Author: shyam

The Dao of Collusive Trading

When people talk about crypto-currencies, the primary focus so far has been the price of bitcoin, ethereum, etc. However, that is only scratching the surface of what is possible. When you dig a little deeper, you find yourself sucked into a rabbit-hole. One such rabbit-hole is the DAO.

Decentralized Autonomous Organizations

DAO stands for Decentralized Autonomous Organization. A traditional corporation is structured in a top-down hierarchy where decisions are taken by CxOs and handed over to people down the hierarchy to execute. But what if you replace CxOs and workers by code and replace the decisions making mechanism by votes? You get a DAO.

In a DAO, participants buy DAO tokens and vote on what tasks need to be performed. The set of tasks is defined in code. The DAO then uses “smart contracts” to perform those tasks. As long as the output can be digitally verified, the whole thing can be distributed (does not need a central server) and anonymous.

The ability to anonymously create and participate in a DAO could open up a whole can of worms when it comes to securities market regulation. Take synchronized and circular trading, as an example for collusive trading.

Synchronized and Circular Trading

A synchronised trade is a transaction where the buy and sell orders are identical, and are put through at exactly the same time on a stock exchange. A circular trade is where a set of brokers buy and sell shares frequently amongst themselves. The intention could either be to artificially influence price, trading volume or tax avoidance.

Surveillance systems red flag these transactions and SEBI follows up by proving collusion and intent. The latter is possible because there is always a paper-trail and it is possible to draw a relationship diagram between market participants under the lens. But a DAO can upend that.

The DAO can be setup anonymously. The crypto that is required by the DAO can be acquired and traded anonymously. Participants can vote on the stock to be manipulated anonymously. The DAO can execute the code (“smart contract”) that trades these stocks autonomously.

So even if a surveillance system red flags these transactions, it becomes next to impossible to prove collusion. The trades would occur between participants who have not even interacted with each other before – just like what occurs naturally in a stock exchange.

Please tell me that some version of this is not possible.

Links:
DAOs, DACs, DAs and More: An Incomplete Terminology Guide
DACs VS the Corporation
What is a DAO?
How Do Ethereum Smart Contracts Work?

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Portfolio Management vs. Stock Selection

Retail investors and their media diet tend to focus too much on what stocks to buy and which IPOs to subscribe rather than portfolio construction and maintenance. Some of these aspects were touched upon here and here. Below are some portfolio level questions that investors should answer before they dive into stock selection:

  1. Is cash allowed? If enough number of stocks cannot be found to fit the investment thesis, or if the market is “bad,” is the portfolio allowed to hold cash? Remember that cash in the brokerage account earns zero.
  2. What is the maximum number of positions? How much time is to be spent everyday on surviellance?
  3. How much of each stock is to be purchased? Is it going to be equal-weight, cap-weight (free-float or full-float) or factor-weight?
  4. Will there be hard position and sector limits?
  5. How will IPO subscriptions where the target allocation is not filled be handled?
  6. What will be covered in daily surveillance? Things to consider: M&A, management, regulations, competitor profile, government interference, etc.
  7. How will costs be controlled? Direct investing is an extremely expensive proposition in India. What are net portfolio returns after: STT, brokerage, exchange fees, SEBI fees, stamp duty, GST/IGST, demat fees, demat transaction charges, short-term gains tax, long-term gains, etc?
  8. Will the portfolio be bench-marked? Should the appropriate benchmark be a basket of mutual funds that could have otherwise been invested into?
  9. What about risk management? Is it going to be a long-only balls-to-the-wall portfolio or are there going to be hedges, stop-losses, etc.?
  10. What is the re-balancing strategy? Will the whole portfolio be recomputed or will only those positions that strayed too far away from the thesis be looked at? How often will this exercise be undertaken?

Unless investors can think through these questions, stock selection is irrelevant.

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Global Momentum Strategies

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

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Sentiment Analysis of News Articles

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!

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Trend Following vs. Trend Prediction, Part I

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!

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