What does the pair-wise correlation of the NIFTY 50 constituents look like? Is it sticky? (Intro)
What does the high-correlation regime look like? (Correlation vs. Returns & Volatility)
Can correlation states be used for timing? (Correlation Timing)
Category: Collections
Curated list of posts on a specific topic.
Some portfolios you consistently ended up with higher returns, implying that there was something about the market, something systematic, that was driving them. What are these factors? Factors, Intro
Systematically accounting for excess returns became an academic sack race. Factors, The Famous 5
While fundamental factors play a role in explaining excess returns, technical factors cannot be ignored either. Thus, Momentum. While the market can be sliced-and-diced in many different ways, here’s a simple way to go about it: The All Star Backtest.
Most momentum strategies use a skip month. What happens if you don’t? And what happens if you skip two? Does it make sense to rebalance weekly?
Momentum portfolios are extremely volatile. But, is it possible that a portfolio of less volatile stocks out-perform the market? Ergo, the Low Volatility Anomaly.
Turns out, you can boost returns of a low-volatility strategy by adding a bit of momentum. VOLxMOM.
Both Factor Rotation (buying what worked best in the past) and Multi-Factor (buying all factors) work. As long as you stick with it.
Introduction
Years of returns can get wiped out in a month in the markets. While investors mostly focus on the average, the tails end up dictating their actual returns. (Introduction)
Sampling and Measurement
Typically, a uniform sample is taken. The problem with this is it under-represents the tails. This leads to models that work on average but blow up on occasion. One way to overcome this problem is through stratified sampling. (Sampling)
Expected shortfall (ES) is a risk measure that can be used to estimate the loss during tail-events. (Measuring)
Acceptance
All assets have fat tails. It is a feature, not a bug. (Historical)
Simple Moving Average (SMA) is one of the oldest and simplest measurements of trend. Arrived at by taking the average of prices over a period of time, it remains a popular tool for timing investments and risk-management. The following series of posts outlines how investors can use SMAs to get superior risk-adjusted returns.
SMA Strategies using ETFs
SMA strategies that use ETFs to create trend-following portfolios.
Reducing Drawdowns in SMA strategies
Shallower drawdowns allow a bit of leverage to be employed. This could be a good starting point for a NIFTY futures trading strategy.
Slopes vs. Cross-overs
A lagged response will result in higher drawdowns. It could, however, lead to lower transaction costs by papering over short-term mean-reverting moves.
Transaction Costs
Transaction cost analysis to backtests give investors an idea of what gross and net returns of different SMA look-backs look like over buy and hold.
Long-term Returns
Strategy outcomes depend on the underlying index and holding-periods. There is, alas, no magic formula.
A brief introduction to Global Equities Momentum and a look at various alternative scenarios.
Read: Part I
Could it work on value indices?
Read: Part II
Swapping momentum at the final step boosts returns significantly.
Read: Part III
Averaging out returns over different formation periods boosts returns and reduces drawdowns.
Read: Part IV
Track the virtual portfolios we setup using these strategies and follow the trades on our Slack channel.
Details: Trades and Portfolio