Invest Without Emotions
Investing insight to make you a better investor.
StockViz is proud to announce the launch of its momentum strategies on US, UK and Japanese stocks that are powered by the same momentum algorithms that work on our Indian Themes.
Since its launch in late 2013, our Momentum strategy has given 5x returns in India.
Thanks to Interactive Brokers, Indians can now open a global trading account and trade stocks across the world using our strategies sitting right here in India.
Check it out now and let us know what you think! Whatsapp: +918026650232
Markets trend, until they don’t. Markets consolidate until they start trending again. Since the beginning of markets, traders have been trying to figure out when these turning points occur. At StockViz, we have approached this problem from a risk-management angle rather than a prediction angle. We assume that we cannot predict, but we can definitely prepare.
Risk-management, however, is not free. Whether you hedge or employ stop-losses, there is a cost involved. What if we can reduce this cost by employing market sentiment as an input to our risk-management models? One approach could be to widen stop-loss levels when the market is “extremely bearish” and tighten them when they get “extremely bullish.”
To get things started, we have setup a weekly sentiment roundup that looks at a few popular news sources ranked using different lexicons. The first step would be create a time series of these values to see how they relate to market returns.
Stay tuned!
Traditional equity momentum strategies are variations of algos that try to figure out “trending” stocks so that they can be ranked to create a long/short portfolio. The key thing to remember is that these algos are following a trend, the prediction that a trending stock will continue to trend is implicit. However, using machine learning techniques, stocks can be ranked based on their predicted returns over a future time frame.
The simplest momentum strategy looks only at a price series. However, it quickly runs into problems when additional factors are overlaid on top of basic momentum. For example, you may want to filter out volatile stocks out the basket. You can do this either by setting a maximum volatility level or by weighing both momentum and volatility to arrive at a combined rank. Either approach leads to ad hoc decisions of cut off levels and the ratio with which to weigh each of those factors. Luckily, typical machine learning algorithms can work with multiple factors and weigh them based on the training set you supply.
The biggest drawback of using machine learning is that the larger the number of factors/features you use, the less explainable the resulting model becomes. As a trader, if you want to use any of these models, you should have a fairly good idea of what is going in, how the model is setup and what exactly is the model getting trained with.
As a first step in taking a crack at this, we have setup four machine learning algos. Two of them use SVRs and the other two use LR to train on data that is either return-series only or a combination of returns and volatility. You can have look at them here.
We will have more of these machine learning models out as we ramp up our understanding of these models. Stay tuned!
Momentum strategies have been on a tear over the last few years and have generally out-performed pure-value strategies. When we compare momentum returns with mutual funds, the most common criticism we encounter is that mutual fund returns are after transaction costs whereas our “Theme” returns are before transaction costs.
The challenge we face in showing post-cost returns is that we offer different brokerage slabs to different types of clients, making a one-cost-fits-all analysis impossible. However, we can show how different brokerage slabs impact returns.
A gross return of 83.82% translates to returns of 74.20%, 69.58% and 65.08% for brokerage slabs of 0.1%, 0.05% and 0% respectively (STT of 0.1% was assumed.) Momentum out-performs even after transactions costs.
Back in November-2015, we had concluded that it does not make sense following mutual fund entries and exits from individual stocks. Using an expanded data set of 260 funds, we still reach the same conclusion. Median returns over 10, 20 and 50 days on additions were 0.49%, 1.81% and 4.88% whereas exits clocked -0.01%, 0.90% and 3.27%.
Direct equity investors would do well to ignore what fund managers a doing.
Code and results are on github.