Co-authored by Nesiya Josephine A. J.
Modern vehicles are fitted with a gear indicator on the instrument cluster. It not only tells you which gear you are on but also suggests you upshift or downshift depending on the speed or RPMs you are on. Why, one could ask. I have been driving for almost three decades and for the most part, I have relied on my ears and the feel of the engine to choose which gear the car needs to be on. Of course, automatic transmissions and EVs make this conversation redundant, but my latest car has a manual transmission, and I am sure, there are a few more old souls like me who continue to love the stick. Well, the interesting thing is that despite looking down on my car manufacturer for thinking that I require to be told which gear I am on, or I need to be on, I found myself increasingly casting an eye on the instrument cluster to see what the gear indicator is “indicating”. The reason is that those suggestions not only improved the car’s fuel efficiency, but it also improved the comfort level at the passenger seat, as the car was not revved any more than it required to be at each gear, and there was no jerkiness between shifts. In other words, my driving personality changed when I had an objective of fuel efficiency and passenger comfort, and the gear indicator served as an ideal tool. But of course, all these flew out of the window, when I was in the mood of an inspired and enthusiastic driving. So, in other words, while how I drove decided my driving experience, how I wanted my driving experience to be could also have decided how I approached the driving process.
This raises the question, do investment approaches influence objectives, or is it the other way around? To investigate this, Nesiya and I identified three investment approaches randomly and did paper trades, with an amount of 1,00,000 rupees invested in Nifty, along with each of the investment approaches. The experiment was conducted for two-time frames: January 2020 to December 2022 and January 2021 to December 2023, so as to see if the choice of time frames had any influence on the outcome. Let us briefly look at the three investment approaches first.
Investment Approach A- Lumpsum method: Here, the entire sum of 1,00,000 rupees was invested at the beginning of the period and no further action is taken until the end of the calculation period.
Investment Approach B- Systematic method: Here, an equal sum of 10,000 rupees was invested on the 1st trading day of every month for 10 months, and then held till end of the calculation period.
Investment Approach C- Dip method: An equal sum of 20,000 rupees was invested after every dip until the investment amount was exhausted. A dip is defined as five consecutive days of negative returns. The final returns accrued at the end of each investment period are evaluated.
The lump sum approach yielded a final return of 51% in the 2020-2022 period and 59% in the 2021-2023 period. Conversely, investing monthly resulted in a return of 71% in 2020-2022 but dropped to 39% in 2021-2023. Meanwhile, investing during dips garnered returns of 39% in 2020-2022 and 44% in 2021-2023. Overall, the 2020-2022 period saw higher returns on average compared to 2021-2023. The monthly investing method exhibited greater volatility, evident from the percentage change.
Is it so straightforward ever?
In exploring investment strategies, it’s vital to acknowledge the profound impact of behavioural biases that end up altering the financial objectives of the investment approach. Herding, a common bias, occurs when investors follow the crowd rather than conducting independent analysis. Anchoring bias reflects the tendency to heavily rely on initial information, influencing subsequent decisions. There are several such biases that may appear as a cause for pause event, but if we look closely, we have always been hardwired to such biases so much so that those were in a way became objectives in their own right, which decided the choice of investment approach, or its journey. Paramount among these biases is loss aversion, wherein investors fear losses more than they value gains. This emotional response can lead to suboptimal decisions, affecting portfolio performance. Understanding and mitigating loss aversion is crucial for fostering rational decision-making amid market fluctuations, as evidenced in the evaluation of different investment approaches and their corresponding returns.
Let us now analyse the butterfly effect of a loss aversion bias on each of the previously mentioned investment approaches. In the lump sum approach, following an initial investment, the investor encounters a downturn in the Nifty benchmark, prompting panic and subsequent exit to avoid further losses. They re-enter the market when the Nifty reaches the level at which they exited. This strategy yielded a return of 31% in 2020-2022 compared to 53% in 2021-2023. In the monthly investing approach, if the investor observes a negative monthly return in the Nifty, they refrain from investing on the 1st of the subsequent month. This method generated returns of 51% in 2020-2022 and 37% in 2021-2023. In the final approach, assuming the investor skips a dip to avert potential losses or due to fear, returns were 36% in 2020-2022 and 42% in 2021-2023. On average, the 2021-2023 period yielded higher returns, but the lump sum method exhibited greater volatility.
Observations
- The investing period matters. Some investment approaches fare better in trending markets, whereas some fare better during fluctuating markets.
- The systematic monthly approach has the most fluctuation among returns across the two measuring periods. It fared the least during the 2021-23 period as market was largely trending, but the fluctuating nature during the first phase of the 2020-22 measurement period helped return the best among the approaches during that period.
- The impact of bias was the least in the trending period of 2021-23. While the decline in average returns across the investment approaches was only 8.3% during this period, the decline due to bias rose to 27.8% during the 2020-2022, where there was more volatility.
