrophe bonds offered exceptionally high risk-adjusted returns since 2005 These were uncorrelated to equities, making cat bonds attractive for diversification However, cat bonds might have underpriced risk historically, raising concerns going forward INTRODUCTION The global pandemic continues to