Money drives the world. And so does Technology! And when both come in together in financial markets, they move the world much more rapidly.

With the rise in technological advancement, algorithm-based trading (Algo trading) has changed the face of financial markets across the globe. The proliferation of Algo trading has been staggering in global markets.

Algo trading is a computerized rule-based process for placing large orders in the market, which almost eliminates manual human intervention. Though Algo and high-frequency trading (HFT) usually go together, they are not the same.

Algo trading involves placing trades based on defined criteria, either high frequency or low frequency. In high-frequency trading, the trader’s advantage is speed, whereas, in low frequency, the strength is the underlying trading model.

High-frequency trading has become the most pervasive use of Algo trading. However, an increase in total costs due to the increased number of transactions and the risk of trading errors limits the use of high-frequency trading by investors (hedge funds, equity mutual funds, etc.). On the other hand, low-frequency trading strategies where portfolios are rebalanced on a monthly or even annual basis builds on the core strength of Algo trading for formulating profitable trading strategies.

Program-based, low-frequency trading strategies have their foundations in significant equity market anomalies, fundamental and risk-based. Anomalies are occurrences that deviate from the predictions of economic or financial models that challenge those models’ core assumptions.

The presence of an anomaly indicates that a given assumption or model does not hold up in practice. For example, a stock is priced differently from how a model predicts it will be priced. These are some important anomalies that savvy investors have used for decades to beat the market: size (small cap outperforming large-cap firms) and value (firms with low book to market giving better returns than a high book to market firms). Moreover, liquidity anomaly (returns of neglected stocks outpace high liquid stocks) and profitability anomaly (firms with low profits generate relatively higher returns) exist.

The past two decades saw the emergence of risk-based anomalies, which occur when the stocks are classified based on risk measures. There are several alternative risk measures: beta, skewness, idiosyncratic volatility (IVOL), and tail risk.

Beta is defined as a sensitivity coefficient, which measures systematic risk, and is priced in rational asset pricing theory. Prior empirical evidence shows high betas associated with low or negative alphas, also known as a beta anomaly. Theoretically, IVOL is a risk that can be diversified away. However, investors start becoming mindful of total risk when there are limited diversification opportunities. They start pricing entire risk rather than systematic risk leading to a positive risk premium for high IVOL stocks. The demand for lottery-like stocks, where stocks with high probabilities of large short-term up moves in the stock price are bid up by the lottery investors.

Ample empirical evidence confirms the presence of risk anomalies and a risk puzzle, where low risk gives higher returns. Equity investors often overpay for risky stocks. In the Indian context, stocks with high idiosyncratic volatility have high expected returns because investors cannot fully diversify away from the firm-specific risk. Since all measures are related to risk, there is a possibility that these anomalies entangle with each other.

Over the years, another strand of thought calls anomalies imaginary (spurious correlation like the Hemline or the Asprin indicator) or irrelevant. It is also true that ​most of the supposed market anomalies academics have identified don’t exist or are too small to matter.

Nevertheless, even after so many years, anomalies exist because of irrationality in investor behavior and different types of investors present in the market. Both of these factors are not going to fade anytime. Therefore, the significance of behavioral factors in explaining the relationship between risk-based anomalies and stock returns piques the interest of many. For instance, prospect theory suggests that investors exhibit a risk preference when facing a prior capital loss. It is interesting to understand that the performance of both fundamental and risk-based anomalies varies due to factors like information uncertainty, level of market development, investor sentiment, and degree of capital flow restrictions.

One can benefit from this understanding and gain vital insights for portfolio creation and strategy formulation. It is also possible to make investment strategies combining different anomalies. One can write an algorithm on relevant anomalies accounting for their interaction with the market. Remember, portfolio construction matters, and it matters a lot. How frequently is it rebalanced? What is the weightage given to different stocks or sectors?

So, my dear investors, it’s time to upskill yourselves to understand the rapidly evolving stock market maths and economics. Let us make sense of these anomalies and translate market inefficiencies into profitable opportunities.

Best Regards
Dr. Tarunika Jain Agarwal Ph.D.

Alpha Research