Quant Mashup - OSM

Hidden miners [OSM]

We conclude our discussion of market regime detection by examining Hidden Markov Models (HMMs). Recall this series was inspired by a post from PyQuant News that highlighted a longer article from the London Stock Exchange Group (LSEG). Those who took the CFA exams probably forgot using HMMs in the

*- 3 weeks ago, 30 May 2024, 04:34am -*

Gaussian gold [OSM]

Our previous post, used hierarchical clustering to identify market regimes in the gold miners ETF, GDX. This was inspired by a post from PyQuant News that highlighted a longer article from the London Stock Exchange Group (LSEG). In this post, we’ll continue looking at identifying market regimes

*- 1 month ago, 21 May 2024, 09:18pm -*

Golden clusters [OSM]

We recently saw a post from PyQuant News that piqued our interest, compelling us to dust off the old blog files and get back into the saddle. The post highlights a longer article from the London Stock Exchange Group (LSEG) on how to use different machine learning models to identify and forecast

*- 1 month ago, 17 May 2024, 06:21pm -*

One-N against the world! [OSM]

We’re taking a short break from neural networks to return to portfolio optimization. Our last posts in the portfolio series discussed risk-constrained optimization. Before that we examined satisificing vs. mean-variance optimization (MVO). In our last post on that topic, we simulated 1,000

*- 2 years ago, 3 Nov 2021, 10:52am -*

Netting income [OSM]

For fundamental equity investors, the financial statement is the launchpad for the search for value. True, quants use financial statements too. But they spend less time on what the numbers mean, than on what they are. To produce a financial statement that adequately captures the economic (not GAAP

*- 2 years ago, 15 Sep 2021, 10:03pm -*

Trees and networks [OSM]

It’s been over a month since our last post and for that we must apologize. We endeavor to be more prolific, but sometimes work and life get in the way. On the work front, let’s just say we won’t have to spend as much time selling encyclopedias door-to-door, which should free up more time to

*- 3 years ago, 21 May 2021, 10:51pm -*

Not so soft softmax [OSM]

Our last post examined the correspondence between a logistic regression and a simple neural network using a sigmoid activation function. The downside with such models is that they only produce binary outcomes. While we argued (not very forcefully) that if investing is about assessing the probability

*- 3 years ago, 2 Apr 2021, 10:07pm -*

Activate sigmoid! [OSM]

In our last post, we introduced neural networks and formulated some of the questions we want to explore over this series. We explained the underlying architecture, the basics of the algorithm, and showed how a simple neural network could approximate the results and parameters of a linear regression.

*- 3 years ago, 12 Mar 2021, 08:49pm -*

Nothing but (neural) net [OSM]

We start a new series on neural networks and deep learning. Neural networks and their use in finance are not new. But are still only a fraction of the research output. A recent Google scholar search found only 6% of the articles on stock price price forecasting discussed neural networks.1 Artificial

*- 3 years ago, 26 Feb 2021, 09:19pm -*

Risk-constrained optimization [OSM]

Our last post parsed portfolio optimization outputs and examined some of the nuances around the efficient frontier. We noted that when you start building portfolios with a large number of assets, brute force simulation can miss the optimal weighting scheme for a given return or risk profile. While

*- 3 years ago, 8 Feb 2021, 08:32am -*

Parsing portfolio optimization [OSM]

Our last few posts on risk factor models haven’t discussed how we might use such a model in the portfolio optimization process. Indeed, although we’ve touched on mean-variance optimization, efficient frontiers, and maximum Sharpe ratios in this portfolio series, we haven’t discussed portfolio

*- 3 years ago, 31 Jan 2021, 08:05pm -*

More factors, more variance...explained [OSM]

Risk factor models are at the core of quantitative investing. We’ve been exploring their application within our portfolio series to see if we could create such a model to quantify risk better than using a simplistic volatility measure. That is, given our four portfolios (Satisfactory, Naive, Max

*- 3 years ago, 16 Jan 2021, 10:15am -*

Macro variance [OSM]

In our last post, we looked at using a risk factor model to identify potential sources of variance for our 30,000 portfolio simulations. We introduced the process with a view ultimately to construct a model that could help to quantify, and thus mitigate, sources of risk beyond a simplistic

*- 3 years ago, 31 Dec 2020, 11:08pm -*

Explaining variance [OSM]

We’re returning to our portfolio discussion after detours into topics on the put-write index and non-linear correlations. We’ll be investigating alternative methods to analyze, quantify, and mitigate risk, including risk-constrained optimization, a topic that figures large in factor research.

*- 3 years ago, 15 Dec 2020, 07:53pm -*

Round about the kernel [OSM]

In our last post, we took our analysis of rolling average pairwise correlations on the constituents of the XLI ETF one step further by applying kernel regressions to the data and comparing those results with linear regressions. Using a cross-validation approach to analyze prediction error and

*- 3 years ago, 12 Nov 2020, 06:58pm -*

Kernel of error [OSM]

In our last post, we looked at a rolling average of pairwise correlations for the constituents of XLI, an ETF that tracks the industrials sector of the S&P 500. We found that spikes in the three-month average coincided with declines in the underlying index. There was some graphical evidence of a

*- 3 years ago, 26 Oct 2020, 11:40pm -*

Corr-correlation [OSM]

We recently read two blog posts from Robot Wealth and FOSS Trading on calculating rolling pairwise correlations for the constituents of an S&P 500 sector index. Both posts were very interesting and offered informative ways to solve the problem using different packages in R: tidyverse or xts.

*- 3 years ago, 9 Oct 2020, 11:13pm -*

Writing conundrums [OSM]

We’re taking a break from our portfolio series and million sample simulations to return to a subject that we haven’t discussed of late despite its featured spot in this blog’s name—options. In this post, we’ll look at the buy-write (BXM) and put-write (PUT) indices on the S&P 500, as

*- 3 years ago, 25 Sep 2020, 08:13pm -*

Sequential satisficing [OSM]

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

*- 3 years ago, 18 Sep 2020, 09:58pm -*

Satisficing and optimizing [OSM]

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

*- 3 years ago, 26 Aug 2020, 09:38pm -*

I like to MVO it! [OSM]

In our last post, we ran through a bunch of weighting scenarios using our returns simulation. This resulted in three million portfolios comprised in part, or total, of four assets: stocks, bonds, gold, and real estate. These simulations relaxed the allocation constraints to allow us to exclude

*- 3 years ago, 31 Jul 2020, 11:59pm -*

Weighting on a friend [OSM]

Our last few posts on portfolio construction have simulated various weighting schemes to create a range of possible portfolios. We’ve then chosen portfolios whose average weights yield the type of risk and return we’d like to achieve. However, we’ve noted there is more to portfolio

*- 3 years ago, 24 Jul 2020, 11:48pm -*

Testing expectations [OSM]

In our last post, we analyzed the performance of our portfolio, built using the historical average method to set return expectations. We calculated return and risk contributions and examined changes in allocation weights due to asset performance. We briefly considered whether such changes warranted

*- 3 years ago, 10 Jul 2020, 12:41pm -*

Performance anxiety [OSM]

In our last post, we took a quick look at building a portfolio based on the historical averages method for setting return expectations. Beginning in 1987, we used the first five years of monthly return data to simulate a thousand possible portfolio weights, found the average weights that met our

*- 3 years ago, 26 Jun 2020, 04:57am -*

Portfolio simulations [OSM]

In our last post, we compared the three most common methods used to set return expectations prior to building a portfolio. Of the three—historical averages, discounted cash flow models, and risk premia models—no single method dominated the others on average annual returns over one, three, and

*- 4 years ago, 13 Jun 2020, 12:20pm -*

Mad methods [OSM]

Over the past few weeks, we’ve examined the three major methods used to set return expectations as part of the portfolio allocation process. Those methods were historical averages, discounted cash flow models, and risk premia models. Today, we’ll bring all these models together to compare and

*- 4 years ago, 29 May 2020, 09:03pm -*

Implied risk premia [OSM]

In our last post, we applied machine learning to the Capital Aset Pricing Model (CAPM) to try to predict future returns for the S&P 500. This analysis was part of our overall project to analyze the various methods to set return expectations when seeking to build a satisfactory portfolio. Others

*- 4 years ago, 19 May 2020, 12:11pm -*

Machined risk premia [OSM]

Over the last few posts, we’ve discussed methods to set return expectations to construct a satisfactory portfolio. These methods are historical averages, discounted cash flow models, and risk premia. our last post, focused on the third method: risk premia. Using the Capital Asset Pricing Model

*- 4 years ago, 8 May 2020, 09:34pm -*

Risk premia [OSM]

Our last post discussed using the discounted cash flow model (DCF) as a method to set return expectations that one would ultimately employ in building a satisfactory portfolio. We noted that if one were able to have a reasonably good estimate of the cash flow growth rate of an asset, then it would

*- 4 years ago, 25 Apr 2020, 12:04pm -*

Discounted expectations [OSM]

After our little detour into GARCHery, we’re back to discuss capital market expectations. In Mean expectations, we examined using the historical average return to set return expectations when constructing a portfolio. We noted hurdles to this approach due to factors like non-normal distributions,

*- 4 years ago, 15 Apr 2020, 10:13pm -*

GARCHery [OSM]

In our last post, we discussed using the historical average return as one method for setting capital market expectations prior to constructing a satisfactory portfolio. We glossed over setting expectations for future volatility, mainly because it is such a thorny issue. However, we read an excellent

*- 4 years ago, 5 Apr 2020, 05:22am -*

Mean expectations [OSM]

We’re taking a break from our extended analysis of rebalancing to get back to the other salient parts of portfolio construction. We haven’t given up on the deep dive into the merits or drawbacks of rebalancing, but we feel we need to move the discussion along to keep the momentum. This should

*- 4 years ago, 29 Mar 2020, 12:27pm -*

Rebalancing history [OSM]

Our last post on rebalancing struck an equivocal note. We ran a thousand simulations using historical averages across different rebalancing regimes to test whether rebalancing produced better absolute or risk-adjusted returns. The results suggested it did not. But we noted many problems with the

*- 4 years ago, 21 Mar 2020, 09:16pm -*

Rebalancing ruminations [OSM]

Back in the rebalancing saddle! In our last post on rebalancing, we analyzed whether rebalancing over different periods would have any effect on mean or risk-adjusted returns for our three (equal, naive, and risky) portfolios. We found little evidence that returns were much different whether we

*- 4 years ago, 14 Mar 2020, 01:29pm -*

Drawdowns by the data [OSM]

We’re taking a break from our series on portfolio construction for two reasons: life and the recent market sell-off. Life got in the way of focusing on the next couple of posts on rebalancing. And given the market sell-off we were too busy gamma hedging our convexity exposure, looking for cheap

*- 4 years ago, 3 Mar 2020, 10:14am -*

Rebalancing! Really? [OSM]

In our last post, we introduced benchmarking as a way to analyze our hero’s investment results apart from comparing it to alternate weightings or Sharpe ratios. In this case, the benchmark was meant to capture the returns available to a global aggregate of investable risk assets. If you could own

*- 4 years ago, 23 Feb 2020, 10:04am -*

Benchmarking the portfolio [OSM]

In our last post, we looked at one measure of risk-adjusted returns, the Sharpe ratio, to help our hero decide whether he wanted to alter his portfolio allocations. Then, as opposed to finding the maximum return for our hero’s initial level of risk, we broadened the risk parameters and searched

*- 4 years ago, 14 Feb 2020, 08:26pm -*

SHARPEn your portfolio [OSM]

In our last post, we started building the intuition around constructing a reasonable portfolio to achieve an acceptable return. The hero of our story had built up a small nest egg and then decided to invest it equally across the three major asset classes: stocks, bonds, and real assets. For that we

*- 4 years ago, 8 Feb 2020, 08:54pm -*

Portfolio starter kit [OSM]

Say you’ve built a little nest egg thanks to some discipline and frugality. And now you realize that you should probably invest that money so that you’ve got something to live off of in retirement. Or perhaps you simply want to earn a better return than stashing your cash underneath your bed, I

*- 4 years ago, 26 Jan 2020, 11:16am -*

Skew who? [OSM]

In our last post on the SKEW index we looked at how good the index was in pricing two standard deviation (2SD) down moves. The answer: not very. But, we conjectured that this poor performance may be due to the fact that it is more accurate at pricing larger moves, which occur with greater frequency

*- 4 years ago, 21 Jan 2020, 10:27pm -*